report.html

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<title>SageMaker Project</title>

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  content: "\e252";
}
.glyphicon-triangle-top:before {
  content: "\e253";
}
.glyphicon-console:before {
  content: "\e254";
}
.glyphicon-superscript:before {
  content: "\e255";
}
.glyphicon-subscript:before {
  content: "\e256";
}
.glyphicon-menu-left:before {
  content: "\e257";
}
.glyphicon-menu-right:before {
  content: "\e258";
}
.glyphicon-menu-down:before {
  content: "\e259";
}
.glyphicon-menu-up:before {
  content: "\e260";
}
* {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
*:before,
*:after {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
html {
  font-size: 10px;
  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-size: 13px;
  line-height: 1.42857143;
  color: #000;
  background-color: #fff;
}
input,
button,
select,
textarea {
  font-family: inherit;
  font-size: inherit;
  line-height: inherit;
}
a {
  color: #337ab7;
  text-decoration: none;
}
a:hover,
a:focus {
  color: #23527c;
  text-decoration: underline;
}
a:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
figure {
  margin: 0;
}
img {
  vertical-align: middle;
}
.img-responsive,
.thumbnail > img,
.thumbnail a > img,
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  display: block;
  max-width: 100%;
  height: auto;
}
.img-rounded {
  border-radius: 3px;
}
.img-thumbnail {
  padding: 4px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: all 0.2s ease-in-out;
  -o-transition: all 0.2s ease-in-out;
  transition: all 0.2s ease-in-out;
  display: inline-block;
  max-width: 100%;
  height: auto;
}
.img-circle {
  border-radius: 50%;
}
hr {
  margin-top: 18px;
  margin-bottom: 18px;
  border: 0;
  border-top: 1px solid #eeeeee;
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  margin: -1px;
  padding: 0;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
[role="button"] {
  cursor: pointer;
}
h1,
h2,
h3,
h4,
h5,
h6,
.h1,
.h2,
.h3,
.h4,
.h5,
.h6 {
  font-family: inherit;
  font-weight: 500;
  line-height: 1.1;
  color: inherit;
}
h1 small,
h2 small,
h3 small,
h4 small,
h5 small,
h6 small,
.h1 small,
.h2 small,
.h3 small,
.h4 small,
.h5 small,
.h6 small,
h1 .small,
h2 .small,
h3 .small,
h4 .small,
h5 .small,
h6 .small,
.h1 .small,
.h2 .small,
.h3 .small,
.h4 .small,
.h5 .small,
.h6 .small {
  font-weight: normal;
  line-height: 1;
  color: #777777;
}
h1,
.h1,
h2,
.h2,
h3,
.h3 {
  margin-top: 18px;
  margin-bottom: 9px;
}
h1 small,
.h1 small,
h2 small,
.h2 small,
h3 small,
.h3 small,
h1 .small,
.h1 .small,
h2 .small,
.h2 .small,
h3 .small,
.h3 .small {
  font-size: 65%;
}
h4,
.h4,
h5,
.h5,
h6,
.h6 {
  margin-top: 9px;
  margin-bottom: 9px;
}
h4 small,
.h4 small,
h5 small,
.h5 small,
h6 small,
.h6 small,
h4 .small,
.h4 .small,
h5 .small,
.h5 .small,
h6 .small,
.h6 .small {
  font-size: 75%;
}
h1,
.h1 {
  font-size: 33px;
}
h2,
.h2 {
  font-size: 27px;
}
h3,
.h3 {
  font-size: 23px;
}
h4,
.h4 {
  font-size: 17px;
}
h5,
.h5 {
  font-size: 13px;
}
h6,
.h6 {
  font-size: 12px;
}
p {
  margin: 0 0 9px;
}
.lead {
  margin-bottom: 18px;
  font-size: 14px;
  font-weight: 300;
  line-height: 1.4;
}
@media (min-width: 768px) {
  .lead {
    font-size: 19.5px;
  }
}
small,
.small {
  font-size: 92%;
}
mark,
.mark {
  background-color: #fcf8e3;
  padding: .2em;
}
.text-left {
  text-align: left;
}
.text-right {
  text-align: right;
}
.text-center {
  text-align: center;
}
.text-justify {
  text-align: justify;
}
.text-nowrap {
  white-space: nowrap;
}
.text-lowercase {
  text-transform: lowercase;
}
.text-uppercase {
  text-transform: uppercase;
}
.text-capitalize {
  text-transform: capitalize;
}
.text-muted {
  color: #777777;
}
.text-primary {
  color: #337ab7;
}
a.text-primary:hover,
a.text-primary:focus {
  color: #286090;
}
.text-success {
  color: #3c763d;
}
a.text-success:hover,
a.text-success:focus {
  color: #2b542c;
}
.text-info {
  color: #31708f;
}
a.text-info:hover,
a.text-info:focus {
  color: #245269;
}
.text-warning {
  color: #8a6d3b;
}
a.text-warning:hover,
a.text-warning:focus {
  color: #66512c;
}
.text-danger {
  color: #a94442;
}
a.text-danger:hover,
a.text-danger:focus {
  color: #843534;
}
.bg-primary {
  color: #fff;
  background-color: #337ab7;
}
a.bg-primary:hover,
a.bg-primary:focus {
  background-color: #286090;
}
.bg-success {
  background-color: #dff0d8;
}
a.bg-success:hover,
a.bg-success:focus {
  background-color: #c1e2b3;
}
.bg-info {
  background-color: #d9edf7;
}
a.bg-info:hover,
a.bg-info:focus {
  background-color: #afd9ee;
}
.bg-warning {
  background-color: #fcf8e3;
}
a.bg-warning:hover,
a.bg-warning:focus {
  background-color: #f7ecb5;
}
.bg-danger {
  background-color: #f2dede;
}
a.bg-danger:hover,
a.bg-danger:focus {
  background-color: #e4b9b9;
}
.page-header {
  padding-bottom: 8px;
  margin: 36px 0 18px;
  border-bottom: 1px solid #eeeeee;
}
ul,
ol {
  margin-top: 0;
  margin-bottom: 9px;
}
ul ul,
ol ul,
ul ol,
ol ol {
  margin-bottom: 0;
}
.list-unstyled {
  padding-left: 0;
  list-style: none;
}
.list-inline {
  padding-left: 0;
  list-style: none;
  margin-left: -5px;
}
.list-inline > li {
  display: inline-block;
  padding-left: 5px;
  padding-right: 5px;
}
dl {
  margin-top: 0;
  margin-bottom: 18px;
}
dt,
dd {
  line-height: 1.42857143;
}
dt {
  font-weight: bold;
}
dd {
  margin-left: 0;
}
@media (min-width: 541px) {
  .dl-horizontal dt {
    float: left;
    width: 160px;
    clear: left;
    text-align: right;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
  }
  .dl-horizontal dd {
    margin-left: 180px;
  }
}
abbr[title],
abbr[data-original-title] {
  cursor: help;
  border-bottom: 1px dotted #777777;
}
.initialism {
  font-size: 90%;
  text-transform: uppercase;
}
blockquote {
  padding: 9px 18px;
  margin: 0 0 18px;
  font-size: inherit;
  border-left: 5px solid #eeeeee;
}
blockquote p:last-child,
blockquote ul:last-child,
blockquote ol:last-child {
  margin-bottom: 0;
}
blockquote footer,
blockquote small,
blockquote .small {
  display: block;
  font-size: 80%;
  line-height: 1.42857143;
  color: #777777;
}
blockquote footer:before,
blockquote small:before,
blockquote .small:before {
  content: '\2014 \00A0';
}
.blockquote-reverse,
blockquote.pull-right {
  padding-right: 15px;
  padding-left: 0;
  border-right: 5px solid #eeeeee;
  border-left: 0;
  text-align: right;
}
.blockquote-reverse footer:before,
blockquote.pull-right footer:before,
.blockquote-reverse small:before,
blockquote.pull-right small:before,
.blockquote-reverse .small:before,
blockquote.pull-right .small:before {
  content: '';
}
.blockquote-reverse footer:after,
blockquote.pull-right footer:after,
.blockquote-reverse small:after,
blockquote.pull-right small:after,
.blockquote-reverse .small:after,
blockquote.pull-right .small:after {
  content: '\00A0 \2014';
}
address {
  margin-bottom: 18px;
  font-style: normal;
  line-height: 1.42857143;
}
code,
kbd,
pre,
samp {
  font-family: monospace;
}
code {
  padding: 2px 4px;
  font-size: 90%;
  color: #c7254e;
  background-color: #f9f2f4;
  border-radius: 2px;
}
kbd {
  padding: 2px 4px;
  font-size: 90%;
  color: #888;
  background-color: transparent;
  border-radius: 1px;
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
}
kbd kbd {
  padding: 0;
  font-size: 100%;
  font-weight: bold;
  box-shadow: none;
}
pre {
  display: block;
  padding: 8.5px;
  margin: 0 0 9px;
  font-size: 12px;
  line-height: 1.42857143;
  word-break: break-all;
  word-wrap: break-word;
  color: #333333;
  background-color: #f5f5f5;
  border: 1px solid #ccc;
  border-radius: 2px;
}
pre code {
  padding: 0;
  font-size: inherit;
  color: inherit;
  white-space: pre-wrap;
  background-color: transparent;
  border-radius: 0;
}
.pre-scrollable {
  max-height: 340px;
  overflow-y: scroll;
}
.container {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
@media (min-width: 768px) {
  .container {
    width: 768px;
  }
}
@media (min-width: 992px) {
  .container {
    width: 940px;
  }
}
@media (min-width: 1200px) {
  .container {
    width: 1140px;
  }
}
.container-fluid {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
.row {
  margin-left: 0px;
  margin-right: 0px;
}
.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
  position: relative;
  min-height: 1px;
  padding-left: 0px;
  padding-right: 0px;
}
.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
  float: left;
}
.col-xs-12 {
  width: 100%;
}
.col-xs-11 {
  width: 91.66666667%;
}
.col-xs-10 {
  width: 83.33333333%;
}
.col-xs-9 {
  width: 75%;
}
.col-xs-8 {
  width: 66.66666667%;
}
.col-xs-7 {
  width: 58.33333333%;
}
.col-xs-6 {
  width: 50%;
}
.col-xs-5 {
  width: 41.66666667%;
}
.col-xs-4 {
  width: 33.33333333%;
}
.col-xs-3 {
  width: 25%;
}
.col-xs-2 {
  width: 16.66666667%;
}
.col-xs-1 {
  width: 8.33333333%;
}
.col-xs-pull-12 {
  right: 100%;
}
.col-xs-pull-11 {
  right: 91.66666667%;
}
.col-xs-pull-10 {
  right: 83.33333333%;
}
.col-xs-pull-9 {
  right: 75%;
}
.col-xs-pull-8 {
  right: 66.66666667%;
}
.col-xs-pull-7 {
  right: 58.33333333%;
}
.col-xs-pull-6 {
  right: 50%;
}
.col-xs-pull-5 {
  right: 41.66666667%;
}
.col-xs-pull-4 {
  right: 33.33333333%;
}
.col-xs-pull-3 {
  right: 25%;
}
.col-xs-pull-2 {
  right: 16.66666667%;
}
.col-xs-pull-1 {
  right: 8.33333333%;
}
.col-xs-pull-0 {
  right: auto;
}
.col-xs-push-12 {
  left: 100%;
}
.col-xs-push-11 {
  left: 91.66666667%;
}
.col-xs-push-10 {
  left: 83.33333333%;
}
.col-xs-push-9 {
  left: 75%;
}
.col-xs-push-8 {
  left: 66.66666667%;
}
.col-xs-push-7 {
  left: 58.33333333%;
}
.col-xs-push-6 {
  left: 50%;
}
.col-xs-push-5 {
  left: 41.66666667%;
}
.col-xs-push-4 {
  left: 33.33333333%;
}
.col-xs-push-3 {
  left: 25%;
}
.col-xs-push-2 {
  left: 16.66666667%;
}
.col-xs-push-1 {
  left: 8.33333333%;
}
.col-xs-push-0 {
  left: auto;
}
.col-xs-offset-12 {
  margin-left: 100%;
}
.col-xs-offset-11 {
  margin-left: 91.66666667%;
}
.col-xs-offset-10 {
  margin-left: 83.33333333%;
}
.col-xs-offset-9 {
  margin-left: 75%;
}
.col-xs-offset-8 {
  margin-left: 66.66666667%;
}
.col-xs-offset-7 {
  margin-left: 58.33333333%;
}
.col-xs-offset-6 {
  margin-left: 50%;
}
.col-xs-offset-5 {
  margin-left: 41.66666667%;
}
.col-xs-offset-4 {
  margin-left: 33.33333333%;
}
.col-xs-offset-3 {
  margin-left: 25%;
}
.col-xs-offset-2 {
  margin-left: 16.66666667%;
}
.col-xs-offset-1 {
  margin-left: 8.33333333%;
}
.col-xs-offset-0 {
  margin-left: 0%;
}
@media (min-width: 768px) {
  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
    float: left;
  }
  .col-sm-12 {
    width: 100%;
  }
  .col-sm-11 {
    width: 91.66666667%;
  }
  .col-sm-10 {
    width: 83.33333333%;
  }
  .col-sm-9 {
    width: 75%;
  }
  .col-sm-8 {
    width: 66.66666667%;
  }
  .col-sm-7 {
    width: 58.33333333%;
  }
  .col-sm-6 {
    width: 50%;
  }
  .col-sm-5 {
    width: 41.66666667%;
  }
  .col-sm-4 {
    width: 33.33333333%;
  }
  .col-sm-3 {
    width: 25%;
  }
  .col-sm-2 {
    width: 16.66666667%;
  }
  .col-sm-1 {
    width: 8.33333333%;
  }
  .col-sm-pull-12 {
    right: 100%;
  }
  .col-sm-pull-11 {
    right: 91.66666667%;
  }
  .col-sm-pull-10 {
    right: 83.33333333%;
  }
  .col-sm-pull-9 {
    right: 75%;
  }
  .col-sm-pull-8 {
    right: 66.66666667%;
  }
  .col-sm-pull-7 {
    right: 58.33333333%;
  }
  .col-sm-pull-6 {
    right: 50%;
  }
  .col-sm-pull-5 {
    right: 41.66666667%;
  }
  .col-sm-pull-4 {
    right: 33.33333333%;
  }
  .col-sm-pull-3 {
    right: 25%;
  }
  .col-sm-pull-2 {
    right: 16.66666667%;
  }
  .col-sm-pull-1 {
    right: 8.33333333%;
  }
  .col-sm-pull-0 {
    right: auto;
  }
  .col-sm-push-12 {
    left: 100%;
  }
  .col-sm-push-11 {
    left: 91.66666667%;
  }
  .col-sm-push-10 {
    left: 83.33333333%;
  }
  .col-sm-push-9 {
    left: 75%;
  }
  .col-sm-push-8 {
    left: 66.66666667%;
  }
  .col-sm-push-7 {
    left: 58.33333333%;
  }
  .col-sm-push-6 {
    left: 50%;
  }
  .col-sm-push-5 {
    left: 41.66666667%;
  }
  .col-sm-push-4 {
    left: 33.33333333%;
  }
  .col-sm-push-3 {
    left: 25%;
  }
  .col-sm-push-2 {
    left: 16.66666667%;
  }
  .col-sm-push-1 {
    left: 8.33333333%;
  }
  .col-sm-push-0 {
    left: auto;
  }
  .col-sm-offset-12 {
    margin-left: 100%;
  }
  .col-sm-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-sm-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-sm-offset-9 {
    margin-left: 75%;
  }
  .col-sm-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-sm-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-sm-offset-6 {
    margin-left: 50%;
  }
  .col-sm-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-sm-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-sm-offset-3 {
    margin-left: 25%;
  }
  .col-sm-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-sm-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-sm-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 992px) {
  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
    float: left;
  }
  .col-md-12 {
    width: 100%;
  }
  .col-md-11 {
    width: 91.66666667%;
  }
  .col-md-10 {
    width: 83.33333333%;
  }
  .col-md-9 {
    width: 75%;
  }
  .col-md-8 {
    width: 66.66666667%;
  }
  .col-md-7 {
    width: 58.33333333%;
  }
  .col-md-6 {
    width: 50%;
  }
  .col-md-5 {
    width: 41.66666667%;
  }
  .col-md-4 {
    width: 33.33333333%;
  }
  .col-md-3 {
    width: 25%;
  }
  .col-md-2 {
    width: 16.66666667%;
  }
  .col-md-1 {
    width: 8.33333333%;
  }
  .col-md-pull-12 {
    right: 100%;
  }
  .col-md-pull-11 {
    right: 91.66666667%;
  }
  .col-md-pull-10 {
    right: 83.33333333%;
  }
  .col-md-pull-9 {
    right: 75%;
  }
  .col-md-pull-8 {
    right: 66.66666667%;
  }
  .col-md-pull-7 {
    right: 58.33333333%;
  }
  .col-md-pull-6 {
    right: 50%;
  }
  .col-md-pull-5 {
    right: 41.66666667%;
  }
  .col-md-pull-4 {
    right: 33.33333333%;
  }
  .col-md-pull-3 {
    right: 25%;
  }
  .col-md-pull-2 {
    right: 16.66666667%;
  }
  .col-md-pull-1 {
    right: 8.33333333%;
  }
  .col-md-pull-0 {
    right: auto;
  }
  .col-md-push-12 {
    left: 100%;
  }
  .col-md-push-11 {
    left: 91.66666667%;
  }
  .col-md-push-10 {
    left: 83.33333333%;
  }
  .col-md-push-9 {
    left: 75%;
  }
  .col-md-push-8 {
    left: 66.66666667%;
  }
  .col-md-push-7 {
    left: 58.33333333%;
  }
  .col-md-push-6 {
    left: 50%;
  }
  .col-md-push-5 {
    left: 41.66666667%;
  }
  .col-md-push-4 {
    left: 33.33333333%;
  }
  .col-md-push-3 {
    left: 25%;
  }
  .col-md-push-2 {
    left: 16.66666667%;
  }
  .col-md-push-1 {
    left: 8.33333333%;
  }
  .col-md-push-0 {
    left: auto;
  }
  .col-md-offset-12 {
    margin-left: 100%;
  }
  .col-md-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-md-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-md-offset-9 {
    margin-left: 75%;
  }
  .col-md-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-md-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-md-offset-6 {
    margin-left: 50%;
  }
  .col-md-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-md-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-md-offset-3 {
    margin-left: 25%;
  }
  .col-md-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-md-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-md-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 1200px) {
  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
    float: left;
  }
  .col-lg-12 {
    width: 100%;
  }
  .col-lg-11 {
    width: 91.66666667%;
  }
  .col-lg-10 {
    width: 83.33333333%;
  }
  .col-lg-9 {
    width: 75%;
  }
  .col-lg-8 {
    width: 66.66666667%;
  }
  .col-lg-7 {
    width: 58.33333333%;
  }
  .col-lg-6 {
    width: 50%;
  }
  .col-lg-5 {
    width: 41.66666667%;
  }
  .col-lg-4 {
    width: 33.33333333%;
  }
  .col-lg-3 {
    width: 25%;
  }
  .col-lg-2 {
    width: 16.66666667%;
  }
  .col-lg-1 {
    width: 8.33333333%;
  }
  .col-lg-pull-12 {
    right: 100%;
  }
  .col-lg-pull-11 {
    right: 91.66666667%;
  }
  .col-lg-pull-10 {
    right: 83.33333333%;
  }
  .col-lg-pull-9 {
    right: 75%;
  }
  .col-lg-pull-8 {
    right: 66.66666667%;
  }
  .col-lg-pull-7 {
    right: 58.33333333%;
  }
  .col-lg-pull-6 {
    right: 50%;
  }
  .col-lg-pull-5 {
    right: 41.66666667%;
  }
  .col-lg-pull-4 {
    right: 33.33333333%;
  }
  .col-lg-pull-3 {
    right: 25%;
  }
  .col-lg-pull-2 {
    right: 16.66666667%;
  }
  .col-lg-pull-1 {
    right: 8.33333333%;
  }
  .col-lg-pull-0 {
    right: auto;
  }
  .col-lg-push-12 {
    left: 100%;
  }
  .col-lg-push-11 {
    left: 91.66666667%;
  }
  .col-lg-push-10 {
    left: 83.33333333%;
  }
  .col-lg-push-9 {
    left: 75%;
  }
  .col-lg-push-8 {
    left: 66.66666667%;
  }
  .col-lg-push-7 {
    left: 58.33333333%;
  }
  .col-lg-push-6 {
    left: 50%;
  }
  .col-lg-push-5 {
    left: 41.66666667%;
  }
  .col-lg-push-4 {
    left: 33.33333333%;
  }
  .col-lg-push-3 {
    left: 25%;
  }
  .col-lg-push-2 {
    left: 16.66666667%;
  }
  .col-lg-push-1 {
    left: 8.33333333%;
  }
  .col-lg-push-0 {
    left: auto;
  }
  .col-lg-offset-12 {
    margin-left: 100%;
  }
  .col-lg-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-lg-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-lg-offset-9 {
    margin-left: 75%;
  }
  .col-lg-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-lg-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-lg-offset-6 {
    margin-left: 50%;
  }
  .col-lg-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-lg-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-lg-offset-3 {
    margin-left: 25%;
  }
  .col-lg-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-lg-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-lg-offset-0 {
    margin-left: 0%;
  }
}
table {
  background-color: transparent;
}
caption {
  padding-top: 8px;
  padding-bottom: 8px;
  color: #777777;
  text-align: left;
}
th {
  text-align: left;
}
.table {
  width: 100%;
  max-width: 100%;
  margin-bottom: 18px;
}
.table > thead > tr > th,
.table > tbody > tr > th,
.table > tfoot > tr > th,
.table > thead > tr > td,
.table > tbody > tr > td,
.table > tfoot > tr > td {
  padding: 8px;
  line-height: 1.42857143;
  vertical-align: top;
  border-top: 1px solid #ddd;
}
.table > thead > tr > th {
  vertical-align: bottom;
  border-bottom: 2px solid #ddd;
}
.table > caption + thead > tr:first-child > th,
.table > colgroup + thead > tr:first-child > th,
.table > thead:first-child > tr:first-child > th,
.table > caption + thead > tr:first-child > td,
.table > colgroup + thead > tr:first-child > td,
.table > thead:first-child > tr:first-child > td {
  border-top: 0;
}
.table > tbody + tbody {
  border-top: 2px solid #ddd;
}
.table .table {
  background-color: #fff;
}
.table-condensed > thead > tr > th,
.table-condensed > tbody > tr > th,
.table-condensed > tfoot > tr > th,
.table-condensed > thead > tr > td,
.table-condensed > tbody > tr > td,
.table-condensed > tfoot > tr > td {
  padding: 5px;
}
.table-bordered {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > tbody > tr > th,
.table-bordered > tfoot > tr > th,
.table-bordered > thead > tr > td,
.table-bordered > tbody > tr > td,
.table-bordered > tfoot > tr > td {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > thead > tr > td {
  border-bottom-width: 2px;
}
.table-striped > tbody > tr:nth-of-type(odd) {
  background-color: #f9f9f9;
}
.table-hover > tbody > tr:hover {
  background-color: #f5f5f5;
}
table col[class*="col-"] {
  position: static;
  float: none;
  display: table-column;
}
table td[class*="col-"],
table th[class*="col-"] {
  position: static;
  float: none;
  display: table-cell;
}
.table > thead > tr > td.active,
.table > tbody > tr > td.active,
.table > tfoot > tr > td.active,
.table > thead > tr > th.active,
.table > tbody > tr > th.active,
.table > tfoot > tr > th.active,
.table > thead > tr.active > td,
.table > tbody > tr.active > td,
.table > tfoot > tr.active > td,
.table > thead > tr.active > th,
.table > tbody > tr.active > th,
.table > tfoot > tr.active > th {
  background-color: #f5f5f5;
}
.table-hover > tbody > tr > td.active:hover,
.table-hover > tbody > tr > th.active:hover,
.table-hover > tbody > tr.active:hover > td,
.table-hover > tbody > tr:hover > .active,
.table-hover > tbody > tr.active:hover > th {
  background-color: #e8e8e8;
}
.table > thead > tr > td.success,
.table > tbody > tr > td.success,
.table > tfoot > tr > td.success,
.table > thead > tr > th.success,
.table > tbody > tr > th.success,
.table > tfoot > tr > th.success,
.table > thead > tr.success > td,
.table > tbody > tr.success > td,
.table > tfoot > tr.success > td,
.table > thead > tr.success > th,
.table > tbody > tr.success > th,
.table > tfoot > tr.success > th {
  background-color: #dff0d8;
}
.table-hover > tbody > tr > td.success:hover,
.table-hover > tbody > tr > th.success:hover,
.table-hover > tbody > tr.success:hover > td,
.table-hover > tbody > tr:hover > .success,
.table-hover > tbody > tr.success:hover > th {
  background-color: #d0e9c6;
}
.table > thead > tr > td.info,
.table > tbody > tr > td.info,
.table > tfoot > tr > td.info,
.table > thead > tr > th.info,
.table > tbody > tr > th.info,
.table > tfoot > tr > th.info,
.table > thead > tr.info > td,
.table > tbody > tr.info > td,
.table > tfoot > tr.info > td,
.table > thead > tr.info > th,
.table > tbody > tr.info > th,
.table > tfoot > tr.info > th {
  background-color: #d9edf7;
}
.table-hover > tbody > tr > td.info:hover,
.table-hover > tbody > tr > th.info:hover,
.table-hover > tbody > tr.info:hover > td,
.table-hover > tbody > tr:hover > .info,
.table-hover > tbody > tr.info:hover > th {
  background-color: #c4e3f3;
}
.table > thead > tr > td.warning,
.table > tbody > tr > td.warning,
.table > tfoot > tr > td.warning,
.table > thead > tr > th.warning,
.table > tbody > tr > th.warning,
.table > tfoot > tr > th.warning,
.table > thead > tr.warning > td,
.table > tbody > tr.warning > td,
.table > tfoot > tr.warning > td,
.table > thead > tr.warning > th,
.table > tbody > tr.warning > th,
.table > tfoot > tr.warning > th {
  background-color: #fcf8e3;
}
.table-hover > tbody > tr > td.warning:hover,
.table-hover > tbody > tr > th.warning:hover,
.table-hover > tbody > tr.warning:hover > td,
.table-hover > tbody > tr:hover > .warning,
.table-hover > tbody > tr.warning:hover > th {
  background-color: #faf2cc;
}
.table > thead > tr > td.danger,
.table > tbody > tr > td.danger,
.table > tfoot > tr > td.danger,
.table > thead > tr > th.danger,
.table > tbody > tr > th.danger,
.table > tfoot > tr > th.danger,
.table > thead > tr.danger > td,
.table > tbody > tr.danger > td,
.table > tfoot > tr.danger > td,
.table > thead > tr.danger > th,
.table > tbody > tr.danger > th,
.table > tfoot > tr.danger > th {
  background-color: #f2dede;
}
.table-hover > tbody > tr > td.danger:hover,
.table-hover > tbody > tr > th.danger:hover,
.table-hover > tbody > tr.danger:hover > td,
.table-hover > tbody > tr:hover > .danger,
.table-hover > tbody > tr.danger:hover > th {
  background-color: #ebcccc;
}
.table-responsive {
  overflow-x: auto;
  min-height: 0.01%;
}
@media screen and (max-width: 767px) {
  .table-responsive {
    width: 100%;
    margin-bottom: 13.5px;
    overflow-y: hidden;
    -ms-overflow-style: -ms-autohiding-scrollbar;
    border: 1px solid #ddd;
  }
  .table-responsive > .table {
    margin-bottom: 0;
  }
  .table-responsive > .table > thead > tr > th,
  .table-responsive > .table > tbody > tr > th,
  .table-responsive > .table > tfoot > tr > th,
  .table-responsive > .table > thead > tr > td,
  .table-responsive > .table > tbody > tr > td,
  .table-responsive > .table > tfoot > tr > td {
    white-space: nowrap;
  }
  .table-responsive > .table-bordered {
    border: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:first-child,
  .table-responsive > .table-bordered > tbody > tr > th:first-child,
  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
  .table-responsive > .table-bordered > thead > tr > td:first-child,
  .table-responsive > .table-bordered > tbody > tr > td:first-child,
  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
    border-left: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:last-child,
  .table-responsive > .table-bordered > tbody > tr > th:last-child,
  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
  .table-responsive > .table-bordered > thead > tr > td:last-child,
  .table-responsive > .table-bordered > tbody > tr > td:last-child,
  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
    border-right: 0;
  }
  .table-responsive > .table-bordered > tbody > tr:last-child > th,
  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
  .table-responsive > .table-bordered > tbody > tr:last-child > td,
  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
    border-bottom: 0;
  }
}
fieldset {
  padding: 0;
  margin: 0;
  border: 0;
  min-width: 0;
}
legend {
  display: block;
  width: 100%;
  padding: 0;
  margin-bottom: 18px;
  font-size: 19.5px;
  line-height: inherit;
  color: #333333;
  border: 0;
  border-bottom: 1px solid #e5e5e5;
}
label {
  display: inline-block;
  max-width: 100%;
  margin-bottom: 5px;
  font-weight: bold;
}
input[type="search"] {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
input[type="radio"],
input[type="checkbox"] {
  margin: 4px 0 0;
  margin-top: 1px \9;
  line-height: normal;
}
input[type="file"] {
  display: block;
}
input[type="range"] {
  display: block;
  width: 100%;
}
select[multiple],
select[size] {
  height: auto;
}
input[type="file"]:focus,
input[type="radio"]:focus,
input[type="checkbox"]:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
output {
  display: block;
  padding-top: 7px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
}
.form-control {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
}
.form-control:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.form-control::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.form-control:-ms-input-placeholder {
  color: #999;
}
.form-control::-webkit-input-placeholder {
  color: #999;
}
.form-control::-ms-expand {
  border: 0;
  background-color: transparent;
}
.form-control[disabled],
.form-control[readonly],
fieldset[disabled] .form-control {
  background-color: #eeeeee;
  opacity: 1;
}
.form-control[disabled],
fieldset[disabled] .form-control {
  cursor: not-allowed;
}
textarea.form-control {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: none;
}
@media screen and (-webkit-min-device-pixel-ratio: 0) {
  input[type="date"].form-control,
  input[type="time"].form-control,
  input[type="datetime-local"].form-control,
  input[type="month"].form-control {
    line-height: 32px;
  }
  input[type="date"].input-sm,
  input[type="time"].input-sm,
  input[type="datetime-local"].input-sm,
  input[type="month"].input-sm,
  .input-group-sm input[type="date"],
  .input-group-sm input[type="time"],
  .input-group-sm input[type="datetime-local"],
  .input-group-sm input[type="month"] {
    line-height: 30px;
  }
  input[type="date"].input-lg,
  input[type="time"].input-lg,
  input[type="datetime-local"].input-lg,
  input[type="month"].input-lg,
  .input-group-lg input[type="date"],
  .input-group-lg input[type="time"],
  .input-group-lg input[type="datetime-local"],
  .input-group-lg input[type="month"] {
    line-height: 45px;
  }
}
.form-group {
  margin-bottom: 15px;
}
.radio,
.checkbox {
  position: relative;
  display: block;
  margin-top: 10px;
  margin-bottom: 10px;
}
.radio label,
.checkbox label {
  min-height: 18px;
  padding-left: 20px;
  margin-bottom: 0;
  font-weight: normal;
  cursor: pointer;
}
.radio input[type="radio"],
.radio-inline input[type="radio"],
.checkbox input[type="checkbox"],
.checkbox-inline input[type="checkbox"] {
  position: absolute;
  margin-left: -20px;
  margin-top: 4px \9;
}
.radio + .radio,
.checkbox + .checkbox {
  margin-top: -5px;
}
.radio-inline,
.checkbox-inline {
  position: relative;
  display: inline-block;
  padding-left: 20px;
  margin-bottom: 0;
  vertical-align: middle;
  font-weight: normal;
  cursor: pointer;
}
.radio-inline + .radio-inline,
.checkbox-inline + .checkbox-inline {
  margin-top: 0;
  margin-left: 10px;
}
input[type="radio"][disabled],
input[type="checkbox"][disabled],
input[type="radio"].disabled,
input[type="checkbox"].disabled,
fieldset[disabled] input[type="radio"],
fieldset[disabled] input[type="checkbox"] {
  cursor: not-allowed;
}
.radio-inline.disabled,
.checkbox-inline.disabled,
fieldset[disabled] .radio-inline,
fieldset[disabled] .checkbox-inline {
  cursor: not-allowed;
}
.radio.disabled label,
.checkbox.disabled label,
fieldset[disabled] .radio label,
fieldset[disabled] .checkbox label {
  cursor: not-allowed;
}
.form-control-static {
  padding-top: 7px;
  padding-bottom: 7px;
  margin-bottom: 0;
  min-height: 31px;
}
.form-control-static.input-lg,
.form-control-static.input-sm {
  padding-left: 0;
  padding-right: 0;
}
.input-sm {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-sm {
  height: 30px;
  line-height: 30px;
}
textarea.input-sm,
select[multiple].input-sm {
  height: auto;
}
.form-group-sm .form-control {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.form-group-sm select.form-control {
  height: 30px;
  line-height: 30px;
}
.form-group-sm textarea.form-control,
.form-group-sm select[multiple].form-control {
  height: auto;
}
.form-group-sm .form-control-static {
  height: 30px;
  min-height: 30px;
  padding: 6px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.input-lg {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-lg {
  height: 45px;
  line-height: 45px;
}
textarea.input-lg,
select[multiple].input-lg {
  height: auto;
}
.form-group-lg .form-control {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.form-group-lg select.form-control {
  height: 45px;
  line-height: 45px;
}
.form-group-lg textarea.form-control,
.form-group-lg select[multiple].form-control {
  height: auto;
}
.form-group-lg .form-control-static {
  height: 45px;
  min-height: 35px;
  padding: 11px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.has-feedback {
  position: relative;
}
.has-feedback .form-control {
  padding-right: 40px;
}
.form-control-feedback {
  position: absolute;
  top: 0;
  right: 0;
  z-index: 2;
  display: block;
  width: 32px;
  height: 32px;
  line-height: 32px;
  text-align: center;
  pointer-events: none;
}
.input-lg + .form-control-feedback,
.input-group-lg + .form-control-feedback,
.form-group-lg .form-control + .form-control-feedback {
  width: 45px;
  height: 45px;
  line-height: 45px;
}
.input-sm + .form-control-feedback,
.input-group-sm + .form-control-feedback,
.form-group-sm .form-control + .form-control-feedback {
  width: 30px;
  height: 30px;
  line-height: 30px;
}
.has-success .help-block,
.has-success .control-label,
.has-success .radio,
.has-success .checkbox,
.has-success .radio-inline,
.has-success .checkbox-inline,
.has-success.radio label,
.has-success.checkbox label,
.has-success.radio-inline label,
.has-success.checkbox-inline label {
  color: #3c763d;
}
.has-success .form-control {
  border-color: #3c763d;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-success .form-control:focus {
  border-color: #2b542c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
}
.has-success .input-group-addon {
  color: #3c763d;
  border-color: #3c763d;
  background-color: #dff0d8;
}
.has-success .form-control-feedback {
  color: #3c763d;
}
.has-warning .help-block,
.has-warning .control-label,
.has-warning .radio,
.has-warning .checkbox,
.has-warning .radio-inline,
.has-warning .checkbox-inline,
.has-warning.radio label,
.has-warning.checkbox label,
.has-warning.radio-inline label,
.has-warning.checkbox-inline label {
  color: #8a6d3b;
}
.has-warning .form-control {
  border-color: #8a6d3b;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-warning .form-control:focus {
  border-color: #66512c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
}
.has-warning .input-group-addon {
  color: #8a6d3b;
  border-color: #8a6d3b;
  background-color: #fcf8e3;
}
.has-warning .form-control-feedback {
  color: #8a6d3b;
}
.has-error .help-block,
.has-error .control-label,
.has-error .radio,
.has-error .checkbox,
.has-error .radio-inline,
.has-error .checkbox-inline,
.has-error.radio label,
.has-error.checkbox label,
.has-error.radio-inline label,
.has-error.checkbox-inline label {
  color: #a94442;
}
.has-error .form-control {
  border-color: #a94442;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-error .form-control:focus {
  border-color: #843534;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
}
.has-error .input-group-addon {
  color: #a94442;
  border-color: #a94442;
  background-color: #f2dede;
}
.has-error .form-control-feedback {
  color: #a94442;
}
.has-feedback label ~ .form-control-feedback {
  top: 23px;
}
.has-feedback label.sr-only ~ .form-control-feedback {
  top: 0;
}
.help-block {
  display: block;
  margin-top: 5px;
  margin-bottom: 10px;
  color: #404040;
}
@media (min-width: 768px) {
  .form-inline .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .form-inline .form-control-static {
    display: inline-block;
  }
  .form-inline .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .form-inline .input-group .input-group-addon,
  .form-inline .input-group .input-group-btn,
  .form-inline .input-group .form-control {
    width: auto;
  }
  .form-inline .input-group > .form-control {
    width: 100%;
  }
  .form-inline .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio,
  .form-inline .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio label,
  .form-inline .checkbox label {
    padding-left: 0;
  }
  .form-inline .radio input[type="radio"],
  .form-inline .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .form-inline .has-feedback .form-control-feedback {
    top: 0;
  }
}
.form-horizontal .radio,
.form-horizontal .checkbox,
.form-horizontal .radio-inline,
.form-horizontal .checkbox-inline {
  margin-top: 0;
  margin-bottom: 0;
  padding-top: 7px;
}
.form-horizontal .radio,
.form-horizontal .checkbox {
  min-height: 25px;
}
.form-horizontal .form-group {
  margin-left: 0px;
  margin-right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .control-label {
    text-align: right;
    margin-bottom: 0;
    padding-top: 7px;
  }
}
.form-horizontal .has-feedback .form-control-feedback {
  right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .form-group-lg .control-label {
    padding-top: 11px;
    font-size: 17px;
  }
}
@media (min-width: 768px) {
  .form-horizontal .form-group-sm .control-label {
    padding-top: 6px;
    font-size: 12px;
  }
}
.btn {
  display: inline-block;
  margin-bottom: 0;
  font-weight: normal;
  text-align: center;
  vertical-align: middle;
  touch-action: manipulation;
  cursor: pointer;
  background-image: none;
  border: 1px solid transparent;
  white-space: nowrap;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  border-radius: 2px;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}
.btn:focus,
.btn:active:focus,
.btn.active:focus,
.btn.focus,
.btn:active.focus,
.btn.active.focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
.btn:hover,
.btn:focus,
.btn.focus {
  color: #333;
  text-decoration: none;
}
.btn:active,
.btn.active {
  outline: 0;
  background-image: none;
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn.disabled,
.btn[disabled],
fieldset[disabled] .btn {
  cursor: not-allowed;
  opacity: 0.65;
  filter: alpha(opacity=65);
  -webkit-box-shadow: none;
  box-shadow: none;
}
a.btn.disabled,
fieldset[disabled] a.btn {
  pointer-events: none;
}
.btn-default {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.btn-default:focus,
.btn-default.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.btn-default:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active:hover,
.btn-default.active:hover,
.open > .dropdown-toggle.btn-default:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.open > .dropdown-toggle.btn-default:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn-default.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  background-image: none;
}
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
  background-color: #fff;
  border-color: #ccc;
}
.btn-default .badge {
  color: #fff;
  background-color: #333;
}
.btn-primary {
  color: #fff;
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary:focus,
.btn-primary.focus {
  color: #fff;
  background-color: #286090;
  border-color: #122b40;
}
.btn-primary:hover {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active:hover,
.btn-primary.active:hover,
.open > .dropdown-toggle.btn-primary:hover,
.btn-primary:active:focus,
.btn-primary.active:focus,
.open > .dropdown-toggle.btn-primary:focus,
.btn-primary:active.focus,
.btn-primary.active.focus,
.open > .dropdown-toggle.btn-primary.focus {
  color: #fff;
  background-color: #204d74;
  border-color: #122b40;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  background-image: none;
}
.btn-primary.disabled:hover,
.btn-primary[disabled]:hover,
fieldset[disabled] .btn-primary:hover,
.btn-primary.disabled:focus,
.btn-primary[disabled]:focus,
fieldset[disabled] .btn-primary:focus,
.btn-primary.disabled.focus,
.btn-primary[disabled].focus,
fieldset[disabled] .btn-primary.focus {
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary .badge {
  color: #337ab7;
  background-color: #fff;
}
.btn-success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success:focus,
.btn-success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.btn-success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active:hover,
.btn-success.active:hover,
.open > .dropdown-toggle.btn-success:hover,
.btn-success:active:focus,
.btn-success.active:focus,
.open > .dropdown-toggle.btn-success:focus,
.btn-success:active.focus,
.btn-success.active.focus,
.open > .dropdown-toggle.btn-success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  background-image: none;
}
.btn-success.disabled:hover,
.btn-success[disabled]:hover,
fieldset[disabled] .btn-success:hover,
.btn-success.disabled:focus,
.btn-success[disabled]:focus,
fieldset[disabled] .btn-success:focus,
.btn-success.disabled.focus,
.btn-success[disabled].focus,
fieldset[disabled] .btn-success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.btn-info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info:focus,
.btn-info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.btn-info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active:hover,
.btn-info.active:hover,
.open > .dropdown-toggle.btn-info:hover,
.btn-info:active:focus,
.btn-info.active:focus,
.open > .dropdown-toggle.btn-info:focus,
.btn-info:active.focus,
.btn-info.active.focus,
.open > .dropdown-toggle.btn-info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  background-image: none;
}
.btn-info.disabled:hover,
.btn-info[disabled]:hover,
fieldset[disabled] .btn-info:hover,
.btn-info.disabled:focus,
.btn-info[disabled]:focus,
fieldset[disabled] .btn-info:focus,
.btn-info.disabled.focus,
.btn-info[disabled].focus,
fieldset[disabled] .btn-info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.btn-warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning:focus,
.btn-warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.btn-warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active:hover,
.btn-warning.active:hover,
.open > .dropdown-toggle.btn-warning:hover,
.btn-warning:active:focus,
.btn-warning.active:focus,
.open > .dropdown-toggle.btn-warning:focus,
.btn-warning:active.focus,
.btn-warning.active.focus,
.open > .dropdown-toggle.btn-warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  background-image: none;
}
.btn-warning.disabled:hover,
.btn-warning[disabled]:hover,
fieldset[disabled] .btn-warning:hover,
.btn-warning.disabled:focus,
.btn-warning[disabled]:focus,
fieldset[disabled] .btn-warning:focus,
.btn-warning.disabled.focus,
.btn-warning[disabled].focus,
fieldset[disabled] .btn-warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.btn-danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger:focus,
.btn-danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.btn-danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active:hover,
.btn-danger.active:hover,
.open > .dropdown-toggle.btn-danger:hover,
.btn-danger:active:focus,
.btn-danger.active:focus,
.open > .dropdown-toggle.btn-danger:focus,
.btn-danger:active.focus,
.btn-danger.active.focus,
.open > .dropdown-toggle.btn-danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  background-image: none;
}
.btn-danger.disabled:hover,
.btn-danger[disabled]:hover,
fieldset[disabled] .btn-danger:hover,
.btn-danger.disabled:focus,
.btn-danger[disabled]:focus,
fieldset[disabled] .btn-danger:focus,
.btn-danger.disabled.focus,
.btn-danger[disabled].focus,
fieldset[disabled] .btn-danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger .badge {
  color: #d9534f;
  background-color: #fff;
}
.btn-link {
  color: #337ab7;
  font-weight: normal;
  border-radius: 0;
}
.btn-link,
.btn-link:active,
.btn-link.active,
.btn-link[disabled],
fieldset[disabled] .btn-link {
  background-color: transparent;
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn-link,
.btn-link:hover,
.btn-link:focus,
.btn-link:active {
  border-color: transparent;
}
.btn-link:hover,
.btn-link:focus {
  color: #23527c;
  text-decoration: underline;
  background-color: transparent;
}
.btn-link[disabled]:hover,
fieldset[disabled] .btn-link:hover,
.btn-link[disabled]:focus,
fieldset[disabled] .btn-link:focus {
  color: #777777;
  text-decoration: none;
}
.btn-lg,
.btn-group-lg > .btn {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.btn-sm,
.btn-group-sm > .btn {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-xs,
.btn-group-xs > .btn {
  padding: 1px 5px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-block {
  display: block;
  width: 100%;
}
.btn-block + .btn-block {
  margin-top: 5px;
}
input[type="submit"].btn-block,
input[type="reset"].btn-block,
input[type="button"].btn-block {
  width: 100%;
}
.fade {
  opacity: 0;
  -webkit-transition: opacity 0.15s linear;
  -o-transition: opacity 0.15s linear;
  transition: opacity 0.15s linear;
}
.fade.in {
  opacity: 1;
}
.collapse {
  display: none;
}
.collapse.in {
  display: block;
}
tr.collapse.in {
  display: table-row;
}
tbody.collapse.in {
  display: table-row-group;
}
.collapsing {
  position: relative;
  height: 0;
  overflow: hidden;
  -webkit-transition-property: height, visibility;
  transition-property: height, visibility;
  -webkit-transition-duration: 0.35s;
  transition-duration: 0.35s;
  -webkit-transition-timing-function: ease;
  transition-timing-function: ease;
}
.caret {
  display: inline-block;
  width: 0;
  height: 0;
  margin-left: 2px;
  vertical-align: middle;
  border-top: 4px dashed;
  border-top: 4px solid \9;
  border-right: 4px solid transparent;
  border-left: 4px solid transparent;
}
.dropup,
.dropdown {
  position: relative;
}
.dropdown-toggle:focus {
  outline: 0;
}
.dropdown-menu {
  position: absolute;
  top: 100%;
  left: 0;
  z-index: 1000;
  display: none;
  float: left;
  min-width: 160px;
  padding: 5px 0;
  margin: 2px 0 0;
  list-style: none;
  font-size: 13px;
  text-align: left;
  background-color: #fff;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.15);
  border-radius: 2px;
  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  background-clip: padding-box;
}
.dropdown-menu.pull-right {
  right: 0;
  left: auto;
}
.dropdown-menu .divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.dropdown-menu > li > a {
  display: block;
  padding: 3px 20px;
  clear: both;
  font-weight: normal;
  line-height: 1.42857143;
  color: #333333;
  white-space: nowrap;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
  text-decoration: none;
  color: #262626;
  background-color: #f5f5f5;
}
.dropdown-menu > .active > a,
.dropdown-menu > .active > a:hover,
.dropdown-menu > .active > a:focus {
  color: #fff;
  text-decoration: none;
  outline: 0;
  background-color: #337ab7;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  color: #777777;
}
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  text-decoration: none;
  background-color: transparent;
  background-image: none;
  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
  cursor: not-allowed;
}
.open > .dropdown-menu {
  display: block;
}
.open > a {
  outline: 0;
}
.dropdown-menu-right {
  left: auto;
  right: 0;
}
.dropdown-menu-left {
  left: 0;
  right: auto;
}
.dropdown-header {
  display: block;
  padding: 3px 20px;
  font-size: 12px;
  line-height: 1.42857143;
  color: #777777;
  white-space: nowrap;
}
.dropdown-backdrop {
  position: fixed;
  left: 0;
  right: 0;
  bottom: 0;
  top: 0;
  z-index: 990;
}
.pull-right > .dropdown-menu {
  right: 0;
  left: auto;
}
.dropup .caret,
.navbar-fixed-bottom .dropdown .caret {
  border-top: 0;
  border-bottom: 4px dashed;
  border-bottom: 4px solid \9;
  content: "";
}
.dropup .dropdown-menu,
.navbar-fixed-bottom .dropdown .dropdown-menu {
  top: auto;
  bottom: 100%;
  margin-bottom: 2px;
}
@media (min-width: 541px) {
  .navbar-right .dropdown-menu {
    left: auto;
    right: 0;
  }
  .navbar-right .dropdown-menu-left {
    left: 0;
    right: auto;
  }
}
.btn-group,
.btn-group-vertical {
  position: relative;
  display: inline-block;
  vertical-align: middle;
}
.btn-group > .btn,
.btn-group-vertical > .btn {
  position: relative;
  float: left;
}
.btn-group > .btn:hover,
.btn-group-vertical > .btn:hover,
.btn-group > .btn:focus,
.btn-group-vertical > .btn:focus,
.btn-group > .btn:active,
.btn-group-vertical > .btn:active,
.btn-group > .btn.active,
.btn-group-vertical > .btn.active {
  z-index: 2;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
  margin-left: -1px;
}
.btn-toolbar {
  margin-left: -5px;
}
.btn-toolbar .btn,
.btn-toolbar .btn-group,
.btn-toolbar .input-group {
  float: left;
}
.btn-toolbar > .btn,
.btn-toolbar > .btn-group,
.btn-toolbar > .input-group {
  margin-left: 5px;
}
.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
  border-radius: 0;
}
.btn-group > .btn:first-child {
  margin-left: 0;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn:last-child:not(:first-child),
.btn-group > .dropdown-toggle:not(:first-child) {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group > .btn-group {
  float: left;
}
.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group .dropdown-toggle:active,
.btn-group.open .dropdown-toggle {
  outline: 0;
}
.btn-group > .btn + .dropdown-toggle {
  padding-left: 8px;
  padding-right: 8px;
}
.btn-group > .btn-lg + .dropdown-toggle {
  padding-left: 12px;
  padding-right: 12px;
}
.btn-group.open .dropdown-toggle {
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn-group.open .dropdown-toggle.btn-link {
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn .caret {
  margin-left: 0;
}
.btn-lg .caret {
  border-width: 5px 5px 0;
  border-bottom-width: 0;
}
.dropup .btn-lg .caret {
  border-width: 0 5px 5px;
}
.btn-group-vertical > .btn,
.btn-group-vertical > .btn-group,
.btn-group-vertical > .btn-group > .btn {
  display: block;
  float: none;
  width: 100%;
  max-width: 100%;
}
.btn-group-vertical > .btn-group > .btn {
  float: none;
}
.btn-group-vertical > .btn + .btn,
.btn-group-vertical > .btn + .btn-group,
.btn-group-vertical > .btn-group + .btn,
.btn-group-vertical > .btn-group + .btn-group {
  margin-top: -1px;
  margin-left: 0;
}
.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.btn-group-vertical > .btn:first-child:not(:last-child) {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn:last-child:not(:first-child) {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.btn-group-justified {
  display: table;
  width: 100%;
  table-layout: fixed;
  border-collapse: separate;
}
.btn-group-justified > .btn,
.btn-group-justified > .btn-group {
  float: none;
  display: table-cell;
  width: 1%;
}
.btn-group-justified > .btn-group .btn {
  width: 100%;
}
.btn-group-justified > .btn-group .dropdown-menu {
  left: auto;
}
[data-toggle="buttons"] > .btn input[type="radio"],
[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
[data-toggle="buttons"] > .btn input[type="checkbox"],
[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
  position: absolute;
  clip: rect(0, 0, 0, 0);
  pointer-events: none;
}
.input-group {
  position: relative;
  display: table;
  border-collapse: separate;
}
.input-group[class*="col-"] {
  float: none;
  padding-left: 0;
  padding-right: 0;
}
.input-group .form-control {
  position: relative;
  z-index: 2;
  float: left;
  width: 100%;
  margin-bottom: 0;
}
.input-group .form-control:focus {
  z-index: 3;
}
.input-group-lg > .form-control,
.input-group-lg > .input-group-addon,
.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-group-lg > .form-control,
select.input-group-lg > .input-group-addon,
select.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  line-height: 45px;
}
textarea.input-group-lg > .form-control,
textarea.input-group-lg > .input-group-addon,
textarea.input-group-lg > .input-group-btn > .btn,
select[multiple].input-group-lg > .form-control,
select[multiple].input-group-lg > .input-group-addon,
select[multiple].input-group-lg > .input-group-btn > .btn {
  height: auto;
}
.input-group-sm > .form-control,
.input-group-sm > .input-group-addon,
.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-group-sm > .form-control,
select.input-group-sm > .input-group-addon,
select.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  line-height: 30px;
}
textarea.input-group-sm > .form-control,
textarea.input-group-sm > .input-group-addon,
textarea.input-group-sm > .input-group-btn > .btn,
select[multiple].input-group-sm > .form-control,
select[multiple].input-group-sm > .input-group-addon,
select[multiple].input-group-sm > .input-group-btn > .btn {
  height: auto;
}
.input-group-addon,
.input-group-btn,
.input-group .form-control {
  display: table-cell;
}
.input-group-addon:not(:first-child):not(:last-child),
.input-group-btn:not(:first-child):not(:last-child),
.input-group .form-control:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.input-group-addon,
.input-group-btn {
  width: 1%;
  white-space: nowrap;
  vertical-align: middle;
}
.input-group-addon {
  padding: 6px 12px;
  font-size: 13px;
  font-weight: normal;
  line-height: 1;
  color: #555555;
  text-align: center;
  background-color: #eeeeee;
  border: 1px solid #ccc;
  border-radius: 2px;
}
.input-group-addon.input-sm {
  padding: 5px 10px;
  font-size: 12px;
  border-radius: 1px;
}
.input-group-addon.input-lg {
  padding: 10px 16px;
  font-size: 17px;
  border-radius: 3px;
}
.input-group-addon input[type="radio"],
.input-group-addon input[type="checkbox"] {
  margin-top: 0;
}
.input-group .form-control:first-child,
.input-group-addon:first-child,
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group > .btn,
.input-group-btn:first-child > .dropdown-toggle,
.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.input-group-addon:first-child {
  border-right: 0;
}
.input-group .form-control:last-child,
.input-group-addon:last-child,
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group > .btn,
.input-group-btn:last-child > .dropdown-toggle,
.input-group-btn:first-child > .btn:not(:first-child),
.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.input-group-addon:last-child {
  border-left: 0;
}
.input-group-btn {
  position: relative;
  font-size: 0;
  white-space: nowrap;
}
.input-group-btn > .btn {
  position: relative;
}
.input-group-btn > .btn + .btn {
  margin-left: -1px;
}
.input-group-btn > .btn:hover,
.input-group-btn > .btn:focus,
.input-group-btn > .btn:active {
  z-index: 2;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
  margin-right: -1px;
}
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group {
  z-index: 2;
  margin-left: -1px;
}
.nav {
  margin-bottom: 0;
  padding-left: 0;
  list-style: none;
}
.nav > li {
  position: relative;
  display: block;
}
.nav > li > a {
  position: relative;
  display: block;
  padding: 10px 15px;
}
.nav > li > a:hover,
.nav > li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.nav > li.disabled > a {
  color: #777777;
}
.nav > li.disabled > a:hover,
.nav > li.disabled > a:focus {
  color: #777777;
  text-decoration: none;
  background-color: transparent;
  cursor: not-allowed;
}
.nav .open > a,
.nav .open > a:hover,
.nav .open > a:focus {
  background-color: #eeeeee;
  border-color: #337ab7;
}
.nav .nav-divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.nav > li > a > img {
  max-width: none;
}
.nav-tabs {
  border-bottom: 1px solid #ddd;
}
.nav-tabs > li {
  float: left;
  margin-bottom: -1px;
}
.nav-tabs > li > a {
  margin-right: 2px;
  line-height: 1.42857143;
  border: 1px solid transparent;
  border-radius: 2px 2px 0 0;
}
.nav-tabs > li > a:hover {
  border-color: #eeeeee #eeeeee #ddd;
}
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
  color: #555555;
  background-color: #fff;
  border: 1px solid #ddd;
  border-bottom-color: transparent;
  cursor: default;
}
.nav-tabs.nav-justified {
  width: 100%;
  border-bottom: 0;
}
.nav-tabs.nav-justified > li {
  float: none;
}
.nav-tabs.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-tabs.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-tabs.nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs.nav-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs.nav-justified > .active > a,
  .nav-tabs.nav-justified > .active > a:hover,
  .nav-tabs.nav-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.nav-pills > li {
  float: left;
}
.nav-pills > li > a {
  border-radius: 2px;
}
.nav-pills > li + li {
  margin-left: 2px;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
  color: #fff;
  background-color: #337ab7;
}
.nav-stacked > li {
  float: none;
}
.nav-stacked > li + li {
  margin-top: 2px;
  margin-left: 0;
}
.nav-justified {
  width: 100%;
}
.nav-justified > li {
  float: none;
}
.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs-justified {
  border-bottom: 0;
}
.nav-tabs-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs-justified > .active > a,
  .nav-tabs-justified > .active > a:hover,
  .nav-tabs-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.tab-content > .tab-pane {
  display: none;
}
.tab-content > .active {
  display: block;
}
.nav-tabs .dropdown-menu {
  margin-top: -1px;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar {
  position: relative;
  min-height: 30px;
  margin-bottom: 18px;
  border: 1px solid transparent;
}
@media (min-width: 541px) {
  .navbar {
    border-radius: 2px;
  }
}
@media (min-width: 541px) {
  .navbar-header {
    float: left;
  }
}
.navbar-collapse {
  overflow-x: visible;
  padding-right: 0px;
  padding-left: 0px;
  border-top: 1px solid transparent;
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
  -webkit-overflow-scrolling: touch;
}
.navbar-collapse.in {
  overflow-y: auto;
}
@media (min-width: 541px) {
  .navbar-collapse {
    width: auto;
    border-top: 0;
    box-shadow: none;
  }
  .navbar-collapse.collapse {
    display: block !important;
    height: auto !important;
    padding-bottom: 0;
    overflow: visible !important;
  }
  .navbar-collapse.in {
    overflow-y: visible;
  }
  .navbar-fixed-top .navbar-collapse,
  .navbar-static-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    padding-left: 0;
    padding-right: 0;
  }
}
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
  max-height: 340px;
}
@media (max-device-width: 540px) and (orientation: landscape) {
  .navbar-fixed-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    max-height: 200px;
  }
}
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
  margin-right: 0px;
  margin-left: 0px;
}
@media (min-width: 541px) {
  .container > .navbar-header,
  .container-fluid > .navbar-header,
  .container > .navbar-collapse,
  .container-fluid > .navbar-collapse {
    margin-right: 0;
    margin-left: 0;
  }
}
.navbar-static-top {
  z-index: 1000;
  border-width: 0 0 1px;
}
@media (min-width: 541px) {
  .navbar-static-top {
    border-radius: 0;
  }
}
.navbar-fixed-top,
.navbar-fixed-bottom {
  position: fixed;
  right: 0;
  left: 0;
  z-index: 1030;
}
@media (min-width: 541px) {
  .navbar-fixed-top,
  .navbar-fixed-bottom {
    border-radius: 0;
  }
}
.navbar-fixed-top {
  top: 0;
  border-width: 0 0 1px;
}
.navbar-fixed-bottom {
  bottom: 0;
  margin-bottom: 0;
  border-width: 1px 0 0;
}
.navbar-brand {
  float: left;
  padding: 6px 0px;
  font-size: 17px;
  line-height: 18px;
  height: 30px;
}
.navbar-brand:hover,
.navbar-brand:focus {
  text-decoration: none;
}
.navbar-brand > img {
  display: block;
}
@media (min-width: 541px) {
  .navbar > .container .navbar-brand,
  .navbar > .container-fluid .navbar-brand {
    margin-left: 0px;
  }
}
.navbar-toggle {
  position: relative;
  float: right;
  margin-right: 0px;
  padding: 9px 10px;
  margin-top: -2px;
  margin-bottom: -2px;
  background-color: transparent;
  background-image: none;
  border: 1px solid transparent;
  border-radius: 2px;
}
.navbar-toggle:focus {
  outline: 0;
}
.navbar-toggle .icon-bar {
  display: block;
  width: 22px;
  height: 2px;
  border-radius: 1px;
}
.navbar-toggle .icon-bar + .icon-bar {
  margin-top: 4px;
}
@media (min-width: 541px) {
  .navbar-toggle {
    display: none;
  }
}
.navbar-nav {
  margin: 3px 0px;
}
.navbar-nav > li > a {
  padding-top: 10px;
  padding-bottom: 10px;
  line-height: 18px;
}
@media (max-width: 540px) {
  .navbar-nav .open .dropdown-menu {
    position: static;
    float: none;
    width: auto;
    margin-top: 0;
    background-color: transparent;
    border: 0;
    box-shadow: none;
  }
  .navbar-nav .open .dropdown-menu > li > a,
  .navbar-nav .open .dropdown-menu .dropdown-header {
    padding: 5px 15px 5px 25px;
  }
  .navbar-nav .open .dropdown-menu > li > a {
    line-height: 18px;
  }
  .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-nav .open .dropdown-menu > li > a:focus {
    background-image: none;
  }
}
@media (min-width: 541px) {
  .navbar-nav {
    float: left;
    margin: 0;
  }
  .navbar-nav > li {
    float: left;
  }
  .navbar-nav > li > a {
    padding-top: 6px;
    padding-bottom: 6px;
  }
}
.navbar-form {
  margin-left: 0px;
  margin-right: 0px;
  padding: 10px 0px;
  border-top: 1px solid transparent;
  border-bottom: 1px solid transparent;
  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  margin-top: -1px;
  margin-bottom: -1px;
}
@media (min-width: 768px) {
  .navbar-form .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .navbar-form .form-control-static {
    display: inline-block;
  }
  .navbar-form .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .navbar-form .input-group .input-group-addon,
  .navbar-form .input-group .input-group-btn,
  .navbar-form .input-group .form-control {
    width: auto;
  }
  .navbar-form .input-group > .form-control {
    width: 100%;
  }
  .navbar-form .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio,
  .navbar-form .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio label,
  .navbar-form .checkbox label {
    padding-left: 0;
  }
  .navbar-form .radio input[type="radio"],
  .navbar-form .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .navbar-form .has-feedback .form-control-feedback {
    top: 0;
  }
}
@media (max-width: 540px) {
  .navbar-form .form-group {
    margin-bottom: 5px;
  }
  .navbar-form .form-group:last-child {
    margin-bottom: 0;
  }
}
@media (min-width: 541px) {
  .navbar-form {
    width: auto;
    border: 0;
    margin-left: 0;
    margin-right: 0;
    padding-top: 0;
    padding-bottom: 0;
    -webkit-box-shadow: none;
    box-shadow: none;
  }
}
.navbar-nav > li > .dropdown-menu {
  margin-top: 0;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
  margin-bottom: 0;
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.navbar-btn {
  margin-top: -1px;
  margin-bottom: -1px;
}
.navbar-btn.btn-sm {
  margin-top: 0px;
  margin-bottom: 0px;
}
.navbar-btn.btn-xs {
  margin-top: 4px;
  margin-bottom: 4px;
}
.navbar-text {
  margin-top: 6px;
  margin-bottom: 6px;
}
@media (min-width: 541px) {
  .navbar-text {
    float: left;
    margin-left: 0px;
    margin-right: 0px;
  }
}
@media (min-width: 541px) {
  .navbar-left {
    float: left !important;
    float: left;
  }
  .navbar-right {
    float: right !important;
    float: right;
    margin-right: 0px;
  }
  .navbar-right ~ .navbar-right {
    margin-right: 0;
  }
}
.navbar-default {
  background-color: #f8f8f8;
  border-color: #e7e7e7;
}
.navbar-default .navbar-brand {
  color: #777;
}
.navbar-default .navbar-brand:hover,
.navbar-default .navbar-brand:focus {
  color: #5e5e5e;
  background-color: transparent;
}
.navbar-default .navbar-text {
  color: #777;
}
.navbar-default .navbar-nav > li > a {
  color: #777;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
  color: #333;
  background-color: transparent;
}
.navbar-default .navbar-nav > .active > a,
.navbar-default .navbar-nav > .active > a:hover,
.navbar-default .navbar-nav > .active > a:focus {
  color: #555;
  background-color: #e7e7e7;
}
.navbar-default .navbar-nav > .disabled > a,
.navbar-default .navbar-nav > .disabled > a:hover,
.navbar-default .navbar-nav > .disabled > a:focus {
  color: #ccc;
  background-color: transparent;
}
.navbar-default .navbar-toggle {
  border-color: #ddd;
}
.navbar-default .navbar-toggle:hover,
.navbar-default .navbar-toggle:focus {
  background-color: #ddd;
}
.navbar-default .navbar-toggle .icon-bar {
  background-color: #888;
}
.navbar-default .navbar-collapse,
.navbar-default .navbar-form {
  border-color: #e7e7e7;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
  background-color: #e7e7e7;
  color: #555;
}
@media (max-width: 540px) {
  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
    color: #777;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #333;
    background-color: transparent;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #555;
    background-color: #e7e7e7;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #ccc;
    background-color: transparent;
  }
}
.navbar-default .navbar-link {
  color: #777;
}
.navbar-default .navbar-link:hover {
  color: #333;
}
.navbar-default .btn-link {
  color: #777;
}
.navbar-default .btn-link:hover,
.navbar-default .btn-link:focus {
  color: #333;
}
.navbar-default .btn-link[disabled]:hover,
fieldset[disabled] .navbar-default .btn-link:hover,
.navbar-default .btn-link[disabled]:focus,
fieldset[disabled] .navbar-default .btn-link:focus {
  color: #ccc;
}
.navbar-inverse {
  background-color: #222;
  border-color: #080808;
}
.navbar-inverse .navbar-brand {
  color: #9d9d9d;
}
.navbar-inverse .navbar-brand:hover,
.navbar-inverse .navbar-brand:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-text {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a:hover,
.navbar-inverse .navbar-nav > li > a:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-nav > .active > a,
.navbar-inverse .navbar-nav > .active > a:hover,
.navbar-inverse .navbar-nav > .active > a:focus {
  color: #fff;
  background-color: #080808;
}
.navbar-inverse .navbar-nav > .disabled > a,
.navbar-inverse .navbar-nav > .disabled > a:hover,
.navbar-inverse .navbar-nav > .disabled > a:focus {
  color: #444;
  background-color: transparent;
}
.navbar-inverse .navbar-toggle {
  border-color: #333;
}
.navbar-inverse .navbar-toggle:hover,
.navbar-inverse .navbar-toggle:focus {
  background-color: #333;
}
.navbar-inverse .navbar-toggle .icon-bar {
  background-color: #fff;
}
.navbar-inverse .navbar-collapse,
.navbar-inverse .navbar-form {
  border-color: #101010;
}
.navbar-inverse .navbar-nav > .open > a,
.navbar-inverse .navbar-nav > .open > a:hover,
.navbar-inverse .navbar-nav > .open > a:focus {
  background-color: #080808;
  color: #fff;
}
@media (max-width: 540px) {
  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
    border-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
    color: #9d9d9d;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #fff;
    background-color: transparent;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #fff;
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #444;
    background-color: transparent;
  }
}
.navbar-inverse .navbar-link {
  color: #9d9d9d;
}
.navbar-inverse .navbar-link:hover {
  color: #fff;
}
.navbar-inverse .btn-link {
  color: #9d9d9d;
}
.navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link:focus {
  color: #fff;
}
.navbar-inverse .btn-link[disabled]:hover,
fieldset[disabled] .navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link[disabled]:focus,
fieldset[disabled] .navbar-inverse .btn-link:focus {
  color: #444;
}
.breadcrumb {
  padding: 8px 15px;
  margin-bottom: 18px;
  list-style: none;
  background-color: #f5f5f5;
  border-radius: 2px;
}
.breadcrumb > li {
  display: inline-block;
}
.breadcrumb > li + li:before {
  content: "/\00a0";
  padding: 0 5px;
  color: #5e5e5e;
}
.breadcrumb > .active {
  color: #777777;
}
.pagination {
  display: inline-block;
  padding-left: 0;
  margin: 18px 0;
  border-radius: 2px;
}
.pagination > li {
  display: inline;
}
.pagination > li > a,
.pagination > li > span {
  position: relative;
  float: left;
  padding: 6px 12px;
  line-height: 1.42857143;
  text-decoration: none;
  color: #337ab7;
  background-color: #fff;
  border: 1px solid #ddd;
  margin-left: -1px;
}
.pagination > li:first-child > a,
.pagination > li:first-child > span {
  margin-left: 0;
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.pagination > li:last-child > a,
.pagination > li:last-child > span {
  border-bottom-right-radius: 2px;
  border-top-right-radius: 2px;
}
.pagination > li > a:hover,
.pagination > li > span:hover,
.pagination > li > a:focus,
.pagination > li > span:focus {
  z-index: 2;
  color: #23527c;
  background-color: #eeeeee;
  border-color: #ddd;
}
.pagination > .active > a,
.pagination > .active > span,
.pagination > .active > a:hover,
.pagination > .active > span:hover,
.pagination > .active > a:focus,
.pagination > .active > span:focus {
  z-index: 3;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
  cursor: default;
}
.pagination > .disabled > span,
.pagination > .disabled > span:hover,
.pagination > .disabled > span:focus,
.pagination > .disabled > a,
.pagination > .disabled > a:hover,
.pagination > .disabled > a:focus {
  color: #777777;
  background-color: #fff;
  border-color: #ddd;
  cursor: not-allowed;
}
.pagination-lg > li > a,
.pagination-lg > li > span {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.pagination-lg > li:first-child > a,
.pagination-lg > li:first-child > span {
  border-bottom-left-radius: 3px;
  border-top-left-radius: 3px;
}
.pagination-lg > li:last-child > a,
.pagination-lg > li:last-child > span {
  border-bottom-right-radius: 3px;
  border-top-right-radius: 3px;
}
.pagination-sm > li > a,
.pagination-sm > li > span {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.pagination-sm > li:first-child > a,
.pagination-sm > li:first-child > span {
  border-bottom-left-radius: 1px;
  border-top-left-radius: 1px;
}
.pagination-sm > li:last-child > a,
.pagination-sm > li:last-child > span {
  border-bottom-right-radius: 1px;
  border-top-right-radius: 1px;
}
.pager {
  padding-left: 0;
  margin: 18px 0;
  list-style: none;
  text-align: center;
}
.pager li {
  display: inline;
}
.pager li > a,
.pager li > span {
  display: inline-block;
  padding: 5px 14px;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 15px;
}
.pager li > a:hover,
.pager li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.pager .next > a,
.pager .next > span {
  float: right;
}
.pager .previous > a,
.pager .previous > span {
  float: left;
}
.pager .disabled > a,
.pager .disabled > a:hover,
.pager .disabled > a:focus,
.pager .disabled > span {
  color: #777777;
  background-color: #fff;
  cursor: not-allowed;
}
.label {
  display: inline;
  padding: .2em .6em .3em;
  font-size: 75%;
  font-weight: bold;
  line-height: 1;
  color: #fff;
  text-align: center;
  white-space: nowrap;
  vertical-align: baseline;
  border-radius: .25em;
}
a.label:hover,
a.label:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.label:empty {
  display: none;
}
.btn .label {
  position: relative;
  top: -1px;
}
.label-default {
  background-color: #777777;
}
.label-default[href]:hover,
.label-default[href]:focus {
  background-color: #5e5e5e;
}
.label-primary {
  background-color: #337ab7;
}
.label-primary[href]:hover,
.label-primary[href]:focus {
  background-color: #286090;
}
.label-success {
  background-color: #5cb85c;
}
.label-success[href]:hover,
.label-success[href]:focus {
  background-color: #449d44;
}
.label-info {
  background-color: #5bc0de;
}
.label-info[href]:hover,
.label-info[href]:focus {
  background-color: #31b0d5;
}
.label-warning {
  background-color: #f0ad4e;
}
.label-warning[href]:hover,
.label-warning[href]:focus {
  background-color: #ec971f;
}
.label-danger {
  background-color: #d9534f;
}
.label-danger[href]:hover,
.label-danger[href]:focus {
  background-color: #c9302c;
}
.badge {
  display: inline-block;
  min-width: 10px;
  padding: 3px 7px;
  font-size: 12px;
  font-weight: bold;
  color: #fff;
  line-height: 1;
  vertical-align: middle;
  white-space: nowrap;
  text-align: center;
  background-color: #777777;
  border-radius: 10px;
}
.badge:empty {
  display: none;
}
.btn .badge {
  position: relative;
  top: -1px;
}
.btn-xs .badge,
.btn-group-xs > .btn .badge {
  top: 0;
  padding: 1px 5px;
}
a.badge:hover,
a.badge:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.list-group-item.active > .badge,
.nav-pills > .active > a > .badge {
  color: #337ab7;
  background-color: #fff;
}
.list-group-item > .badge {
  float: right;
}
.list-group-item > .badge + .badge {
  margin-right: 5px;
}
.nav-pills > li > a > .badge {
  margin-left: 3px;
}
.jumbotron {
  padding-top: 30px;
  padding-bottom: 30px;
  margin-bottom: 30px;
  color: inherit;
  background-color: #eeeeee;
}
.jumbotron h1,
.jumbotron .h1 {
  color: inherit;
}
.jumbotron p {
  margin-bottom: 15px;
  font-size: 20px;
  font-weight: 200;
}
.jumbotron > hr {
  border-top-color: #d5d5d5;
}
.container .jumbotron,
.container-fluid .jumbotron {
  border-radius: 3px;
  padding-left: 0px;
  padding-right: 0px;
}
.jumbotron .container {
  max-width: 100%;
}
@media screen and (min-width: 768px) {
  .jumbotron {
    padding-top: 48px;
    padding-bottom: 48px;
  }
  .container .jumbotron,
  .container-fluid .jumbotron {
    padding-left: 60px;
    padding-right: 60px;
  }
  .jumbotron h1,
  .jumbotron .h1 {
    font-size: 59px;
  }
}
.thumbnail {
  display: block;
  padding: 4px;
  margin-bottom: 18px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: border 0.2s ease-in-out;
  -o-transition: border 0.2s ease-in-out;
  transition: border 0.2s ease-in-out;
}
.thumbnail > img,
.thumbnail a > img {
  margin-left: auto;
  margin-right: auto;
}
a.thumbnail:hover,
a.thumbnail:focus,
a.thumbnail.active {
  border-color: #337ab7;
}
.thumbnail .caption {
  padding: 9px;
  color: #000;
}
.alert {
  padding: 15px;
  margin-bottom: 18px;
  border: 1px solid transparent;
  border-radius: 2px;
}
.alert h4 {
  margin-top: 0;
  color: inherit;
}
.alert .alert-link {
  font-weight: bold;
}
.alert > p,
.alert > ul {
  margin-bottom: 0;
}
.alert > p + p {
  margin-top: 5px;
}
.alert-dismissable,
.alert-dismissible {
  padding-right: 35px;
}
.alert-dismissable .close,
.alert-dismissible .close {
  position: relative;
  top: -2px;
  right: -21px;
  color: inherit;
}
.alert-success {
  background-color: #dff0d8;
  border-color: #d6e9c6;
  color: #3c763d;
}
.alert-success hr {
  border-top-color: #c9e2b3;
}
.alert-success .alert-link {
  color: #2b542c;
}
.alert-info {
  background-color: #d9edf7;
  border-color: #bce8f1;
  color: #31708f;
}
.alert-info hr {
  border-top-color: #a6e1ec;
}
.alert-info .alert-link {
  color: #245269;
}
.alert-warning {
  background-color: #fcf8e3;
  border-color: #faebcc;
  color: #8a6d3b;
}
.alert-warning hr {
  border-top-color: #f7e1b5;
}
.alert-warning .alert-link {
  color: #66512c;
}
.alert-danger {
  background-color: #f2dede;
  border-color: #ebccd1;
  color: #a94442;
}
.alert-danger hr {
  border-top-color: #e4b9c0;
}
.alert-danger .alert-link {
  color: #843534;
}
@-webkit-keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
@keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
.progress {
  overflow: hidden;
  height: 18px;
  margin-bottom: 18px;
  background-color: #f5f5f5;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
}
.progress-bar {
  float: left;
  width: 0%;
  height: 100%;
  font-size: 12px;
  line-height: 18px;
  color: #fff;
  text-align: center;
  background-color: #337ab7;
  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  -webkit-transition: width 0.6s ease;
  -o-transition: width 0.6s ease;
  transition: width 0.6s ease;
}
.progress-striped .progress-bar,
.progress-bar-striped {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-size: 40px 40px;
}
.progress.active .progress-bar,
.progress-bar.active {
  -webkit-animation: progress-bar-stripes 2s linear infinite;
  -o-animation: progress-bar-stripes 2s linear infinite;
  animation: progress-bar-stripes 2s linear infinite;
}
.progress-bar-success {
  background-color: #5cb85c;
}
.progress-striped .progress-bar-success {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-info {
  background-color: #5bc0de;
}
.progress-striped .progress-bar-info {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-warning {
  background-color: #f0ad4e;
}
.progress-striped .progress-bar-warning {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-danger {
  background-color: #d9534f;
}
.progress-striped .progress-bar-danger {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.media {
  margin-top: 15px;
}
.media:first-child {
  margin-top: 0;
}
.media,
.media-body {
  zoom: 1;
  overflow: hidden;
}
.media-body {
  width: 10000px;
}
.media-object {
  display: block;
}
.media-object.img-thumbnail {
  max-width: none;
}
.media-right,
.media > .pull-right {
  padding-left: 10px;
}
.media-left,
.media > .pull-left {
  padding-right: 10px;
}
.media-left,
.media-right,
.media-body {
  display: table-cell;
  vertical-align: top;
}
.media-middle {
  vertical-align: middle;
}
.media-bottom {
  vertical-align: bottom;
}
.media-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.media-list {
  padding-left: 0;
  list-style: none;
}
.list-group {
  margin-bottom: 20px;
  padding-left: 0;
}
.list-group-item {
  position: relative;
  display: block;
  padding: 10px 15px;
  margin-bottom: -1px;
  background-color: #fff;
  border: 1px solid #ddd;
}
.list-group-item:first-child {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
}
.list-group-item:last-child {
  margin-bottom: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
a.list-group-item,
button.list-group-item {
  color: #555;
}
a.list-group-item .list-group-item-heading,
button.list-group-item .list-group-item-heading {
  color: #333;
}
a.list-group-item:hover,
button.list-group-item:hover,
a.list-group-item:focus,
button.list-group-item:focus {
  text-decoration: none;
  color: #555;
  background-color: #f5f5f5;
}
button.list-group-item {
  width: 100%;
  text-align: left;
}
.list-group-item.disabled,
.list-group-item.disabled:hover,
.list-group-item.disabled:focus {
  background-color: #eeeeee;
  color: #777777;
  cursor: not-allowed;
}
.list-group-item.disabled .list-group-item-heading,
.list-group-item.disabled:hover .list-group-item-heading,
.list-group-item.disabled:focus .list-group-item-heading {
  color: inherit;
}
.list-group-item.disabled .list-group-item-text,
.list-group-item.disabled:hover .list-group-item-text,
.list-group-item.disabled:focus .list-group-item-text {
  color: #777777;
}
.list-group-item.active,
.list-group-item.active:hover,
.list-group-item.active:focus {
  z-index: 2;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.list-group-item.active .list-group-item-heading,
.list-group-item.active:hover .list-group-item-heading,
.list-group-item.active:focus .list-group-item-heading,
.list-group-item.active .list-group-item-heading > small,
.list-group-item.active:hover .list-group-item-heading > small,
.list-group-item.active:focus .list-group-item-heading > small,
.list-group-item.active .list-group-item-heading > .small,
.list-group-item.active:hover .list-group-item-heading > .small,
.list-group-item.active:focus .list-group-item-heading > .small {
  color: inherit;
}
.list-group-item.active .list-group-item-text,
.list-group-item.active:hover .list-group-item-text,
.list-group-item.active:focus .list-group-item-text {
  color: #c7ddef;
}
.list-group-item-success {
  color: #3c763d;
  background-color: #dff0d8;
}
a.list-group-item-success,
button.list-group-item-success {
  color: #3c763d;
}
a.list-group-item-success .list-group-item-heading,
button.list-group-item-success .list-group-item-heading {
  color: inherit;
}
a.list-group-item-success:hover,
button.list-group-item-success:hover,
a.list-group-item-success:focus,
button.list-group-item-success:focus {
  color: #3c763d;
  background-color: #d0e9c6;
}
a.list-group-item-success.active,
button.list-group-item-success.active,
a.list-group-item-success.active:hover,
button.list-group-item-success.active:hover,
a.list-group-item-success.active:focus,
button.list-group-item-success.active:focus {
  color: #fff;
  background-color: #3c763d;
  border-color: #3c763d;
}
.list-group-item-info {
  color: #31708f;
  background-color: #d9edf7;
}
a.list-group-item-info,
button.list-group-item-info {
  color: #31708f;
}
a.list-group-item-info .list-group-item-heading,
button.list-group-item-info .list-group-item-heading {
  color: inherit;
}
a.list-group-item-info:hover,
button.list-group-item-info:hover,
a.list-group-item-info:focus,
button.list-group-item-info:focus {
  color: #31708f;
  background-color: #c4e3f3;
}
a.list-group-item-info.active,
button.list-group-item-info.active,
a.list-group-item-info.active:hover,
button.list-group-item-info.active:hover,
a.list-group-item-info.active:focus,
button.list-group-item-info.active:focus {
  color: #fff;
  background-color: #31708f;
  border-color: #31708f;
}
.list-group-item-warning {
  color: #8a6d3b;
  background-color: #fcf8e3;
}
a.list-group-item-warning,
button.list-group-item-warning {
  color: #8a6d3b;
}
a.list-group-item-warning .list-group-item-heading,
button.list-group-item-warning .list-group-item-heading {
  color: inherit;
}
a.list-group-item-warning:hover,
button.list-group-item-warning:hover,
a.list-group-item-warning:focus,
button.list-group-item-warning:focus {
  color: #8a6d3b;
  background-color: #faf2cc;
}
a.list-group-item-warning.active,
button.list-group-item-warning.active,
a.list-group-item-warning.active:hover,
button.list-group-item-warning.active:hover,
a.list-group-item-warning.active:focus,
button.list-group-item-warning.active:focus {
  color: #fff;
  background-color: #8a6d3b;
  border-color: #8a6d3b;
}
.list-group-item-danger {
  color: #a94442;
  background-color: #f2dede;
}
a.list-group-item-danger,
button.list-group-item-danger {
  color: #a94442;
}
a.list-group-item-danger .list-group-item-heading,
button.list-group-item-danger .list-group-item-heading {
  color: inherit;
}
a.list-group-item-danger:hover,
button.list-group-item-danger:hover,
a.list-group-item-danger:focus,
button.list-group-item-danger:focus {
  color: #a94442;
  background-color: #ebcccc;
}
a.list-group-item-danger.active,
button.list-group-item-danger.active,
a.list-group-item-danger.active:hover,
button.list-group-item-danger.active:hover,
a.list-group-item-danger.active:focus,
button.list-group-item-danger.active:focus {
  color: #fff;
  background-color: #a94442;
  border-color: #a94442;
}
.list-group-item-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.list-group-item-text {
  margin-bottom: 0;
  line-height: 1.3;
}
.panel {
  margin-bottom: 18px;
  background-color: #fff;
  border: 1px solid transparent;
  border-radius: 2px;
  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
}
.panel-body {
  padding: 15px;
}
.panel-heading {
  padding: 10px 15px;
  border-bottom: 1px solid transparent;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel-heading > .dropdown .dropdown-toggle {
  color: inherit;
}
.panel-title {
  margin-top: 0;
  margin-bottom: 0;
  font-size: 15px;
  color: inherit;
}
.panel-title > a,
.panel-title > small,
.panel-title > .small,
.panel-title > small > a,
.panel-title > .small > a {
  color: inherit;
}
.panel-footer {
  padding: 10px 15px;
  background-color: #f5f5f5;
  border-top: 1px solid #ddd;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .list-group,
.panel > .panel-collapse > .list-group {
  margin-bottom: 0;
}
.panel > .list-group .list-group-item,
.panel > .panel-collapse > .list-group .list-group-item {
  border-width: 1px 0;
  border-radius: 0;
}
.panel > .list-group:first-child .list-group-item:first-child,
.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
  border-top: 0;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .list-group:last-child .list-group-item:last-child,
.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
  border-bottom: 0;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.panel-heading + .list-group .list-group-item:first-child {
  border-top-width: 0;
}
.list-group + .panel-footer {
  border-top-width: 0;
}
.panel > .table,
.panel > .table-responsive > .table,
.panel > .panel-collapse > .table {
  margin-bottom: 0;
}
.panel > .table caption,
.panel > .table-responsive > .table caption,
.panel > .panel-collapse > .table caption {
  padding-left: 15px;
  padding-right: 15px;
}
.panel > .table:first-child,
.panel > .table-responsive:first-child > .table:first-child {
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
  border-top-left-radius: 1px;
  border-top-right-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
  border-top-right-radius: 1px;
}
.panel > .table:last-child,
.panel > .table-responsive:last-child > .table:last-child {
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
  border-bottom-left-radius: 1px;
  border-bottom-right-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
  border-bottom-right-radius: 1px;
}
.panel > .panel-body + .table,
.panel > .panel-body + .table-responsive,
.panel > .table + .panel-body,
.panel > .table-responsive + .panel-body {
  border-top: 1px solid #ddd;
}
.panel > .table > tbody:first-child > tr:first-child th,
.panel > .table > tbody:first-child > tr:first-child td {
  border-top: 0;
}
.panel > .table-bordered,
.panel > .table-responsive > .table-bordered {
  border: 0;
}
.panel > .table-bordered > thead > tr > th:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
.panel > .table-bordered > tbody > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
.panel > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-bordered > thead > tr > td:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
.panel > .table-bordered > tbody > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
.panel > .table-bordered > tfoot > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
  border-left: 0;
}
.panel > .table-bordered > thead > tr > th:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
.panel > .table-bordered > tbody > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
.panel > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-bordered > thead > tr > td:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
.panel > .table-bordered > tbody > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
.panel > .table-bordered > tfoot > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
  border-right: 0;
}
.panel > .table-bordered > thead > tr:first-child > td,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
.panel > .table-bordered > tbody > tr:first-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
.panel > .table-bordered > thead > tr:first-child > th,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
.panel > .table-bordered > tbody > tr:first-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
  border-bottom: 0;
}
.panel > .table-bordered > tbody > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
.panel > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-bordered > tbody > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
.panel > .table-bordered > tfoot > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
  border-bottom: 0;
}
.panel > .table-responsive {
  border: 0;
  margin-bottom: 0;
}
.panel-group {
  margin-bottom: 18px;
}
.panel-group .panel {
  margin-bottom: 0;
  border-radius: 2px;
}
.panel-group .panel + .panel {
  margin-top: 5px;
}
.panel-group .panel-heading {
  border-bottom: 0;
}
.panel-group .panel-heading + .panel-collapse > .panel-body,
.panel-group .panel-heading + .panel-collapse > .list-group {
  border-top: 1px solid #ddd;
}
.panel-group .panel-footer {
  border-top: 0;
}
.panel-group .panel-footer + .panel-collapse .panel-body {
  border-bottom: 1px solid #ddd;
}
.panel-default {
  border-color: #ddd;
}
.panel-default > .panel-heading {
  color: #333333;
  background-color: #f5f5f5;
  border-color: #ddd;
}
.panel-default > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ddd;
}
.panel-default > .panel-heading .badge {
  color: #f5f5f5;
  background-color: #333333;
}
.panel-default > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ddd;
}
.panel-primary {
  border-color: #337ab7;
}
.panel-primary > .panel-heading {
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.panel-primary > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #337ab7;
}
.panel-primary > .panel-heading .badge {
  color: #337ab7;
  background-color: #fff;
}
.panel-primary > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #337ab7;
}
.panel-success {
  border-color: #d6e9c6;
}
.panel-success > .panel-heading {
  color: #3c763d;
  background-color: #dff0d8;
  border-color: #d6e9c6;
}
.panel-success > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #d6e9c6;
}
.panel-success > .panel-heading .badge {
  color: #dff0d8;
  background-color: #3c763d;
}
.panel-success > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #d6e9c6;
}
.panel-info {
  border-color: #bce8f1;
}
.panel-info > .panel-heading {
  color: #31708f;
  background-color: #d9edf7;
  border-color: #bce8f1;
}
.panel-info > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #bce8f1;
}
.panel-info > .panel-heading .badge {
  color: #d9edf7;
  background-color: #31708f;
}
.panel-info > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #bce8f1;
}
.panel-warning {
  border-color: #faebcc;
}
.panel-warning > .panel-heading {
  color: #8a6d3b;
  background-color: #fcf8e3;
  border-color: #faebcc;
}
.panel-warning > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #faebcc;
}
.panel-warning > .panel-heading .badge {
  color: #fcf8e3;
  background-color: #8a6d3b;
}
.panel-warning > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #faebcc;
}
.panel-danger {
  border-color: #ebccd1;
}
.panel-danger > .panel-heading {
  color: #a94442;
  background-color: #f2dede;
  border-color: #ebccd1;
}
.panel-danger > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ebccd1;
}
.panel-danger > .panel-heading .badge {
  color: #f2dede;
  background-color: #a94442;
}
.panel-danger > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ebccd1;
}
.embed-responsive {
  position: relative;
  display: block;
  height: 0;
  padding: 0;
  overflow: hidden;
}
.embed-responsive .embed-responsive-item,
.embed-responsive iframe,
.embed-responsive embed,
.embed-responsive object,
.embed-responsive video {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  height: 100%;
  width: 100%;
  border: 0;
}
.embed-responsive-16by9 {
  padding-bottom: 56.25%;
}
.embed-responsive-4by3 {
  padding-bottom: 75%;
}
.well {
  min-height: 20px;
  padding: 19px;
  margin-bottom: 20px;
  background-color: #f5f5f5;
  border: 1px solid #e3e3e3;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
}
.well blockquote {
  border-color: #ddd;
  border-color: rgba(0, 0, 0, 0.15);
}
.well-lg {
  padding: 24px;
  border-radius: 3px;
}
.well-sm {
  padding: 9px;
  border-radius: 1px;
}
.close {
  float: right;
  font-size: 19.5px;
  font-weight: bold;
  line-height: 1;
  color: #000;
  text-shadow: 0 1px 0 #fff;
  opacity: 0.2;
  filter: alpha(opacity=20);
}
.close:hover,
.close:focus {
  color: #000;
  text-decoration: none;
  cursor: pointer;
  opacity: 0.5;
  filter: alpha(opacity=50);
}
button.close {
  padding: 0;
  cursor: pointer;
  background: transparent;
  border: 0;
  -webkit-appearance: none;
}
.modal-open {
  overflow: hidden;
}
.modal {
  display: none;
  overflow: hidden;
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1050;
  -webkit-overflow-scrolling: touch;
  outline: 0;
}
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, -25%);
  -ms-transform: translate(0, -25%);
  -o-transform: translate(0, -25%);
  transform: translate(0, -25%);
  -webkit-transition: -webkit-transform 0.3s ease-out;
  -moz-transition: -moz-transform 0.3s ease-out;
  -o-transition: -o-transform 0.3s ease-out;
  transition: transform 0.3s ease-out;
}
.modal.in .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
.modal-open .modal {
  overflow-x: hidden;
  overflow-y: auto;
}
.modal-dialog {
  position: relative;
  width: auto;
  margin: 10px;
}
.modal-content {
  position: relative;
  background-color: #fff;
  border: 1px solid #999;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  background-clip: padding-box;
  outline: 0;
}
.modal-backdrop {
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1040;
  background-color: #000;
}
.modal-backdrop.fade {
  opacity: 0;
  filter: alpha(opacity=0);
}
.modal-backdrop.in {
  opacity: 0.5;
  filter: alpha(opacity=50);
}
.modal-header {
  padding: 15px;
  border-bottom: 1px solid #e5e5e5;
}
.modal-header .close {
  margin-top: -2px;
}
.modal-title {
  margin: 0;
  line-height: 1.42857143;
}
.modal-body {
  position: relative;
  padding: 15px;
}
.modal-footer {
  padding: 15px;
  text-align: right;
  border-top: 1px solid #e5e5e5;
}
.modal-footer .btn + .btn {
  margin-left: 5px;
  margin-bottom: 0;
}
.modal-footer .btn-group .btn + .btn {
  margin-left: -1px;
}
.modal-footer .btn-block + .btn-block {
  margin-left: 0;
}
.modal-scrollbar-measure {
  position: absolute;
  top: -9999px;
  width: 50px;
  height: 50px;
  overflow: scroll;
}
@media (min-width: 768px) {
  .modal-dialog {
    width: 600px;
    margin: 30px auto;
  }
  .modal-content {
    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
  }
  .modal-sm {
    width: 300px;
  }
}
@media (min-width: 992px) {
  .modal-lg {
    width: 900px;
  }
}
.tooltip {
  position: absolute;
  z-index: 1070;
  display: block;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 12px;
  opacity: 0;
  filter: alpha(opacity=0);
}
.tooltip.in {
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.tooltip.top {
  margin-top: -3px;
  padding: 5px 0;
}
.tooltip.right {
  margin-left: 3px;
  padding: 0 5px;
}
.tooltip.bottom {
  margin-top: 3px;
  padding: 5px 0;
}
.tooltip.left {
  margin-left: -3px;
  padding: 0 5px;
}
.tooltip-inner {
  max-width: 200px;
  padding: 3px 8px;
  color: #fff;
  text-align: center;
  background-color: #000;
  border-radius: 2px;
}
.tooltip-arrow {
  position: absolute;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.tooltip.top .tooltip-arrow {
  bottom: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-left .tooltip-arrow {
  bottom: 0;
  right: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-right .tooltip-arrow {
  bottom: 0;
  left: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.right .tooltip-arrow {
  top: 50%;
  left: 0;
  margin-top: -5px;
  border-width: 5px 5px 5px 0;
  border-right-color: #000;
}
.tooltip.left .tooltip-arrow {
  top: 50%;
  right: 0;
  margin-top: -5px;
  border-width: 5px 0 5px 5px;
  border-left-color: #000;
}
.tooltip.bottom .tooltip-arrow {
  top: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-left .tooltip-arrow {
  top: 0;
  right: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-right .tooltip-arrow {
  top: 0;
  left: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.popover {
  position: absolute;
  top: 0;
  left: 0;
  z-index: 1060;
  display: none;
  max-width: 276px;
  padding: 1px;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 13px;
  background-color: #fff;
  background-clip: padding-box;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.popover.top {
  margin-top: -10px;
}
.popover.right {
  margin-left: 10px;
}
.popover.bottom {
  margin-top: 10px;
}
.popover.left {
  margin-left: -10px;
}
.popover-title {
  margin: 0;
  padding: 8px 14px;
  font-size: 13px;
  background-color: #f7f7f7;
  border-bottom: 1px solid #ebebeb;
  border-radius: 2px 2px 0 0;
}
.popover-content {
  padding: 9px 14px;
}
.popover > .arrow,
.popover > .arrow:after {
  position: absolute;
  display: block;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.popover > .arrow {
  border-width: 11px;
}
.popover > .arrow:after {
  border-width: 10px;
  content: "";
}
.popover.top > .arrow {
  left: 50%;
  margin-left: -11px;
  border-bottom-width: 0;
  border-top-color: #999999;
  border-top-color: rgba(0, 0, 0, 0.25);
  bottom: -11px;
}
.popover.top > .arrow:after {
  content: " ";
  bottom: 1px;
  margin-left: -10px;
  border-bottom-width: 0;
  border-top-color: #fff;
}
.popover.right > .arrow {
  top: 50%;
  left: -11px;
  margin-top: -11px;
  border-left-width: 0;
  border-right-color: #999999;
  border-right-color: rgba(0, 0, 0, 0.25);
}
.popover.right > .arrow:after {
  content: " ";
  left: 1px;
  bottom: -10px;
  border-left-width: 0;
  border-right-color: #fff;
}
.popover.bottom > .arrow {
  left: 50%;
  margin-left: -11px;
  border-top-width: 0;
  border-bottom-color: #999999;
  border-bottom-color: rgba(0, 0, 0, 0.25);
  top: -11px;
}
.popover.bottom > .arrow:after {
  content: " ";
  top: 1px;
  margin-left: -10px;
  border-top-width: 0;
  border-bottom-color: #fff;
}
.popover.left > .arrow {
  top: 50%;
  right: -11px;
  margin-top: -11px;
  border-right-width: 0;
  border-left-color: #999999;
  border-left-color: rgba(0, 0, 0, 0.25);
}
.popover.left > .arrow:after {
  content: " ";
  right: 1px;
  border-right-width: 0;
  border-left-color: #fff;
  bottom: -10px;
}
.carousel {
  position: relative;
}
.carousel-inner {
  position: relative;
  overflow: hidden;
  width: 100%;
}
.carousel-inner > .item {
  display: none;
  position: relative;
  -webkit-transition: 0.6s ease-in-out left;
  -o-transition: 0.6s ease-in-out left;
  transition: 0.6s ease-in-out left;
}
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  line-height: 1;
}
@media all and (transform-3d), (-webkit-transform-3d) {
  .carousel-inner > .item {
    -webkit-transition: -webkit-transform 0.6s ease-in-out;
    -moz-transition: -moz-transform 0.6s ease-in-out;
    -o-transition: -o-transform 0.6s ease-in-out;
    transition: transform 0.6s ease-in-out;
    -webkit-backface-visibility: hidden;
    -moz-backface-visibility: hidden;
    backface-visibility: hidden;
    -webkit-perspective: 1000px;
    -moz-perspective: 1000px;
    perspective: 1000px;
  }
  .carousel-inner > .item.next,
  .carousel-inner > .item.active.right {
    -webkit-transform: translate3d(100%, 0, 0);
    transform: translate3d(100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.prev,
  .carousel-inner > .item.active.left {
    -webkit-transform: translate3d(-100%, 0, 0);
    transform: translate3d(-100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.next.left,
  .carousel-inner > .item.prev.right,
  .carousel-inner > .item.active {
    -webkit-transform: translate3d(0, 0, 0);
    transform: translate3d(0, 0, 0);
    left: 0;
  }
}
.carousel-inner > .active,
.carousel-inner > .next,
.carousel-inner > .prev {
  display: block;
}
.carousel-inner > .active {
  left: 0;
}
.carousel-inner > .next,
.carousel-inner > .prev {
  position: absolute;
  top: 0;
  width: 100%;
}
.carousel-inner > .next {
  left: 100%;
}
.carousel-inner > .prev {
  left: -100%;
}
.carousel-inner > .next.left,
.carousel-inner > .prev.right {
  left: 0;
}
.carousel-inner > .active.left {
  left: -100%;
}
.carousel-inner > .active.right {
  left: 100%;
}
.carousel-control {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  width: 15%;
  opacity: 0.5;
  filter: alpha(opacity=50);
  font-size: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
  background-color: rgba(0, 0, 0, 0);
}
.carousel-control.left {
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
}
.carousel-control.right {
  left: auto;
  right: 0;
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
}
.carousel-control:hover,
.carousel-control:focus {
  outline: 0;
  color: #fff;
  text-decoration: none;
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.carousel-control .icon-prev,
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right {
  position: absolute;
  top: 50%;
  margin-top: -10px;
  z-index: 5;
  display: inline-block;
}
.carousel-control .icon-prev,
.carousel-control .glyphicon-chevron-left {
  left: 50%;
  margin-left: -10px;
}
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-right {
  right: 50%;
  margin-right: -10px;
}
.carousel-control .icon-prev,
.carousel-control .icon-next {
  width: 20px;
  height: 20px;
  line-height: 1;
  font-family: serif;
}
.carousel-control .icon-prev:before {
  content: '\2039';
}
.carousel-control .icon-next:before {
  content: '\203a';
}
.carousel-indicators {
  position: absolute;
  bottom: 10px;
  left: 50%;
  z-index: 15;
  width: 60%;
  margin-left: -30%;
  padding-left: 0;
  list-style: none;
  text-align: center;
}
.carousel-indicators li {
  display: inline-block;
  width: 10px;
  height: 10px;
  margin: 1px;
  text-indent: -999px;
  border: 1px solid #fff;
  border-radius: 10px;
  cursor: pointer;
  background-color: #000 \9;
  background-color: rgba(0, 0, 0, 0);
}
.carousel-indicators .active {
  margin: 0;
  width: 12px;
  height: 12px;
  background-color: #fff;
}
.carousel-caption {
  position: absolute;
  left: 15%;
  right: 15%;
  bottom: 20px;
  z-index: 10;
  padding-top: 20px;
  padding-bottom: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
}
.carousel-caption .btn {
  text-shadow: none;
}
@media screen and (min-width: 768px) {
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-prev,
  .carousel-control .icon-next {
    width: 30px;
    height: 30px;
    margin-top: -10px;
    font-size: 30px;
  }
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .icon-prev {
    margin-left: -10px;
  }
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-next {
    margin-right: -10px;
  }
  .carousel-caption {
    left: 20%;
    right: 20%;
    padding-bottom: 30px;
  }
  .carousel-indicators {
    bottom: 20px;
  }
}
.clearfix:before,
.clearfix:after,
.dl-horizontal dd:before,
.dl-horizontal dd:after,
.container:before,
.container:after,
.container-fluid:before,
.container-fluid:after,
.row:before,
.row:after,
.form-horizontal .form-group:before,
.form-horizontal .form-group:after,
.btn-toolbar:before,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:before,
.btn-group-vertical > .btn-group:after,
.nav:before,
.nav:after,
.navbar:before,
.navbar:after,
.navbar-header:before,
.navbar-header:after,
.navbar-collapse:before,
.navbar-collapse:after,
.pager:before,
.pager:after,
.panel-body:before,
.panel-body:after,
.modal-header:before,
.modal-header:after,
.modal-footer:before,
.modal-footer:after,
.item_buttons:before,
.item_buttons:after {
  content: " ";
  display: table;
}
.clearfix:after,
.dl-horizontal dd:after,
.container:after,
.container-fluid:after,
.row:after,
.form-horizontal .form-group:after,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:after,
.nav:after,
.navbar:after,
.navbar-header:after,
.navbar-collapse:after,
.pager:after,
.panel-body:after,
.modal-header:after,
.modal-footer:after,
.item_buttons:after {
  clear: both;
}
.center-block {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.pull-right {
  float: right !important;
}
.pull-left {
  float: left !important;
}
.hide {
  display: none !important;
}
.show {
  display: block !important;
}
.invisible {
  visibility: hidden;
}
.text-hide {
  font: 0/0 a;
  color: transparent;
  text-shadow: none;
  background-color: transparent;
  border: 0;
}
.hidden {
  display: none !important;
}
.affix {
  position: fixed;
}
@-ms-viewport {
  width: device-width;
}
.visible-xs,
.visible-sm,
.visible-md,
.visible-lg {
  display: none !important;
}
.visible-xs-block,
.visible-xs-inline,
.visible-xs-inline-block,
.visible-sm-block,
.visible-sm-inline,
.visible-sm-inline-block,
.visible-md-block,
.visible-md-inline,
.visible-md-inline-block,
.visible-lg-block,
.visible-lg-inline,
.visible-lg-inline-block {
  display: none !important;
}
@media (max-width: 767px) {
  .visible-xs {
    display: block !important;
  }
  table.visible-xs {
    display: table !important;
  }
  tr.visible-xs {
    display: table-row !important;
  }
  th.visible-xs,
  td.visible-xs {
    display: table-cell !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-block {
    display: block !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline {
    display: inline !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm {
    display: block !important;
  }
  table.visible-sm {
    display: table !important;
  }
  tr.visible-sm {
    display: table-row !important;
  }
  th.visible-sm,
  td.visible-sm {
    display: table-cell !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-block {
    display: block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline {
    display: inline !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md {
    display: block !important;
  }
  table.visible-md {
    display: table !important;
  }
  tr.visible-md {
    display: table-row !important;
  }
  th.visible-md,
  td.visible-md {
    display: table-cell !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-block {
    display: block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline {
    display: inline !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg {
    display: block !important;
  }
  table.visible-lg {
    display: table !important;
  }
  tr.visible-lg {
    display: table-row !important;
  }
  th.visible-lg,
  td.visible-lg {
    display: table-cell !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-block {
    display: block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline {
    display: inline !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline-block {
    display: inline-block !important;
  }
}
@media (max-width: 767px) {
  .hidden-xs {
    display: none !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .hidden-sm {
    display: none !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .hidden-md {
    display: none !important;
  }
}
@media (min-width: 1200px) {
  .hidden-lg {
    display: none !important;
  }
}
.visible-print {
  display: none !important;
}
@media print {
  .visible-print {
    display: block !important;
  }
  table.visible-print {
    display: table !important;
  }
  tr.visible-print {
    display: table-row !important;
  }
  th.visible-print,
  td.visible-print {
    display: table-cell !important;
  }
}
.visible-print-block {
  display: none !important;
}
@media print {
  .visible-print-block {
    display: block !important;
  }
}
.visible-print-inline {
  display: none !important;
}
@media print {
  .visible-print-inline {
    display: inline !important;
  }
}
.visible-print-inline-block {
  display: none !important;
}
@media print {
  .visible-print-inline-block {
    display: inline-block !important;
  }
}
@media print {
  .hidden-print {
    display: none !important;
  }
}
/*!
*
* Font Awesome
*
*/
/*!
 *  Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome
 *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
 */
/* FONT PATH
 * -------------------------- */
@font-face {
  font-family: 'FontAwesome';
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.7.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff2?v=4.7.0') format('woff2'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.7.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.7.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular') format('svg');
  font-weight: normal;
  font-style: normal;
}
.fa {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
/* makes the font 33% larger relative to the icon container */
.fa-lg {
  font-size: 1.33333333em;
  line-height: 0.75em;
  vertical-align: -15%;
}
.fa-2x {
  font-size: 2em;
}
.fa-3x {
  font-size: 3em;
}
.fa-4x {
  font-size: 4em;
}
.fa-5x {
  font-size: 5em;
}
.fa-fw {
  width: 1.28571429em;
  text-align: center;
}
.fa-ul {
  padding-left: 0;
  margin-left: 2.14285714em;
  list-style-type: none;
}
.fa-ul > li {
  position: relative;
}
.fa-li {
  position: absolute;
  left: -2.14285714em;
  width: 2.14285714em;
  top: 0.14285714em;
  text-align: center;
}
.fa-li.fa-lg {
  left: -1.85714286em;
}
.fa-border {
  padding: .2em .25em .15em;
  border: solid 0.08em #eee;
  border-radius: .1em;
}
.fa-pull-left {
  float: left;
}
.fa-pull-right {
  float: right;
}
.fa.fa-pull-left {
  margin-right: .3em;
}
.fa.fa-pull-right {
  margin-left: .3em;
}
/* Deprecated as of 4.4.0 */
.pull-right {
  float: right;
}
.pull-left {
  float: left;
}
.fa.pull-left {
  margin-right: .3em;
}
.fa.pull-right {
  margin-left: .3em;
}
.fa-spin {
  -webkit-animation: fa-spin 2s infinite linear;
  animation: fa-spin 2s infinite linear;
}
.fa-pulse {
  -webkit-animation: fa-spin 1s infinite steps(8);
  animation: fa-spin 1s infinite steps(8);
}
@-webkit-keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
@keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
.fa-rotate-90 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=1)";
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2)";
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=3)";
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1)";
  -webkit-transform: scale(-1, 1);
  -ms-transform: scale(-1, 1);
  transform: scale(-1, 1);
}
.fa-flip-vertical {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1)";
  -webkit-transform: scale(1, -1);
  -ms-transform: scale(1, -1);
  transform: scale(1, -1);
}
:root .fa-rotate-90,
:root .fa-rotate-180,
:root .fa-rotate-270,
:root .fa-flip-horizontal,
:root .fa-flip-vertical {
  filter: none;
}
.fa-stack {
  position: relative;
  display: inline-block;
  width: 2em;
  height: 2em;
  line-height: 2em;
  vertical-align: middle;
}
.fa-stack-1x,
.fa-stack-2x {
  position: absolute;
  left: 0;
  width: 100%;
  text-align: center;
}
.fa-stack-1x {
  line-height: inherit;
}
.fa-stack-2x {
  font-size: 2em;
}
.fa-inverse {
  color: #fff;
}
/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
   readers do not read off random characters that represent icons */
.fa-glass:before {
  content: "\f000";
}
.fa-music:before {
  content: "\f001";
}
.fa-search:before {
  content: "\f002";
}
.fa-envelope-o:before {
  content: "\f003";
}
.fa-heart:before {
  content: "\f004";
}
.fa-star:before {
  content: "\f005";
}
.fa-star-o:before {
  content: "\f006";
}
.fa-user:before {
  content: "\f007";
}
.fa-film:before {
  content: "\f008";
}
.fa-th-large:before {
  content: "\f009";
}
.fa-th:before {
  content: "\f00a";
}
.fa-th-list:before {
  content: "\f00b";
}
.fa-check:before {
  content: "\f00c";
}
.fa-remove:before,
.fa-close:before,
.fa-times:before {
  content: "\f00d";
}
.fa-search-plus:before {
  content: "\f00e";
}
.fa-search-minus:before {
  content: "\f010";
}
.fa-power-off:before {
  content: "\f011";
}
.fa-signal:before {
  content: "\f012";
}
.fa-gear:before,
.fa-cog:before {
  content: "\f013";
}
.fa-trash-o:before {
  content: "\f014";
}
.fa-home:before {
  content: "\f015";
}
.fa-file-o:before {
  content: "\f016";
}
.fa-clock-o:before {
  content: "\f017";
}
.fa-road:before {
  content: "\f018";
}
.fa-download:before {
  content: "\f019";
}
.fa-arrow-circle-o-down:before {
  content: "\f01a";
}
.fa-arrow-circle-o-up:before {
  content: "\f01b";
}
.fa-inbox:before {
  content: "\f01c";
}
.fa-play-circle-o:before {
  content: "\f01d";
}
.fa-rotate-right:before,
.fa-repeat:before {
  content: "\f01e";
}
.fa-refresh:before {
  content: "\f021";
}
.fa-list-alt:before {
  content: "\f022";
}
.fa-lock:before {
  content: "\f023";
}
.fa-flag:before {
  content: "\f024";
}
.fa-headphones:before {
  content: "\f025";
}
.fa-volume-off:before {
  content: "\f026";
}
.fa-volume-down:before {
  content: "\f027";
}
.fa-volume-up:before {
  content: "\f028";
}
.fa-qrcode:before {
  content: "\f029";
}
.fa-barcode:before {
  content: "\f02a";
}
.fa-tag:before {
  content: "\f02b";
}
.fa-tags:before {
  content: "\f02c";
}
.fa-book:before {
  content: "\f02d";
}
.fa-bookmark:before {
  content: "\f02e";
}
.fa-print:before {
  content: "\f02f";
}
.fa-camera:before {
  content: "\f030";
}
.fa-font:before {
  content: "\f031";
}
.fa-bold:before {
  content: "\f032";
}
.fa-italic:before {
  content: "\f033";
}
.fa-text-height:before {
  content: "\f034";
}
.fa-text-width:before {
  content: "\f035";
}
.fa-align-left:before {
  content: "\f036";
}
.fa-align-center:before {
  content: "\f037";
}
.fa-align-right:before {
  content: "\f038";
}
.fa-align-justify:before {
  content: "\f039";
}
.fa-list:before {
  content: "\f03a";
}
.fa-dedent:before,
.fa-outdent:before {
  content: "\f03b";
}
.fa-indent:before {
  content: "\f03c";
}
.fa-video-camera:before {
  content: "\f03d";
}
.fa-photo:before,
.fa-image:before,
.fa-picture-o:before {
  content: "\f03e";
}
.fa-pencil:before {
  content: "\f040";
}
.fa-map-marker:before {
  content: "\f041";
}
.fa-adjust:before {
  content: "\f042";
}
.fa-tint:before {
  content: "\f043";
}
.fa-edit:before,
.fa-pencil-square-o:before {
  content: "\f044";
}
.fa-share-square-o:before {
  content: "\f045";
}
.fa-check-square-o:before {
  content: "\f046";
}
.fa-arrows:before {
  content: "\f047";
}
.fa-step-backward:before {
  content: "\f048";
}
.fa-fast-backward:before {
  content: "\f049";
}
.fa-backward:before {
  content: "\f04a";
}
.fa-play:before {
  content: "\f04b";
}
.fa-pause:before {
  content: "\f04c";
}
.fa-stop:before {
  content: "\f04d";
}
.fa-forward:before {
  content: "\f04e";
}
.fa-fast-forward:before {
  content: "\f050";
}
.fa-step-forward:before {
  content: "\f051";
}
.fa-eject:before {
  content: "\f052";
}
.fa-chevron-left:before {
  content: "\f053";
}
.fa-chevron-right:before {
  content: "\f054";
}
.fa-plus-circle:before {
  content: "\f055";
}
.fa-minus-circle:before {
  content: "\f056";
}
.fa-times-circle:before {
  content: "\f057";
}
.fa-check-circle:before {
  content: "\f058";
}
.fa-question-circle:before {
  content: "\f059";
}
.fa-info-circle:before {
  content: "\f05a";
}
.fa-crosshairs:before {
  content: "\f05b";
}
.fa-times-circle-o:before {
  content: "\f05c";
}
.fa-check-circle-o:before {
  content: "\f05d";
}
.fa-ban:before {
  content: "\f05e";
}
.fa-arrow-left:before {
  content: "\f060";
}
.fa-arrow-right:before {
  content: "\f061";
}
.fa-arrow-up:before {
  content: "\f062";
}
.fa-arrow-down:before {
  content: "\f063";
}
.fa-mail-forward:before,
.fa-share:before {
  content: "\f064";
}
.fa-expand:before {
  content: "\f065";
}
.fa-compress:before {
  content: "\f066";
}
.fa-plus:before {
  content: "\f067";
}
.fa-minus:before {
  content: "\f068";
}
.fa-asterisk:before {
  content: "\f069";
}
.fa-exclamation-circle:before {
  content: "\f06a";
}
.fa-gift:before {
  content: "\f06b";
}
.fa-leaf:before {
  content: "\f06c";
}
.fa-fire:before {
  content: "\f06d";
}
.fa-eye:before {
  content: "\f06e";
}
.fa-eye-slash:before {
  content: "\f070";
}
.fa-warning:before,
.fa-exclamation-triangle:before {
  content: "\f071";
}
.fa-plane:before {
  content: "\f072";
}
.fa-calendar:before {
  content: "\f073";
}
.fa-random:before {
  content: "\f074";
}
.fa-comment:before {
  content: "\f075";
}
.fa-magnet:before {
  content: "\f076";
}
.fa-chevron-up:before {
  content: "\f077";
}
.fa-chevron-down:before {
  content: "\f078";
}
.fa-retweet:before {
  content: "\f079";
}
.fa-shopping-cart:before {
  content: "\f07a";
}
.fa-folder:before {
  content: "\f07b";
}
.fa-folder-open:before {
  content: "\f07c";
}
.fa-arrows-v:before {
  content: "\f07d";
}
.fa-arrows-h:before {
  content: "\f07e";
}
.fa-bar-chart-o:before,
.fa-bar-chart:before {
  content: "\f080";
}
.fa-twitter-square:before {
  content: "\f081";
}
.fa-facebook-square:before {
  content: "\f082";
}
.fa-camera-retro:before {
  content: "\f083";
}
.fa-key:before {
  content: "\f084";
}
.fa-gears:before,
.fa-cogs:before {
  content: "\f085";
}
.fa-comments:before {
  content: "\f086";
}
.fa-thumbs-o-up:before {
  content: "\f087";
}
.fa-thumbs-o-down:before {
  content: "\f088";
}
.fa-star-half:before {
  content: "\f089";
}
.fa-heart-o:before {
  content: "\f08a";
}
.fa-sign-out:before {
  content: "\f08b";
}
.fa-linkedin-square:before {
  content: "\f08c";
}
.fa-thumb-tack:before {
  content: "\f08d";
}
.fa-external-link:before {
  content: "\f08e";
}
.fa-sign-in:before {
  content: "\f090";
}
.fa-trophy:before {
  content: "\f091";
}
.fa-github-square:before {
  content: "\f092";
}
.fa-upload:before {
  content: "\f093";
}
.fa-lemon-o:before {
  content: "\f094";
}
.fa-phone:before {
  content: "\f095";
}
.fa-square-o:before {
  content: "\f096";
}
.fa-bookmark-o:before {
  content: "\f097";
}
.fa-phone-square:before {
  content: "\f098";
}
.fa-twitter:before {
  content: "\f099";
}
.fa-facebook-f:before,
.fa-facebook:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.fa-feed:before,
.fa-rss:before {
  content: "\f09e";
}
.fa-hdd-o:before {
  content: "\f0a0";
}
.fa-bullhorn:before {
  content: "\f0a1";
}
.fa-bell:before {
  content: "\f0f3";
}
.fa-certificate:before {
  content: "\f0a3";
}
.fa-hand-o-right:before {
  content: "\f0a4";
}
.fa-hand-o-left:before {
  content: "\f0a5";
}
.fa-hand-o-up:before {
  content: "\f0a6";
}
.fa-hand-o-down:before {
  content: "\f0a7";
}
.fa-arrow-circle-left:before {
  content: "\f0a8";
}
.fa-arrow-circle-right:before {
  content: "\f0a9";
}
.fa-arrow-circle-up:before {
  content: "\f0aa";
}
.fa-arrow-circle-down:before {
  content: "\f0ab";
}
.fa-globe:before {
  content: "\f0ac";
}
.fa-wrench:before {
  content: "\f0ad";
}
.fa-tasks:before {
  content: "\f0ae";
}
.fa-filter:before {
  content: "\f0b0";
}
.fa-briefcase:before {
  content: "\f0b1";
}
.fa-arrows-alt:before {
  content: "\f0b2";
}
.fa-group:before,
.fa-users:before {
  content: "\f0c0";
}
.fa-chain:before,
.fa-link:before {
  content: "\f0c1";
}
.fa-cloud:before {
  content: "\f0c2";
}
.fa-flask:before {
  content: "\f0c3";
}
.fa-cut:before,
.fa-scissors:before {
  content: "\f0c4";
}
.fa-copy:before,
.fa-files-o:before {
  content: "\f0c5";
}
.fa-paperclip:before {
  content: "\f0c6";
}
.fa-save:before,
.fa-floppy-o:before {
  content: "\f0c7";
}
.fa-square:before {
  content: "\f0c8";
}
.fa-navicon:before,
.fa-reorder:before,
.fa-bars:before {
  content: "\f0c9";
}
.fa-list-ul:before {
  content: "\f0ca";
}
.fa-list-ol:before {
  content: "\f0cb";
}
.fa-strikethrough:before {
  content: "\f0cc";
}
.fa-underline:before {
  content: "\f0cd";
}
.fa-table:before {
  content: "\f0ce";
}
.fa-magic:before {
  content: "\f0d0";
}
.fa-truck:before {
  content: "\f0d1";
}
.fa-pinterest:before {
  content: "\f0d2";
}
.fa-pinterest-square:before {
  content: "\f0d3";
}
.fa-google-plus-square:before {
  content: "\f0d4";
}
.fa-google-plus:before {
  content: "\f0d5";
}
.fa-money:before {
  content: "\f0d6";
}
.fa-caret-down:before {
  content: "\f0d7";
}
.fa-caret-up:before {
  content: "\f0d8";
}
.fa-caret-left:before {
  content: "\f0d9";
}
.fa-caret-right:before {
  content: "\f0da";
}
.fa-columns:before {
  content: "\f0db";
}
.fa-unsorted:before,
.fa-sort:before {
  content: "\f0dc";
}
.fa-sort-down:before,
.fa-sort-desc:before {
  content: "\f0dd";
}
.fa-sort-up:before,
.fa-sort-asc:before {
  content: "\f0de";
}
.fa-envelope:before {
  content: "\f0e0";
}
.fa-linkedin:before {
  content: "\f0e1";
}
.fa-rotate-left:before,
.fa-undo:before {
  content: "\f0e2";
}
.fa-legal:before,
.fa-gavel:before {
  content: "\f0e3";
}
.fa-dashboard:before,
.fa-tachometer:before {
  content: "\f0e4";
}
.fa-comment-o:before {
  content: "\f0e5";
}
.fa-comments-o:before {
  content: "\f0e6";
}
.fa-flash:before,
.fa-bolt:before {
  content: "\f0e7";
}
.fa-sitemap:before {
  content: "\f0e8";
}
.fa-umbrella:before {
  content: "\f0e9";
}
.fa-paste:before,
.fa-clipboard:before {
  content: "\f0ea";
}
.fa-lightbulb-o:before {
  content: "\f0eb";
}
.fa-exchange:before {
  content: "\f0ec";
}
.fa-cloud-download:before {
  content: "\f0ed";
}
.fa-cloud-upload:before {
  content: "\f0ee";
}
.fa-user-md:before {
  content: "\f0f0";
}
.fa-stethoscope:before {
  content: "\f0f1";
}
.fa-suitcase:before {
  content: "\f0f2";
}
.fa-bell-o:before {
  content: "\f0a2";
}
.fa-coffee:before {
  content: "\f0f4";
}
.fa-cutlery:before {
  content: "\f0f5";
}
.fa-file-text-o:before {
  content: "\f0f6";
}
.fa-building-o:before {
  content: "\f0f7";
}
.fa-hospital-o:before {
  content: "\f0f8";
}
.fa-ambulance:before {
  content: "\f0f9";
}
.fa-medkit:before {
  content: "\f0fa";
}
.fa-fighter-jet:before {
  content: "\f0fb";
}
.fa-beer:before {
  content: "\f0fc";
}
.fa-h-square:before {
  content: "\f0fd";
}
.fa-plus-square:before {
  content: "\f0fe";
}
.fa-angle-double-left:before {
  content: "\f100";
}
.fa-angle-double-right:before {
  content: "\f101";
}
.fa-angle-double-up:before {
  content: "\f102";
}
.fa-angle-double-down:before {
  content: "\f103";
}
.fa-angle-left:before {
  content: "\f104";
}
.fa-angle-right:before {
  content: "\f105";
}
.fa-angle-up:before {
  content: "\f106";
}
.fa-angle-down:before {
  content: "\f107";
}
.fa-desktop:before {
  content: "\f108";
}
.fa-laptop:before {
  content: "\f109";
}
.fa-tablet:before {
  content: "\f10a";
}
.fa-mobile-phone:before,
.fa-mobile:before {
  content: "\f10b";
}
.fa-circle-o:before {
  content: "\f10c";
}
.fa-quote-left:before {
  content: "\f10d";
}
.fa-quote-right:before {
  content: "\f10e";
}
.fa-spinner:before {
  content: "\f110";
}
.fa-circle:before {
  content: "\f111";
}
.fa-mail-reply:before,
.fa-reply:before {
  content: "\f112";
}
.fa-github-alt:before {
  content: "\f113";
}
.fa-folder-o:before {
  content: "\f114";
}
.fa-folder-open-o:before {
  content: "\f115";
}
.fa-smile-o:before {
  content: "\f118";
}
.fa-frown-o:before {
  content: "\f119";
}
.fa-meh-o:before {
  content: "\f11a";
}
.fa-gamepad:before {
  content: "\f11b";
}
.fa-keyboard-o:before {
  content: "\f11c";
}
.fa-flag-o:before {
  content: "\f11d";
}
.fa-flag-checkered:before {
  content: "\f11e";
}
.fa-terminal:before {
  content: "\f120";
}
.fa-code:before {
  content: "\f121";
}
.fa-mail-reply-all:before,
.fa-reply-all:before {
  content: "\f122";
}
.fa-star-half-empty:before,
.fa-star-half-full:before,
.fa-star-half-o:before {
  content: "\f123";
}
.fa-location-arrow:before {
  content: "\f124";
}
.fa-crop:before {
  content: "\f125";
}
.fa-code-fork:before {
  content: "\f126";
}
.fa-unlink:before,
.fa-chain-broken:before {
  content: "\f127";
}
.fa-question:before {
  content: "\f128";
}
.fa-info:before {
  content: "\f129";
}
.fa-exclamation:before {
  content: "\f12a";
}
.fa-superscript:before {
  content: "\f12b";
}
.fa-subscript:before {
  content: "\f12c";
}
.fa-eraser:before {
  content: "\f12d";
}
.fa-puzzle-piece:before {
  content: "\f12e";
}
.fa-microphone:before {
  content: "\f130";
}
.fa-microphone-slash:before {
  content: "\f131";
}
.fa-shield:before {
  content: "\f132";
}
.fa-calendar-o:before {
  content: "\f133";
}
.fa-fire-extinguisher:before {
  content: "\f134";
}
.fa-rocket:before {
  content: "\f135";
}
.fa-maxcdn:before {
  content: "\f136";
}
.fa-chevron-circle-left:before {
  content: "\f137";
}
.fa-chevron-circle-right:before {
  content: "\f138";
}
.fa-chevron-circle-up:before {
  content: "\f139";
}
.fa-chevron-circle-down:before {
  content: "\f13a";
}
.fa-html5:before {
  content: "\f13b";
}
.fa-css3:before {
  content: "\f13c";
}
.fa-anchor:before {
  content: "\f13d";
}
.fa-unlock-alt:before {
  content: "\f13e";
}
.fa-bullseye:before {
  content: "\f140";
}
.fa-ellipsis-h:before {
  content: "\f141";
}
.fa-ellipsis-v:before {
  content: "\f142";
}
.fa-rss-square:before {
  content: "\f143";
}
.fa-play-circle:before {
  content: "\f144";
}
.fa-ticket:before {
  content: "\f145";
}
.fa-minus-square:before {
  content: "\f146";
}
.fa-minus-square-o:before {
  content: "\f147";
}
.fa-level-up:before {
  content: "\f148";
}
.fa-level-down:before {
  content: "\f149";
}
.fa-check-square:before {
  content: "\f14a";
}
.fa-pencil-square:before {
  content: "\f14b";
}
.fa-external-link-square:before {
  content: "\f14c";
}
.fa-share-square:before {
  content: "\f14d";
}
.fa-compass:before {
  content: "\f14e";
}
.fa-toggle-down:before,
.fa-caret-square-o-down:before {
  content: "\f150";
}
.fa-toggle-up:before,
.fa-caret-square-o-up:before {
  content: "\f151";
}
.fa-toggle-right:before,
.fa-caret-square-o-right:before {
  content: "\f152";
}
.fa-euro:before,
.fa-eur:before {
  content: "\f153";
}
.fa-gbp:before {
  content: "\f154";
}
.fa-dollar:before,
.fa-usd:before {
  content: "\f155";
}
.fa-rupee:before,
.fa-inr:before {
  content: "\f156";
}
.fa-cny:before,
.fa-rmb:before,
.fa-yen:before,
.fa-jpy:before {
  content: "\f157";
}
.fa-ruble:before,
.fa-rouble:before,
.fa-rub:before {
  content: "\f158";
}
.fa-won:before,
.fa-krw:before {
  content: "\f159";
}
.fa-bitcoin:before,
.fa-btc:before {
  content: "\f15a";
}
.fa-file:before {
  content: "\f15b";
}
.fa-file-text:before {
  content: "\f15c";
}
.fa-sort-alpha-asc:before {
  content: "\f15d";
}
.fa-sort-alpha-desc:before {
  content: "\f15e";
}
.fa-sort-amount-asc:before {
  content: "\f160";
}
.fa-sort-amount-desc:before {
  content: "\f161";
}
.fa-sort-numeric-asc:before {
  content: "\f162";
}
.fa-sort-numeric-desc:before {
  content: "\f163";
}
.fa-thumbs-up:before {
  content: "\f164";
}
.fa-thumbs-down:before {
  content: "\f165";
}
.fa-youtube-square:before {
  content: "\f166";
}
.fa-youtube:before {
  content: "\f167";
}
.fa-xing:before {
  content: "\f168";
}
.fa-xing-square:before {
  content: "\f169";
}
.fa-youtube-play:before {
  content: "\f16a";
}
.fa-dropbox:before {
  content: "\f16b";
}
.fa-stack-overflow:before {
  content: "\f16c";
}
.fa-instagram:before {
  content: "\f16d";
}
.fa-flickr:before {
  content: "\f16e";
}
.fa-adn:before {
  content: "\f170";
}
.fa-bitbucket:before {
  content: "\f171";
}
.fa-bitbucket-square:before {
  content: "\f172";
}
.fa-tumblr:before {
  content: "\f173";
}
.fa-tumblr-square:before {
  content: "\f174";
}
.fa-long-arrow-down:before {
  content: "\f175";
}
.fa-long-arrow-up:before {
  content: "\f176";
}
.fa-long-arrow-left:before {
  content: "\f177";
}
.fa-long-arrow-right:before {
  content: "\f178";
}
.fa-apple:before {
  content: "\f179";
}
.fa-windows:before {
  content: "\f17a";
}
.fa-android:before {
  content: "\f17b";
}
.fa-linux:before {
  content: "\f17c";
}
.fa-dribbble:before {
  content: "\f17d";
}
.fa-skype:before {
  content: "\f17e";
}
.fa-foursquare:before {
  content: "\f180";
}
.fa-trello:before {
  content: "\f181";
}
.fa-female:before {
  content: "\f182";
}
.fa-male:before {
  content: "\f183";
}
.fa-gittip:before,
.fa-gratipay:before {
  content: "\f184";
}
.fa-sun-o:before {
  content: "\f185";
}
.fa-moon-o:before {
  content: "\f186";
}
.fa-archive:before {
  content: "\f187";
}
.fa-bug:before {
  content: "\f188";
}
.fa-vk:before {
  content: "\f189";
}
.fa-weibo:before {
  content: "\f18a";
}
.fa-renren:before {
  content: "\f18b";
}
.fa-pagelines:before {
  content: "\f18c";
}
.fa-stack-exchange:before {
  content: "\f18d";
}
.fa-arrow-circle-o-right:before {
  content: "\f18e";
}
.fa-arrow-circle-o-left:before {
  content: "\f190";
}
.fa-toggle-left:before,
.fa-caret-square-o-left:before {
  content: "\f191";
}
.fa-dot-circle-o:before {
  content: "\f192";
}
.fa-wheelchair:before {
  content: "\f193";
}
.fa-vimeo-square:before {
  content: "\f194";
}
.fa-turkish-lira:before,
.fa-try:before {
  content: "\f195";
}
.fa-plus-square-o:before {
  content: "\f196";
}
.fa-space-shuttle:before {
  content: "\f197";
}
.fa-slack:before {
  content: "\f198";
}
.fa-envelope-square:before {
  content: "\f199";
}
.fa-wordpress:before {
  content: "\f19a";
}
.fa-openid:before {
  content: "\f19b";
}
.fa-institution:before,
.fa-bank:before,
.fa-university:before {
  content: "\f19c";
}
.fa-mortar-board:before,
.fa-graduation-cap:before {
  content: "\f19d";
}
.fa-yahoo:before {
  content: "\f19e";
}
.fa-google:before {
  content: "\f1a0";
}
.fa-reddit:before {
  content: "\f1a1";
}
.fa-reddit-square:before {
  content: "\f1a2";
}
.fa-stumbleupon-circle:before {
  content: "\f1a3";
}
.fa-stumbleupon:before {
  content: "\f1a4";
}
.fa-delicious:before {
  content: "\f1a5";
}
.fa-digg:before {
  content: "\f1a6";
}
.fa-pied-piper-pp:before {
  content: "\f1a7";
}
.fa-pied-piper-alt:before {
  content: "\f1a8";
}
.fa-drupal:before {
  content: "\f1a9";
}
.fa-joomla:before {
  content: "\f1aa";
}
.fa-language:before {
  content: "\f1ab";
}
.fa-fax:before {
  content: "\f1ac";
}
.fa-building:before {
  content: "\f1ad";
}
.fa-child:before {
  content: "\f1ae";
}
.fa-paw:before {
  content: "\f1b0";
}
.fa-spoon:before {
  content: "\f1b1";
}
.fa-cube:before {
  content: "\f1b2";
}
.fa-cubes:before {
  content: "\f1b3";
}
.fa-behance:before {
  content: "\f1b4";
}
.fa-behance-square:before {
  content: "\f1b5";
}
.fa-steam:before {
  content: "\f1b6";
}
.fa-steam-square:before {
  content: "\f1b7";
}
.fa-recycle:before {
  content: "\f1b8";
}
.fa-automobile:before,
.fa-car:before {
  content: "\f1b9";
}
.fa-cab:before,
.fa-taxi:before {
  content: "\f1ba";
}
.fa-tree:before {
  content: "\f1bb";
}
.fa-spotify:before {
  content: "\f1bc";
}
.fa-deviantart:before {
  content: "\f1bd";
}
.fa-soundcloud:before {
  content: "\f1be";
}
.fa-database:before {
  content: "\f1c0";
}
.fa-file-pdf-o:before {
  content: "\f1c1";
}
.fa-file-word-o:before {
  content: "\f1c2";
}
.fa-file-excel-o:before {
  content: "\f1c3";
}
.fa-file-powerpoint-o:before {
  content: "\f1c4";
}
.fa-file-photo-o:before,
.fa-file-picture-o:before,
.fa-file-image-o:before {
  content: "\f1c5";
}
.fa-file-zip-o:before,
.fa-file-archive-o:before {
  content: "\f1c6";
}
.fa-file-sound-o:before,
.fa-file-audio-o:before {
  content: "\f1c7";
}
.fa-file-movie-o:before,
.fa-file-video-o:before {
  content: "\f1c8";
}
.fa-file-code-o:before {
  content: "\f1c9";
}
.fa-vine:before {
  content: "\f1ca";
}
.fa-codepen:before {
  content: "\f1cb";
}
.fa-jsfiddle:before {
  content: "\f1cc";
}
.fa-life-bouy:before,
.fa-life-buoy:before,
.fa-life-saver:before,
.fa-support:before,
.fa-life-ring:before {
  content: "\f1cd";
}
.fa-circle-o-notch:before {
  content: "\f1ce";
}
.fa-ra:before,
.fa-resistance:before,
.fa-rebel:before {
  content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
  content: "\f1d1";
}
.fa-git-square:before {
  content: "\f1d2";
}
.fa-git:before {
  content: "\f1d3";
}
.fa-y-combinator-square:before,
.fa-yc-square:before,
.fa-hacker-news:before {
  content: "\f1d4";
}
.fa-tencent-weibo:before {
  content: "\f1d5";
}
.fa-qq:before {
  content: "\f1d6";
}
.fa-wechat:before,
.fa-weixin:before {
  content: "\f1d7";
}
.fa-send:before,
.fa-paper-plane:before {
  content: "\f1d8";
}
.fa-send-o:before,
.fa-paper-plane-o:before {
  content: "\f1d9";
}
.fa-history:before {
  content: "\f1da";
}
.fa-circle-thin:before {
  content: "\f1db";
}
.fa-header:before {
  content: "\f1dc";
}
.fa-paragraph:before {
  content: "\f1dd";
}
.fa-sliders:before {
  content: "\f1de";
}
.fa-share-alt:before {
  content: "\f1e0";
}
.fa-share-alt-square:before {
  content: "\f1e1";
}
.fa-bomb:before {
  content: "\f1e2";
}
.fa-soccer-ball-o:before,
.fa-futbol-o:before {
  content: "\f1e3";
}
.fa-tty:before {
  content: "\f1e4";
}
.fa-binoculars:before {
  content: "\f1e5";
}
.fa-plug:before {
  content: "\f1e6";
}
.fa-slideshare:before {
  content: "\f1e7";
}
.fa-twitch:before {
  content: "\f1e8";
}
.fa-yelp:before {
  content: "\f1e9";
}
.fa-newspaper-o:before {
  content: "\f1ea";
}
.fa-wifi:before {
  content: "\f1eb";
}
.fa-calculator:before {
  content: "\f1ec";
}
.fa-paypal:before {
  content: "\f1ed";
}
.fa-google-wallet:before {
  content: "\f1ee";
}
.fa-cc-visa:before {
  content: "\f1f0";
}
.fa-cc-mastercard:before {
  content: "\f1f1";
}
.fa-cc-discover:before {
  content: "\f1f2";
}
.fa-cc-amex:before {
  content: "\f1f3";
}
.fa-cc-paypal:before {
  content: "\f1f4";
}
.fa-cc-stripe:before {
  content: "\f1f5";
}
.fa-bell-slash:before {
  content: "\f1f6";
}
.fa-bell-slash-o:before {
  content: "\f1f7";
}
.fa-trash:before {
  content: "\f1f8";
}
.fa-copyright:before {
  content: "\f1f9";
}
.fa-at:before {
  content: "\f1fa";
}
.fa-eyedropper:before {
  content: "\f1fb";
}
.fa-paint-brush:before {
  content: "\f1fc";
}
.fa-birthday-cake:before {
  content: "\f1fd";
}
.fa-area-chart:before {
  content: "\f1fe";
}
.fa-pie-chart:before {
  content: "\f200";
}
.fa-line-chart:before {
  content: "\f201";
}
.fa-lastfm:before {
  content: "\f202";
}
.fa-lastfm-square:before {
  content: "\f203";
}
.fa-toggle-off:before {
  content: "\f204";
}
.fa-toggle-on:before {
  content: "\f205";
}
.fa-bicycle:before {
  content: "\f206";
}
.fa-bus:before {
  content: "\f207";
}
.fa-ioxhost:before {
  content: "\f208";
}
.fa-angellist:before {
  content: "\f209";
}
.fa-cc:before {
  content: "\f20a";
}
.fa-shekel:before,
.fa-sheqel:before,
.fa-ils:before {
  content: "\f20b";
}
.fa-meanpath:before {
  content: "\f20c";
}
.fa-buysellads:before {
  content: "\f20d";
}
.fa-connectdevelop:before {
  content: "\f20e";
}
.fa-dashcube:before {
  content: "\f210";
}
.fa-forumbee:before {
  content: "\f211";
}
.fa-leanpub:before {
  content: "\f212";
}
.fa-sellsy:before {
  content: "\f213";
}
.fa-shirtsinbulk:before {
  content: "\f214";
}
.fa-simplybuilt:before {
  content: "\f215";
}
.fa-skyatlas:before {
  content: "\f216";
}
.fa-cart-plus:before {
  content: "\f217";
}
.fa-cart-arrow-down:before {
  content: "\f218";
}
.fa-diamond:before {
  content: "\f219";
}
.fa-ship:before {
  content: "\f21a";
}
.fa-user-secret:before {
  content: "\f21b";
}
.fa-motorcycle:before {
  content: "\f21c";
}
.fa-street-view:before {
  content: "\f21d";
}
.fa-heartbeat:before {
  content: "\f21e";
}
.fa-venus:before {
  content: "\f221";
}
.fa-mars:before {
  content: "\f222";
}
.fa-mercury:before {
  content: "\f223";
}
.fa-intersex:before,
.fa-transgender:before {
  content: "\f224";
}
.fa-transgender-alt:before {
  content: "\f225";
}
.fa-venus-double:before {
  content: "\f226";
}
.fa-mars-double:before {
  content: "\f227";
}
.fa-venus-mars:before {
  content: "\f228";
}
.fa-mars-stroke:before {
  content: "\f229";
}
.fa-mars-stroke-v:before {
  content: "\f22a";
}
.fa-mars-stroke-h:before {
  content: "\f22b";
}
.fa-neuter:before {
  content: "\f22c";
}
.fa-genderless:before {
  content: "\f22d";
}
.fa-facebook-official:before {
  content: "\f230";
}
.fa-pinterest-p:before {
  content: "\f231";
}
.fa-whatsapp:before {
  content: "\f232";
}
.fa-server:before {
  content: "\f233";
}
.fa-user-plus:before {
  content: "\f234";
}
.fa-user-times:before {
  content: "\f235";
}
.fa-hotel:before,
.fa-bed:before {
  content: "\f236";
}
.fa-viacoin:before {
  content: "\f237";
}
.fa-train:before {
  content: "\f238";
}
.fa-subway:before {
  content: "\f239";
}
.fa-medium:before {
  content: "\f23a";
}
.fa-yc:before,
.fa-y-combinator:before {
  content: "\f23b";
}
.fa-optin-monster:before {
  content: "\f23c";
}
.fa-opencart:before {
  content: "\f23d";
}
.fa-expeditedssl:before {
  content: "\f23e";
}
.fa-battery-4:before,
.fa-battery:before,
.fa-battery-full:before {
  content: "\f240";
}
.fa-battery-3:before,
.fa-battery-three-quarters:before {
  content: "\f241";
}
.fa-battery-2:before,
.fa-battery-half:before {
  content: "\f242";
}
.fa-battery-1:before,
.fa-battery-quarter:before {
  content: "\f243";
}
.fa-battery-0:before,
.fa-battery-empty:before {
  content: "\f244";
}
.fa-mouse-pointer:before {
  content: "\f245";
}
.fa-i-cursor:before {
  content: "\f246";
}
.fa-object-group:before {
  content: "\f247";
}
.fa-object-ungroup:before {
  content: "\f248";
}
.fa-sticky-note:before {
  content: "\f249";
}
.fa-sticky-note-o:before {
  content: "\f24a";
}
.fa-cc-jcb:before {
  content: "\f24b";
}
.fa-cc-diners-club:before {
  content: "\f24c";
}
.fa-clone:before {
  content: "\f24d";
}
.fa-balance-scale:before {
  content: "\f24e";
}
.fa-hourglass-o:before {
  content: "\f250";
}
.fa-hourglass-1:before,
.fa-hourglass-start:before {
  content: "\f251";
}
.fa-hourglass-2:before,
.fa-hourglass-half:before {
  content: "\f252";
}
.fa-hourglass-3:before,
.fa-hourglass-end:before {
  content: "\f253";
}
.fa-hourglass:before {
  content: "\f254";
}
.fa-hand-grab-o:before,
.fa-hand-rock-o:before {
  content: "\f255";
}
.fa-hand-stop-o:before,
.fa-hand-paper-o:before {
  content: "\f256";
}
.fa-hand-scissors-o:before {
  content: "\f257";
}
.fa-hand-lizard-o:before {
  content: "\f258";
}
.fa-hand-spock-o:before {
  content: "\f259";
}
.fa-hand-pointer-o:before {
  content: "\f25a";
}
.fa-hand-peace-o:before {
  content: "\f25b";
}
.fa-trademark:before {
  content: "\f25c";
}
.fa-registered:before {
  content: "\f25d";
}
.fa-creative-commons:before {
  content: "\f25e";
}
.fa-gg:before {
  content: "\f260";
}
.fa-gg-circle:before {
  content: "\f261";
}
.fa-tripadvisor:before {
  content: "\f262";
}
.fa-odnoklassniki:before {
  content: "\f263";
}
.fa-odnoklassniki-square:before {
  content: "\f264";
}
.fa-get-pocket:before {
  content: "\f265";
}
.fa-wikipedia-w:before {
  content: "\f266";
}
.fa-safari:before {
  content: "\f267";
}
.fa-chrome:before {
  content: "\f268";
}
.fa-firefox:before {
  content: "\f269";
}
.fa-opera:before {
  content: "\f26a";
}
.fa-internet-explorer:before {
  content: "\f26b";
}
.fa-tv:before,
.fa-television:before {
  content: "\f26c";
}
.fa-contao:before {
  content: "\f26d";
}
.fa-500px:before {
  content: "\f26e";
}
.fa-amazon:before {
  content: "\f270";
}
.fa-calendar-plus-o:before {
  content: "\f271";
}
.fa-calendar-minus-o:before {
  content: "\f272";
}
.fa-calendar-times-o:before {
  content: "\f273";
}
.fa-calendar-check-o:before {
  content: "\f274";
}
.fa-industry:before {
  content: "\f275";
}
.fa-map-pin:before {
  content: "\f276";
}
.fa-map-signs:before {
  content: "\f277";
}
.fa-map-o:before {
  content: "\f278";
}
.fa-map:before {
  content: "\f279";
}
.fa-commenting:before {
  content: "\f27a";
}
.fa-commenting-o:before {
  content: "\f27b";
}
.fa-houzz:before {
  content: "\f27c";
}
.fa-vimeo:before {
  content: "\f27d";
}
.fa-black-tie:before {
  content: "\f27e";
}
.fa-fonticons:before {
  content: "\f280";
}
.fa-reddit-alien:before {
  content: "\f281";
}
.fa-edge:before {
  content: "\f282";
}
.fa-credit-card-alt:before {
  content: "\f283";
}
.fa-codiepie:before {
  content: "\f284";
}
.fa-modx:before {
  content: "\f285";
}
.fa-fort-awesome:before {
  content: "\f286";
}
.fa-usb:before {
  content: "\f287";
}
.fa-product-hunt:before {
  content: "\f288";
}
.fa-mixcloud:before {
  content: "\f289";
}
.fa-scribd:before {
  content: "\f28a";
}
.fa-pause-circle:before {
  content: "\f28b";
}
.fa-pause-circle-o:before {
  content: "\f28c";
}
.fa-stop-circle:before {
  content: "\f28d";
}
.fa-stop-circle-o:before {
  content: "\f28e";
}
.fa-shopping-bag:before {
  content: "\f290";
}
.fa-shopping-basket:before {
  content: "\f291";
}
.fa-hashtag:before {
  content: "\f292";
}
.fa-bluetooth:before {
  content: "\f293";
}
.fa-bluetooth-b:before {
  content: "\f294";
}
.fa-percent:before {
  content: "\f295";
}
.fa-gitlab:before {
  content: "\f296";
}
.fa-wpbeginner:before {
  content: "\f297";
}
.fa-wpforms:before {
  content: "\f298";
}
.fa-envira:before {
  content: "\f299";
}
.fa-universal-access:before {
  content: "\f29a";
}
.fa-wheelchair-alt:before {
  content: "\f29b";
}
.fa-question-circle-o:before {
  content: "\f29c";
}
.fa-blind:before {
  content: "\f29d";
}
.fa-audio-description:before {
  content: "\f29e";
}
.fa-volume-control-phone:before {
  content: "\f2a0";
}
.fa-braille:before {
  content: "\f2a1";
}
.fa-assistive-listening-systems:before {
  content: "\f2a2";
}
.fa-asl-interpreting:before,
.fa-american-sign-language-interpreting:before {
  content: "\f2a3";
}
.fa-deafness:before,
.fa-hard-of-hearing:before,
.fa-deaf:before {
  content: "\f2a4";
}
.fa-glide:before {
  content: "\f2a5";
}
.fa-glide-g:before {
  content: "\f2a6";
}
.fa-signing:before,
.fa-sign-language:before {
  content: "\f2a7";
}
.fa-low-vision:before {
  content: "\f2a8";
}
.fa-viadeo:before {
  content: "\f2a9";
}
.fa-viadeo-square:before {
  content: "\f2aa";
}
.fa-snapchat:before {
  content: "\f2ab";
}
.fa-snapchat-ghost:before {
  content: "\f2ac";
}
.fa-snapchat-square:before {
  content: "\f2ad";
}
.fa-pied-piper:before {
  content: "\f2ae";
}
.fa-first-order:before {
  content: "\f2b0";
}
.fa-yoast:before {
  content: "\f2b1";
}
.fa-themeisle:before {
  content: "\f2b2";
}
.fa-google-plus-circle:before,
.fa-google-plus-official:before {
  content: "\f2b3";
}
.fa-fa:before,
.fa-font-awesome:before {
  content: "\f2b4";
}
.fa-handshake-o:before {
  content: "\f2b5";
}
.fa-envelope-open:before {
  content: "\f2b6";
}
.fa-envelope-open-o:before {
  content: "\f2b7";
}
.fa-linode:before {
  content: "\f2b8";
}
.fa-address-book:before {
  content: "\f2b9";
}
.fa-address-book-o:before {
  content: "\f2ba";
}
.fa-vcard:before,
.fa-address-card:before {
  content: "\f2bb";
}
.fa-vcard-o:before,
.fa-address-card-o:before {
  content: "\f2bc";
}
.fa-user-circle:before {
  content: "\f2bd";
}
.fa-user-circle-o:before {
  content: "\f2be";
}
.fa-user-o:before {
  content: "\f2c0";
}
.fa-id-badge:before {
  content: "\f2c1";
}
.fa-drivers-license:before,
.fa-id-card:before {
  content: "\f2c2";
}
.fa-drivers-license-o:before,
.fa-id-card-o:before {
  content: "\f2c3";
}
.fa-quora:before {
  content: "\f2c4";
}
.fa-free-code-camp:before {
  content: "\f2c5";
}
.fa-telegram:before {
  content: "\f2c6";
}
.fa-thermometer-4:before,
.fa-thermometer:before,
.fa-thermometer-full:before {
  content: "\f2c7";
}
.fa-thermometer-3:before,
.fa-thermometer-three-quarters:before {
  content: "\f2c8";
}
.fa-thermometer-2:before,
.fa-thermometer-half:before {
  content: "\f2c9";
}
.fa-thermometer-1:before,
.fa-thermometer-quarter:before {
  content: "\f2ca";
}
.fa-thermometer-0:before,
.fa-thermometer-empty:before {
  content: "\f2cb";
}
.fa-shower:before {
  content: "\f2cc";
}
.fa-bathtub:before,
.fa-s15:before,
.fa-bath:before {
  content: "\f2cd";
}
.fa-podcast:before {
  content: "\f2ce";
}
.fa-window-maximize:before {
  content: "\f2d0";
}
.fa-window-minimize:before {
  content: "\f2d1";
}
.fa-window-restore:before {
  content: "\f2d2";
}
.fa-times-rectangle:before,
.fa-window-close:before {
  content: "\f2d3";
}
.fa-times-rectangle-o:before,
.fa-window-close-o:before {
  content: "\f2d4";
}
.fa-bandcamp:before {
  content: "\f2d5";
}
.fa-grav:before {
  content: "\f2d6";
}
.fa-etsy:before {
  content: "\f2d7";
}
.fa-imdb:before {
  content: "\f2d8";
}
.fa-ravelry:before {
  content: "\f2d9";
}
.fa-eercast:before {
  content: "\f2da";
}
.fa-microchip:before {
  content: "\f2db";
}
.fa-snowflake-o:before {
  content: "\f2dc";
}
.fa-superpowers:before {
  content: "\f2dd";
}
.fa-wpexplorer:before {
  content: "\f2de";
}
.fa-meetup:before {
  content: "\f2e0";
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  padding: 0;
  margin: -1px;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
/*!
*
* IPython base
*
*/
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
code {
  color: #000;
}
pre {
  font-size: inherit;
  line-height: inherit;
}
label {
  font-weight: normal;
}
/* Make the page background atleast 100% the height of the view port */
/* Make the page itself atleast 70% the height of the view port */
.border-box-sizing {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.corner-all {
  border-radius: 2px;
}
.no-padding {
  padding: 0px;
}
/* Flexible box model classes */
/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
/* This file is a compatability layer.  It allows the usage of flexible box 
model layouts accross multiple browsers, including older browsers.  The newest,
universal implementation of the flexible box model is used when available (see
`Modern browsers` comments below).  Browsers that are known to implement this 
new spec completely include:

    Firefox 28.0+
    Chrome 29.0+
    Internet Explorer 11+ 
    Opera 17.0+

Browsers not listed, including Safari, are supported via the styling under the
`Old browsers` comments below.
*/
.hbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
.hbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.vbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
.vbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.hbox.reverse,
.vbox.reverse,
.reverse {
  /* Old browsers */
  -webkit-box-direction: reverse;
  -moz-box-direction: reverse;
  box-direction: reverse;
  /* Modern browsers */
  flex-direction: row-reverse;
}
.hbox.box-flex0,
.vbox.box-flex0,
.box-flex0 {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
  width: auto;
}
.hbox.box-flex1,
.vbox.box-flex1,
.box-flex1 {
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex,
.vbox.box-flex,
.box-flex {
  /* Old browsers */
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex2,
.vbox.box-flex2,
.box-flex2 {
  /* Old browsers */
  -webkit-box-flex: 2;
  -moz-box-flex: 2;
  box-flex: 2;
  /* Modern browsers */
  flex: 2;
}
.box-group1 {
  /*  Deprecated */
  -webkit-box-flex-group: 1;
  -moz-box-flex-group: 1;
  box-flex-group: 1;
}
.box-group2 {
  /* Deprecated */
  -webkit-box-flex-group: 2;
  -moz-box-flex-group: 2;
  box-flex-group: 2;
}
.hbox.start,
.vbox.start,
.start {
  /* Old browsers */
  -webkit-box-pack: start;
  -moz-box-pack: start;
  box-pack: start;
  /* Modern browsers */
  justify-content: flex-start;
}
.hbox.end,
.vbox.end,
.end {
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
}
.hbox.center,
.vbox.center,
.center {
  /* Old browsers */
  -webkit-box-pack: center;
  -moz-box-pack: center;
  box-pack: center;
  /* Modern browsers */
  justify-content: center;
}
.hbox.baseline,
.vbox.baseline,
.baseline {
  /* Old browsers */
  -webkit-box-pack: baseline;
  -moz-box-pack: baseline;
  box-pack: baseline;
  /* Modern browsers */
  justify-content: baseline;
}
.hbox.stretch,
.vbox.stretch,
.stretch {
  /* Old browsers */
  -webkit-box-pack: stretch;
  -moz-box-pack: stretch;
  box-pack: stretch;
  /* Modern browsers */
  justify-content: stretch;
}
.hbox.align-start,
.vbox.align-start,
.align-start {
  /* Old browsers */
  -webkit-box-align: start;
  -moz-box-align: start;
  box-align: start;
  /* Modern browsers */
  align-items: flex-start;
}
.hbox.align-end,
.vbox.align-end,
.align-end {
  /* Old browsers */
  -webkit-box-align: end;
  -moz-box-align: end;
  box-align: end;
  /* Modern browsers */
  align-items: flex-end;
}
.hbox.align-center,
.vbox.align-center,
.align-center {
  /* Old browsers */
  -webkit-box-align: center;
  -moz-box-align: center;
  box-align: center;
  /* Modern browsers */
  align-items: center;
}
.hbox.align-baseline,
.vbox.align-baseline,
.align-baseline {
  /* Old browsers */
  -webkit-box-align: baseline;
  -moz-box-align: baseline;
  box-align: baseline;
  /* Modern browsers */
  align-items: baseline;
}
.hbox.align-stretch,
.vbox.align-stretch,
.align-stretch {
  /* Old browsers */
  -webkit-box-align: stretch;
  -moz-box-align: stretch;
  box-align: stretch;
  /* Modern browsers */
  align-items: stretch;
}
div.error {
  margin: 2em;
  text-align: center;
}
div.error > h1 {
  font-size: 500%;
  line-height: normal;
}
div.error > p {
  font-size: 200%;
  line-height: normal;
}
div.traceback-wrapper {
  text-align: left;
  max-width: 800px;
  margin: auto;
}
div.traceback-wrapper pre.traceback {
  max-height: 600px;
  overflow: auto;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
body {
  background-color: #fff;
  /* This makes sure that the body covers the entire window and needs to
       be in a different element than the display: box in wrapper below */
  position: absolute;
  left: 0px;
  right: 0px;
  top: 0px;
  bottom: 0px;
  overflow: visible;
}
body > #header {
  /* Initially hidden to prevent FLOUC */
  display: none;
  background-color: #fff;
  /* Display over codemirror */
  position: relative;
  z-index: 100;
}
body > #header #header-container {
  display: flex;
  flex-direction: row;
  justify-content: space-between;
  padding: 5px;
  padding-bottom: 5px;
  padding-top: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
body > #header .header-bar {
  width: 100%;
  height: 1px;
  background: #e7e7e7;
  margin-bottom: -1px;
}
@media print {
  body > #header {
    display: none !important;
  }
}
#header-spacer {
  width: 100%;
  visibility: hidden;
}
@media print {
  #header-spacer {
    display: none;
  }
}
#ipython_notebook {
  padding-left: 0px;
  padding-top: 1px;
  padding-bottom: 1px;
}
[dir="rtl"] #ipython_notebook {
  margin-right: 10px;
  margin-left: 0;
}
[dir="rtl"] #ipython_notebook.pull-left {
  float: right !important;
  float: right;
}
.flex-spacer {
  flex: 1;
}
#noscript {
  width: auto;
  padding-top: 16px;
  padding-bottom: 16px;
  text-align: center;
  font-size: 22px;
  color: red;
  font-weight: bold;
}
#ipython_notebook img {
  height: 28px;
}
#site {
  width: 100%;
  display: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  overflow: auto;
}
@media print {
  #site {
    height: auto !important;
  }
}
/* Smaller buttons */
.ui-button .ui-button-text {
  padding: 0.2em 0.8em;
  font-size: 77%;
}
input.ui-button {
  padding: 0.3em 0.9em;
}
span#kernel_logo_widget {
  margin: 0 10px;
}
span#login_widget {
  float: right;
}
[dir="rtl"] span#login_widget {
  float: left;
}
span#login_widget > .button,
#logout {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button:focus,
#logout:focus,
span#login_widget > .button.focus,
#logout.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
span#login_widget > .button:hover,
#logout:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active:hover,
#logout:active:hover,
span#login_widget > .button.active:hover,
#logout.active:hover,
.open > .dropdown-togglespan#login_widget > .button:hover,
.open > .dropdown-toggle#logout:hover,
span#login_widget > .button:active:focus,
#logout:active:focus,
span#login_widget > .button.active:focus,
#logout.active:focus,
.open > .dropdown-togglespan#login_widget > .button:focus,
.open > .dropdown-toggle#logout:focus,
span#login_widget > .button:active.focus,
#logout:active.focus,
span#login_widget > .button.active.focus,
#logout.active.focus,
.open > .dropdown-togglespan#login_widget > .button.focus,
.open > .dropdown-toggle#logout.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  background-image: none;
}
span#login_widget > .button.disabled:hover,
#logout.disabled:hover,
span#login_widget > .button[disabled]:hover,
#logout[disabled]:hover,
fieldset[disabled] span#login_widget > .button:hover,
fieldset[disabled] #logout:hover,
span#login_widget > .button.disabled:focus,
#logout.disabled:focus,
span#login_widget > .button[disabled]:focus,
#logout[disabled]:focus,
fieldset[disabled] span#login_widget > .button:focus,
fieldset[disabled] #logout:focus,
span#login_widget > .button.disabled.focus,
#logout.disabled.focus,
span#login_widget > .button[disabled].focus,
#logout[disabled].focus,
fieldset[disabled] span#login_widget > .button.focus,
fieldset[disabled] #logout.focus {
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button .badge,
#logout .badge {
  color: #fff;
  background-color: #333;
}
.nav-header {
  text-transform: none;
}
#header > span {
  margin-top: 10px;
}
.modal_stretch .modal-dialog {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  min-height: 80vh;
}
.modal_stretch .modal-dialog .modal-body {
  max-height: calc(100vh - 200px);
  overflow: auto;
  flex: 1;
}
.modal-header {
  cursor: move;
}
@media (min-width: 768px) {
  .modal .modal-dialog {
    width: 700px;
  }
}
@media (min-width: 768px) {
  select.form-control {
    margin-left: 12px;
    margin-right: 12px;
  }
}
/*!
*
* IPython auth
*
*/
.center-nav {
  display: inline-block;
  margin-bottom: -4px;
}
[dir="rtl"] .center-nav form.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] .center-nav .navbar-text {
  float: right;
}
[dir="rtl"] .navbar-inner {
  text-align: right;
}
[dir="rtl"] div.text-left {
  text-align: right;
}
/*!
*
* IPython tree view
*
*/
/* We need an invisible input field on top of the sentense*/
/* "Drag file onto the list ..." */
.alternate_upload {
  background-color: none;
  display: inline;
}
.alternate_upload.form {
  padding: 0;
  margin: 0;
}
.alternate_upload input.fileinput {
  position: absolute;
  display: block;
  width: 100%;
  height: 100%;
  overflow: hidden;
  cursor: pointer;
  opacity: 0;
  z-index: 2;
}
.alternate_upload .btn-xs > input.fileinput {
  margin: -1px -5px;
}
.alternate_upload .btn-upload {
  position: relative;
  height: 22px;
}
::-webkit-file-upload-button {
  cursor: pointer;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
ul#tabs {
  margin-bottom: 4px;
}
ul#tabs a {
  padding-top: 6px;
  padding-bottom: 4px;
}
[dir="rtl"] ul#tabs.nav-tabs > li {
  float: right;
}
[dir="rtl"] ul#tabs.nav.nav-tabs {
  padding-right: 0;
}
ul.breadcrumb a:focus,
ul.breadcrumb a:hover {
  text-decoration: none;
}
ul.breadcrumb i.icon-home {
  font-size: 16px;
  margin-right: 4px;
}
ul.breadcrumb span {
  color: #5e5e5e;
}
.list_toolbar {
  padding: 4px 0 4px 0;
  vertical-align: middle;
}
.list_toolbar .tree-buttons {
  padding-top: 1px;
}
[dir="rtl"] .list_toolbar .tree-buttons .pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .list_toolbar .col-sm-4,
[dir="rtl"] .list_toolbar .col-sm-8 {
  float: right;
}
.dynamic-buttons {
  padding-top: 3px;
  display: inline-block;
}
.list_toolbar [class*="span"] {
  min-height: 24px;
}
.list_header {
  font-weight: bold;
  background-color: #EEE;
}
.list_placeholder {
  font-weight: bold;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
}
.list_container {
  margin-top: 4px;
  margin-bottom: 20px;
  border: 1px solid #ddd;
  border-radius: 2px;
}
.list_container > div {
  border-bottom: 1px solid #ddd;
}
.list_container > div:hover .list-item {
  background-color: red;
}
.list_container > div:last-child {
  border: none;
}
.list_item:hover .list_item {
  background-color: #ddd;
}
.list_item a {
  text-decoration: none;
}
.list_item:hover {
  background-color: #fafafa;
}
.list_header > div,
.list_item > div {
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
.list_header > div input,
.list_item > div input {
  margin-right: 7px;
  margin-left: 14px;
  vertical-align: text-bottom;
  line-height: 22px;
  position: relative;
  top: -1px;
}
.list_header > div .item_link,
.list_item > div .item_link {
  margin-left: -1px;
  vertical-align: baseline;
  line-height: 22px;
}
[dir="rtl"] .list_item > div input {
  margin-right: 0;
}
.new-file input[type=checkbox] {
  visibility: hidden;
}
.item_name {
  line-height: 22px;
  height: 24px;
}
.item_icon {
  font-size: 14px;
  color: #5e5e5e;
  margin-right: 7px;
  margin-left: 7px;
  line-height: 22px;
  vertical-align: baseline;
}
.item_modified {
  margin-right: 7px;
  margin-left: 7px;
}
[dir="rtl"] .item_modified.pull-right {
  float: left !important;
  float: left;
}
.item_buttons {
  line-height: 1em;
  margin-left: -5px;
}
.item_buttons .btn,
.item_buttons .btn-group,
.item_buttons .input-group {
  float: left;
}
.item_buttons > .btn,
.item_buttons > .btn-group,
.item_buttons > .input-group {
  margin-left: 5px;
}
.item_buttons .btn {
  min-width: 13ex;
}
.item_buttons .running-indicator {
  padding-top: 4px;
  color: #5cb85c;
}
.item_buttons .kernel-name {
  padding-top: 4px;
  color: #5bc0de;
  margin-right: 7px;
  float: left;
}
[dir="rtl"] .item_buttons.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .item_buttons .kernel-name {
  margin-left: 7px;
  float: right;
}
.toolbar_info {
  height: 24px;
  line-height: 24px;
}
.list_item input:not([type=checkbox]) {
  padding-top: 3px;
  padding-bottom: 3px;
  height: 22px;
  line-height: 14px;
  margin: 0px;
}
.highlight_text {
  color: blue;
}
#project_name {
  display: inline-block;
  padding-left: 7px;
  margin-left: -2px;
}
#project_name > .breadcrumb {
  padding: 0px;
  margin-bottom: 0px;
  background-color: transparent;
  font-weight: bold;
}
.sort_button {
  display: inline-block;
  padding-left: 7px;
}
[dir="rtl"] .sort_button.pull-right {
  float: left !important;
  float: left;
}
#tree-selector {
  padding-right: 0px;
}
#button-select-all {
  min-width: 50px;
}
[dir="rtl"] #button-select-all.btn {
  float: right ;
}
#select-all {
  margin-left: 7px;
  margin-right: 2px;
  margin-top: 2px;
  height: 16px;
}
[dir="rtl"] #select-all.pull-left {
  float: right !important;
  float: right;
}
.menu_icon {
  margin-right: 2px;
}
.tab-content .row {
  margin-left: 0px;
  margin-right: 0px;
}
.folder_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f114";
}
.folder_icon:before.fa-pull-left {
  margin-right: .3em;
}
.folder_icon:before.fa-pull-right {
  margin-left: .3em;
}
.folder_icon:before.pull-left {
  margin-right: .3em;
}
.folder_icon:before.pull-right {
  margin-left: .3em;
}
.notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
}
.notebook_icon:before.fa-pull-left {
  margin-right: .3em;
}
.notebook_icon:before.fa-pull-right {
  margin-left: .3em;
}
.notebook_icon:before.pull-left {
  margin-right: .3em;
}
.notebook_icon:before.pull-right {
  margin-left: .3em;
}
.running_notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
  color: #5cb85c;
}
.running_notebook_icon:before.fa-pull-left {
  margin-right: .3em;
}
.running_notebook_icon:before.fa-pull-right {
  margin-left: .3em;
}
.running_notebook_icon:before.pull-left {
  margin-right: .3em;
}
.running_notebook_icon:before.pull-right {
  margin-left: .3em;
}
.file_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f016";
  position: relative;
  top: -2px;
}
.file_icon:before.fa-pull-left {
  margin-right: .3em;
}
.file_icon:before.fa-pull-right {
  margin-left: .3em;
}
.file_icon:before.pull-left {
  margin-right: .3em;
}
.file_icon:before.pull-right {
  margin-left: .3em;
}
#notebook_toolbar .pull-right {
  padding-top: 0px;
  margin-right: -1px;
}
ul#new-menu {
  left: auto;
  right: 0;
}
#new-menu .dropdown-header {
  font-size: 10px;
  border-bottom: 1px solid #e5e5e5;
  padding: 0 0 3px;
  margin: -3px 20px 0;
}
.kernel-menu-icon {
  padding-right: 12px;
  width: 24px;
  content: "\f096";
}
.kernel-menu-icon:before {
  content: "\f096";
}
.kernel-menu-icon-current:before {
  content: "\f00c";
}
#tab_content {
  padding-top: 20px;
}
#running .panel-group .panel {
  margin-top: 3px;
  margin-bottom: 1em;
}
#running .panel-group .panel .panel-heading {
  background-color: #EEE;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
#running .panel-group .panel .panel-heading a:focus,
#running .panel-group .panel .panel-heading a:hover {
  text-decoration: none;
}
#running .panel-group .panel .panel-body {
  padding: 0px;
}
#running .panel-group .panel .panel-body .list_container {
  margin-top: 0px;
  margin-bottom: 0px;
  border: 0px;
  border-radius: 0px;
}
#running .panel-group .panel .panel-body .list_container .list_item {
  border-bottom: 1px solid #ddd;
}
#running .panel-group .panel .panel-body .list_container .list_item:last-child {
  border-bottom: 0px;
}
.delete-button {
  display: none;
}
.duplicate-button {
  display: none;
}
.rename-button {
  display: none;
}
.move-button {
  display: none;
}
.download-button {
  display: none;
}
.shutdown-button {
  display: none;
}
.dynamic-instructions {
  display: inline-block;
  padding-top: 4px;
}
/*!
*
* IPython text editor webapp
*
*/
.selected-keymap i.fa {
  padding: 0px 5px;
}
.selected-keymap i.fa:before {
  content: "\f00c";
}
#mode-menu {
  overflow: auto;
  max-height: 20em;
}
.edit_app #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.edit_app #menubar .navbar {
  /* Use a negative 1 bottom margin, so the border overlaps the border of the
    header */
  margin-bottom: -1px;
}
.dirty-indicator {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator.pull-left {
  margin-right: .3em;
}
.dirty-indicator.pull-right {
  margin-left: .3em;
}
.dirty-indicator-dirty {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-dirty.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-dirty.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-dirty.pull-left {
  margin-right: .3em;
}
.dirty-indicator-dirty.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-clean.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f00c";
}
.dirty-indicator-clean:before.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean:before.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean:before.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean:before.pull-right {
  margin-left: .3em;
}
#filename {
  font-size: 16pt;
  display: table;
  padding: 0px 5px;
}
#current-mode {
  padding-left: 5px;
  padding-right: 5px;
}
#texteditor-backdrop {
  padding-top: 20px;
  padding-bottom: 20px;
}
@media not print {
  #texteditor-backdrop {
    background-color: #EEE;
  }
}
@media print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container {
    padding: 0px;
    background-color: #fff;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
.CodeMirror-dialog {
  background-color: #fff;
}
/*!
*
* IPython notebook
*
*/
/* CSS font colors for translated ANSI escape sequences */
/* The color values are a mix of
   http://www.xcolors.net/dl/baskerville-ivorylight and
   http://www.xcolors.net/dl/euphrasia */
.ansi-black-fg {
  color: #3E424D;
}
.ansi-black-bg {
  background-color: #3E424D;
}
.ansi-black-intense-fg {
  color: #282C36;
}
.ansi-black-intense-bg {
  background-color: #282C36;
}
.ansi-red-fg {
  color: #E75C58;
}
.ansi-red-bg {
  background-color: #E75C58;
}
.ansi-red-intense-fg {
  color: #B22B31;
}
.ansi-red-intense-bg {
  background-color: #B22B31;
}
.ansi-green-fg {
  color: #00A250;
}
.ansi-green-bg {
  background-color: #00A250;
}
.ansi-green-intense-fg {
  color: #007427;
}
.ansi-green-intense-bg {
  background-color: #007427;
}
.ansi-yellow-fg {
  color: #DDB62B;
}
.ansi-yellow-bg {
  background-color: #DDB62B;
}
.ansi-yellow-intense-fg {
  color: #B27D12;
}
.ansi-yellow-intense-bg {
  background-color: #B27D12;
}
.ansi-blue-fg {
  color: #208FFB;
}
.ansi-blue-bg {
  background-color: #208FFB;
}
.ansi-blue-intense-fg {
  color: #0065CA;
}
.ansi-blue-intense-bg {
  background-color: #0065CA;
}
.ansi-magenta-fg {
  color: #D160C4;
}
.ansi-magenta-bg {
  background-color: #D160C4;
}
.ansi-magenta-intense-fg {
  color: #A03196;
}
.ansi-magenta-intense-bg {
  background-color: #A03196;
}
.ansi-cyan-fg {
  color: #60C6C8;
}
.ansi-cyan-bg {
  background-color: #60C6C8;
}
.ansi-cyan-intense-fg {
  color: #258F8F;
}
.ansi-cyan-intense-bg {
  background-color: #258F8F;
}
.ansi-white-fg {
  color: #C5C1B4;
}
.ansi-white-bg {
  background-color: #C5C1B4;
}
.ansi-white-intense-fg {
  color: #A1A6B2;
}
.ansi-white-intense-bg {
  background-color: #A1A6B2;
}
.ansi-default-inverse-fg {
  color: #FFFFFF;
}
.ansi-default-inverse-bg {
  background-color: #000000;
}
.ansi-bold {
  font-weight: bold;
}
.ansi-underline {
  text-decoration: underline;
}
/* The following styles are deprecated an will be removed in a future version */
.ansibold {
  font-weight: bold;
}
.ansi-inverse {
  outline: 0.5px dotted;
}
/* use dark versions for foreground, to improve visibility */
.ansiblack {
  color: black;
}
.ansired {
  color: darkred;
}
.ansigreen {
  color: darkgreen;
}
.ansiyellow {
  color: #c4a000;
}
.ansiblue {
  color: darkblue;
}
.ansipurple {
  color: darkviolet;
}
.ansicyan {
  color: steelblue;
}
.ansigray {
  color: gray;
}
/* and light for background, for the same reason */
.ansibgblack {
  background-color: black;
}
.ansibgred {
  background-color: red;
}
.ansibggreen {
  background-color: green;
}
.ansibgyellow {
  background-color: yellow;
}
.ansibgblue {
  background-color: blue;
}
.ansibgpurple {
  background-color: magenta;
}
.ansibgcyan {
  background-color: cyan;
}
.ansibggray {
  background-color: gray;
}
div.cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  border-radius: 2px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  border-width: 1px;
  border-style: solid;
  border-color: transparent;
  width: 100%;
  padding: 5px;
  /* This acts as a spacer between cells, that is outside the border */
  margin: 0px;
  outline: none;
  position: relative;
  overflow: visible;
}
div.cell:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: transparent;
}
div.cell.jupyter-soft-selected {
  border-left-color: #E3F2FD;
  border-left-width: 1px;
  padding-left: 5px;
  border-right-color: #E3F2FD;
  border-right-width: 1px;
  background: #E3F2FD;
}
@media print {
  div.cell.jupyter-soft-selected {
    border-color: transparent;
  }
}
div.cell.selected,
div.cell.selected.jupyter-soft-selected {
  border-color: #ababab;
}
div.cell.selected:before,
div.cell.selected.jupyter-soft-selected:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: #42A5F5;
}
@media print {
  div.cell.selected,
  div.cell.selected.jupyter-soft-selected {
    border-color: transparent;
  }
}
.edit_mode div.cell.selected {
  border-color: #66BB6A;
}
.edit_mode div.cell.selected:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: #66BB6A;
}
@media print {
  .edit_mode div.cell.selected {
    border-color: transparent;
  }
}
.prompt {
  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
  min-width: 14ex;
  /* This padding is tuned to match the padding on the CodeMirror editor. */
  padding: 0.4em;
  margin: 0px;
  font-family: monospace;
  text-align: right;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
  /* Don't highlight prompt number selection */
  -webkit-touch-callout: none;
  -webkit-user-select: none;
  -khtml-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
  /* Use default cursor */
  cursor: default;
}
@media (max-width: 540px) {
  .prompt {
    text-align: left;
  }
}
div.inner_cell {
  min-width: 0;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_area {
  border: 1px solid #cfcfcf;
  border-radius: 2px;
  background: #f7f7f7;
  line-height: 1.21429em;
}
/* This is needed so that empty prompt areas can collapse to zero height when there
   is no content in the output_subarea and the prompt. The main purpose of this is
   to make sure that empty JavaScript output_subareas have no height. */
div.prompt:empty {
  padding-top: 0;
  padding-bottom: 0;
}
div.unrecognized_cell {
  padding: 5px 5px 5px 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.unrecognized_cell .inner_cell {
  border-radius: 2px;
  padding: 5px;
  font-weight: bold;
  color: red;
  border: 1px solid #cfcfcf;
  background: #eaeaea;
}
div.unrecognized_cell .inner_cell a {
  color: inherit;
  text-decoration: none;
}
div.unrecognized_cell .inner_cell a:hover {
  color: inherit;
  text-decoration: none;
}
@media (max-width: 540px) {
  div.unrecognized_cell > div.prompt {
    display: none;
  }
}
div.code_cell {
  /* avoid page breaking on code cells when printing */
}
@media print {
  div.code_cell {
    page-break-inside: avoid;
  }
}
/* any special styling for code cells that are currently running goes here */
div.input {
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.input {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_prompt {
  color: #303F9F;
  border-top: 1px solid transparent;
}
div.input_area > div.highlight {
  margin: 0.4em;
  border: none;
  padding: 0px;
  background-color: transparent;
}
div.input_area > div.highlight > pre {
  margin: 0px;
  border: none;
  padding: 0px;
  background-color: transparent;
}
/* The following gets added to the <head> if it is detected that the user has a
 * monospace font with inconsistent normal/bold/italic height.  See
 * notebookmain.js.  Such fonts will have keywords vertically offset with
 * respect to the rest of the text.  The user should select a better font.
 * See: https://github.com/ipython/ipython/issues/1503
 *
 * .CodeMirror span {
 *      vertical-align: bottom;
 * }
 */
.CodeMirror {
  line-height: 1.21429em;
  /* Changed from 1em to our global default */
  font-size: 14px;
  height: auto;
  /* Changed to auto to autogrow */
  background: none;
  /* Changed from white to allow our bg to show through */
}
.CodeMirror-scroll {
  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
  overflow-y: hidden;
  overflow-x: auto;
}
.CodeMirror-lines {
  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
  /* we have set a different line-height and want this to scale with that. */
  /* Note that this should set vertical padding only, since CodeMirror assumes
       that horizontal padding will be set on CodeMirror pre */
  padding: 0.4em 0;
}
.CodeMirror-linenumber {
  padding: 0 8px 0 4px;
}
.CodeMirror-gutters {
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.CodeMirror pre {
  /* In CM3 this went to 4px from 0 in CM2. This sets horizontal padding only,
    use .CodeMirror-lines for vertical */
  padding: 0 0.4em;
  border: 0;
  border-radius: 0;
}
.CodeMirror-cursor {
  border-left: 1.4px solid black;
}
@media screen and (min-width: 2138px) and (max-width: 4319px) {
  .CodeMirror-cursor {
    border-left: 2px solid black;
  }
}
@media screen and (min-width: 4320px) {
  .CodeMirror-cursor {
    border-left: 4px solid black;
  }
}
/*

Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
Adapted from GitHub theme

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
div.output_area .mglyph > img {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 1px 0 1px 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul:not(.list-inline),
.rendered_html ol:not(.list-inline) {
  padding-left: 2em;
}
.rendered_html ul {
  list-style: disc;
}
.rendered_html ul ul {
  list-style: square;
  margin-top: 0;
}
.rendered_html ul ul ul {
  list-style: circle;
}
.rendered_html ol {
  list-style: decimal;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin-top: 0;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
  padding: 0px;
  background-color: #fff;
}
.rendered_html code {
  background-color: #eff0f1;
}
.rendered_html p code {
  padding: 1px 5px;
}
.rendered_html pre code {
  background-color: #fff;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  color: #000;
  font-size: 100%;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: none;
  border-collapse: collapse;
  border-spacing: 0;
  color: black;
  font-size: 12px;
  table-layout: fixed;
}
.rendered_html thead {
  border-bottom: 1px solid black;
  vertical-align: bottom;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  text-align: right;
  vertical-align: middle;
  padding: 0.5em 0.5em;
  line-height: normal;
  white-space: normal;
  max-width: none;
  border: none;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html tbody tr:nth-child(odd) {
  background: #f5f5f5;
}
.rendered_html tbody tr:hover {
  background: rgba(66, 165, 245, 0.2);
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
.rendered_html .alert {
  margin-bottom: initial;
}
.rendered_html * + .alert {
  margin-top: 1em;
}
[dir="rtl"] .rendered_html p {
  text-align: right;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.rendered .rendered_html tr,
.text_cell.rendered .rendered_html th,
.text_cell.rendered .rendered_html td {
  max-width: none;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.text_cell .dropzone .input_area {
  border: 2px dashed #bababa;
  margin: -1px;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
.jupyter-keybindings {
  padding: 1px;
  line-height: 24px;
  border-bottom: 1px solid gray;
}
.jupyter-keybindings input {
  margin: 0;
  padding: 0;
  border: none;
}
.jupyter-keybindings i {
  padding: 6px;
}
.well code {
  background-color: #ffffff;
  border-color: #ababab;
  border-width: 1px;
  border-style: solid;
  padding: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.tags_button_container {
  width: 100%;
  display: flex;
}
.tag-container {
  display: flex;
  flex-direction: row;
  flex-grow: 1;
  overflow: hidden;
  position: relative;
}
.tag-container > * {
  margin: 0 4px;
}
.remove-tag-btn {
  margin-left: 4px;
}
.tags-input {
  display: flex;
}
.cell-tag:last-child:after {
  content: "";
  position: absolute;
  right: 0;
  width: 40px;
  height: 100%;
  /* Fade to background color of cell toolbar */
  background: linear-gradient(to right, rgba(0, 0, 0, 0), #EEE);
}
.tags-input > * {
  margin-left: 4px;
}
.cell-tag,
.tags-input input,
.tags-input button {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  box-shadow: none;
  width: inherit;
  font-size: inherit;
  height: 22px;
  line-height: 22px;
  padding: 0px 4px;
  display: inline-block;
}
.cell-tag:focus,
.tags-input input:focus,
.tags-input button:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.cell-tag::-moz-placeholder,
.tags-input input::-moz-placeholder,
.tags-input button::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.cell-tag:-ms-input-placeholder,
.tags-input input:-ms-input-placeholder,
.tags-input button:-ms-input-placeholder {
  color: #999;
}
.cell-tag::-webkit-input-placeholder,
.tags-input input::-webkit-input-placeholder,
.tags-input button::-webkit-input-placeholder {
  color: #999;
}
.cell-tag::-ms-expand,
.tags-input input::-ms-expand,
.tags-input button::-ms-expand {
  border: 0;
  background-color: transparent;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
.cell-tag[readonly],
.tags-input input[readonly],
.tags-input button[readonly],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  background-color: #eeeeee;
  opacity: 1;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  cursor: not-allowed;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button {
  height: auto;
}
select.cell-tag,
select.tags-input input,
select.tags-input button {
  height: 30px;
  line-height: 30px;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button,
select[multiple].cell-tag,
select[multiple].tags-input input,
select[multiple].tags-input button {
  height: auto;
}
.cell-tag,
.tags-input button {
  padding: 0px 4px;
}
.cell-tag {
  background-color: #fff;
  white-space: nowrap;
}
.tags-input input[type=text]:focus {
  outline: none;
  box-shadow: none;
  border-color: #ccc;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
[dir="rtl"] #kernel_logo_widget {
  float: left !important;
  float: left;
}
.modal .modal-body .move-path {
  display: flex;
  flex-direction: row;
  justify-content: space;
  align-items: center;
}
.modal .modal-body .move-path .server-root {
  padding-right: 20px;
}
.modal .modal-body .move-path .path-input {
  flex: 1;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
[dir="rtl"] #menubar .navbar-toggle {
  float: right;
}
[dir="rtl"] #menubar .navbar-collapse {
  clear: right;
}
[dir="rtl"] #menubar .navbar-nav {
  float: right;
}
[dir="rtl"] #menubar .nav {
  padding-right: 0px;
}
[dir="rtl"] #menubar .navbar-nav > li {
  float: right;
}
[dir="rtl"] #menubar .navbar-right {
  float: left !important;
}
[dir="rtl"] ul.dropdown-menu {
  text-align: right;
  left: auto;
}
[dir="rtl"] ul#new-menu.dropdown-menu {
  right: auto;
  left: 0;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
[dir="rtl"] i.menu-icon.pull-right {
  float: left !important;
  float: left;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
[dir="rtl"] ul#help_menu li a {
  padding-left: 2.2em;
}
[dir="rtl"] ul#help_menu li a i {
  margin-right: 0;
  margin-left: -1.2em;
}
[dir="rtl"] ul#help_menu li a i.pull-right {
  float: left !important;
  float: left;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
[dir="rtl"] .dropdown-submenu > .dropdown-menu {
  right: 100%;
  margin-right: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.fa-pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.fa-pull-right {
  margin-left: .3em;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
[dir="rtl"] .dropdown-submenu > a:after {
  float: left;
  content: "\f0d9";
  margin-right: 0;
  margin-left: -10px;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
[dir="rtl"] #notification_area {
  float: left !important;
  float: left;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] .indicator_area {
  float: left !important;
  float: left;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
[dir="rtl"] #kernel_indicator {
  float: left !important;
  float: left;
  border-left: 0;
  border-right: 1px solid;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] #modal_indicator {
  float: left !important;
  float: left;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  height: 30px;
  margin-top: 4px;
  display: flex;
  justify-content: flex-start;
  align-items: baseline;
  width: 50%;
  flex: 1;
}
span.save_widget span.filename {
  height: 100%;
  line-height: 1em;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  text-overflow: ellipsis;
  overflow: hidden;
  white-space: nowrap;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
[dir="rtl"] span.save_widget.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] span.save_widget span.filename {
  margin-left: 0;
  margin-right: 16px;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
  white-space: nowrap;
  padding: 0 5px;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
    padding: 0 0 0 5px;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
.toolbar-btn-label {
  margin-left: 6px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
  padding-top: 11px;
  padding-bottom: 3px;
}
#maintoolbar .navbar-text {
  float: none;
  vertical-align: middle;
  text-align: right;
  margin-left: 5px;
  margin-right: 0px;
  margin-top: 0px;
}
.select-xs {
  height: 24px;
}
[dir="rtl"] .btn-group > .btn,
.btn-group-vertical > .btn {
  float: right;
}
.pulse,
.dropdown-menu > li > a.pulse,
li.pulse > a.dropdown-toggle,
li.pulse.open > a.dropdown-toggle {
  background-color: #F37626;
  color: white;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
 * of chance of beeing generated from the ../less/[samename].less file, you can
 * try to get back the less file by reverting somme commit in history
 **/
/*
 * We'll try to get something pretty, so we
 * have some strange css to have the scroll bar on
 * the left with fix button on the top right of the tooltip
 */
@-moz-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-webkit-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-moz-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
@-webkit-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
/*properties of tooltip after "expand"*/
.bigtooltip {
  overflow: auto;
  height: 200px;
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
}
/*properties of tooltip before "expand"*/
.smalltooltip {
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
  text-overflow: ellipsis;
  overflow: hidden;
  height: 80px;
}
.tooltipbuttons {
  position: absolute;
  padding-right: 15px;
  top: 0px;
  right: 0px;
}
.tooltiptext {
  /*avoid the button to overlap on some docstring*/
  padding-right: 30px;
}
.ipython_tooltip {
  max-width: 700px;
  /*fade-in animation when inserted*/
  -webkit-animation: fadeOut 400ms;
  -moz-animation: fadeOut 400ms;
  animation: fadeOut 400ms;
  -webkit-animation: fadeIn 400ms;
  -moz-animation: fadeIn 400ms;
  animation: fadeIn 400ms;
  vertical-align: middle;
  background-color: #f7f7f7;
  overflow: visible;
  border: #ababab 1px solid;
  outline: none;
  padding: 3px;
  margin: 0px;
  padding-left: 7px;
  font-family: monospace;
  min-height: 50px;
  -moz-box-shadow: 0px 6px 10px -1px #adadad;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  border-radius: 2px;
  position: absolute;
  z-index: 1000;
}
.ipython_tooltip a {
  float: right;
}
.ipython_tooltip .tooltiptext pre {
  border: 0;
  border-radius: 0;
  font-size: 100%;
  background-color: #f7f7f7;
}
.pretooltiparrow {
  left: 0px;
  margin: 0px;
  top: -16px;
  width: 40px;
  height: 16px;
  overflow: hidden;
  position: absolute;
}
.pretooltiparrow:before {
  background-color: #f7f7f7;
  border: 1px #ababab solid;
  z-index: 11;
  content: "";
  position: absolute;
  left: 15px;
  top: 10px;
  width: 25px;
  height: 25px;
  -webkit-transform: rotate(45deg);
  -moz-transform: rotate(45deg);
  -ms-transform: rotate(45deg);
  -o-transform: rotate(45deg);
}
ul.typeahead-list i {
  margin-left: -10px;
  width: 18px;
}
[dir="rtl"] ul.typeahead-list i {
  margin-left: 0;
  margin-right: -10px;
}
ul.typeahead-list {
  max-height: 80vh;
  overflow: auto;
}
ul.typeahead-list > li > a {
  /** Firefox bug **/
  /* see https://github.com/jupyter/notebook/issues/559 */
  white-space: normal;
}
ul.typeahead-list  > li > a.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .typeahead-list {
  text-align: right;
}
.cmd-palette .modal-body {
  padding: 7px;
}
.cmd-palette form {
  background: white;
}
.cmd-palette input {
  outline: none;
}
.no-shortcut {
  min-width: 20px;
  color: transparent;
}
[dir="rtl"] .no-shortcut.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .command-shortcut.pull-right {
  float: left !important;
  float: left;
}
.command-shortcut:before {
  content: "(command mode)";
  padding-right: 3px;
  color: #777777;
}
.edit-shortcut:before {
  content: "(edit)";
  padding-right: 3px;
  color: #777777;
}
[dir="rtl"] .edit-shortcut.pull-right {
  float: left !important;
  float: left;
}
#find-and-replace #replace-preview .match,
#find-and-replace #replace-preview .insert {
  background-color: #BBDEFB;
  border-color: #90CAF9;
  border-style: solid;
  border-width: 1px;
  border-radius: 0px;
}
[dir="ltr"] #find-and-replace .input-group-btn + .form-control {
  border-left: none;
}
[dir="rtl"] #find-and-replace .input-group-btn + .form-control {
  border-right: none;
}
#find-and-replace #replace-preview .replace .match {
  background-color: #FFCDD2;
  border-color: #EF9A9A;
  border-radius: 0px;
}
#find-and-replace #replace-preview .replace .insert {
  background-color: #C8E6C9;
  border-color: #A5D6A7;
  border-radius: 0px;
}
#find-and-replace #replace-preview {
  max-height: 60vh;
  overflow: auto;
}
#find-and-replace #replace-preview pre {
  padding: 5px 10px;
}
.terminal-app {
  background: #EEE;
}
.terminal-app #header {
  background: #fff;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.terminal-app .terminal {
  width: 100%;
  float: left;
  font-family: monospace;
  color: white;
  background: black;
  padding: 0.4em;
  border-radius: 2px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
}
.terminal-app .terminal,
.terminal-app .terminal dummy-screen {
  line-height: 1em;
  font-size: 14px;
}
.terminal-app .terminal .xterm-rows {
  padding: 10px;
}
.terminal-app .terminal-cursor {
  color: black;
  background: white;
}
.terminal-app #terminado-container {
  margin-top: 20px;
}
/*# sourceMappingURL=style.min.css.map */
    </style>
<style type="text/css">
    .highlight .hll { background-color: #ffffcc }
.highlight  { background: #f8f8f8; }
.highlight .c { color: #408080; font-style: italic } /* Comment */
.highlight .err { border: 1px solid #FF0000 } /* Error */
.highlight .k { color: #008000; font-weight: bold } /* Keyword */
.highlight .o { color: #666666 } /* Operator */
.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
.highlight .gd { color: #A00000 } /* Generic.Deleted */
.highlight .ge { font-style: italic } /* Generic.Emph */
.highlight .gr { color: #FF0000 } /* Generic.Error */
.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
.highlight .gi { color: #00A000 } /* Generic.Inserted */
.highlight .go { color: #888888 } /* Generic.Output */
.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
.highlight .gs { font-weight: bold } /* Generic.Strong */
.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
.highlight .gt { color: #0044DD } /* Generic.Traceback */
.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
.highlight .kp { color: #008000 } /* Keyword.Pseudo */
.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
.highlight .kt { color: #B00040 } /* Keyword.Type */
.highlight .m { color: #666666 } /* Literal.Number */
.highlight .s { color: #BA2121 } /* Literal.String */
.highlight .na { color: #7D9029 } /* Name.Attribute */
.highlight .nb { color: #008000 } /* Name.Builtin */
.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
.highlight .no { color: #880000 } /* Name.Constant */
.highlight .nd { color: #AA22FF } /* Name.Decorator */
.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
.highlight .nf { color: #0000FF } /* Name.Function */
.highlight .nl { color: #A0A000 } /* Name.Label */
.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
.highlight .nv { color: #19177C } /* Name.Variable */
.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
.highlight .w { color: #bbbbbb } /* Text.Whitespace */
.highlight .mb { color: #666666 } /* Literal.Number.Bin */
.highlight .mf { color: #666666 } /* Literal.Number.Float */
.highlight .mh { color: #666666 } /* Literal.Number.Hex */
.highlight .mi { color: #666666 } /* Literal.Number.Integer */
.highlight .mo { color: #666666 } /* Literal.Number.Oct */
.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
.highlight .sc { color: #BA2121 } /* Literal.String.Char */
.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
.highlight .sx { color: #008000 } /* Literal.String.Other */
.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
.highlight .ss { color: #19177C } /* Literal.String.Symbol */
.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
.highlight .fm { color: #0000FF } /* Name.Function.Magic */
.highlight .vc { color: #19177C } /* Name.Variable.Class */
.highlight .vg { color: #19177C } /* Name.Variable.Global */
.highlight .vi { color: #19177C } /* Name.Variable.Instance */
.highlight .vm { color: #19177C } /* Name.Variable.Magic */
.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
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<style type="text/css">
    
/* Temporary definitions which will become obsolete with Notebook release 5.0 */
.ansi-black-fg { color: #3E424D; }
.ansi-black-bg { background-color: #3E424D; }
.ansi-black-intense-fg { color: #282C36; }
.ansi-black-intense-bg { background-color: #282C36; }
.ansi-red-fg { color: #E75C58; }
.ansi-red-bg { background-color: #E75C58; }
.ansi-red-intense-fg { color: #B22B31; }
.ansi-red-intense-bg { background-color: #B22B31; }
.ansi-green-fg { color: #00A250; }
.ansi-green-bg { background-color: #00A250; }
.ansi-green-intense-fg { color: #007427; }
.ansi-green-intense-bg { background-color: #007427; }
.ansi-yellow-fg { color: #DDB62B; }
.ansi-yellow-bg { background-color: #DDB62B; }
.ansi-yellow-intense-fg { color: #B27D12; }
.ansi-yellow-intense-bg { background-color: #B27D12; }
.ansi-blue-fg { color: #208FFB; }
.ansi-blue-bg { background-color: #208FFB; }
.ansi-blue-intense-fg { color: #0065CA; }
.ansi-blue-intense-bg { background-color: #0065CA; }
.ansi-magenta-fg { color: #D160C4; }
.ansi-magenta-bg { background-color: #D160C4; }
.ansi-magenta-intense-fg { color: #A03196; }
.ansi-magenta-intense-bg { background-color: #A03196; }
.ansi-cyan-fg { color: #60C6C8; }
.ansi-cyan-bg { background-color: #60C6C8; }
.ansi-cyan-intense-fg { color: #258F8F; }
.ansi-cyan-intense-bg { background-color: #258F8F; }
.ansi-white-fg { color: #C5C1B4; }
.ansi-white-bg { background-color: #C5C1B4; }
.ansi-white-intense-fg { color: #A1A6B2; }
.ansi-white-intense-bg { background-color: #A1A6B2; }

.ansi-bold { font-weight: bold; }

    </style>


<style type="text/css">
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<h1 id="Creating-a-Sentiment-Analysis-Web-App">Creating a Sentiment Analysis Web App<a class="anchor-link" href="#Creating-a-Sentiment-Analysis-Web-App">&#182;</a></h1><h2 id="Using-PyTorch-and-SageMaker">Using PyTorch and SageMaker<a class="anchor-link" href="#Using-PyTorch-and-SageMaker">&#182;</a></h2><p><em>Deep Learning Nanodegree Program | Deployment</em></p>
<hr>
<p>Now that we have a basic understanding of how SageMaker works we will try to use it to construct a complete project from end to end. Our goal will be to have a simple web page which a user can use to enter a movie review. The web page will then send the review off to our deployed model which will predict the sentiment of the entered review.</p>
<h2 id="Instructions">Instructions<a class="anchor-link" href="#Instructions">&#182;</a></h2><p>Some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this notebook. You will not need to modify the included code beyond what is requested. Sections that begin with '<strong>TODO</strong>' in the header indicate that you need to complete or implement some portion within them. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a <code># TODO: ...</code> comment. Please be sure to read the instructions carefully!</p>
<p>In addition to implementing code, there will be questions for you to answer which relate to the task and your implementation. Each section where you will answer a question is preceded by a '<strong>Question:</strong>' header. Carefully read each question and provide your answer below the '<strong>Answer:</strong>' header by editing the Markdown cell.</p>
<blockquote><p><strong>Note</strong>: Code and Markdown cells can be executed using the <strong>Shift+Enter</strong> keyboard shortcut. In addition, a cell can be edited by typically clicking it (double-click for Markdown cells) or by pressing <strong>Enter</strong> while it is highlighted.</p>
</blockquote>
<h2 id="General-Outline">General Outline<a class="anchor-link" href="#General-Outline">&#182;</a></h2><p>Recall the general outline for SageMaker projects using a notebook instance.</p>
<ol>
<li>Download or otherwise retrieve the data.</li>
<li>Process / Prepare the data.</li>
<li>Upload the processed data to S3.</li>
<li>Train a chosen model.</li>
<li>Test the trained model (typically using a batch transform job).</li>
<li>Deploy the trained model.</li>
<li>Use the deployed model.</li>
</ol>
<p>For this project, you will be following the steps in the general outline with some modifications.</p>
<p>First, you will not be testing the model in its own step. You will still be testing the model, however, you will do it by deploying your model and then using the deployed model by sending the test data to it. One of the reasons for doing this is so that you can make sure that your deployed model is working correctly before moving forward.</p>
<p>In addition, you will deploy and use your trained model a second time. In the second iteration you will customize the way that your trained model is deployed by including some of your own code. In addition, your newly deployed model will be used in the sentiment analysis web app.</p>

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<h2 id="Step-1:-Downloading-the-data">Step 1: Downloading the data<a class="anchor-link" href="#Step-1:-Downloading-the-data">&#182;</a></h2><p>As in the XGBoost in SageMaker notebook, we will be using the <a href="http://ai.stanford.edu/~amaas/data/sentiment/">IMDb dataset</a></p>
<blockquote><p>Maas, Andrew L., et al. <a href="http://ai.stanford.edu/~amaas/data/sentiment/">Learning Word Vectors for Sentiment Analysis</a>. In <em>Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies</em>. Association for Computational Linguistics, 2011.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">mkdir</span> ../data
<span class="o">!</span>wget -O ../data/aclImdb_v1.tar.gz http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
<span class="o">!</span>tar -zxf ../data/aclImdb_v1.tar.gz -C ../data
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<pre>mkdir: cannot create directory ‘../data’: File exists
--2019-05-24 09:08:48--  http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
Resolving ai.stanford.edu (ai.stanford.edu)... 171.64.68.10
Connecting to ai.stanford.edu (ai.stanford.edu)|171.64.68.10|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 84125825 (80M) [application/x-gzip]
Saving to: ‘../data/aclImdb_v1.tar.gz’

../data/aclImdb_v1. 100%[===================&gt;]  80.23M  5.92MB/s    in 20s     

2019-05-24 09:09:10 (3.93 MB/s) - ‘../data/aclImdb_v1.tar.gz’ saved [84125825/84125825]

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<h2 id="Step-2:-Preparing-and-Processing-the-data">Step 2: Preparing and Processing the data<a class="anchor-link" href="#Step-2:-Preparing-and-Processing-the-data">&#182;</a></h2><p>Also, as in the XGBoost notebook, we will be doing some initial data processing. The first few steps are the same as in the XGBoost example. To begin with, we will read in each of the reviews and combine them into a single input structure. Then, we will split the dataset into a training set and a testing set.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">glob</span>

<span class="k">def</span> <span class="nf">read_imdb_data</span><span class="p">(</span><span class="n">data_dir</span><span class="o">=</span><span class="s1">&#39;../data/aclImdb&#39;</span><span class="p">):</span>
    <span class="n">data</span> <span class="o">=</span> <span class="p">{}</span>
    <span class="n">labels</span> <span class="o">=</span> <span class="p">{}</span>
    
    <span class="k">for</span> <span class="n">data_type</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="s1">&#39;test&#39;</span><span class="p">]:</span>
        <span class="n">data</span><span class="p">[</span><span class="n">data_type</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
        <span class="n">labels</span><span class="p">[</span><span class="n">data_type</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
        
        <span class="k">for</span> <span class="n">sentiment</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;pos&#39;</span><span class="p">,</span> <span class="s1">&#39;neg&#39;</span><span class="p">]:</span>
            <span class="n">data</span><span class="p">[</span><span class="n">data_type</span><span class="p">][</span><span class="n">sentiment</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
            <span class="n">labels</span><span class="p">[</span><span class="n">data_type</span><span class="p">][</span><span class="n">sentiment</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
            
            <span class="n">path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="n">data_type</span><span class="p">,</span> <span class="n">sentiment</span><span class="p">,</span> <span class="s1">&#39;*.txt&#39;</span><span class="p">)</span>
            <span class="n">files</span> <span class="o">=</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
            
            <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">files</span><span class="p">:</span>
                <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="k">as</span> <span class="n">review</span><span class="p">:</span>
                    <span class="n">data</span><span class="p">[</span><span class="n">data_type</span><span class="p">][</span><span class="n">sentiment</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">review</span><span class="o">.</span><span class="n">read</span><span class="p">())</span>
                    <span class="c1"># Here we represent a positive review by &#39;1&#39; and a negative review by &#39;0&#39;</span>
                    <span class="n">labels</span><span class="p">[</span><span class="n">data_type</span><span class="p">][</span><span class="n">sentiment</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span> <span class="k">if</span> <span class="n">sentiment</span> <span class="o">==</span> <span class="s1">&#39;pos&#39;</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>
                    
            <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">data_type</span><span class="p">][</span><span class="n">sentiment</span><span class="p">])</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">labels</span><span class="p">[</span><span class="n">data_type</span><span class="p">][</span><span class="n">sentiment</span><span class="p">]),</span> \
                    <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">/</span><span class="si">{}</span><span class="s2"> data size does not match labels size&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">data_type</span><span class="p">,</span> <span class="n">sentiment</span><span class="p">)</span>
                
    <span class="k">return</span> <span class="n">data</span><span class="p">,</span> <span class="n">labels</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">read_imdb_data</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;IMDB reviews: train = </span><span class="si">{}</span><span class="s2"> pos / </span><span class="si">{}</span><span class="s2"> neg, test = </span><span class="si">{}</span><span class="s2"> pos / </span><span class="si">{}</span><span class="s2"> neg&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
            <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">][</span><span class="s1">&#39;pos&#39;</span><span class="p">]),</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">][</span><span class="s1">&#39;neg&#39;</span><span class="p">]),</span>
            <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">][</span><span class="s1">&#39;pos&#39;</span><span class="p">]),</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">][</span><span class="s1">&#39;neg&#39;</span><span class="p">])))</span>
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<pre>IMDB reviews: train = 12500 pos / 12500 neg, test = 12500 pos / 12500 neg
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<p>Now that we've read the raw training and testing data from the downloaded dataset, we will combine the positive and negative reviews and shuffle the resulting records.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="k">import</span> <span class="n">shuffle</span>

<span class="k">def</span> <span class="nf">prepare_imdb_data</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Prepare training and test sets from IMDb movie reviews.&quot;&quot;&quot;</span>
    
    <span class="c1">#Combine positive and negative reviews and labels</span>
    <span class="n">data_train</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">][</span><span class="s1">&#39;pos&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">][</span><span class="s1">&#39;neg&#39;</span><span class="p">]</span>
    <span class="n">data_test</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">][</span><span class="s1">&#39;pos&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">][</span><span class="s1">&#39;neg&#39;</span><span class="p">]</span>
    <span class="n">labels_train</span> <span class="o">=</span> <span class="n">labels</span><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">][</span><span class="s1">&#39;pos&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">labels</span><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">][</span><span class="s1">&#39;neg&#39;</span><span class="p">]</span>
    <span class="n">labels_test</span> <span class="o">=</span> <span class="n">labels</span><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">][</span><span class="s1">&#39;pos&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">labels</span><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">][</span><span class="s1">&#39;neg&#39;</span><span class="p">]</span>
    
    <span class="c1">#Shuffle reviews and corresponding labels within training and test sets</span>
    <span class="n">data_train</span><span class="p">,</span> <span class="n">labels_train</span> <span class="o">=</span> <span class="n">shuffle</span><span class="p">(</span><span class="n">data_train</span><span class="p">,</span> <span class="n">labels_train</span><span class="p">)</span>
    <span class="n">data_test</span><span class="p">,</span> <span class="n">labels_test</span> <span class="o">=</span> <span class="n">shuffle</span><span class="p">(</span><span class="n">data_test</span><span class="p">,</span> <span class="n">labels_test</span><span class="p">)</span>
    
    <span class="c1"># Return a unified training data, test data, training labels, test labets</span>
    <span class="k">return</span> <span class="n">data_train</span><span class="p">,</span> <span class="n">data_test</span><span class="p">,</span> <span class="n">labels_train</span><span class="p">,</span> <span class="n">labels_test</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">train_X</span><span class="p">,</span> <span class="n">test_X</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">test_y</span> <span class="o">=</span> <span class="n">prepare_imdb_data</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;IMDb reviews (combined): train = </span><span class="si">{}</span><span class="s2">, test = </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">train_X</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">test_X</span><span class="p">)))</span>
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<pre>IMDb reviews (combined): train = 25000, test = 25000
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<p>Now that we have our training and testing sets unified and prepared, we should do a quick check and see an example of the data our model will be trained on. This is generally a good idea as it allows you to see how each of the further processing steps affects the reviews and it also ensures that the data has been loaded correctly.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">train_X</span><span class="p">[</span><span class="mi">100</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">train_y</span><span class="p">[</span><span class="mi">100</span><span class="p">])</span>
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<pre>A Christmas Story Is A Holiday Classic And My Favorite Movie. So Naturally, I Was Elated When This Movie Came Out In 1994. I Saw It Opening Day and Was Prepared To Enjoy Myself. I Came Away Revolted And Digusted. The Anticipation that Rang True In A Christmas Story Is Curiously Missing from This mess. A Red Ryder BB Gun Is Better to get than a chinese top.And It Is Not Very Funny At all. Charles Grodin Is Good but the Buck Stops There. Bottom Line:1 Star. Don&#39;t Even Bother.
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<p>The first step in processing the reviews is to make sure that any html tags that appear should be removed. In addition we wish to tokenize our input, that way words such as <em>entertained</em> and <em>entertaining</em> are considered the same with regard to sentiment analysis.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">nltk</span>
<span class="kn">from</span> <span class="nn">nltk.corpus</span> <span class="k">import</span> <span class="n">stopwords</span>
<span class="kn">from</span> <span class="nn">nltk.stem.porter</span> <span class="k">import</span> <span class="o">*</span>

<span class="kn">import</span> <span class="nn">re</span>
<span class="kn">from</span> <span class="nn">bs4</span> <span class="k">import</span> <span class="n">BeautifulSoup</span>

<span class="k">def</span> <span class="nf">review_to_words</span><span class="p">(</span><span class="n">review</span><span class="p">):</span>
    <span class="n">nltk</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="s2">&quot;stopwords&quot;</span><span class="p">,</span> <span class="n">quiet</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    <span class="n">stemmer</span> <span class="o">=</span> <span class="n">PorterStemmer</span><span class="p">()</span>
    
    <span class="n">text</span> <span class="o">=</span> <span class="n">BeautifulSoup</span><span class="p">(</span><span class="n">review</span><span class="p">,</span> <span class="s2">&quot;html.parser&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">get_text</span><span class="p">()</span> <span class="c1"># Remove HTML tags</span>
    <span class="n">text</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;[^a-zA-Z0-9]&quot;</span><span class="p">,</span> <span class="s2">&quot; &quot;</span><span class="p">,</span> <span class="n">text</span><span class="o">.</span><span class="n">lower</span><span class="p">())</span> <span class="c1"># Convert to lower case</span>
    <span class="n">words</span> <span class="o">=</span> <span class="n">text</span><span class="o">.</span><span class="n">split</span><span class="p">()</span> <span class="c1"># Split string into words</span>
    <span class="n">words</span> <span class="o">=</span> <span class="p">[</span><span class="n">w</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">words</span> <span class="k">if</span> <span class="n">w</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">stopwords</span><span class="o">.</span><span class="n">words</span><span class="p">(</span><span class="s2">&quot;english&quot;</span><span class="p">)]</span> <span class="c1"># Remove stopwords</span>
    <span class="n">words</span> <span class="o">=</span> <span class="p">[</span><span class="n">PorterStemmer</span><span class="p">()</span><span class="o">.</span><span class="n">stem</span><span class="p">(</span><span class="n">w</span><span class="p">)</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">words</span><span class="p">]</span> <span class="c1"># stem</span>
    
    <span class="k">return</span> <span class="n">words</span>
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<p>The <code>review_to_words</code> method defined above uses <code>BeautifulSoup</code> to remove any html tags that appear and uses the <code>nltk</code> package to tokenize the reviews. As a check to ensure we know how everything is working, try applying <code>review_to_words</code> to one of the reviews in the training set.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Apply review_to_words to a review (train_X[100] or any other review)</span>
<span class="n">review_to_words</span><span class="p">(</span><span class="n">train_X</span><span class="p">[</span><span class="mi">100</span><span class="p">])</span>
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<pre>[&#39;christma&#39;,
 &#39;stori&#39;,
 &#39;holiday&#39;,
 &#39;classic&#39;,
 &#39;favorit&#39;,
 &#39;movi&#39;,
 &#39;natur&#39;,
 &#39;elat&#39;,
 &#39;movi&#39;,
 &#39;came&#39;,
 &#39;1994&#39;,
 &#39;saw&#39;,
 &#39;open&#39;,
 &#39;day&#39;,
 &#39;prepar&#39;,
 &#39;enjoy&#39;,
 &#39;came&#39;,
 &#39;away&#39;,
 &#39;revolt&#39;,
 &#39;digust&#39;,
 &#39;anticip&#39;,
 &#39;rang&#39;,
 &#39;true&#39;,
 &#39;christma&#39;,
 &#39;stori&#39;,
 &#39;curious&#39;,
 &#39;miss&#39;,
 &#39;mess&#39;,
 &#39;red&#39;,
 &#39;ryder&#39;,
 &#39;bb&#39;,
 &#39;gun&#39;,
 &#39;better&#39;,
 &#39;get&#39;,
 &#39;chines&#39;,
 &#39;top&#39;,
 &#39;funni&#39;,
 &#39;charl&#39;,
 &#39;grodin&#39;,
 &#39;good&#39;,
 &#39;buck&#39;,
 &#39;stop&#39;,
 &#39;bottom&#39;,
 &#39;line&#39;,
 &#39;1&#39;,
 &#39;star&#39;,
 &#39;even&#39;,
 &#39;bother&#39;]</pre>
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<p><strong>Question:</strong> Above we mentioned that <code>review_to_words</code> method removes html formatting and allows us to tokenize the words found in a review, for example, converting <em>entertained</em> and <em>entertaining</em> into <em>entertain</em> so that they are treated as though they are the same word. What else, if anything, does this method do to the input?</p>

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<p><strong>Answer:</strong></p>
<p>The review_to_words method performs few operations to process the words before it can be used. They are as follows -</p>
<ul>
<li>First it removes all the HTML tags using BeautifulSoup.</li>
<li>Then it replaces all the non-alpha-numeric tags with space and converts everything to lower case.</li>
<li>Converts the sentence into a list of words.</li>
<li>Removes all the stopwords(the common words which doesn't carry much information like 'The', 'is') from the list.</li>
<li>Stemming is performed on each word. Basically stripping each word of its suffixes and prefixes so that only the root word is left.</li>
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<p>The method below applies the <code>review_to_words</code> method to each of the reviews in the training and testing datasets. In addition it caches the results. This is because performing this processing step can take a long time. This way if you are unable to complete the notebook in the current session, you can come back without needing to process the data a second time.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pickle</span>

<span class="n">cache_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s2">&quot;../cache&quot;</span><span class="p">,</span> <span class="s2">&quot;sentiment_analysis&quot;</span><span class="p">)</span>  <span class="c1"># where to store cache files</span>
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">cache_dir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>  <span class="c1"># ensure cache directory exists</span>

<span class="k">def</span> <span class="nf">preprocess_data</span><span class="p">(</span><span class="n">data_train</span><span class="p">,</span> <span class="n">data_test</span><span class="p">,</span> <span class="n">labels_train</span><span class="p">,</span> <span class="n">labels_test</span><span class="p">,</span>
                    <span class="n">cache_dir</span><span class="o">=</span><span class="n">cache_dir</span><span class="p">,</span> <span class="n">cache_file</span><span class="o">=</span><span class="s2">&quot;preprocessed_data.pkl&quot;</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Convert each review to words; read from cache if available.&quot;&quot;&quot;</span>

    <span class="c1"># If cache_file is not None, try to read from it first</span>
    <span class="n">cache_data</span> <span class="o">=</span> <span class="kc">None</span>
    <span class="k">if</span> <span class="n">cache_file</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">try</span><span class="p">:</span>
            <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">cache_dir</span><span class="p">,</span> <span class="n">cache_file</span><span class="p">),</span> <span class="s2">&quot;rb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
                <span class="n">cache_data</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Read preprocessed data from cache file:&quot;</span><span class="p">,</span> <span class="n">cache_file</span><span class="p">)</span>
        <span class="k">except</span><span class="p">:</span>
            <span class="k">pass</span>  <span class="c1"># unable to read from cache, but that&#39;s okay</span>
    
    <span class="c1"># If cache is missing, then do the heavy lifting</span>
    <span class="k">if</span> <span class="n">cache_data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="c1"># Preprocess training and test data to obtain words for each review</span>
        <span class="c1">#words_train = list(map(review_to_words, data_train))</span>
        <span class="c1">#words_test = list(map(review_to_words, data_test))</span>
        <span class="n">words_train</span> <span class="o">=</span> <span class="p">[</span><span class="n">review_to_words</span><span class="p">(</span><span class="n">review</span><span class="p">)</span> <span class="k">for</span> <span class="n">review</span> <span class="ow">in</span> <span class="n">data_train</span><span class="p">]</span>
        <span class="n">words_test</span> <span class="o">=</span> <span class="p">[</span><span class="n">review_to_words</span><span class="p">(</span><span class="n">review</span><span class="p">)</span> <span class="k">for</span> <span class="n">review</span> <span class="ow">in</span> <span class="n">data_test</span><span class="p">]</span>
        
        <span class="c1"># Write to cache file for future runs</span>
        <span class="k">if</span> <span class="n">cache_file</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">cache_data</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">words_train</span><span class="o">=</span><span class="n">words_train</span><span class="p">,</span> <span class="n">words_test</span><span class="o">=</span><span class="n">words_test</span><span class="p">,</span>
                              <span class="n">labels_train</span><span class="o">=</span><span class="n">labels_train</span><span class="p">,</span> <span class="n">labels_test</span><span class="o">=</span><span class="n">labels_test</span><span class="p">)</span>
            <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">cache_dir</span><span class="p">,</span> <span class="n">cache_file</span><span class="p">),</span> <span class="s2">&quot;wb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
                <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">cache_data</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span>
            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Wrote preprocessed data to cache file:&quot;</span><span class="p">,</span> <span class="n">cache_file</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="c1"># Unpack data loaded from cache file</span>
        <span class="n">words_train</span><span class="p">,</span> <span class="n">words_test</span><span class="p">,</span> <span class="n">labels_train</span><span class="p">,</span> <span class="n">labels_test</span> <span class="o">=</span> <span class="p">(</span><span class="n">cache_data</span><span class="p">[</span><span class="s1">&#39;words_train&#39;</span><span class="p">],</span>
                <span class="n">cache_data</span><span class="p">[</span><span class="s1">&#39;words_test&#39;</span><span class="p">],</span> <span class="n">cache_data</span><span class="p">[</span><span class="s1">&#39;labels_train&#39;</span><span class="p">],</span> <span class="n">cache_data</span><span class="p">[</span><span class="s1">&#39;labels_test&#39;</span><span class="p">])</span>
    
    <span class="k">return</span> <span class="n">words_train</span><span class="p">,</span> <span class="n">words_test</span><span class="p">,</span> <span class="n">labels_train</span><span class="p">,</span> <span class="n">labels_test</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Preprocess data</span>
<span class="n">train_X</span><span class="p">,</span> <span class="n">test_X</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">test_y</span> <span class="o">=</span> <span class="n">preprocess_data</span><span class="p">(</span><span class="n">train_X</span><span class="p">,</span> <span class="n">test_X</span><span class="p">,</span> <span class="n">train_y</span><span class="p">,</span> <span class="n">test_y</span><span class="p">)</span>
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<pre>Read preprocessed data from cache file: preprocessed_data.pkl
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">train_X</span><span class="p">[</span><span class="mi">100</span><span class="p">])</span>
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<pre>[&#39;cheap&#39;, &#39;mind&#39;, &#39;blisteringli&#39;, &#39;dull&#39;, &#39;stori&#39;, &#39;act&#39;, &#39;singl&#39;, &#39;good&#39;, &#39;line&#39;, &#39;even&#39;, &#39;line&#39;, &#39;bad&#39;, &#39;enough&#39;, &#39;good&#39;, &#39;memor&#39;, &#39;deliveri&#39;, &#39;even&#39;, &#39;blooper&#39;, &#39;reel&#39;, &#39;includ&#39;, &#39;dvd&#39;, &#39;show&#39;, &#39;inept&#39;, &#39;actor&#39;, &#39;littl&#39;, &#39;fun&#39;, &#39;esoter&#39;, &#39;occult&#39;, &#39;basi&#39;, &#39;apathet&#39;, &#39;inauthent&#39;, &#39;antagonist&#39;, &#39;fail&#39;, &#39;creepi&#39;, &#39;believ&#39;, &#39;homoerot&#39;, &#39;overton&#39;, &#39;pointlessli&#39;, &#39;tame&#39;, &#39;dissatisfi&#39;, &#39;limit&#39;, &#39;young&#39;, &#39;boy&#39;, &#39;caress&#39;, &#39;chest&#39;, &#39;flaccid&#39;, &#39;boxer&#39;, &#39;gay&#39;, &#39;enough&#39;, &#39;appreci&#39;, &#39;littl&#39;, &#39;action&#39;, &#39;might&#39;, &#39;least&#39;, &#39;kept&#39;, &#39;girlfriend&#39;, &#39;awak&#39;]
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<h2 id="Transform-the-data">Transform the data<a class="anchor-link" href="#Transform-the-data">&#182;</a></h2><p>In the XGBoost notebook we transformed the data from its word representation to a bag-of-words feature representation. For the model we are going to construct in this notebook we will construct a feature representation which is very similar. To start, we will represent each word as an integer. Of course, some of the words that appear in the reviews occur very infrequently and so likely don't contain much information for the purposes of sentiment analysis. The way we will deal with this problem is that we will fix the size of our working vocabulary and we will only include the words that appear most frequently. We will then combine all of the infrequent words into a single category and, in our case, we will label it as <code>1</code>.</p>
<p>Since we will be using a recurrent neural network, it will be convenient if the length of each review is the same. To do this, we will fix a size for our reviews and then pad short reviews with the category 'no word' (which we will label <code>0</code>) and truncate long reviews.</p>

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<h3 id="(TODO)-Create-a-word-dictionary">(TODO) Create a word dictionary<a class="anchor-link" href="#(TODO)-Create-a-word-dictionary">&#182;</a></h3><p>To begin with, we need to construct a way to map words that appear in the reviews to integers. Here we fix the size of our vocabulary (including the 'no word' and 'infrequent' categories) to be <code>5000</code> but you may wish to change this to see how it affects the model.</p>
<blockquote><p><strong>TODO:</strong> Complete the implementation for the <code>build_dict()</code> method below. Note that even though the vocab_size is set to <code>5000</code>, we only want to construct a mapping for the most frequently appearing <code>4998</code> words. This is because we want to reserve the special labels <code>0</code> for 'no word' and <code>1</code> for 'infrequent word'.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="k">import</span> <span class="n">Counter</span>

<span class="k">def</span> <span class="nf">build_dict</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">vocab_size</span> <span class="o">=</span> <span class="mi">5000</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Construct and return a dictionary mapping each of the most frequently appearing words to a unique integer.&quot;&quot;&quot;</span>
    
    <span class="c1"># TODO: Determine how often each word appears in `data`. Note that `data` is a list of sentences and that a</span>
    <span class="c1">#       sentence is a list of words.</span>
    
    <span class="n">word_count</span> <span class="o">=</span> <span class="p">{}</span> <span class="c1"># A dict storing the words that appear in the reviews along with how often they occur</span>
    
    <span class="k">for</span> <span class="n">review</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span>
        <span class="n">count</span> <span class="o">=</span> <span class="n">Counter</span><span class="p">(</span><span class="n">review</span><span class="p">)</span>
        <span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">count</span><span class="p">:</span>
            <span class="n">word_count</span><span class="p">[</span><span class="n">word</span><span class="p">]</span> <span class="o">=</span> <span class="n">count</span><span class="p">[</span><span class="n">word</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span><span class="n">word_count</span><span class="p">[</span><span class="n">word</span><span class="p">]</span> <span class="k">if</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">word_count</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>
        
    <span class="c1"># TODO: Sort the words found in `data` so that sorted_words[0] is the most frequently appearing word and</span>
    <span class="c1">#       sorted_words[-1] is the least frequently appearing word.</span>
    
    <span class="n">sorted_words</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">word_count</span><span class="p">,</span> <span class="n">key</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">w</span><span class="p">:</span>  <span class="n">word_count</span><span class="p">[</span><span class="n">w</span><span class="p">],</span> <span class="n">reverse</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
    
    <span class="n">word_dict</span> <span class="o">=</span> <span class="p">{}</span> <span class="c1"># This is what we are building, a dictionary that translates words into integers</span>
    <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">word</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sorted_words</span><span class="p">[:</span><span class="n">vocab_size</span> <span class="o">-</span> <span class="mi">2</span><span class="p">]):</span> <span class="c1"># The -2 is so that we save room for the &#39;no word&#39;</span>
        <span class="n">word_dict</span><span class="p">[</span><span class="n">word</span><span class="p">]</span> <span class="o">=</span> <span class="n">idx</span> <span class="o">+</span> <span class="mi">2</span>                              <span class="c1"># &#39;infrequent&#39; labels</span>
        
    <span class="k">return</span> <span class="n">word_dict</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">word_dict</span> <span class="o">=</span> <span class="n">build_dict</span><span class="p">(</span><span class="n">train_X</span><span class="p">)</span>
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<p><strong>Question:</strong> What are the five most frequently appearing (tokenized) words in the training set? Does it makes sense that these words appear frequently in the training set?</p>

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<p><strong>Answer:</strong>
In the 'word_dict' the words with less index value are the more frequently appearing words. So the five most appearing words are -</p>
<ol>
<li>movie</li>
<li>film</li>
<li>one</li>
<li>like</li>
<li>time</li>
</ol>
<p>'movie' and 'film' are the most frequently used words, this makes sense as these words are bound to come up when you are writing a review about a movie/film. Other three words are also very frequently used in reviews, in common phrases like 'one of the best/worst' or 'had a great/bad time'.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Use this space to determine the five most frequently appearing words in the training set.</span>
<span class="nb">print</span><span class="p">(</span><span class="n">word_dict</span><span class="p">)</span>
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<pre>{&#39;movi&#39;: 2, &#39;film&#39;: 3, &#39;one&#39;: 4, &#39;like&#39;: 5, &#39;time&#39;: 6, &#39;good&#39;: 7, &#39;make&#39;: 8, &#39;charact&#39;: 9, &#39;get&#39;: 10, &#39;see&#39;: 11, &#39;watch&#39;: 12, &#39;stori&#39;: 13, &#39;even&#39;: 14, &#39;would&#39;: 15, &#39;realli&#39;: 16, &#39;well&#39;: 17, &#39;scene&#39;: 18, &#39;look&#39;: 19, &#39;show&#39;: 20, &#39;much&#39;: 21, &#39;end&#39;: 22, &#39;peopl&#39;: 23, &#39;bad&#39;: 24, &#39;go&#39;: 25, &#39;great&#39;: 26, &#39;also&#39;: 27, &#39;first&#39;: 28, &#39;love&#39;: 29, &#39;think&#39;: 30, &#39;way&#39;: 31, &#39;act&#39;: 32, &#39;play&#39;: 33, &#39;made&#39;: 34, &#39;thing&#39;: 35, &#39;could&#39;: 36, &#39;know&#39;: 37, &#39;say&#39;: 38, &#39;seem&#39;: 39, &#39;work&#39;: 40, &#39;plot&#39;: 41, &#39;two&#39;: 42, &#39;actor&#39;: 43, &#39;year&#39;: 44, &#39;come&#39;: 45, &#39;mani&#39;: 46, &#39;seen&#39;: 47, &#39;take&#39;: 48, &#39;want&#39;: 49, &#39;life&#39;: 50, &#39;never&#39;: 51, &#39;littl&#39;: 52, &#39;best&#39;: 53, &#39;tri&#39;: 54, &#39;man&#39;: 55, &#39;ever&#39;: 56, &#39;give&#39;: 57, &#39;better&#39;: 58, &#39;still&#39;: 59, &#39;perform&#39;: 60, &#39;find&#39;: 61, &#39;feel&#39;: 62, &#39;part&#39;: 63, &#39;back&#39;: 64, &#39;use&#39;: 65, &#39;someth&#39;: 66, &#39;director&#39;: 67, &#39;actual&#39;: 68, &#39;interest&#39;: 69, &#39;lot&#39;: 70, &#39;real&#39;: 71, &#39;old&#39;: 72, &#39;cast&#39;: 73, &#39;though&#39;: 74, &#39;live&#39;: 75, &#39;star&#39;: 76, &#39;enjoy&#39;: 77, &#39;guy&#39;: 78, &#39;anoth&#39;: 79, &#39;new&#39;: 80, &#39;role&#39;: 81, &#39;noth&#39;: 82, &#39;10&#39;: 83, &#39;funni&#39;: 84, &#39;music&#39;: 85, &#39;point&#39;: 86, &#39;start&#39;: 87, &#39;set&#39;: 88, &#39;girl&#39;: 89, &#39;origin&#39;: 90, &#39;day&#39;: 91, &#39;world&#39;: 92, &#39;everi&#39;: 93, &#39;believ&#39;: 94, &#39;turn&#39;: 95, &#39;quit&#39;: 96, &#39;direct&#39;: 97, &#39;us&#39;: 98, &#39;thought&#39;: 99, &#39;fact&#39;: 100, &#39;minut&#39;: 101, &#39;horror&#39;: 102, &#39;kill&#39;: 103, &#39;action&#39;: 104, &#39;comedi&#39;: 105, &#39;pretti&#39;: 106, &#39;young&#39;: 107, &#39;wonder&#39;: 108, &#39;happen&#39;: 109, &#39;around&#39;: 110, &#39;got&#39;: 111, &#39;effect&#39;: 112, &#39;right&#39;: 113, &#39;long&#39;: 114, &#39;howev&#39;: 115, &#39;big&#39;: 116, &#39;line&#39;: 117, &#39;famili&#39;: 118, &#39;enough&#39;: 119, &#39;seri&#39;: 120, &#39;may&#39;: 121, &#39;need&#39;: 122, &#39;fan&#39;: 123, &#39;bit&#39;: 124, &#39;script&#39;: 125, &#39;beauti&#39;: 126, &#39;person&#39;: 127, &#39;becom&#39;: 128, &#39;without&#39;: 129, &#39;must&#39;: 130, &#39;alway&#39;: 131, &#39;friend&#39;: 132, &#39;tell&#39;: 133, &#39;reason&#39;: 134, &#39;saw&#39;: 135, &#39;last&#39;: 136, &#39;final&#39;: 137, &#39;kid&#39;: 138, &#39;almost&#39;: 139, &#39;put&#39;: 140, &#39;least&#39;: 141, &#39;sure&#39;: 142, &#39;done&#39;: 143, &#39;whole&#39;: 144, &#39;place&#39;: 145, &#39;complet&#39;: 146, &#39;kind&#39;: 147, &#39;differ&#39;: 148, &#39;expect&#39;: 149, &#39;shot&#39;: 150, &#39;far&#39;: 151, &#39;mean&#39;: 152, &#39;anyth&#39;: 153, &#39;book&#39;: 154, &#39;laugh&#39;: 155, &#39;might&#39;: 156, &#39;name&#39;: 157, &#39;sinc&#39;: 158, &#39;begin&#39;: 159, &#39;2&#39;: 160, &#39;probabl&#39;: 161, &#39;woman&#39;: 162, &#39;help&#39;: 163, &#39;entertain&#39;: 164, &#39;let&#39;: 165, &#39;screen&#39;: 166, &#39;call&#39;: 167, &#39;tv&#39;: 168, &#39;moment&#39;: 169, &#39;away&#39;: 170, &#39;read&#39;: 171, &#39;yet&#39;: 172, &#39;rather&#39;: 173, &#39;worst&#39;: 174, &#39;run&#39;: 175, &#39;fun&#39;: 176, &#39;lead&#39;: 177, &#39;hard&#39;: 178, &#39;audienc&#39;: 179, &#39;idea&#39;: 180, &#39;anyon&#39;: 181, &#39;episod&#39;: 182, &#39;american&#39;: 183, &#39;found&#39;: 184, &#39;appear&#39;: 185, &#39;bore&#39;: 186, &#39;especi&#39;: 187, &#39;although&#39;: 188, &#39;hope&#39;: 189, &#39;keep&#39;: 190, &#39;cours&#39;: 191, &#39;anim&#39;: 192, &#39;job&#39;: 193, &#39;goe&#39;: 194, &#39;move&#39;: 195, &#39;sens&#39;: 196, &#39;dvd&#39;: 197, &#39;version&#39;: 198, &#39;war&#39;: 199, &#39;money&#39;: 200, &#39;someon&#39;: 201, &#39;mind&#39;: 202, &#39;mayb&#39;: 203, &#39;problem&#39;: 204, &#39;true&#39;: 205, &#39;hous&#39;: 206, &#39;everyth&#39;: 207, &#39;nice&#39;: 208, &#39;second&#39;: 209, &#39;rate&#39;: 210, &#39;three&#39;: 211, &#39;night&#39;: 212, &#39;face&#39;: 213, &#39;follow&#39;: 214, &#39;recommend&#39;: 215, &#39;main&#39;: 216, &#39;product&#39;: 217, &#39;worth&#39;: 218, &#39;leav&#39;: 219, &#39;human&#39;: 220, &#39;special&#39;: 221, &#39;excel&#39;: 222, &#39;togeth&#39;: 223, &#39;wast&#39;: 224, &#39;everyon&#39;: 225, &#39;sound&#39;: 226, &#39;john&#39;: 227, &#39;hand&#39;: 228, &#39;1&#39;: 229, &#39;father&#39;: 230, &#39;later&#39;: 231, &#39;eye&#39;: 232, &#39;said&#39;: 233, &#39;view&#39;: 234, &#39;instead&#39;: 235, &#39;review&#39;: 236, &#39;boy&#39;: 237, &#39;high&#39;: 238, &#39;hour&#39;: 239, &#39;miss&#39;: 240, &#39;talk&#39;: 241, &#39;classic&#39;: 242, &#39;wife&#39;: 243, &#39;understand&#39;: 244, &#39;left&#39;: 245, &#39;care&#39;: 246, &#39;black&#39;: 247, &#39;death&#39;: 248, &#39;open&#39;: 249, &#39;murder&#39;: 250, &#39;write&#39;: 251, &#39;half&#39;: 252, &#39;head&#39;: 253, &#39;rememb&#39;: 254, &#39;chang&#39;: 255, &#39;viewer&#39;: 256, &#39;fight&#39;: 257, &#39;gener&#39;: 258, &#39;surpris&#39;: 259, &#39;includ&#39;: 260, &#39;short&#39;: 261, &#39;die&#39;: 262, &#39;fall&#39;: 263, &#39;less&#39;: 264, &#39;els&#39;: 265, &#39;entir&#39;: 266, &#39;piec&#39;: 267, &#39;involv&#39;: 268, &#39;pictur&#39;: 269, &#39;simpli&#39;: 270, &#39;home&#39;: 271, &#39;top&#39;: 272, &#39;power&#39;: 273, &#39;total&#39;: 274, &#39;usual&#39;: 275, &#39;budget&#39;: 276, &#39;attempt&#39;: 277, &#39;suppos&#39;: 278, &#39;releas&#39;: 279, &#39;hollywood&#39;: 280, &#39;terribl&#39;: 281, &#39;song&#39;: 282, &#39;men&#39;: 283, &#39;possibl&#39;: 284, &#39;featur&#39;: 285, &#39;portray&#39;: 286, &#39;disappoint&#39;: 287, &#39;poor&#39;: 288, &#39;3&#39;: 289, &#39;coupl&#39;: 290, &#39;stupid&#39;: 291, &#39;camera&#39;: 292, &#39;dead&#39;: 293, &#39;wrong&#39;: 294, &#39;low&#39;: 295, &#39;produc&#39;: 296, &#39;either&#39;: 297, &#39;video&#39;: 298, &#39;aw&#39;: 299, &#39;definit&#39;: 300, &#39;except&#39;: 301, &#39;rest&#39;: 302, &#39;given&#39;: 303, &#39;absolut&#39;: 304, &#39;women&#39;: 305, &#39;lack&#39;: 306, &#39;word&#39;: 307, &#39;writer&#39;: 308, &#39;titl&#39;: 309, &#39;talent&#39;: 310, &#39;decid&#39;: 311, &#39;full&#39;: 312, &#39;perfect&#39;: 313, &#39;along&#39;: 314, &#39;style&#39;: 315, &#39;close&#39;: 316, &#39;truli&#39;: 317, &#39;school&#39;: 318, &#39;save&#39;: 319, &#39;emot&#39;: 320, &#39;sex&#39;: 321, &#39;age&#39;: 322, &#39;next&#39;: 323, &#39;bring&#39;: 324, &#39;mr&#39;: 325, &#39;case&#39;: 326, &#39;killer&#39;: 327, &#39;heart&#39;: 328, &#39;comment&#39;: 329, &#39;sort&#39;: 330, &#39;creat&#39;: 331, &#39;perhap&#39;: 332, &#39;came&#39;: 333, &#39;brother&#39;: 334, &#39;sever&#39;: 335, &#39;joke&#39;: 336, &#39;art&#39;: 337, &#39;dialogu&#39;: 338, &#39;game&#39;: 339, &#39;small&#39;: 340, &#39;base&#39;: 341, &#39;flick&#39;: 342, &#39;written&#39;: 343, &#39;sequenc&#39;: 344, &#39;meet&#39;: 345, &#39;earli&#39;: 346, &#39;often&#39;: 347, &#39;other&#39;: 348, &#39;mother&#39;: 349, &#39;develop&#39;: 350, &#39;humor&#39;: 351, &#39;actress&#39;: 352, &#39;consid&#39;: 353, &#39;dark&#39;: 354, &#39;guess&#39;: 355, &#39;amaz&#39;: 356, &#39;unfortun&#39;: 357, &#39;light&#39;: 358, &#39;lost&#39;: 359, &#39;exampl&#39;: 360, &#39;cinema&#39;: 361, &#39;drama&#39;: 362, &#39;white&#39;: 363, &#39;ye&#39;: 364, &#39;experi&#39;: 365, &#39;imagin&#39;: 366, &#39;mention&#39;: 367, &#39;stop&#39;: 368, &#39;natur&#39;: 369, &#39;forc&#39;: 370, &#39;manag&#39;: 371, &#39;felt&#39;: 372, &#39;cut&#39;: 373, &#39;present&#39;: 374, &#39;children&#39;: 375, &#39;fail&#39;: 376, &#39;son&#39;: 377, &#39;support&#39;: 378, &#39;car&#39;: 379, &#39;qualiti&#39;: 380, &#39;ask&#39;: 381, &#39;hit&#39;: 382, &#39;side&#39;: 383, &#39;voic&#39;: 384, &#39;extrem&#39;: 385, &#39;impress&#39;: 386, &#39;wors&#39;: 387, &#39;evil&#39;: 388, &#39;stand&#39;: 389, &#39;went&#39;: 390, &#39;certainli&#39;: 391, &#39;basic&#39;: 392, &#39;oh&#39;: 393, &#39;overal&#39;: 394, &#39;favorit&#39;: 395, &#39;horribl&#39;: 396, &#39;mysteri&#39;: 397, &#39;number&#39;: 398, &#39;type&#39;: 399, &#39;danc&#39;: 400, &#39;wait&#39;: 401, &#39;hero&#39;: 402, &#39;5&#39;: 403, &#39;alreadi&#39;: 404, &#39;learn&#39;: 405, &#39;matter&#39;: 406, &#39;4&#39;: 407, &#39;michael&#39;: 408, &#39;genr&#39;: 409, &#39;fine&#39;: 410, &#39;despit&#39;: 411, &#39;throughout&#39;: 412, &#39;walk&#39;: 413, &#39;success&#39;: 414, &#39;histori&#39;: 415, &#39;question&#39;: 416, &#39;zombi&#39;: 417, &#39;town&#39;: 418, &#39;realiz&#39;: 419, &#39;relationship&#39;: 420, &#39;child&#39;: 421, &#39;past&#39;: 422, &#39;daughter&#39;: 423, &#39;late&#39;: 424, &#39;b&#39;: 425, &#39;wish&#39;: 426, &#39;hate&#39;: 427, &#39;credit&#39;: 428, &#39;event&#39;: 429, &#39;theme&#39;: 430, &#39;touch&#39;: 431, &#39;citi&#39;: 432, &#39;today&#39;: 433, &#39;sometim&#39;: 434, &#39;behind&#39;: 435, &#39;god&#39;: 436, &#39;twist&#39;: 437, &#39;sit&#39;: 438, &#39;deal&#39;: 439, &#39;annoy&#39;: 440, &#39;stay&#39;: 441, &#39;abl&#39;: 442, &#39;rent&#39;: 443, &#39;pleas&#39;: 444, &#39;edit&#39;: 445, &#39;blood&#39;: 446, &#39;deserv&#39;: 447, &#39;anyway&#39;: 448, &#39;comic&#39;: 449, &#39;appar&#39;: 450, &#39;soon&#39;: 451, &#39;gave&#39;: 452, &#39;etc&#39;: 453, &#39;level&#39;: 454, &#39;slow&#39;: 455, &#39;chanc&#39;: 456, &#39;score&#39;: 457, &#39;bodi&#39;: 458, &#39;brilliant&#39;: 459, &#39;incred&#39;: 460, &#39;figur&#39;: 461, &#39;situat&#39;: 462, &#39;major&#39;: 463, &#39;self&#39;: 464, &#39;stuff&#39;: 465, &#39;decent&#39;: 466, &#39;element&#39;: 467, &#39;dream&#39;: 468, &#39;return&#39;: 469, &#39;obvious&#39;: 470, &#39;continu&#39;: 471, &#39;order&#39;: 472, &#39;pace&#39;: 473, &#39;ridicul&#39;: 474, &#39;happi&#39;: 475, &#39;add&#39;: 476, &#39;group&#39;: 477, &#39;highli&#39;: 478, &#39;thank&#39;: 479, &#39;ladi&#39;: 480, &#39;novel&#39;: 481, &#39;speak&#39;: 482, &#39;pain&#39;: 483, &#39;career&#39;: 484, &#39;shoot&#39;: 485, &#39;strang&#39;: 486, &#39;heard&#39;: 487, &#39;sad&#39;: 488, &#39;husband&#39;: 489, &#39;polic&#39;: 490, &#39;import&#39;: 491, &#39;break&#39;: 492, &#39;took&#39;: 493, &#39;cannot&#39;: 494, &#39;strong&#39;: 495, &#39;predict&#39;: 496, &#39;robert&#39;: 497, &#39;violenc&#39;: 498, &#39;hilari&#39;: 499, &#39;recent&#39;: 500, &#39;countri&#39;: 501, &#39;known&#39;: 502, &#39;particularli&#39;: 503, &#39;pick&#39;: 504, &#39;documentari&#39;: 505, &#39;season&#39;: 506, &#39;critic&#39;: 507, &#39;jame&#39;: 508, &#39;compar&#39;: 509, &#39;alon&#39;: 510, &#39;obviou&#39;: 511, &#39;told&#39;: 512, &#39;state&#39;: 513, &#39;visual&#39;: 514, &#39;rock&#39;: 515, &#39;exist&#39;: 516, &#39;offer&#39;: 517, &#39;theater&#39;: 518, &#39;opinion&#39;: 519, &#39;gore&#39;: 520, &#39;crap&#39;: 521, &#39;hold&#39;: 522, &#39;result&#39;: 523, &#39;hear&#39;: 524, &#39;room&#39;: 525, &#39;realiti&#39;: 526, &#39;effort&#39;: 527, &#39;clich&#39;: 528, &#39;thriller&#39;: 529, &#39;caus&#39;: 530, &#39;explain&#39;: 531, &#39;sequel&#39;: 532, &#39;serious&#39;: 533, &#39;king&#39;: 534, &#39;local&#39;: 535, &#39;ago&#39;: 536, &#39;hell&#39;: 537, &#39;none&#39;: 538, &#39;note&#39;: 539, &#39;allow&#39;: 540, &#39;david&#39;: 541, &#39;sister&#39;: 542, &#39;simpl&#39;: 543, &#39;femal&#39;: 544, &#39;deliv&#39;: 545, &#39;ok&#39;: 546, &#39;convinc&#39;: 547, &#39;class&#39;: 548, &#39;check&#39;: 549, &#39;suspens&#39;: 550, 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&#39;clock&#39;: 3023, &#39;kudo&#39;: 3024, &#39;grasp&#39;: 3025, &#39;closet&#39;: 3026, &#39;brosnan&#39;: 3027, &#39;importantli&#39;: 3028, &#39;eighti&#39;: 3029, &#39;declar&#39;: 3030, &#39;gray&#39;: 3031, &#39;sidekick&#39;: 3032, &#39;pal&#39;: 3033, &#39;wipe&#39;: 3034, &#39;mitchel&#39;: 3035, &#39;fifteen&#39;: 3036, &#39;miracl&#39;: 3037, &#39;bleak&#39;: 3038, &#39;selfish&#39;: 3039, &#39;norman&#39;: 3040, &#39;communist&#39;: 3041, &#39;clown&#39;: 3042, &#39;sloppi&#39;: 3043, &#39;partli&#39;: 3044, &#39;evok&#39;: 3045, &#39;cliff&#39;: 3046, &#39;seller&#39;: 3047, &#39;inconsist&#39;: 3048, &#39;debat&#39;: 3049, &#39;chew&#39;: 3050, &#39;seed&#39;: 3051, &#39;enthusiast&#39;: 3052, &#39;lifestyl&#39;: 3053, &#39;psychiatrist&#39;: 3054, &#39;superbl&#39;: 3055, &#39;piano&#39;: 3056, &#39;aforement&#39;: 3057, &#39;destin&#39;: 3058, &#39;stoog&#39;: 3059, &#39;flawless&#39;: 3060, &#39;ho&#39;: 3061, &#39;farc&#39;: 3062, &#39;australia&#39;: 3063, &#39;45&#39;: 3064, &#39;cheek&#39;: 3065, &#39;websit&#39;: 3066, &#39;dire&#39;: 3067, &#39;drivel&#39;: 3068, &#39;incompet&#39;: 3069, &#39;pressur&#39;: 3070, &#39;bash&#39;: 3071, &#39;regardless&#39;: 3072, &#39;wrestl&#39;: 3073, &#39;kitchen&#39;: 3074, &#39;directori&#39;: 3075, &#39;dash&#39;: 3076, &#39;slice&#39;: 3077, &#39;akshay&#39;: 3078, &#39;emili&#39;: 3079, &#39;anni&#39;: 3080, &#39;abc&#39;: 3081, &#39;wick&#39;: 3082, &#39;splatter&#39;: 3083, &#39;soviet&#39;: 3084, &#39;jar&#39;: 3085, &#39;increas&#39;: 3086, &#39;recov&#39;: 3087, &#39;helicopt&#39;: 3088, &#39;seduc&#39;: 3089, &#39;judi&#39;: 3090, &#39;cia&#39;: 3091, &#39;distant&#39;: 3092, &#39;chapter&#39;: 3093, &#39;glow&#39;: 3094, &#39;flower&#39;: 3095, &#39;cameron&#39;: 3096, &#39;suppli&#39;: 3097, &#39;ken&#39;: 3098, &#39;artifici&#39;: 3099, &#39;boil&#39;: 3100, &#39;curios&#39;: 3101, &#39;mann&#39;: 3102, &#39;doo&#39;: 3103, &#39;prize&#39;: 3104, &#39;pleasantli&#39;: 3105, &#39;lou&#39;: 3106, &#39;blob&#39;: 3107, &#39;duo&#39;: 3108, &#39;beaten&#39;: 3109, &#39;dave&#39;: 3110, &#39;cagney&#39;: 3111, &#39;turner&#39;: 3112, &#39;web&#39;: 3113, &#39;goldberg&#39;: 3114, &#39;favour&#39;: 3115, &#39;eleg&#39;: 3116, &#39;hop&#39;: 3117, &#39;drunken&#39;: 3118, &#39;craig&#39;: 3119, &#39;laurel&#39;: 3120, &#39;panic&#39;: 3121, &#39;craven&#39;: 3122, &#39;ellen&#39;: 3123, &#39;combat&#39;: 3124, &#39;splendid&#39;: 3125, &#39;francisco&#39;: 3126, &#39;ranger&#39;: 3127, &#39;glenn&#39;: 3128, &#39;perri&#39;: 3129, &#39;psychot&#39;: 3130, &#39;flip&#39;: 3131, &#39;min&#39;: 3132, &#39;hatr&#39;: 3133, &#39;graduat&#39;: 3134, &#39;wizard&#39;: 3135, &#39;gentl&#39;: 3136, &#39;shortli&#39;: 3137, &#39;rid&#39;: 3138, &#39;philosophi&#39;: 3139, &#39;fx&#39;: 3140, &#39;falk&#39;: 3141, &#39;20th&#39;: 3142, &#39;plausibl&#39;: 3143, &#39;ruth&#39;: 3144, &#39;slightest&#39;: 3145, &#39;gandhi&#39;: 3146, &#39;greek&#39;: 3147, &#39;alexand&#39;: 3148, &#39;modesti&#39;: 3149, &#39;ocean&#39;: 3150, &#39;holi&#39;: 3151, &#39;harm&#39;: 3152, &#39;preciou&#39;: 3153, &#39;tall&#39;: 3154, &#39;knight&#39;: 3155, &#39;we&#39;: 3156, &#39;futurist&#39;: 3157, &#39;dracula&#39;: 3158, &#39;lend&#39;: 3159, &#39;legal&#39;: 3160, &#39;fund&#39;: 3161, &#39;jealou&#39;: 3162, &#39;manhattan&#39;: 3163, &#39;unpleas&#39;: 3164, &#39;felix&#39;: 3165, &#39;thread&#39;: 3166, &#39;scientif&#39;: 3167, &#39;forbidden&#39;: 3168, &#39;giallo&#39;: 3169, &#39;explicit&#39;: 3170, &#39;mock&#39;: 3171, &#39;ami&#39;: 3172, &#39;bless&#39;: 3173, &#39;digniti&#39;: 3174, &#39;tank&#39;: 3175, &#39;reviv&#39;: 3176, &#39;overdon&#39;: 3177, &#39;childish&#39;: 3178, &#39;nod&#39;: 3179, &#39;margaret&#39;: 3180, &#39;99&#39;: 3181, &#39;elderli&#39;: 3182, &#39;unwatch&#39;: 3183, &#39;torment&#39;: 3184, &#39;pirat&#39;: 3185, &#39;fever&#39;: 3186, &#39;awaken&#39;: 3187, &#39;awe&#39;: 3188, &#39;eve&#39;: 3189, &#39;verhoeven&#39;: 3190, &#39;nerv&#39;: 3191, &#39;2004&#39;: 3192, &#39;repeatedli&#39;: 3193, &#39;thick&#39;: 3194, &#39;yesterday&#39;: 3195, &#39;broad&#39;: 3196, &#39;mel&#39;: 3197, &#39;ambit&#39;: 3198, &#39;custom&#39;: 3199, &#39;eas&#39;: 3200, &#39;ah&#39;: 3201, &#39;royal&#39;: 3202, &#39;politician&#39;: 3203, &#39;bin&#39;: 3204, &#39;uniform&#39;: 3205, &#39;launch&#39;: 3206, &#39;automat&#39;: 3207, &#39;stiller&#39;: 3208, &#39;acclaim&#39;: 3209, &#39;rivet&#39;: 3210, &#39;absenc&#39;: 3211, &#39;roman&#39;: 3212, &#39;romero&#39;: 3213, &#39;griffith&#39;: 3214, &#39;kay&#39;: 3215, &#39;timothi&#39;: 3216, &#39;publish&#39;: 3217, &#39;lean&#39;: 3218, &#39;foul&#39;: 3219, &#39;crook&#39;: 3220, &#39;termin&#39;: 3221, &#39;phrase&#39;: 3222, &#39;sunshin&#39;: 3223, &#39;pulp&#39;: 3224, &#39;transport&#39;: 3225, &#39;homicid&#39;: 3226, &#39;bathroom&#39;: 3227, &#39;wallac&#39;: 3228, &#39;darker&#39;: 3229, &#39;purpl&#39;: 3230, &#39;antic&#39;: 3231, &#39;stinker&#39;: 3232, &#39;warren&#39;: 3233, &#39;tomato&#39;: 3234, &#39;pierc&#39;: 3235, &#39;gabriel&#39;: 3236, &#39;evolv&#39;: 3237, &#39;hollow&#39;: 3238, &#39;contrari&#39;: 3239, &#39;choreographi&#39;: 3240, &#39;karen&#39;: 3241, &#39;revolutionari&#39;: 3242, &#39;coloni&#39;: 3243, &#39;juvenil&#39;: 3244, &#39;2003&#39;: 3245, &#39;packag&#39;: 3246, &#39;awak&#39;: 3247, &#39;ought&#39;: 3248, &#39;pray&#39;: 3249, &#39;li&#39;: 3250, &#39;kenneth&#39;: 3251, &#39;album&#39;: 3252, &#39;rambo&#39;: 3253, &#39;brazil&#39;: 3254, &#39;sixti&#39;: 3255, &#39;horrid&#39;: 3256, &#39;marin&#39;: 3257, &#39;eyr&#39;: 3258, &#39;q&#39;: 3259, &#39;saint&#39;: 3260, &#39;viciou&#39;: 3261, &#39;prom&#39;: 3262, &#39;donna&#39;: 3263, &#39;overr&#39;: 3264, &#39;candid&#39;: 3265, &#39;blade&#39;: 3266, &#39;twelv&#39;: 3267, &#39;mummi&#39;: 3268, &#39;boast&#39;: 3269, &#39;nerd&#39;: 3270, &#39;kapoor&#39;: 3271, &#39;option&#39;: 3272, &#39;conserv&#39;: 3273, &#39;stole&#39;: 3274, &#39;defi&#39;: 3275, &#39;ireland&#39;: 3276, &#39;mildr&#39;: 3277, &#39;dose&#39;: 3278, &#39;ramon&#39;: 3279, &#39;beatti&#39;: 3280, &#39;confirm&#39;: 3281, &#39;global&#39;: 3282, &#39;detract&#39;: 3283, &#39;funer&#39;: 3284, &#39;flame&#39;: 3285, &#39;fulci&#39;: 3286, &#39;collabor&#39;: 3287, &#39;protest&#39;: 3288, &#39;astonish&#39;: 3289, &#39;kirk&#39;: 3290, &#39;natali&#39;: 3291, &#39;trio&#39;: 3292, &#39;jazz&#39;: 3293, &#39;altman&#39;: 3294, &#39;leap&#39;: 3295, &#39;audio&#39;: 3296, &#39;racial&#39;: 3297, &#39;whip&#39;: 3298, &#39;mystic&#39;: 3299, &#39;enterpris&#39;: 3300, &#39;delici&#39;: 3301, &#39;nicholson&#39;: 3302, &#39;blake&#39;: 3303, &#39;yellow&#39;: 3304, &#39;spit&#39;: 3305, &#39;bottl&#39;: 3306, &#39;bull&#39;: 3307, &#39;destini&#39;: 3308, &#39;shade&#39;: 3309, &#39;tommi&#39;: 3310, &#39;fonda&#39;: 3311, &#39;neo&#39;: 3312, &#39;harder&#39;: 3313, &#39;adolesc&#39;: 3314, &#39;popcorn&#39;: 3315, &#39;enchant&#39;: 3316, &#39;reunit&#39;: 3317, &#39;bedroom&#39;: 3318, &#39;threw&#39;: 3319, &#39;staff&#39;: 3320, &#39;swedish&#39;: 3321, &#39;pseudo&#39;: 3322, &#39;altogeth&#39;: 3323, &#39;merci&#39;: 3324, &#39;meaningless&#39;: 3325, &#39;inherit&#39;: 3326, &#39;visibl&#39;: 3327, &#39;todd&#39;: 3328, &#39;vivid&#39;: 3329, &#39;decor&#39;: 3330, &#39;leonard&#39;: 3331, &#39;bust&#39;: 3332, &#39;exhibit&#39;: 3333, &#39;moodi&#39;: 3334, &#39;jew&#39;: 3335, &#39;respond&#39;: 3336, &#39;tip&#39;: 3337, &#39;lawrenc&#39;: 3338, &#39;suspici&#39;: 3339, &#39;await&#39;: 3340, &#39;roommat&#39;: 3341, &#39;uneven&#39;: 3342, &#39;ruthless&#39;: 3343, &#39;wire&#39;: 3344, &#39;fanat&#39;: 3345, &#39;lemmon&#39;: 3346, &#39;atlanti&#39;: 3347, &#39;crocodil&#39;: 3348, &#39;synopsi&#39;: 3349, &#39;befriend&#39;: 3350, &#39;voight&#39;: 3351, &#39;madonna&#39;: 3352, &#39;kennedi&#39;: 3353, &#39;reserv&#39;: 3354, &#39;edi&#39;: 3355, &#39;bold&#39;: 3356, &#39;voyag&#39;: 3357, &#39;carl&#39;: 3358, &#39;bradi&#39;: 3359, &#39;centr&#39;: 3360, &#39;dimens&#39;: 3361, &#39;audit&#39;: 3362, &#39;chao&#39;: 3363, &#39;ventur&#39;: 3364, &#39;2007&#39;: 3365, &#39;unsettl&#39;: 3366, &#39;garner&#39;: 3367, &#39;palma&#39;: 3368, &#39;holli&#39;: 3369, &#39;clumsi&#39;: 3370, &#39;incident&#39;: 3371, &#39;abysm&#39;: 3372, &#39;bargain&#39;: 3373, &#39;rural&#39;: 3374, &#39;clint&#39;: 3375, &#39;elimin&#39;: 3376, &#39;mall&#39;: 3377, &#39;characterist&#39;: 3378, &#39;versu&#39;: 3379, &#39;echo&#39;: 3380, &#39;daddi&#39;: 3381, &#39;imperson&#39;: 3382, &#39;cari&#39;: 3383, &#39;hart&#39;: 3384, &#39;troop&#39;: 3385, &#39;humili&#39;: 3386, &#39;immigr&#39;: 3387, &#39;neglect&#39;: 3388, &#39;cuba&#39;: 3389, &#39;ant&#39;: 3390, &#39;trail&#39;: 3391, &#39;timon&#39;: 3392, &#39;cd&#39;: 3393, &#39;wealth&#39;: 3394, &#39;lit&#39;: 3395, &#39;acknowledg&#39;: 3396, &#39;2nd&#39;: 3397, &#39;nearbi&#39;: 3398, &#39;poetic&#39;: 3399, &#39;tiger&#39;: 3400, &#39;jeffrey&#39;: 3401, &#39;domest&#39;: 3402, &#39;prejudic&#39;: 3403, &#39;collaps&#39;: 3404, &#39;mickey&#39;: 3405, &#39;saga&#39;: 3406, &#39;infect&#39;: 3407, &#39;repuls&#39;: 3408, &#39;solo&#39;: 3409, &#39;paus&#39;: 3410, &#39;pun&#39;: 3411, &#39;marshal&#39;: 3412, &#39;celluloid&#39;: 3413, &#39;homer&#39;: 3414, &#39;mistaken&#39;: 3415, &#39;harvey&#39;: 3416, &#39;1996&#39;: 3417, &#39;leon&#39;: 3418, &#39;assembl&#39;: 3419, &#39;inan&#39;: 3420, &#39;gear&#39;: 3421, &#39;tribe&#39;: 3422, &#39;undoubtedli&#39;: 3423, &#39;promin&#39;: 3424, &#39;milk&#39;: 3425, &#39;chest&#39;: 3426, &#39;inappropri&#39;: 3427, &#39;sore&#39;: 3428, &#39;cake&#39;: 3429, &#39;apolog&#39;: 3430, &#39;equip&#39;: 3431, &#39;interrupt&#39;: 3432, &#39;hbo&#39;: 3433, &#39;ginger&#39;: 3434, &#39;coffe&#39;: 3435, &#39;pant&#39;: 3436, &#39;coat&#39;: 3437, &#39;olivi&#39;: 3438, &#39;vulgar&#39;: 3439, &#39;retain&#39;: 3440, &#39;embrac&#39;: 3441, &#39;colleagu&#39;: 3442, &#39;maggi&#39;: 3443, &#39;brooklyn&#39;: 3444, &#39;consum&#39;: 3445, &#39;florida&#39;: 3446, &#39;pen&#39;: 3447, &#39;instant&#39;: 3448, &#39;exot&#39;: 3449, &#39;solut&#39;: 3450, &#39;pot&#39;: 3451, &#39;institut&#39;: 3452, &#39;trace&#39;: 3453, &#39;primari&#39;: 3454, &#39;polanski&#39;: 3455, &#39;aveng&#39;: 3456, &#39;jenni&#39;: 3457, &#39;airplan&#39;: 3458, &#39;furthermor&#39;: 3459, &#39;devast&#39;: 3460, &#39;humbl&#39;: 3461, &#39;colonel&#39;: 3462, &#39;highest&#39;: 3463, &#39;rick&#39;: 3464, &#39;1999&#39;: 3465, &#39;bowl&#39;: 3466, &#39;gender&#39;: 3467, &#39;descend&#39;: 3468, &#39;cope&#39;: 3469, &#39;wive&#39;: 3470, &#39;3rd&#39;: 3471, &#39;disabl&#39;: 3472, &#39;poke&#39;: 3473, &#39;illog&#39;: 3474, &#39;sale&#39;: 3475, &#39;outer&#39;: 3476, &#39;strain&#39;: 3477, &#39;linda&#39;: 3478, &#39;seduct&#39;: 3479, &#39;godzilla&#39;: 3480, &#39;ya&#39;: 3481, &#39;smaller&#39;: 3482, &#39;dian&#39;: 3483, &#39;principl&#39;: 3484, &#39;dutch&#39;: 3485, &#39;primarili&#39;: 3486, &#39;dive&#39;: 3487, &#39;bubbl&#39;: 3488, &#39;scope&#39;: 3489, &#39;gloriou&#39;: 3490, &#39;dud&#39;: 3491, &#39;mixtur&#39;: 3492, &#39;beneath&#39;: 3493, &#39;lol&#39;: 3494, &#39;blatant&#39;: 3495, &#39;yard&#39;: 3496, &#39;vast&#39;: 3497, &#39;gundam&#39;: 3498, &#39;hal&#39;: 3499, &#39;predecessor&#39;: 3500, &#39;secondli&#39;: 3501, &#39;cue&#39;: 3502, &#39;inferior&#39;: 3503, &#39;rabbit&#39;: 3504, &#39;glamor&#39;: 3505, &#39;devoid&#39;: 3506, &#39;disjoint&#39;: 3507, &#39;z&#39;: 3508, &#39;trademark&#39;: 3509, &#39;et&#39;: 3510, &#39;senseless&#39;: 3511, &#39;domino&#39;: 3512, &#39;aggress&#39;: 3513, &#39;simplist&#39;: 3514, &#39;myer&#39;: 3515, &#39;museum&#39;: 3516, &#39;shelf&#39;: 3517, &#39;hideou&#39;: 3518, &#39;invas&#39;: 3519, &#39;april&#39;: 3520, &#39;pearl&#39;: 3521, &#39;alfr&#39;: 3522, &#39;arab&#39;: 3523, &#39;casual&#39;: 3524, &#39;grinch&#39;: 3525, 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3567, &#39;soccer&#39;: 3568, &#39;emphasi&#39;: 3569, &#39;hartley&#39;: 3570, &#39;tenant&#39;: 3571, &#39;psychic&#39;: 3572, &#39;counter&#39;: 3573, &#39;span&#39;: 3574, &#39;topless&#39;: 3575, &#39;fri&#39;: 3576, &#39;recruit&#39;: 3577, &#39;blew&#39;: 3578, &#39;scroog&#39;: 3579, &#39;diana&#39;: 3580, &#39;rifl&#39;: 3581, &#39;berlin&#39;: 3582, &#39;tempt&#39;: 3583, &#39;wont&#39;: 3584, &#39;riot&#39;: 3585, &#39;ration&#39;: 3586, &#39;wet&#39;: 3587, &#39;trashi&#39;: 3588, &#39;faster&#39;: 3589, &#39;goer&#39;: 3590, &#39;resurrect&#39;: 3591, &#39;revolt&#39;: 3592, &#39;bitch&#39;: 3593, &#39;justin&#39;: 3594, &#39;lumet&#39;: 3595, &#39;sympath&#39;: 3596, &#39;shed&#39;: 3597, &#39;woo&#39;: 3598, &#39;shaw&#39;: 3599, &#39;porno&#39;: 3600, &#39;parad&#39;: 3601, &#39;intim&#39;: 3602, &#39;sibl&#39;: 3603, &#39;niro&#39;: 3604, &#39;ethnic&#39;: 3605, &#39;wheel&#39;: 3606, &#39;partial&#39;: 3607, &#39;wendi&#39;: 3608, &#39;patriot&#39;: 3609, 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3736, &#39;heal&#39;: 3737, &#39;arguabl&#39;: 3738, &#39;taught&#39;: 3739, &#39;pervert&#39;: 3740, &#39;belt&#39;: 3741, &#39;stilt&#39;: 3742, &#39;1983&#39;: 3743, &#39;attorney&#39;: 3744, &#39;shelley&#39;: 3745, &#39;unpredict&#39;: 3746, &#39;pursuit&#39;: 3747, &#39;tackl&#39;: 3748, &#39;tooth&#39;: 3749, &#39;dazzl&#39;: 3750, &#39;melodi&#39;: 3751, &#39;champion&#39;: 3752, &#39;programm&#39;: 3753, &#39;pervers&#39;: 3754, &#39;paltrow&#39;: 3755, &#39;employe&#39;: 3756, &#39;mesmer&#39;: 3757, &#39;passeng&#39;: 3758, &#39;chicken&#39;: 3759, &#39;bela&#39;: 3760, &#39;cleverli&#39;: 3761, &#39;sensat&#39;: 3762, &#39;graham&#39;: 3763, &#39;uplift&#39;: 3764, &#39;conneri&#39;: 3765, &#39;vocal&#39;: 3766, &#39;closest&#39;: 3767, &#39;rubi&#39;: 3768, &#39;duval&#39;: 3769, &#39;maid&#39;: 3770, &#39;vengeanc&#39;: 3771, &#39;franki&#39;: 3772, &#39;gambl&#39;: 3773, &#39;virginia&#39;: 3774, &#39;raymond&#39;: 3775, &#39;orang&#39;: 3776, &#39;plight&#39;: 3777, 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&#39;grotesqu&#39;: 3819, &#39;abort&#39;: 3820, &#39;fed&#39;: 3821, &#39;backward&#39;: 3822, &#39;surpass&#39;: 3823, &#39;linger&#39;: 3824, &#39;ethan&#39;: 3825, &#39;poetri&#39;: 3826, &#39;taxi&#39;: 3827, &#39;franci&#39;: 3828, &#39;earl&#39;: 3829, &#39;plod&#39;: 3830, &#39;profess&#39;: 3831, &#39;spock&#39;: 3832, &#39;underst&#39;: 3833, &#39;dispos&#39;: 3834, &#39;expand&#39;: 3835, &#39;nichola&#39;: 3836, &#39;bend&#39;: 3837, &#39;meander&#39;: 3838, &#39;chicago&#39;: 3839, &#39;richardson&#39;: 3840, &#39;im&#39;: 3841, &#39;lowest&#39;: 3842, &#39;austen&#39;: 3843, &#39;septemb&#39;: 3844, &#39;muddl&#39;: 3845, &#39;stallon&#39;: 3846, &#39;mundan&#39;: 3847, &#39;spoke&#39;: 3848, &#39;literatur&#39;: 3849, &#39;tourist&#39;: 3850, &#39;catchi&#39;: 3851, &#39;descent&#39;: 3852, &#39;lure&#39;: 3853, &#39;compliment&#39;: 3854, &#39;dysfunct&#39;: 3855, &#39;econom&#39;: 3856, &#39;instrument&#39;: 3857, &#39;rant&#39;: 3858, &#39;eugen&#39;: 3859, &#39;household&#39;: 3860, &#39;myth&#39;: 3861, &#39;cannon&#39;: 3862, &#39;greedi&#39;: 3863, &#39;hum&#39;: 3864, &#39;simplic&#39;: 3865, &#39;waitress&#39;: 3866, &#39;nostalgia&#39;: 3867, &#39;rubber&#39;: 3868, &#39;sue&#39;: 3869, &#39;der&#39;: 3870, &#39;crucial&#39;: 3871, &#39;duck&#39;: 3872, &#39;hello&#39;: 3873, &#39;mortal&#39;: 3874, &#39;map&#39;: 3875, &#39;occupi&#39;: 3876, &#39;furi&#39;: 3877, &#39;dictat&#39;: 3878, &#39;firstli&#39;: 3879, &#39;recognis&#39;: 3880, &#39;phoni&#39;: 3881, &#39;insur&#39;: 3882, &#39;phantom&#39;: 3883, &#39;dement&#39;: 3884, &#39;flee&#39;: 3885, &#39;recognit&#39;: 3886, &#39;mankind&#39;: 3887, &#39;june&#39;: 3888, &#39;alongsid&#39;: 3889, &#39;omen&#39;: 3890, &#39;irrelev&#39;: 3891, &#39;cent&#39;: 3892, &#39;sissi&#39;: 3893, &#39;eaten&#39;: 3894, &#39;deaf&#39;: 3895, &#39;lang&#39;: 3896, &#39;stale&#39;: 3897, &#39;louis&#39;: 3898, &#39;randi&#39;: 3899, &#39;carel&#39;: 3900, &#39;bacal&#39;: 3901, &#39;equival&#39;: 3902, &#39;damon&#39;: 3903, &#39;molli&#39;: 3904, &#39;coast&#39;: 3905, &#39;freez&#39;: 3906, &#39;labor&#39;: 3907, &#39;damm&#39;: 3908, &#39;lengthi&#39;: 3909, &#39;1973&#39;: 3910, &#39;onlin&#39;: 3911, &#39;cyborg&#39;: 3912, &#39;heel&#39;: 3913, &#39;antwon&#39;: 3914, &#39;grayson&#39;: 3915, &#39;newli&#39;: 3916, &#39;reign&#39;: 3917, &#39;rooney&#39;: 3918, &#39;daisi&#39;: 3919, &#39;bike&#39;: 3920, &#39;blackmail&#39;: 3921, &#39;loyalti&#39;: 3922, &#39;wisdom&#39;: 3923, &#39;twilight&#39;: 3924, &#39;drake&#39;: 3925, &#39;rude&#39;: 3926, &#39;ashley&#39;: 3927, &#39;bump&#39;: 3928, &#39;dreari&#39;: 3929, &#39;distinguish&#39;: 3930, &#39;likewis&#39;: 3931, &#39;biko&#39;: 3932, &#39;buffalo&#39;: 3933, &#39;startl&#39;: 3934, &#39;unorigin&#39;: 3935, &#39;interior&#39;: 3936, &#39;pink&#39;: 3937, &#39;baddi&#39;: 3938, &#39;prey&#39;: 3939, &#39;provoc&#39;: 3940, &#39;attribut&#39;: 3941, &#39;boxer&#39;: 3942, &#39;sailor&#39;: 3943, 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&#39;walsh&#39;: 3985, &#39;julian&#39;: 3986, &#39;belushi&#39;: 3987, &#39;nicol&#39;: 3988, &#39;indiffer&#39;: 3989, &#39;mormon&#39;: 3990, &#39;errol&#39;: 3991, &#39;warmth&#39;: 3992, &#39;disord&#39;: 3993, &#39;edgi&#39;: 3994, &#39;agenda&#39;: 3995, &#39;nyc&#39;: 3996, &#39;hay&#39;: 3997, &#39;mtv&#39;: 3998, &#39;roof&#39;: 3999, &#39;alarm&#39;: 4000, &#39;exquisit&#39;: 4001, &#39;rukh&#39;: 4002, &#39;alison&#39;: 4003, &#39;unawar&#39;: 4004, &#39;dolph&#39;: 4005, &#39;novak&#39;: 4006, &#39;3d&#39;: 4007, &#39;enthusiasm&#39;: 4008, &#39;marion&#39;: 4009, &#39;priceless&#39;: 4010, &#39;lampoon&#39;: 4011, &#39;palac&#39;: 4012, &#39;watson&#39;: 4013, &#39;firm&#39;: 4014, &#39;greed&#39;: 4015, &#39;shove&#39;: 4016, &#39;reid&#39;: 4017, &#39;vital&#39;: 4018, &#39;campaign&#39;: 4019, &#39;gestur&#39;: 4020, &#39;glanc&#39;: 4021, &#39;peril&#39;: 4022, &#39;distort&#39;: 4023, &#39;what&#39;: 4024, &#39;thompson&#39;: 4025, &#39;angela&#39;: 4026, 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&#39;rout&#39;: 4068, &#39;crow&#39;: 4069, &#39;scotland&#39;: 4070, &#39;brenda&#39;: 4071, &#39;stroke&#39;: 4072, &#39;travesti&#39;: 4073, &#39;exposit&#39;: 4074, &#39;crawl&#39;: 4075, &#39;wig&#39;: 4076, &#39;calib&#39;: 4077, &#39;represent&#39;: 4078, &#39;han&#39;: 4079, &#39;quinn&#39;: 4080, &#39;reson&#39;: 4081, &#39;shootout&#39;: 4082, &#39;buster&#39;: 4083, &#39;cream&#39;: 4084, &#39;cooki&#39;: 4085, &#39;bro&#39;: 4086, &#39;sabrina&#39;: 4087, &#39;kurosawa&#39;: 4088, &#39;contradict&#39;: 4089, &#39;realm&#39;: 4090, &#39;valley&#39;: 4091, &#39;din&#39;: 4092, &#39;miyazaki&#39;: 4093, &#39;fido&#39;: 4094, &#39;soderbergh&#39;: 4095, &#39;geek&#39;: 4096, &#39;pole&#39;: 4097, &#39;shaki&#39;: 4098, &#39;wax&#39;: 4099, &#39;traumat&#39;: 4100, &#39;greg&#39;: 4101, &#39;passabl&#39;: 4102, &#39;ross&#39;: 4103, &#39;femm&#39;: 4104, &#39;sammi&#39;: 4105, &#39;unsatisfi&#39;: 4106, &#39;sucker&#39;: 4107, &#39;1987&#39;: 4108, &#39;spacey&#39;: 4109, 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&#39;turd&#39;: 4778, &#39;frog&#39;: 4779, &#39;tack&#39;: 4780, &#39;smack&#39;: 4781, &#39;gilbert&#39;: 4782, &#39;audrey&#39;: 4783, &#39;ideolog&#39;: 4784, &#39;comprehend&#39;: 4785, &#39;deem&#39;: 4786, &#39;swept&#39;: 4787, &#39;linear&#39;: 4788, &#39;retriev&#39;: 4789, &#39;clinic&#39;: 4790, &#39;cartoonish&#39;: 4791, &#39;disregard&#39;: 4792, &#39;outdat&#39;: 4793, &#39;oppon&#39;: 4794, &#39;howl&#39;: 4795, &#39;peer&#39;: 4796, &#39;nightclub&#39;: 4797, &#39;ceremoni&#39;: 4798, &#39;affleck&#39;: 4799, &#39;psych&#39;: 4800, &#39;huston&#39;: 4801, &#39;leather&#39;: 4802, &#39;highway&#39;: 4803, &#39;fifth&#39;: 4804, &#39;wrestler&#39;: 4805, &#39;dudley&#39;: 4806, &#39;charlton&#39;: 4807, &#39;styliz&#39;: 4808, &#39;knightley&#39;: 4809, &#39;glare&#39;: 4810, &#39;boyer&#39;: 4811, &#39;shorter&#39;: 4812, &#39;magician&#39;: 4813, &#39;gunga&#39;: 4814, &#39;breakdown&#39;: 4815, &#39;sleaz&#39;: 4816, &#39;tara&#39;: 4817, &#39;spawn&#39;: 4818, &#39;energet&#39;: 4819, &#39;durat&#39;: 4820, &#39;conrad&#39;: 4821, &#39;goldsworthi&#39;: 4822, &#39;spine&#39;: 4823, &#39;salman&#39;: 4824, &#39;corn&#39;: 4825, &#39;1994&#39;: 4826, &#39;greet&#39;: 4827, &#39;moe&#39;: 4828, &#39;boo&#39;: 4829, &#39;evolut&#39;: 4830, &#39;phenomen&#39;: 4831, &#39;toe&#39;: 4832, &#39;cuban&#39;: 4833, &#39;cemeteri&#39;: 4834, &#39;1991&#39;: 4835, &#39;monument&#39;: 4836, &#39;chip&#39;: 4837, &#39;lighter&#39;: 4838, &#39;lavish&#39;: 4839, &#39;summar&#39;: 4840, &#39;btw&#39;: 4841, &#39;senat&#39;: 4842, &#39;snatch&#39;: 4843, &#39;client&#39;: 4844, &#39;compris&#39;: 4845, &#39;plate&#39;: 4846, &#39;bastard&#39;: 4847, &#39;braveheart&#39;: 4848, &#39;collector&#39;: 4849, &#39;4th&#39;: 4850, &#39;potter&#39;: 4851, &#39;bogu&#39;: 4852, &#39;uniformli&#39;: 4853, &#39;newer&#39;: 4854, &#39;einstein&#39;: 4855, &#39;lifeless&#39;: 4856, &#39;whack&#39;: 4857, &#39;deliver&#39;: 4858, &#39;mitch&#39;: 4859, &#39;replay&#39;: 4860, &#39;signal&#39;: 4861, &#39;ie&#39;: 4862, &#39;spectacl&#39;: 4863, &#39;1974&#39;: 4864, &#39;jare&#39;: 4865, &#39;armstrong&#39;: 4866, &#39;evelyn&#39;: 4867, &#39;outright&#39;: 4868, &#39;appl&#39;: 4869, &#39;lex&#39;: 4870, &#39;belli&#39;: 4871, &#39;ol&#39;: 4872, &#39;bori&#39;: 4873, &#39;kazan&#39;: 4874, &#39;undertak&#39;: 4875, &#39;bulk&#39;: 4876, &#39;eleven&#39;: 4877, &#39;luxuri&#39;: 4878, &#39;kent&#39;: 4879, &#39;nina&#39;: 4880, &#39;liu&#39;: 4881, &#39;1977&#39;: 4882, &#39;inaccuraci&#39;: 4883, &#39;jedi&#39;: 4884, &#39;embark&#39;: 4885, &#39;neatli&#39;: 4886, &#39;firmli&#39;: 4887, &#39;wtf&#39;: 4888, &#39;cecil&#39;: 4889, &#39;constitut&#39;: 4890, &#39;clara&#39;: 4891, &#39;mcqueen&#39;: 4892, &#39;fluid&#39;: 4893, &#39;gilliam&#39;: 4894, &#39;jam&#39;: 4895, &#39;historian&#39;: 4896, &#39;creek&#39;: 4897, &#39;sorrow&#39;: 4898, &#39;randolph&#39;: 4899, &#39;healthi&#39;: 4900, &#39;occup&#39;: 4901, &#39;pronounc&#39;: 4902, &#39;capot&#39;: 4903, &#39;jule&#39;: 4904, &#39;judd&#39;: 4905, &#39;trauma&#39;: 4906, &#39;undead&#39;: 4907, &#39;alleg&#39;: 4908, &#39;comprehens&#39;: 4909, &#39;bsg&#39;: 4910, &#39;paula&#39;: 4911, &#39;blur&#39;: 4912, &#39;groan&#39;: 4913, &#39;bait&#39;: 4914, &#39;comb&#39;: 4915, &#39;cape&#39;: 4916, &#39;meal&#39;: 4917, &#39;inclus&#39;: 4918, &#39;lanc&#39;: 4919, &#39;inaccur&#39;: 4920, &#39;unattract&#39;: 4921, &#39;aussi&#39;: 4922, &#39;genet&#39;: 4923, &#39;id&#39;: 4924, &#39;tokyo&#39;: 4925, &#39;relentless&#39;: 4926, &#39;ash&#39;: 4927, &#39;truman&#39;: 4928, &#39;vignett&#39;: 4929, &#39;1985&#39;: 4930, &#39;rosario&#39;: 4931, &#39;basket&#39;: 4932, &#39;propheci&#39;: 4933, &#39;unsuspect&#39;: 4934, &#39;forgiven&#39;: 4935, &#39;decapit&#39;: 4936, &#39;galaxi&#39;: 4937, &#39;mum&#39;: 4938, &#39;abound&#39;: 4939, &#39;miami&#39;: 4940, &#39;palm&#39;: 4941, &#39;knee&#39;: 4942, &#39;conan&#39;: 4943, &#39;vain&#39;: 4944, &#39;carmen&#39;: 4945, &#39;congratul&#39;: 4946, &#39;pioneer&#39;: 4947, &#39;antonio&#39;: 4948, &#39;miniseri&#39;: 4949, &#39;goldblum&#39;: 4950, &#39;spray&#39;: 4951, &#39;fruit&#39;: 4952, &#39;curtain&#39;: 4953, &#39;subtli&#39;: 4954, &#39;kiddi&#39;: 4955, &#39;porter&#39;: 4956, &#39;lauren&#39;: 4957, &#39;roar&#39;: 4958, &#39;walt&#39;: 4959, &#39;sidewalk&#39;: 4960, &#39;poker&#39;: 4961, &#39;pepper&#39;: 4962, &#39;sacrif&#39;: 4963, &#39;frontal&#39;: 4964, &#39;hostil&#39;: 4965, &#39;dubiou&#39;: 4966, &#39;ghetto&#39;: 4967, &#39;incorrect&#39;: 4968, &#39;spill&#39;: 4969, &#39;assort&#39;: 4970, &#39;victorian&#39;: 4971, &#39;macabr&#39;: 4972, &#39;verg&#39;: 4973, &#39;ingrid&#39;: 4974, &#39;epitom&#39;: 4975, &#39;weari&#39;: 4976, &#39;monti&#39;: 4977, &#39;hackman&#39;: 4978, &#39;jill&#39;: 4979, &#39;handicap&#39;: 4980, &#39;sparkl&#39;: 4981, &#39;reincarn&#39;: 4982, &#39;asset&#39;: 4983, &#39;motorcycl&#39;: 4984, &#39;profil&#39;: 4985, &#39;substanti&#39;: 4986, &#39;modest&#39;: 4987, &#39;scariest&#39;: 4988, &#39;mice&#39;: 4989, &#39;cypher&#39;: 4990, &#39;vastli&#39;: 4991, &#39;masterson&#39;: 4992, &#39;21st&#39;: 4993, &#39;detach&#39;: 4994, &#39;playboy&#39;: 4995, &#39;optimist&#39;: 4996, &#39;sophi&#39;: 4997, &#39;rapidli&#39;: 4998, &#39;weaker&#39;: 4999}
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<h3 id="Save-word_dict">Save <code>word_dict</code><a class="anchor-link" href="#Save-word_dict">&#182;</a></h3><p>Later on when we construct an endpoint which processes a submitted review we will need to make use of the <code>word_dict</code> which we have created. As such, we will save it to a file now for future use.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data_dir</span> <span class="o">=</span> <span class="s1">&#39;../data/pytorch&#39;</span> <span class="c1"># The folder we will use for storing data</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">data_dir</span><span class="p">):</span> <span class="c1"># Make sure that the folder exists</span>
    <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">data_dir</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="s1">&#39;word_dict.pkl&#39;</span><span class="p">),</span> <span class="s2">&quot;wb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">word_dict</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span>
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<h3 id="Transform-the-reviews">Transform the reviews<a class="anchor-link" href="#Transform-the-reviews">&#182;</a></h3><p>Now that we have our word dictionary which allows us to transform the words appearing in the reviews into integers, it is time to make use of it and convert our reviews to their integer sequence representation, making sure to pad or truncate to a fixed length, which in our case is <code>500</code>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">convert_and_pad</span><span class="p">(</span><span class="n">word_dict</span><span class="p">,</span> <span class="n">sentence</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">500</span><span class="p">):</span>
    <span class="n">NOWORD</span> <span class="o">=</span> <span class="mi">0</span> <span class="c1"># We will use 0 to represent the &#39;no word&#39; category</span>
    <span class="n">INFREQ</span> <span class="o">=</span> <span class="mi">1</span> <span class="c1"># and we use 1 to represent the infrequent words, i.e., words not appearing in word_dict</span>
    
    <span class="n">working_sentence</span> <span class="o">=</span> <span class="p">[</span><span class="n">NOWORD</span><span class="p">]</span> <span class="o">*</span> <span class="n">pad</span>
    
    <span class="k">for</span> <span class="n">word_index</span><span class="p">,</span> <span class="n">word</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sentence</span><span class="p">[:</span><span class="n">pad</span><span class="p">]):</span>
        <span class="k">if</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">word_dict</span><span class="p">:</span>
            <span class="n">working_sentence</span><span class="p">[</span><span class="n">word_index</span><span class="p">]</span> <span class="o">=</span> <span class="n">word_dict</span><span class="p">[</span><span class="n">word</span><span class="p">]</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">working_sentence</span><span class="p">[</span><span class="n">word_index</span><span class="p">]</span> <span class="o">=</span> <span class="n">INFREQ</span>
            
    <span class="k">return</span> <span class="n">working_sentence</span><span class="p">,</span> <span class="nb">min</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">sentence</span><span class="p">),</span> <span class="n">pad</span><span class="p">)</span>

<span class="k">def</span> <span class="nf">convert_and_pad_data</span><span class="p">(</span><span class="n">word_dict</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">500</span><span class="p">):</span>
    <span class="n">result</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">lengths</span> <span class="o">=</span> <span class="p">[]</span>
    
    <span class="k">for</span> <span class="n">sentence</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span>
        <span class="n">converted</span><span class="p">,</span> <span class="n">leng</span> <span class="o">=</span> <span class="n">convert_and_pad</span><span class="p">(</span><span class="n">word_dict</span><span class="p">,</span> <span class="n">sentence</span><span class="p">,</span> <span class="n">pad</span><span class="p">)</span>
        <span class="n">result</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">converted</span><span class="p">)</span>
        <span class="n">lengths</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">leng</span><span class="p">)</span>
        
    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">result</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">lengths</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">train_X</span><span class="p">,</span> <span class="n">train_X_len</span> <span class="o">=</span> <span class="n">convert_and_pad_data</span><span class="p">(</span><span class="n">word_dict</span><span class="p">,</span> <span class="n">train_X</span><span class="p">)</span>
<span class="n">test_X</span><span class="p">,</span> <span class="n">test_X_len</span> <span class="o">=</span> <span class="n">convert_and_pad_data</span><span class="p">(</span><span class="n">word_dict</span><span class="p">,</span> <span class="n">test_X</span><span class="p">)</span>
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<p>As a quick check to make sure that things are working as intended, check to see what one of the reviews in the training set looks like after having been processeed. Does this look reasonable? What is the length of a review in the training set?</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Use this cell to examine one of the processed reviews to make sure everything is working as intended.</span>
<span class="nb">print</span><span class="p">(</span><span class="n">train_X</span><span class="p">[</span><span class="mi">100</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">train_X</span><span class="p">[</span><span class="mi">100</span><span class="p">]))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">train_X_len</span><span class="p">[</span><span class="mi">100</span><span class="p">])</span>
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    0    0    0    0    0    0    0    0    0    0    0    0    0    0
    0    0    0    0    0    0    0    0    0    0    0    0    0    0
    0    0    0    0    0    0    0    0    0    0    0    0    0    0
    0    0    0    0    0    0    0    0    0    0]
500
57
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<p><strong>Question:</strong> In the cells above we use the <code>preprocess_data</code> and <code>convert_and_pad_data</code> methods to process both the training and testing set. Why or why not might this be a problem?</p>

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<p><strong>Answer:</strong></p>
<p>The 'preprocess_data' method converts the text to a set of words, removes stop words and stems them, which might lead to some of the significant information being lost. For example, when the words are stemmed, the information added by the suffix or prefix is lost, and sometimes it can change the meaning into its complete opposite. Similarly, when stop words like 'not' are removed, it changes the whole meaning of the sentence. For example, the sentences "It was a good movie" and "It was not a good movie" means the same thing.</p>
<p>The convert_and_pad_data method creates a mapping of the words with the frequency of that words in the dataset and pads it appropriately. Additionally, 0 and 1 are used to represent no word and infrequent word respectively. However, the mapping is designed in such a way that the most frequent words are indexed with lower values. So the most frequent words are indexed 2, 3 and so on. There is not much difference numerically between the indexes 1 and  2, but the difference in their frequency is significant. So that might be a problem as it's represented incorrectly.</p>

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<h2 id="Step-3:-Upload-the-data-to-S3">Step 3: Upload the data to S3<a class="anchor-link" href="#Step-3:-Upload-the-data-to-S3">&#182;</a></h2><p>As in the XGBoost notebook, we will need to upload the training dataset to S3 in order for our training code to access it. For now we will save it locally and we will upload to S3 later on.</p>
<h3 id="Save-the-processed-training-dataset-locally">Save the processed training dataset locally<a class="anchor-link" href="#Save-the-processed-training-dataset-locally">&#182;</a></h3><p>It is important to note the format of the data that we are saving as we will need to know it when we write the training code. In our case, each row of the dataset has the form <code>label</code>, <code>length</code>, <code>review[500]</code> where <code>review[500]</code> is a sequence of <code>500</code> integers representing the words in the review.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
    
<span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">train_y</span><span class="p">),</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">train_X_len</span><span class="p">),</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">train_X</span><span class="p">)],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> \
        <span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="s1">&#39;train.csv&#39;</span><span class="p">),</span> <span class="n">header</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<h3 id="Uploading-the-training-data">Uploading the training data<a class="anchor-link" href="#Uploading-the-training-data">&#182;</a></h3><p>Next, we need to upload the training data to the SageMaker default S3 bucket so that we can provide access to it while training our model.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">sagemaker</span>

<span class="n">sagemaker_session</span> <span class="o">=</span> <span class="n">sagemaker</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span>

<span class="n">bucket</span> <span class="o">=</span> <span class="n">sagemaker_session</span><span class="o">.</span><span class="n">default_bucket</span><span class="p">()</span>
<span class="n">prefix</span> <span class="o">=</span> <span class="s1">&#39;sagemaker/sentiment_rnn&#39;</span>

<span class="n">role</span> <span class="o">=</span> <span class="n">sagemaker</span><span class="o">.</span><span class="n">get_execution_role</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">input_data</span> <span class="o">=</span> <span class="n">sagemaker_session</span><span class="o">.</span><span class="n">upload_data</span><span class="p">(</span><span class="n">path</span><span class="o">=</span><span class="n">data_dir</span><span class="p">,</span> <span class="n">bucket</span><span class="o">=</span><span class="n">bucket</span><span class="p">,</span> <span class="n">key_prefix</span><span class="o">=</span><span class="n">prefix</span><span class="p">)</span>
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<p><strong>NOTE:</strong> The cell above uploads the entire contents of our data directory. This includes the <code>word_dict.pkl</code> file. This is fortunate as we will need this later on when we create an endpoint that accepts an arbitrary review. For now, we will just take note of the fact that it resides in the data directory (and so also in the S3 training bucket) and that we will need to make sure it gets saved in the model directory.</p>

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<h2 id="Step-4:-Build-and-Train-the-PyTorch-Model">Step 4: Build and Train the PyTorch Model<a class="anchor-link" href="#Step-4:-Build-and-Train-the-PyTorch-Model">&#182;</a></h2><p>In the XGBoost notebook we discussed what a model is in the SageMaker framework. In particular, a model comprises three objects</p>
<ul>
<li>Model Artifacts,</li>
<li>Training Code, and</li>
<li>Inference Code,</li>
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<p>each of which interact with one another. In the XGBoost example we used training and inference code that was provided by Amazon. Here we will still be using containers provided by Amazon with the added benefit of being able to include our own custom code.</p>
<p>We will start by implementing our own neural network in PyTorch along with a training script. For the purposes of this project we have provided the necessary model object in the <code>model.py</code> file, inside of the <code>train</code> folder. You can see the provided implementation by running the cell below.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>pygmentize train/model.py
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<pre><span class="ansi-blue-fg">import</span> <span class="ansi-cyan-fg ansi-underline">torch.nn</span> <span class="ansi-blue-fg">as</span> <span class="ansi-cyan-fg ansi-underline">nn</span>

<span class="ansi-blue-fg">class</span> <span class="ansi-green-fg ansi-underline">LSTMClassifier</span>(nn.Module):
    <span class="ansi-yellow-fg">&#34;&#34;&#34;</span>
<span class="ansi-yellow-fg">    This is the simple RNN model we will be using to perform Sentiment Analysis.</span>
<span class="ansi-yellow-fg">    &#34;&#34;&#34;</span>

    <span class="ansi-blue-fg">def</span> <span class="ansi-green-fg">__init__</span>(<span class="ansi-cyan-fg">self</span>, embedding_dim, hidden_dim, vocab_size):
        <span class="ansi-yellow-fg">&#34;&#34;&#34;</span>
<span class="ansi-yellow-fg">        Initialize the model by settingg up the various layers.</span>
<span class="ansi-yellow-fg">        &#34;&#34;&#34;</span>
        <span class="ansi-cyan-fg">super</span>(LSTMClassifier, <span class="ansi-cyan-fg">self</span>).<span class="ansi-green-fg">__init__</span>()

        <span class="ansi-cyan-fg">self</span>.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=<span class="ansi-blue-fg">0</span>)
        <span class="ansi-cyan-fg">self</span>.lstm = nn.LSTM(embedding_dim, hidden_dim)
        <span class="ansi-cyan-fg">self</span>.dense = nn.Linear(in_features=hidden_dim, out_features=<span class="ansi-blue-fg">1</span>)
        <span class="ansi-cyan-fg">self</span>.sig = nn.Sigmoid()
        
        <span class="ansi-cyan-fg">self</span>.word_dict = <span class="ansi-cyan-fg">None</span>

    <span class="ansi-blue-fg">def</span> <span class="ansi-green-fg">forward</span>(<span class="ansi-cyan-fg">self</span>, x):
        <span class="ansi-yellow-fg">&#34;&#34;&#34;</span>
<span class="ansi-yellow-fg">        Perform a forward pass of our model on some input.</span>
<span class="ansi-yellow-fg">        &#34;&#34;&#34;</span>
        x = x.t()
        lengths = x[<span class="ansi-blue-fg">0</span>,:]
        reviews = x[<span class="ansi-blue-fg">1</span>:,:]
        embeds = <span class="ansi-cyan-fg">self</span>.embedding(reviews)
        lstm_out, _ = <span class="ansi-cyan-fg">self</span>.lstm(embeds)
        out = <span class="ansi-cyan-fg">self</span>.dense(lstm_out)
        out = out[lengths - <span class="ansi-blue-fg">1</span>, <span class="ansi-cyan-fg">range</span>(<span class="ansi-cyan-fg">len</span>(lengths))]
        <span class="ansi-blue-fg">return</span> <span class="ansi-cyan-fg">self</span>.sig(out.squeeze())
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<p>The important takeaway from the implementation provided is that there are three parameters that we may wish to tweak to improve the performance of our model. These are the embedding dimension, the hidden dimension and the size of the vocabulary. We will likely want to make these parameters configurable in the training script so that if we wish to modify them we do not need to modify the script itself. We will see how to do this later on. To start we will write some of the training code in the notebook so that we can more easily diagnose any issues that arise.</p>
<p>First we will load a small portion of the training data set to use as a sample. It would be very time consuming to try and train the model completely in the notebook as we do not have access to a gpu and the compute instance that we are using is not particularly powerful. However, we can work on a small bit of the data to get a feel for how our training script is behaving.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.utils.data</span>

<span class="c1"># Read in only the first 250 rows</span>
<span class="n">train_sample</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="s1">&#39;train.csv&#39;</span><span class="p">),</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nrows</span><span class="o">=</span><span class="mi">250</span><span class="p">)</span>

<span class="c1"># Turn the input pandas dataframe into tensors</span>
<span class="n">train_sample_y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">train_sample</span><span class="p">[[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
<span class="n">train_sample_X</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">train_sample</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">)</span><span class="o">.</span><span class="n">long</span><span class="p">()</span>

<span class="c1"># Build the dataset</span>
<span class="n">train_sample_ds</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">TensorDataset</span><span class="p">(</span><span class="n">train_sample_X</span><span class="p">,</span> <span class="n">train_sample_y</span><span class="p">)</span>
<span class="c1"># Build the dataloader</span>
<span class="n">train_sample_dl</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">train_sample_ds</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
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<h3 id="(TODO)-Writing-the-training-method">(TODO) Writing the training method<a class="anchor-link" href="#(TODO)-Writing-the-training-method">&#182;</a></h3><p>Next we need to write the training code itself. This should be very similar to training methods that you have written before to train PyTorch models. We will leave any difficult aspects such as model saving / loading and parameter loading until a little later.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">train_loader</span><span class="p">,</span> <span class="n">epochs</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">loss_fn</span><span class="p">,</span> <span class="n">device</span><span class="p">):</span>
    <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">epochs</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
        <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
        <span class="n">total_loss</span> <span class="o">=</span> <span class="mi">0</span>
        
        <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">train_loader</span><span class="p">:</span>         
            <span class="n">batch_X</span><span class="p">,</span> <span class="n">batch_y</span> <span class="o">=</span> <span class="n">batch</span>
            
            <span class="n">batch_X</span> <span class="o">=</span> <span class="n">batch_X</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
            <span class="n">batch_y</span> <span class="o">=</span> <span class="n">batch_y</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
            
            <span class="c1"># TODO: Complete this train method to train the model provided.</span>
            
            <span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
            
            <span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">batch_X</span><span class="p">)</span>
            <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_fn</span><span class="p">(</span><span class="n">output</span><span class="p">,</span><span class="n">batch_y</span><span class="p">)</span>
            <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
            
            <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
            
            <span class="n">total_loss</span> <span class="o">+=</span> <span class="n">loss</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Epoch: </span><span class="si">{}</span><span class="s2">, BCELoss: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch</span><span class="p">,</span> <span class="n">total_loss</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">train_loader</span><span class="p">)))</span>
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<p>Supposing we have the training method above, we will test that it is working by writing a bit of code in the notebook that executes our training method on the small sample training set that we loaded earlier. The reason for doing this in the notebook is so that we have an opportunity to fix any errors that arise early when they are easier to diagnose.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="nn">optim</span>
<span class="kn">from</span> <span class="nn">train.model</span> <span class="k">import</span> <span class="n">LSTMClassifier</span>

<span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">LSTMClassifier</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">5000</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span>
<span class="n">loss_fn</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">BCELoss</span><span class="p">()</span>

<span class="n">train</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">train_sample_dl</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">loss_fn</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>
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<pre>cpu
Epoch: 1, BCELoss: 0.6916956424713134
Epoch: 2, BCELoss: 0.6805495977401733
Epoch: 3, BCELoss: 0.670571255683899
Epoch: 4, BCELoss: 0.6594351768493653
Epoch: 5, BCELoss: 0.6457682371139526
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<p>In order to construct a PyTorch model using SageMaker we must provide SageMaker with a training script. We may optionally include a directory which will be copied to the container and from which our training code will be run. When the training container is executed it will check the uploaded directory (if there is one) for a <code>requirements.txt</code> file and install any required Python libraries, after which the training script will be run.</p>

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<h3 id="(TODO)-Training-the-model">(TODO) Training the model<a class="anchor-link" href="#(TODO)-Training-the-model">&#182;</a></h3><p>When a PyTorch model is constructed in SageMaker, an entry point must be specified. This is the Python file which will be executed when the model is trained. Inside of the <code>train</code> directory is a file called <code>train.py</code> which has been provided and which contains most of the necessary code to train our model. The only thing that is missing is the implementation of the <code>train()</code> method which you wrote earlier in this notebook.</p>
<p><strong>TODO</strong>: Copy the <code>train()</code> method written above and paste it into the <code>train/train.py</code> file where required.</p>
<p>The way that SageMaker passes hyperparameters to the training script is by way of arguments. These arguments can then be parsed and used in the training script. To see how this is done take a look at the provided <code>train/train.py</code> file.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%writefile</span> train/train.py
<span class="kn">import</span> <span class="nn">argparse</span>
<span class="kn">import</span> <span class="nn">json</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">pickle</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">sagemaker_containers</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="nn">optim</span>
<span class="kn">import</span> <span class="nn">torch.utils.data</span>

<span class="kn">from</span> <span class="nn">model</span> <span class="k">import</span> <span class="n">LSTMClassifier</span>

<span class="k">def</span> <span class="nf">model_fn</span><span class="p">(</span><span class="n">model_dir</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Load the PyTorch model from the `model_dir` directory.&quot;&quot;&quot;</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Loading model.&quot;</span><span class="p">)</span>

    <span class="c1"># First, load the parameters used to create the model.</span>
    <span class="n">model_info</span> <span class="o">=</span> <span class="p">{}</span>
    <span class="n">model_info_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">model_dir</span><span class="p">,</span> <span class="s1">&#39;model_info.pth&#39;</span><span class="p">)</span>
    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">model_info_path</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
        <span class="n">model_info</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;model_info: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">model_info</span><span class="p">))</span>

    <span class="c1"># Determine the device and construct the model.</span>
    <span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">LSTMClassifier</span><span class="p">(</span><span class="n">model_info</span><span class="p">[</span><span class="s1">&#39;embedding_dim&#39;</span><span class="p">],</span> <span class="n">model_info</span><span class="p">[</span><span class="s1">&#39;hidden_dim&#39;</span><span class="p">],</span> <span class="n">model_info</span><span class="p">[</span><span class="s1">&#39;vocab_size&#39;</span><span class="p">])</span>

    <span class="c1"># Load the stored model parameters.</span>
    <span class="n">model_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">model_dir</span><span class="p">,</span> <span class="s1">&#39;model.pth&#39;</span><span class="p">)</span>
    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">model_path</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
        <span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">))</span>

    <span class="c1"># Load the saved word_dict.</span>
    <span class="n">word_dict_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">model_dir</span><span class="p">,</span> <span class="s1">&#39;word_dict.pkl&#39;</span><span class="p">)</span>
    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">word_dict_path</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
        <span class="n">model</span><span class="o">.</span><span class="n">word_dict</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>

    <span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Done loading model.&quot;</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">model</span>

<span class="k">def</span> <span class="nf">_get_train_data_loader</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">training_dir</span><span class="p">):</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Get train data loader.&quot;</span><span class="p">)</span>

    <span class="n">train_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">training_dir</span><span class="p">,</span> <span class="s2">&quot;train.csv&quot;</span><span class="p">),</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">names</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>

    <span class="n">train_y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">train_data</span><span class="p">[[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
    <span class="n">train_X</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">train_data</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">)</span><span class="o">.</span><span class="n">long</span><span class="p">()</span>

    <span class="n">train_ds</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">TensorDataset</span><span class="p">(</span><span class="n">train_X</span><span class="p">,</span> <span class="n">train_y</span><span class="p">)</span>

    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">train_ds</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">)</span>


<span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">train_loader</span><span class="p">,</span> <span class="n">epochs</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">loss_fn</span><span class="p">,</span> <span class="n">device</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    This is the training method that is called by the PyTorch training script. The parameters</span>
<span class="sd">    passed are as follows:</span>
<span class="sd">    model        - The PyTorch model that we wish to train.</span>
<span class="sd">    train_loader - The PyTorch DataLoader that should be used during training.</span>
<span class="sd">    epochs       - The total number of epochs to train for.</span>
<span class="sd">    optimizer    - The optimizer to use during training.</span>
<span class="sd">    loss_fn      - The loss function used for training.</span>
<span class="sd">    device       - Where the model and data should be loaded (gpu or cpu).</span>
<span class="sd">    &quot;&quot;&quot;</span>
    
    <span class="c1"># TODO: Paste the train() method developed in the notebook here.</span>
    
    <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">epochs</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
        <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
        <span class="n">total_loss</span> <span class="o">=</span> <span class="mi">0</span>
        
        <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">train_loader</span><span class="p">:</span>         
            <span class="n">batch_X</span><span class="p">,</span> <span class="n">batch_y</span> <span class="o">=</span> <span class="n">batch</span>
            
            <span class="n">batch_X</span> <span class="o">=</span> <span class="n">batch_X</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
            <span class="n">batch_y</span> <span class="o">=</span> <span class="n">batch_y</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
            
            <span class="c1"># TODO: Complete this train method to train the model provided.</span>
            
            <span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
            
            <span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">batch_X</span><span class="p">)</span>
            <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_fn</span><span class="p">(</span><span class="n">output</span><span class="p">,</span><span class="n">batch_y</span><span class="p">)</span>
            <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
            
            <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
            
            <span class="n">total_loss</span> <span class="o">+=</span> <span class="n">loss</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Epoch: </span><span class="si">{}</span><span class="s2">, BCELoss: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch</span><span class="p">,</span> <span class="n">total_loss</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">train_loader</span><span class="p">)))</span>


<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
    <span class="c1"># All of the model parameters and training parameters are sent as arguments when the script</span>
    <span class="c1"># is executed. Here we set up an argument parser to easily access the parameters.</span>

    <span class="n">parser</span> <span class="o">=</span> <span class="n">argparse</span><span class="o">.</span><span class="n">ArgumentParser</span><span class="p">()</span>

    <span class="c1"># Training Parameters</span>
    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;--batch-size&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mi">512</span><span class="p">,</span> <span class="n">metavar</span><span class="o">=</span><span class="s1">&#39;N&#39;</span><span class="p">,</span>
                        <span class="n">help</span><span class="o">=</span><span class="s1">&#39;input batch size for training (default: 512)&#39;</span><span class="p">)</span>
    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;--epochs&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">metavar</span><span class="o">=</span><span class="s1">&#39;N&#39;</span><span class="p">,</span>
                        <span class="n">help</span><span class="o">=</span><span class="s1">&#39;number of epochs to train (default: 10)&#39;</span><span class="p">)</span>
    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;--seed&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">metavar</span><span class="o">=</span><span class="s1">&#39;S&#39;</span><span class="p">,</span>
                        <span class="n">help</span><span class="o">=</span><span class="s1">&#39;random seed (default: 1)&#39;</span><span class="p">)</span>

    <span class="c1"># Model Parameters</span>
    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;--embedding_dim&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">metavar</span><span class="o">=</span><span class="s1">&#39;N&#39;</span><span class="p">,</span>
                        <span class="n">help</span><span class="o">=</span><span class="s1">&#39;size of the word embeddings (default: 32)&#39;</span><span class="p">)</span>
    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;--hidden_dim&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">metavar</span><span class="o">=</span><span class="s1">&#39;N&#39;</span><span class="p">,</span>
                        <span class="n">help</span><span class="o">=</span><span class="s1">&#39;size of the hidden dimension (default: 100)&#39;</span><span class="p">)</span>
    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;--vocab_size&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mi">5000</span><span class="p">,</span> <span class="n">metavar</span><span class="o">=</span><span class="s1">&#39;N&#39;</span><span class="p">,</span>
                        <span class="n">help</span><span class="o">=</span><span class="s1">&#39;size of the vocabulary (default: 5000)&#39;</span><span class="p">)</span>

    <span class="c1"># SageMaker Parameters</span>
    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;--hosts&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">list</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="n">json</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;SM_HOSTS&#39;</span><span class="p">]))</span>
    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;--current-host&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">str</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;SM_CURRENT_HOST&#39;</span><span class="p">])</span>
    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;--model-dir&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">str</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;SM_MODEL_DIR&#39;</span><span class="p">])</span>
    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;--data-dir&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">str</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;SM_CHANNEL_TRAINING&#39;</span><span class="p">])</span>
    <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;--num-gpus&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;SM_NUM_GPUS&#39;</span><span class="p">])</span>

    <span class="n">args</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">parse_args</span><span class="p">()</span>

    <span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Using device </span><span class="si">{}</span><span class="s2">.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">device</span><span class="p">))</span>

    <span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">seed</span><span class="p">)</span>

    <span class="c1"># Load the training data.</span>
    <span class="n">train_loader</span> <span class="o">=</span> <span class="n">_get_train_data_loader</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">data_dir</span><span class="p">)</span>

    <span class="c1"># Build the model.</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">LSTMClassifier</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">embedding_dim</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">hidden_dim</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">vocab_size</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>

    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">data_dir</span><span class="p">,</span> <span class="s2">&quot;word_dict.pkl&quot;</span><span class="p">),</span> <span class="s2">&quot;rb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
        <span class="n">model</span><span class="o">.</span><span class="n">word_dict</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Model loaded with embedding_dim </span><span class="si">{}</span><span class="s2">, hidden_dim </span><span class="si">{}</span><span class="s2">, vocab_size </span><span class="si">{}</span><span class="s2">.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
        <span class="n">args</span><span class="o">.</span><span class="n">embedding_dim</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">hidden_dim</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">vocab_size</span>
    <span class="p">))</span>

    <span class="c1"># Train the model.</span>
    <span class="n">optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span>
    <span class="n">loss_fn</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">BCELoss</span><span class="p">()</span>

    <span class="n">train</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">train_loader</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">epochs</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">loss_fn</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>

    <span class="c1"># Save the parameters used to construct the model</span>
    <span class="n">model_info_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">model_dir</span><span class="p">,</span> <span class="s1">&#39;model_info.pth&#39;</span><span class="p">)</span>
    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">model_info_path</span><span class="p">,</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
        <span class="n">model_info</span> <span class="o">=</span> <span class="p">{</span>
            <span class="s1">&#39;embedding_dim&#39;</span><span class="p">:</span> <span class="n">args</span><span class="o">.</span><span class="n">embedding_dim</span><span class="p">,</span>
            <span class="s1">&#39;hidden_dim&#39;</span><span class="p">:</span> <span class="n">args</span><span class="o">.</span><span class="n">hidden_dim</span><span class="p">,</span>
            <span class="s1">&#39;vocab_size&#39;</span><span class="p">:</span> <span class="n">args</span><span class="o">.</span><span class="n">vocab_size</span><span class="p">,</span>
        <span class="p">}</span>
        <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">model_info</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span>

	<span class="c1"># Save the word_dict</span>
    <span class="n">word_dict_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">model_dir</span><span class="p">,</span> <span class="s1">&#39;word_dict.pkl&#39;</span><span class="p">)</span>
    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">word_dict_path</span><span class="p">,</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
        <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">word_dict</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span>

	<span class="c1"># Save the model parameters</span>
    <span class="n">model_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">model_dir</span><span class="p">,</span> <span class="s1">&#39;model.pth&#39;</span><span class="p">)</span>
    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">model_path</span><span class="p">,</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
        <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="n">f</span><span class="p">)</span>
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<pre>Overwriting train/train.py
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sagemaker.pytorch</span> <span class="k">import</span> <span class="n">PyTorch</span>

<span class="n">estimator</span> <span class="o">=</span> <span class="n">PyTorch</span><span class="p">(</span><span class="n">entry_point</span><span class="o">=</span><span class="s2">&quot;train.py&quot;</span><span class="p">,</span>
                    <span class="n">source_dir</span><span class="o">=</span><span class="s2">&quot;train&quot;</span><span class="p">,</span>
                    <span class="n">role</span><span class="o">=</span><span class="n">role</span><span class="p">,</span>
                    <span class="n">framework_version</span><span class="o">=</span><span class="s1">&#39;0.4.0&#39;</span><span class="p">,</span>
                    <span class="n">train_instance_count</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                    <span class="n">train_instance_type</span><span class="o">=</span><span class="s1">&#39;ml.p2.xlarge&#39;</span><span class="p">,</span>
                    <span class="n">hyperparameters</span><span class="o">=</span><span class="p">{</span>
                        <span class="s1">&#39;epochs&#39;</span><span class="p">:</span> <span class="mi">10</span><span class="p">,</span>
                        <span class="s1">&#39;hidden_dim&#39;</span><span class="p">:</span> <span class="mi">200</span><span class="p">,</span>
                    <span class="p">})</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">estimator</span><span class="o">.</span><span class="n">fit</span><span class="p">({</span><span class="s1">&#39;training&#39;</span><span class="p">:</span> <span class="n">input_data</span><span class="p">})</span>
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<pre>2019-05-24 10:55:57 Starting - Starting the training job...
2019-05-24 10:55:58 Starting - Launching requested ML instances......
2019-05-24 10:57:02 Starting - Preparing the instances for training......
2019-05-24 10:58:07 Downloading - Downloading input data...
2019-05-24 10:58:37 Training - Downloading the training image...
<span class="ansi-red-fg">bash: cannot set terminal process group (-1): Inappropriate ioctl for device</span>
<span class="ansi-red-fg">bash: no job control in this shell</span>
<span class="ansi-red-fg">2019-05-24 10:59:16,352 sagemaker-containers INFO     Imported framework sagemaker_pytorch_container.training</span>
<span class="ansi-red-fg">2019-05-24 10:59:16,375 sagemaker_pytorch_container.training INFO     Block until all host DNS lookups succeed.</span>
<span class="ansi-red-fg">2019-05-24 10:59:19,383 sagemaker_pytorch_container.training INFO     Invoking user training script.</span>
<span class="ansi-red-fg">2019-05-24 10:59:19,622 sagemaker-containers INFO     Module train does not provide a setup.py. </span>
<span class="ansi-red-fg">Generating setup.py</span>
<span class="ansi-red-fg">2019-05-24 10:59:19,622 sagemaker-containers INFO     Generating setup.cfg</span>
<span class="ansi-red-fg">2019-05-24 10:59:19,623 sagemaker-containers INFO     Generating MANIFEST.in</span>
<span class="ansi-red-fg">2019-05-24 10:59:19,623 sagemaker-containers INFO     Installing module with the following command:</span>
<span class="ansi-red-fg">/usr/bin/python -m pip install -U . -r requirements.txt</span>
<span class="ansi-red-fg">Processing /opt/ml/code</span>
<span class="ansi-red-fg">Collecting pandas (from -r requirements.txt (line 1))</span>
<span class="ansi-red-fg">  Downloading https://files.pythonhosted.org/packages/74/24/0cdbf8907e1e3bc5a8da03345c23cbed7044330bb8f73bb12e711a640a00/pandas-0.24.2-cp35-cp35m-manylinux1_x86_64.whl (10.0MB)</span>
<span class="ansi-red-fg">Collecting numpy (from -r requirements.txt (line 2))</span>
<span class="ansi-red-fg">  Downloading https://files.pythonhosted.org/packages/f6/f3/cc6c6745347c1e997cc3e58390584a250b8e22b6dfc45414a7d69a3df016/numpy-1.16.3-cp35-cp35m-manylinux1_x86_64.whl (17.2MB)</span>
<span class="ansi-red-fg">Collecting nltk (from -r requirements.txt (line 3))
  Downloading https://files.pythonhosted.org/packages/73/56/90178929712ce427ebad179f8dc46c8deef4e89d4c853092bee1efd57d05/nltk-3.4.1.zip (3.1MB)</span>
<span class="ansi-red-fg">Collecting beautifulsoup4 (from -r requirements.txt (line 4))
  Downloading https://files.pythonhosted.org/packages/1d/5d/3260694a59df0ec52f8b4883f5d23b130bc237602a1411fa670eae12351e/beautifulsoup4-4.7.1-py3-none-any.whl (94kB)</span>
<span class="ansi-red-fg">Collecting html5lib (from -r requirements.txt (line 5))
  Downloading https://files.pythonhosted.org/packages/a5/62/bbd2be0e7943ec8504b517e62bab011b4946e1258842bc159e5dfde15b96/html5lib-1.0.1-py2.py3-none-any.whl (117kB)</span>
<span class="ansi-red-fg">Requirement already satisfied, skipping upgrade: python-dateutil&gt;=2.5.0 in /usr/local/lib/python3.5/dist-packages (from pandas-&gt;-r requirements.txt (line 1)) (2.7.5)</span>
<span class="ansi-red-fg">Collecting pytz&gt;=2011k (from pandas-&gt;-r requirements.txt (line 1))
  Downloading https://files.pythonhosted.org/packages/3d/73/fe30c2daaaa0713420d0382b16fbb761409f532c56bdcc514bf7b6262bb6/pytz-2019.1-py2.py3-none-any.whl (510kB)</span>
<span class="ansi-red-fg">Requirement already satisfied, skipping upgrade: six in /usr/local/lib/python3.5/dist-packages (from nltk-&gt;-r requirements.txt (line 3)) (1.11.0)</span>
<span class="ansi-red-fg">Collecting soupsieve&gt;=1.2 (from beautifulsoup4-&gt;-r requirements.txt (line 4))
  Downloading https://files.pythonhosted.org/packages/b9/a5/7ea40d0f8676bde6e464a6435a48bc5db09b1a8f4f06d41dd997b8f3c616/soupsieve-1.9.1-py2.py3-none-any.whl</span>
<span class="ansi-red-fg">Collecting webencodings (from html5lib-&gt;-r requirements.txt (line 5))
  Downloading https://files.pythonhosted.org/packages/f4/24/2a3e3df732393fed8b3ebf2ec078f05546de641fe1b667ee316ec1dcf3b7/webencodings-0.5.1-py2.py3-none-any.whl</span>
<span class="ansi-red-fg">Building wheels for collected packages: nltk, train
  Running setup.py bdist_wheel for nltk: started</span>
<span class="ansi-red-fg">  Running setup.py bdist_wheel for nltk: finished with status &#39;done&#39;
  Stored in directory: /root/.cache/pip/wheels/97/8a/10/d646015f33c525688e91986c4544c68019b19a473cb33d3b55
  Running setup.py bdist_wheel for train: started</span>
<span class="ansi-red-fg">  Running setup.py bdist_wheel for train: finished with status &#39;done&#39;
  Stored in directory: /tmp/pip-ephem-wheel-cache-2eup2ihx/wheels/35/24/16/37574d11bf9bde50616c67372a334f94fa8356bc7164af8ca3</span>
<span class="ansi-red-fg">Successfully built nltk train</span>
<span class="ansi-red-fg">Installing collected packages: numpy, pytz, pandas, nltk, soupsieve, beautifulsoup4, webencodings, html5lib, train
  Found existing installation: numpy 1.15.4
    Uninstalling numpy-1.15.4:
      Successfully uninstalled numpy-1.15.4</span>
<span class="ansi-red-fg">Successfully installed beautifulsoup4-4.7.1 html5lib-1.0.1 nltk-3.4.1 numpy-1.16.3 pandas-0.24.2 pytz-2019.1 soupsieve-1.9.1 train-1.0.0 webencodings-0.5.1</span>
<span class="ansi-red-fg">You are using pip version 18.1, however version 19.1.1 is available.</span>
<span class="ansi-red-fg">You should consider upgrading via the &#39;pip install --upgrade pip&#39; command.</span>
<span class="ansi-red-fg">2019-05-24 10:59:31,237 sagemaker-containers INFO     Invoking user script
</span>
<span class="ansi-red-fg">Training Env:
</span>
<span class="ansi-red-fg">{
    &#34;output_dir&#34;: &#34;/opt/ml/output&#34;,
    &#34;additional_framework_parameters&#34;: {},
    &#34;log_level&#34;: 20,
    &#34;hosts&#34;: [
        &#34;algo-1&#34;
    ],
    &#34;current_host&#34;: &#34;algo-1&#34;,
    &#34;input_data_config&#34;: {
        &#34;training&#34;: {
            &#34;S3DistributionType&#34;: &#34;FullyReplicated&#34;,
            &#34;TrainingInputMode&#34;: &#34;File&#34;,
            &#34;RecordWrapperType&#34;: &#34;None&#34;
        }
    },
    &#34;input_config_dir&#34;: &#34;/opt/ml/input/config&#34;,
    &#34;model_dir&#34;: &#34;/opt/ml/model&#34;,
    &#34;resource_config&#34;: {
        &#34;network_interface_name&#34;: &#34;ethwe&#34;,
        &#34;hosts&#34;: [
            &#34;algo-1&#34;
        ],
        &#34;current_host&#34;: &#34;algo-1&#34;
    },
    &#34;hyperparameters&#34;: {
        &#34;hidden_dim&#34;: 200,
        &#34;epochs&#34;: 10
    },
    &#34;user_entry_point&#34;: &#34;train.py&#34;,
    &#34;module_dir&#34;: &#34;s3://sagemaker-ap-south-1-674386208739/sagemaker-pytorch-2019-05-24-10-55-55-342/source/sourcedir.tar.gz&#34;,
    &#34;channel_input_dirs&#34;: {
        &#34;training&#34;: &#34;/opt/ml/input/data/training&#34;
    },
    &#34;input_dir&#34;: &#34;/opt/ml/input&#34;,
    &#34;job_name&#34;: &#34;sagemaker-pytorch-2019-05-24-10-55-55-342&#34;,
    &#34;network_interface_name&#34;: &#34;ethwe&#34;,
    &#34;module_name&#34;: &#34;train&#34;,
    &#34;output_data_dir&#34;: &#34;/opt/ml/output/data&#34;,
    &#34;num_cpus&#34;: 4,
    &#34;framework_module&#34;: &#34;sagemaker_pytorch_container.training:main&#34;,
    &#34;output_intermediate_dir&#34;: &#34;/opt/ml/output/intermediate&#34;,
    &#34;num_gpus&#34;: 1</span>
<span class="ansi-red-fg">}
</span>
<span class="ansi-red-fg">Environment variables:
</span>
<span class="ansi-red-fg">SM_MODEL_DIR=/opt/ml/model</span>
<span class="ansi-red-fg">SM_INPUT_DATA_CONFIG={&#34;training&#34;:{&#34;RecordWrapperType&#34;:&#34;None&#34;,&#34;S3DistributionType&#34;:&#34;FullyReplicated&#34;,&#34;TrainingInputMode&#34;:&#34;File&#34;}}</span>
<span class="ansi-red-fg">SM_LOG_LEVEL=20</span>
<span class="ansi-red-fg">SM_HPS={&#34;epochs&#34;:10,&#34;hidden_dim&#34;:200}</span>
<span class="ansi-red-fg">SM_INPUT_DIR=/opt/ml/input</span>
<span class="ansi-red-fg">SM_CHANNELS=[&#34;training&#34;]</span>
<span class="ansi-red-fg">SM_NUM_GPUS=1</span>
<span class="ansi-red-fg">SM_CHANNEL_TRAINING=/opt/ml/input/data/training</span>
<span class="ansi-red-fg">SM_MODULE_NAME=train</span>
<span class="ansi-red-fg">SM_CURRENT_HOST=algo-1</span>
<span class="ansi-red-fg">SM_RESOURCE_CONFIG={&#34;current_host&#34;:&#34;algo-1&#34;,&#34;hosts&#34;:[&#34;algo-1&#34;],&#34;network_interface_name&#34;:&#34;ethwe&#34;}</span>
<span class="ansi-red-fg">SM_USER_ARGS=[&#34;--epochs&#34;,&#34;10&#34;,&#34;--hidden_dim&#34;,&#34;200&#34;]</span>
<span class="ansi-red-fg">SM_INPUT_CONFIG_DIR=/opt/ml/input/config</span>
<span class="ansi-red-fg">SM_OUTPUT_DATA_DIR=/opt/ml/output/data</span>
<span class="ansi-red-fg">SM_OUTPUT_DIR=/opt/ml/output</span>
<span class="ansi-red-fg">SM_MODULE_DIR=s3://sagemaker-ap-south-1-674386208739/sagemaker-pytorch-2019-05-24-10-55-55-342/source/sourcedir.tar.gz</span>
<span class="ansi-red-fg">SM_HP_EPOCHS=10</span>
<span class="ansi-red-fg">SM_TRAINING_ENV={&#34;additional_framework_parameters&#34;:{},&#34;channel_input_dirs&#34;:{&#34;training&#34;:&#34;/opt/ml/input/data/training&#34;},&#34;current_host&#34;:&#34;algo-1&#34;,&#34;framework_module&#34;:&#34;sagemaker_pytorch_container.training:main&#34;,&#34;hosts&#34;:[&#34;algo-1&#34;],&#34;hyperparameters&#34;:{&#34;epochs&#34;:10,&#34;hidden_dim&#34;:200},&#34;input_config_dir&#34;:&#34;/opt/ml/input/config&#34;,&#34;input_data_config&#34;:{&#34;training&#34;:{&#34;RecordWrapperType&#34;:&#34;None&#34;,&#34;S3DistributionType&#34;:&#34;FullyReplicated&#34;,&#34;TrainingInputMode&#34;:&#34;File&#34;}},&#34;input_dir&#34;:&#34;/opt/ml/input&#34;,&#34;job_name&#34;:&#34;sagemaker-pytorch-2019-05-24-10-55-55-342&#34;,&#34;log_level&#34;:20,&#34;model_dir&#34;:&#34;/opt/ml/model&#34;,&#34;module_dir&#34;:&#34;s3://sagemaker-ap-south-1-674386208739/sagemaker-pytorch-2019-05-24-10-55-55-342/source/sourcedir.tar.gz&#34;,&#34;module_name&#34;:&#34;train&#34;,&#34;network_interface_name&#34;:&#34;ethwe&#34;,&#34;num_cpus&#34;:4,&#34;num_gpus&#34;:1,&#34;output_data_dir&#34;:&#34;/opt/ml/output/data&#34;,&#34;output_dir&#34;:&#34;/opt/ml/output&#34;,&#34;output_intermediate_dir&#34;:&#34;/opt/ml/output/intermediate&#34;,&#34;resource_config&#34;:{&#34;current_host&#34;:&#34;algo-1&#34;,&#34;hosts&#34;:[&#34;algo-1&#34;],&#34;network_interface_name&#34;:&#34;ethwe&#34;},&#34;user_entry_point&#34;:&#34;train.py&#34;}</span>
<span class="ansi-red-fg">SM_FRAMEWORK_PARAMS={}</span>
<span class="ansi-red-fg">SM_NETWORK_INTERFACE_NAME=ethwe</span>
<span class="ansi-red-fg">SM_NUM_CPUS=4</span>
<span class="ansi-red-fg">SM_USER_ENTRY_POINT=train.py</span>
<span class="ansi-red-fg">SM_OUTPUT_INTERMEDIATE_DIR=/opt/ml/output/intermediate</span>
<span class="ansi-red-fg">SM_FRAMEWORK_MODULE=sagemaker_pytorch_container.training:main</span>
<span class="ansi-red-fg">SM_HOSTS=[&#34;algo-1&#34;]</span>
<span class="ansi-red-fg">SM_HP_HIDDEN_DIM=200</span>
<span class="ansi-red-fg">PYTHONPATH=/usr/local/bin:/usr/lib/python35.zip:/usr/lib/python3.5:/usr/lib/python3.5/plat-x86_64-linux-gnu:/usr/lib/python3.5/lib-dynload:/usr/local/lib/python3.5/dist-packages:/usr/lib/python3/dist-packages
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<span class="ansi-red-fg">Invoking script with the following command:
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<span class="ansi-red-fg">/usr/bin/python -m train --epochs 10 --hidden_dim 200

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<span class="ansi-red-fg">Using device cuda.</span>
<span class="ansi-red-fg">Get train data loader.</span>
<span class="ansi-red-fg">Model loaded with embedding_dim 32, hidden_dim 200, vocab_size 5000.</span>

2019-05-24 10:59:45 Training - Training image download completed. Training in progress.<span class="ansi-red-fg">Epoch: 1, BCELoss: 0.6676811296112684</span>
<span class="ansi-red-fg">Epoch: 2, BCELoss: 0.590050390788487</span>
<span class="ansi-red-fg">Epoch: 3, BCELoss: 0.4899488821321604</span>
<span class="ansi-red-fg">Epoch: 4, BCELoss: 0.43150281541201535</span>
<span class="ansi-red-fg">Epoch: 5, BCELoss: 0.3827749022415706</span>
<span class="ansi-red-fg">Epoch: 6, BCELoss: 0.34809774099564067</span>
<span class="ansi-red-fg">Epoch: 7, BCELoss: 0.32510283224436703</span>
<span class="ansi-red-fg">Epoch: 8, BCELoss: 0.30887019543015226</span>
<span class="ansi-red-fg">Epoch: 9, BCELoss: 0.31818822117484347</span>
<span class="ansi-red-fg">Epoch: 10, BCELoss: 0.28481826064537985</span>
<span class="ansi-red-fg">2019-05-24 11:02:33,051 sagemaker-containers INFO     Reporting training SUCCESS</span>

2019-05-24 11:02:43 Uploading - Uploading generated training model
2019-05-24 11:02:43 Completed - Training job completed
Billable seconds: 276
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<h2 id="Step-5:-Testing-the-model">Step 5: Testing the model<a class="anchor-link" href="#Step-5:-Testing-the-model">&#182;</a></h2><p>As mentioned at the top of this notebook, we will be testing this model by first deploying it and then sending the testing data to the deployed endpoint. We will do this so that we can make sure that the deployed model is working correctly.</p>
<h2 id="Step-6:-Deploy-the-model-for-testing">Step 6: Deploy the model for testing<a class="anchor-link" href="#Step-6:-Deploy-the-model-for-testing">&#182;</a></h2><p>Now that we have trained our model, we would like to test it to see how it performs. Currently our model takes input of the form <code>review_length, review[500]</code> where <code>review[500]</code> is a sequence of <code>500</code> integers which describe the words present in the review, encoded using <code>word_dict</code>. Fortunately for us, SageMaker provides built-in inference code for models with simple inputs such as this.</p>
<p>There is one thing that we need to provide, however, and that is a function which loads the saved model. This function must be called <code>model_fn()</code> and takes as its only parameter a path to the directory where the model artifacts are stored. This function must also be present in the python file which we specified as the entry point. In our case the model loading function has been provided and so no changes need to be made.</p>
<p><strong>NOTE</strong>: When the built-in inference code is run it must import the <code>model_fn()</code> method from the <code>train.py</code> file. This is why the training code is wrapped in a main guard ( ie, <code>if __name__ == '__main__':</code> )</p>
<p>Since we don't need to change anything in the code that was uploaded during training, we can simply deploy the current model as-is.</p>
<p><strong>NOTE:</strong> When deploying a model you are asking SageMaker to launch an compute instance that will wait for data to be sent to it. As a result, this compute instance will continue to run until <em>you</em> shut it down. This is important to know since the cost of a deployed endpoint depends on how long it has been running for.</p>
<p>In other words <strong>If you are no longer using a deployed endpoint, shut it down!</strong></p>
<p><strong>TODO:</strong> Deploy the trained model.</p>

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<span class="n">predictor</span> <span class="o">=</span> <span class="n">estimator</span><span class="o">.</span><span class="n">deploy</span><span class="p">(</span><span class="n">initial_instance_count</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">instance_type</span><span class="o">=</span><span class="s1">&#39;ml.p2.xlarge&#39;</span><span class="p">)</span>
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<pre>-------------------------------------------------------------------------------------------------------!</pre>
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<h2 id="Step-7---Use-the-model-for-testing">Step 7 - Use the model for testing<a class="anchor-link" href="#Step-7---Use-the-model-for-testing">&#182;</a></h2><p>Once deployed, we can read in the test data and send it off to our deployed model to get some results. Once we collect all of the results we can determine how accurate our model is.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_X</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">test_X_len</span><span class="p">),</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">test_X</span><span class="p">)],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">test_X_len</span><span class="p">))</span>
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<pre>25000
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># We split the data into chunks and send each chunk seperately, accumulating the results.</span>

<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">rows</span><span class="o">=</span><span class="mi">512</span><span class="p">):</span>
    <span class="n">split_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array_split</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">rows</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
    <span class="n">predictions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([])</span>
    <span class="k">for</span> <span class="n">array</span> <span class="ow">in</span> <span class="n">split_array</span><span class="p">:</span>
        <span class="n">predictions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">predictor</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">array</span><span class="p">))</span>
    
    <span class="k">return</span> <span class="n">predictions</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">predictions</span> <span class="o">=</span> <span class="n">predict</span><span class="p">(</span><span class="n">test_X</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
<span class="n">predictions</span> <span class="o">=</span> <span class="p">[</span><span class="nb">round</span><span class="p">(</span><span class="n">num</span><span class="p">)</span> <span class="k">for</span> <span class="n">num</span> <span class="ow">in</span> <span class="n">predictions</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">accuracy_score</span>
<span class="n">accuracy_score</span><span class="p">(</span><span class="n">test_y</span><span class="p">,</span> <span class="n">predictions</span><span class="p">)</span>
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<pre>0.83608</pre>
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<p><strong>Question:</strong> How does this model compare to the XGBoost model you created earlier? Why might these two models perform differently on this dataset? Which do <em>you</em> think is better for sentiment analysis?</p>

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<p><strong>Answer:</strong>
The accuracy score of the untuned XGBoost model was better than this RNN model. (XGBoost- 0.85344 / RNN- 0.83608 ). Tuning the hyperparameters of the XGBoost model would gain another  2%  of accuracy approximately.</p>
<p>XGBoost performs well for tabular data than neural networks. Neural networks perform better when it comes to unstructured data like images and voice. RNNs in general and LSTMs, in particular, have success when working with sequences of words and paragraphs. Hence, it should be a good fit for Sentiment analysis.</p>
<p>However, in this dataset XGBoost performs much better. This probably is because RNNs require a huge amount of data to perform better. So for this sentiment analysis dataset, I believe XGBoost would perform better and would be more efficient too.</p>

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<h3 id="(TODO)-More-testing">(TODO) More testing<a class="anchor-link" href="#(TODO)-More-testing">&#182;</a></h3><p>We now have a trained model which has been deployed and which we can send processed reviews to and which returns the predicted sentiment. However, ultimately we would like to be able to send our model an unprocessed review. That is, we would like to send the review itself as a string. For example, suppose we wish to send the following review to our model.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_review</span> <span class="o">=</span> <span class="s1">&#39;The simplest pleasures in life are the best, and this film is one of them. Combining a rather basic storyline of love and adventure this movie transcends the usual weekend fair with wit and unmitigated charm.&#39;</span>
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<p>The question we now need to answer is, how do we send this review to our model?</p>
<p>Recall in the first section of this notebook we did a bunch of data processing to the IMDb dataset. In particular, we did two specific things to the provided reviews.</p>
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<li>Removed any html tags and stemmed the input</li>
<li>Encoded the review as a sequence of integers using <code>word_dict</code></li>
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<p>In order process the review we will need to repeat these two steps.</p>
<p><strong>TODO</strong>: Using the <code>review_to_words</code> and <code>convert_and_pad</code> methods from section one, convert <code>test_review</code> into a numpy array <code>test_data</code> suitable to send to our model. Remember that our model expects input of the form <code>review_length, review[500]</code>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Convert test_review into a form usable by the model and save the results in test_data</span>
 
<span class="n">review_words</span><span class="p">,</span> <span class="n">review_words_len</span> <span class="o">=</span> <span class="n">convert_and_pad</span><span class="p">(</span><span class="n">word_dict</span><span class="p">,</span> <span class="n">review_to_words</span><span class="p">(</span><span class="n">test_review</span><span class="p">),</span> <span class="mi">500</span><span class="p">)</span>
<span class="n">data</span> <span class="o">=</span> <span class="p">[[</span><span class="n">review_words_len</span><span class="p">]</span><span class="o">+</span> <span class="n">review_words</span><span class="p">]</span>
<span class="n">test_data</span> <span class="o">=</span>  <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
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<pre>   0    1     2    3    4    5    6    7    8    9   ...   491  492  493  494  \
0   20    1  1374   50   53    3    4  878  173  392 ...     0    0    0    0   

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<p>Now that we have processed the review, we can send the resulting array to our model to predict the sentiment of the review.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">predictor</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test_data</span><span class="p">)</span>
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<pre>array(0.9342659, dtype=float32)</pre>
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<p>Since the return value of our model is close to <code>1</code>, we can be certain that the review we submitted is positive.</p>

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<h3 id="Delete-the-endpoint">Delete the endpoint<a class="anchor-link" href="#Delete-the-endpoint">&#182;</a></h3><p>Of course, just like in the XGBoost notebook, once we've deployed an endpoint it continues to run until we tell it to shut down. Since we are done using our endpoint for now, we can delete it.</p>

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<h2 id="Step-6-(again)---Deploy-the-model-for-the-web-app">Step 6 (again) - Deploy the model for the web app<a class="anchor-link" href="#Step-6-(again)---Deploy-the-model-for-the-web-app">&#182;</a></h2><p>Now that we know that our model is working, it's time to create some custom inference code so that we can send the model a review which has not been processed and have it determine the sentiment of the review.</p>
<p>As we saw above, by default the estimator which we created, when deployed, will use the entry script and directory which we provided when creating the model. However, since we now wish to accept a string as input and our model expects a processed review, we need to write some custom inference code.</p>
<p>We will store the code that we write in the <code>serve</code> directory. Provided in this directory is the <code>model.py</code> file that we used to construct our model, a <code>utils.py</code> file which contains the <code>review_to_words</code> and <code>convert_and_pad</code> pre-processing functions which we used during the initial data processing, and <code>predict.py</code>, the file which will contain our custom inference code. Note also that <code>requirements.txt</code> is present which will tell SageMaker what Python libraries are required by our custom inference code.</p>
<p>When deploying a PyTorch model in SageMaker, you are expected to provide four functions which the SageMaker inference container will use.</p>
<ul>
<li><code>model_fn</code>: This function is the same function that we used in the training script and it tells SageMaker how to load our model.</li>
<li><code>input_fn</code>: This function receives the raw serialized input that has been sent to the model's endpoint and its job is to de-serialize and make the input available for the inference code.</li>
<li><code>output_fn</code>: This function takes the output of the inference code and its job is to serialize this output and return it to the caller of the model's endpoint.</li>
<li><code>predict_fn</code>: The heart of the inference script, this is where the actual prediction is done and is the function which you will need to complete.</li>
</ul>
<p>For the simple website that we are constructing during this project, the <code>input_fn</code> and <code>output_fn</code> methods are relatively straightforward. We only require being able to accept a string as input and we expect to return a single value as output. You might imagine though that in a more complex application the input or output may be image data or some other binary data which would require some effort to serialize.</p>
<h3 id="(TODO)-Writing-inference-code">(TODO) Writing inference code<a class="anchor-link" href="#(TODO)-Writing-inference-code">&#182;</a></h3><p>Before writing our custom inference code, we will begin by taking a look at the code which has been provided.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>pygmentize serve/predict.py
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<pre><span class="ansi-white-fg"># %load serve/predict.py</span>
<span class="ansi-blue-fg">import</span> <span class="ansi-cyan-fg ansi-underline">argparse</span>
<span class="ansi-blue-fg">import</span> <span class="ansi-cyan-fg ansi-underline">json</span>
<span class="ansi-blue-fg">import</span> <span class="ansi-cyan-fg ansi-underline">os</span>
<span class="ansi-blue-fg">import</span> <span class="ansi-cyan-fg ansi-underline">pickle</span>
<span class="ansi-blue-fg">import</span> <span class="ansi-cyan-fg ansi-underline">sys</span>
<span class="ansi-blue-fg">import</span> <span class="ansi-cyan-fg ansi-underline">sagemaker_containers</span>
<span class="ansi-blue-fg">import</span> <span class="ansi-cyan-fg ansi-underline">pandas</span> <span class="ansi-blue-fg">as</span> <span class="ansi-cyan-fg ansi-underline">pd</span>
<span class="ansi-blue-fg">import</span> <span class="ansi-cyan-fg ansi-underline">numpy</span> <span class="ansi-blue-fg">as</span> <span class="ansi-cyan-fg ansi-underline">np</span>
<span class="ansi-blue-fg">import</span> <span class="ansi-cyan-fg ansi-underline">torch</span>
<span class="ansi-blue-fg">import</span> <span class="ansi-cyan-fg ansi-underline">torch.nn</span> <span class="ansi-blue-fg">as</span> <span class="ansi-cyan-fg ansi-underline">nn</span>
<span class="ansi-blue-fg">import</span> <span class="ansi-cyan-fg ansi-underline">torch.optim</span> <span class="ansi-blue-fg">as</span> <span class="ansi-cyan-fg ansi-underline">optim</span>
<span class="ansi-blue-fg">import</span> <span class="ansi-cyan-fg ansi-underline">torch.utils.data</span>

<span class="ansi-blue-fg">from</span> <span class="ansi-cyan-fg ansi-underline">model</span> <span class="ansi-blue-fg">import</span> LSTMClassifier

<span class="ansi-blue-fg">from</span> <span class="ansi-cyan-fg ansi-underline">utils</span> <span class="ansi-blue-fg">import</span> review_to_words, convert_and_pad

<span class="ansi-blue-fg">def</span> <span class="ansi-green-fg">model_fn</span>(model_dir):
    <span class="ansi-yellow-fg">&#34;&#34;&#34;Load the PyTorch model from the `model_dir` directory.&#34;&#34;&#34;</span>
    <span class="ansi-blue-fg">print</span>(<span class="ansi-yellow-fg">&#34;</span><span class="ansi-yellow-fg">Loading model.</span><span class="ansi-yellow-fg">&#34;</span>)

    <span class="ansi-white-fg"># First, load the parameters used to create the model.</span>
    model_info = {}
    model_info_path = os.path.join(model_dir, <span class="ansi-yellow-fg">&#39;</span><span class="ansi-yellow-fg">model_info.pth</span><span class="ansi-yellow-fg">&#39;</span>)
    <span class="ansi-blue-fg">with</span> <span class="ansi-cyan-fg">open</span>(model_info_path, <span class="ansi-yellow-fg">&#39;</span><span class="ansi-yellow-fg">rb</span><span class="ansi-yellow-fg">&#39;</span>) <span class="ansi-blue-fg">as</span> f:
        model_info = torch.load(f)

    <span class="ansi-blue-fg">print</span>(<span class="ansi-yellow-fg">&#34;</span><span class="ansi-yellow-fg">model_info: {}</span><span class="ansi-yellow-fg">&#34;</span>.format(model_info))

    <span class="ansi-white-fg"># Determine the device and construct the model.</span>
    device = torch.device(<span class="ansi-yellow-fg">&#34;</span><span class="ansi-yellow-fg">cuda</span><span class="ansi-yellow-fg">&#34;</span> <span class="ansi-blue-fg">if</span> torch.cuda.is_available() <span class="ansi-blue-fg">else</span> <span class="ansi-yellow-fg">&#34;</span><span class="ansi-yellow-fg">cpu</span><span class="ansi-yellow-fg">&#34;</span>)
    model = LSTMClassifier(model_info[<span class="ansi-yellow-fg">&#39;</span><span class="ansi-yellow-fg">embedding_dim</span><span class="ansi-yellow-fg">&#39;</span>], model_info[<span class="ansi-yellow-fg">&#39;</span><span class="ansi-yellow-fg">hidden_dim</span><span class="ansi-yellow-fg">&#39;</span>], model_info[<span class="ansi-yellow-fg">&#39;</span><span class="ansi-yellow-fg">vocab_size</span><span class="ansi-yellow-fg">&#39;</span>])

    <span class="ansi-white-fg"># Load the store model parameters.</span>
    model_path = os.path.join(model_dir, <span class="ansi-yellow-fg">&#39;</span><span class="ansi-yellow-fg">model.pth</span><span class="ansi-yellow-fg">&#39;</span>)
    <span class="ansi-blue-fg">with</span> <span class="ansi-cyan-fg">open</span>(model_path, <span class="ansi-yellow-fg">&#39;</span><span class="ansi-yellow-fg">rb</span><span class="ansi-yellow-fg">&#39;</span>) <span class="ansi-blue-fg">as</span> f:
        model.load_state_dict(torch.load(f))

    <span class="ansi-white-fg"># Load the saved word_dict.</span>
    word_dict_path = os.path.join(model_dir, <span class="ansi-yellow-fg">&#39;</span><span class="ansi-yellow-fg">word_dict.pkl</span><span class="ansi-yellow-fg">&#39;</span>)
    <span class="ansi-blue-fg">with</span> <span class="ansi-cyan-fg">open</span>(word_dict_path, <span class="ansi-yellow-fg">&#39;</span><span class="ansi-yellow-fg">rb</span><span class="ansi-yellow-fg">&#39;</span>) <span class="ansi-blue-fg">as</span> f:
        model.word_dict = pickle.load(f)

    model.to(device).eval()

    <span class="ansi-blue-fg">print</span>(<span class="ansi-yellow-fg">&#34;</span><span class="ansi-yellow-fg">Done loading model.</span><span class="ansi-yellow-fg">&#34;</span>)
    <span class="ansi-blue-fg">return</span> model

<span class="ansi-blue-fg">def</span> <span class="ansi-green-fg">input_fn</span>(serialized_input_data, content_type):
    <span class="ansi-blue-fg">print</span>(<span class="ansi-yellow-fg">&#39;</span><span class="ansi-yellow-fg">Deserializing the input data.</span><span class="ansi-yellow-fg">&#39;</span>)
    <span class="ansi-blue-fg">if</span> content_type == <span class="ansi-yellow-fg">&#39;</span><span class="ansi-yellow-fg">text/plain</span><span class="ansi-yellow-fg">&#39;</span>:
        data = serialized_input_data.decode(<span class="ansi-yellow-fg">&#39;</span><span class="ansi-yellow-fg">utf-8</span><span class="ansi-yellow-fg">&#39;</span>)
        <span class="ansi-blue-fg">return</span> data
    <span class="ansi-blue-fg">raise</span> <span class="ansi-cyan-fg">Exception</span>(<span class="ansi-yellow-fg">&#39;</span><span class="ansi-yellow-fg">Requested unsupported ContentType in content_type: </span><span class="ansi-yellow-fg">&#39;</span> + content_type)

<span class="ansi-blue-fg">def</span> <span class="ansi-green-fg">output_fn</span>(prediction_output, accept):
    <span class="ansi-blue-fg">print</span>(<span class="ansi-yellow-fg">&#39;</span><span class="ansi-yellow-fg">Serializing the generated output.</span><span class="ansi-yellow-fg">&#39;</span>)
    <span class="ansi-blue-fg">return</span> <span class="ansi-cyan-fg">str</span>(prediction_output)

<span class="ansi-blue-fg">def</span> <span class="ansi-green-fg">predict_fn</span>(input_data, model):
    <span class="ansi-blue-fg">print</span>(<span class="ansi-yellow-fg">&#39;</span><span class="ansi-yellow-fg">Inferring sentiment of input data.</span><span class="ansi-yellow-fg">&#39;</span>)

    device = torch.device(<span class="ansi-yellow-fg">&#34;</span><span class="ansi-yellow-fg">cuda</span><span class="ansi-yellow-fg">&#34;</span> <span class="ansi-blue-fg">if</span> torch.cuda.is_available() <span class="ansi-blue-fg">else</span> <span class="ansi-yellow-fg">&#34;</span><span class="ansi-yellow-fg">cpu</span><span class="ansi-yellow-fg">&#34;</span>)
    
    <span class="ansi-blue-fg">if</span> model.word_dict <span class="ansi-magenta-fg">is</span> <span class="ansi-cyan-fg">None</span>:
        <span class="ansi-blue-fg">raise</span> <span class="ansi-cyan-fg">Exception</span>(<span class="ansi-yellow-fg">&#39;</span><span class="ansi-yellow-fg">Model has not been loaded properly, no word_dict.</span><span class="ansi-yellow-fg">&#39;</span>)
    
    <span class="ansi-white-fg"># TODO: Process input_data so that it is ready to be sent to our model.</span>
    <span class="ansi-white-fg">#       You should produce two variables:</span>
    <span class="ansi-white-fg">#         data_X   - A sequence of length 500 which represents the converted review</span>
    <span class="ansi-white-fg">#         data_len - The length of the review</span>

    data_X , data_len = convert_and_pad(model.word_dict, review_to_words(input_data), <span class="ansi-blue-fg">500</span>)

    <span class="ansi-white-fg"># Using data_X and data_len we construct an appropriate input tensor. Remember</span>
    <span class="ansi-white-fg"># that our model expects input data of the form &#39;len, review[500]&#39;.</span>
    data_pack = np.hstack((data_len, data_X))
    data_pack = data_pack.reshape(<span class="ansi-blue-fg">1</span>, -<span class="ansi-blue-fg">1</span>)

    data = torch.from_numpy(data_pack)
    data = data.to(device)

    <span class="ansi-white-fg"># Make sure to put the model into evaluation mode</span>
    model.eval()

    <span class="ansi-white-fg"># TODO: Compute the result of applying the model to the input data. The variable `result` should</span>
    <span class="ansi-white-fg">#       be a numpy array which contains a single integer which is either 1 or 0</span>
    result = model(data)
    result = <span class="ansi-cyan-fg">round</span>(result.item())

    <span class="ansi-blue-fg">return</span> result
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%%writefile</span> serve/predict.py
<span class="c1"># %load serve/predict.py</span>
<span class="kn">import</span> <span class="nn">argparse</span>
<span class="kn">import</span> <span class="nn">json</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">pickle</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">sagemaker_containers</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="nn">optim</span>
<span class="kn">import</span> <span class="nn">torch.utils.data</span>

<span class="kn">from</span> <span class="nn">model</span> <span class="k">import</span> <span class="n">LSTMClassifier</span>

<span class="kn">from</span> <span class="nn">utils</span> <span class="k">import</span> <span class="n">review_to_words</span><span class="p">,</span> <span class="n">convert_and_pad</span>

<span class="k">def</span> <span class="nf">model_fn</span><span class="p">(</span><span class="n">model_dir</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Load the PyTorch model from the `model_dir` directory.&quot;&quot;&quot;</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Loading model.&quot;</span><span class="p">)</span>

    <span class="c1"># First, load the parameters used to create the model.</span>
    <span class="n">model_info</span> <span class="o">=</span> <span class="p">{}</span>
    <span class="n">model_info_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">model_dir</span><span class="p">,</span> <span class="s1">&#39;model_info.pth&#39;</span><span class="p">)</span>
    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">model_info_path</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
        <span class="n">model_info</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;model_info: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">model_info</span><span class="p">))</span>

    <span class="c1"># Determine the device and construct the model.</span>
    <span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">LSTMClassifier</span><span class="p">(</span><span class="n">model_info</span><span class="p">[</span><span class="s1">&#39;embedding_dim&#39;</span><span class="p">],</span> <span class="n">model_info</span><span class="p">[</span><span class="s1">&#39;hidden_dim&#39;</span><span class="p">],</span> <span class="n">model_info</span><span class="p">[</span><span class="s1">&#39;vocab_size&#39;</span><span class="p">])</span>

    <span class="c1"># Load the store model parameters.</span>
    <span class="n">model_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">model_dir</span><span class="p">,</span> <span class="s1">&#39;model.pth&#39;</span><span class="p">)</span>
    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">model_path</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
        <span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">))</span>

    <span class="c1"># Load the saved word_dict.</span>
    <span class="n">word_dict_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">model_dir</span><span class="p">,</span> <span class="s1">&#39;word_dict.pkl&#39;</span><span class="p">)</span>
    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">word_dict_path</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
        <span class="n">model</span><span class="o">.</span><span class="n">word_dict</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>

    <span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Done loading model.&quot;</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">model</span>

<span class="k">def</span> <span class="nf">input_fn</span><span class="p">(</span><span class="n">serialized_input_data</span><span class="p">,</span> <span class="n">content_type</span><span class="p">):</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Deserializing the input data.&#39;</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">content_type</span> <span class="o">==</span> <span class="s1">&#39;text/plain&#39;</span><span class="p">:</span>
        <span class="n">data</span> <span class="o">=</span> <span class="n">serialized_input_data</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">&#39;utf-8&#39;</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">data</span>
    <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;Requested unsupported ContentType in content_type: &#39;</span> <span class="o">+</span> <span class="n">content_type</span><span class="p">)</span>

<span class="k">def</span> <span class="nf">output_fn</span><span class="p">(</span><span class="n">prediction_output</span><span class="p">,</span> <span class="n">accept</span><span class="p">):</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Serializing the generated output.&#39;</span><span class="p">)</span>
    <span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="n">prediction_output</span><span class="p">)</span>

<span class="k">def</span> <span class="nf">predict_fn</span><span class="p">(</span><span class="n">input_data</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Inferring sentiment of input data.&#39;</span><span class="p">)</span>

    <span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
    
    <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">word_dict</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;Model has not been loaded properly, no word_dict.&#39;</span><span class="p">)</span>
    
    <span class="c1"># TODO: Process input_data so that it is ready to be sent to our model.</span>
    <span class="c1">#       You should produce two variables:</span>
    <span class="c1">#         data_X   - A sequence of length 500 which represents the converted review</span>
    <span class="c1">#         data_len - The length of the review</span>

    <span class="n">data_X</span> <span class="p">,</span> <span class="n">data_len</span> <span class="o">=</span> <span class="n">convert_and_pad</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">word_dict</span><span class="p">,</span> <span class="n">review_to_words</span><span class="p">(</span><span class="n">input_data</span><span class="p">),</span> <span class="mi">500</span><span class="p">)</span>

    <span class="c1"># Using data_X and data_len we construct an appropriate input tensor. Remember</span>
    <span class="c1"># that our model expects input data of the form &#39;len, review[500]&#39;.</span>
    <span class="n">data_pack</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">data_len</span><span class="p">,</span> <span class="n">data_X</span><span class="p">))</span>
    <span class="n">data_pack</span> <span class="o">=</span> <span class="n">data_pack</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>

    <span class="n">data</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">data_pack</span><span class="p">)</span>
    <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>

    <span class="c1"># Make sure to put the model into evaluation mode</span>
    <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>

    <span class="c1"># TODO: Compute the result of applying the model to the input data. The variable `result` should</span>
    <span class="c1">#       be a numpy array which contains a single integer which is either 1 or 0</span>
    <span class="n">result</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
    <span class="n">result</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>

    <span class="k">return</span> <span class="n">result</span>
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<pre>Overwriting serve/predict.py
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<p>As mentioned earlier, the <code>model_fn</code> method is the same as the one provided in the training code and the <code>input_fn</code> and <code>output_fn</code> methods are very simple and your task will be to complete the <code>predict_fn</code> method. Make sure that you save the completed file as <code>predict.py</code> in the <code>serve</code> directory.</p>
<p><strong>TODO</strong>: Complete the <code>predict_fn()</code> method in the <code>serve/predict.py</code> file.</p>

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<h3 id="Deploying-the-model">Deploying the model<a class="anchor-link" href="#Deploying-the-model">&#182;</a></h3><p>Now that the custom inference code has been written, we will create and deploy our model. To begin with, we need to construct a new PyTorchModel object which points to the model artifacts created during training and also points to the inference code that we wish to use. Then we can call the deploy method to launch the deployment container.</p>
<p><strong>NOTE</strong>: The default behaviour for a deployed PyTorch model is to assume that any input passed to the predictor is a <code>numpy</code> array. In our case we want to send a string so we need to construct a simple wrapper around the <code>RealTimePredictor</code> class to accomodate simple strings. In a more complicated situation you may want to provide a serialization object, for example if you wanted to sent image data.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sagemaker.predictor</span> <span class="k">import</span> <span class="n">RealTimePredictor</span>
<span class="kn">from</span> <span class="nn">sagemaker.pytorch</span> <span class="k">import</span> <span class="n">PyTorchModel</span>

<span class="k">class</span> <span class="nc">StringPredictor</span><span class="p">(</span><span class="n">RealTimePredictor</span><span class="p">):</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">endpoint_name</span><span class="p">,</span> <span class="n">sagemaker_session</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">StringPredictor</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">endpoint_name</span><span class="p">,</span> <span class="n">sagemaker_session</span><span class="p">,</span> <span class="n">content_type</span><span class="o">=</span><span class="s1">&#39;text/plain&#39;</span><span class="p">)</span>

<span class="n">model</span> <span class="o">=</span> <span class="n">PyTorchModel</span><span class="p">(</span><span class="n">model_data</span><span class="o">=</span><span class="n">estimator</span><span class="o">.</span><span class="n">model_data</span><span class="p">,</span>
                     <span class="n">role</span> <span class="o">=</span> <span class="n">role</span><span class="p">,</span>
                     <span class="n">framework_version</span><span class="o">=</span><span class="s1">&#39;0.4.0&#39;</span><span class="p">,</span>
                     <span class="n">entry_point</span><span class="o">=</span><span class="s1">&#39;predict.py&#39;</span><span class="p">,</span>
                     <span class="n">source_dir</span><span class="o">=</span><span class="s1">&#39;serve&#39;</span><span class="p">,</span>
                     <span class="n">predictor_cls</span><span class="o">=</span><span class="n">StringPredictor</span><span class="p">)</span>
<span class="n">predictor</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">deploy</span><span class="p">(</span><span class="n">initial_instance_count</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">instance_type</span><span class="o">=</span><span class="s1">&#39;ml.m4.xlarge&#39;</span><span class="p">)</span>
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<pre>--------------------------------------------------------------------------------------!</pre>
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<h3 id="Testing-the-model">Testing the model<a class="anchor-link" href="#Testing-the-model">&#182;</a></h3><p>Now that we have deployed our model with the custom inference code, we should test to see if everything is working. Here we test our model by loading the first <code>250</code> positive and negative reviews and send them to the endpoint, then collect the results. The reason for only sending some of the data is that the amount of time it takes for our model to process the input and then perform inference is quite long and so testing the entire data set would be prohibitive.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">glob</span>

<span class="k">def</span> <span class="nf">test_reviews</span><span class="p">(</span><span class="n">data_dir</span><span class="o">=</span><span class="s1">&#39;../data/aclImdb&#39;</span><span class="p">,</span> <span class="n">stop</span><span class="o">=</span><span class="mi">250</span><span class="p">):</span>
    
    <span class="n">results</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">ground</span> <span class="o">=</span> <span class="p">[]</span>
    
    <span class="c1"># We make sure to test both positive and negative reviews    </span>
    <span class="k">for</span> <span class="n">sentiment</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;pos&#39;</span><span class="p">,</span> <span class="s1">&#39;neg&#39;</span><span class="p">]:</span>
        
        <span class="n">path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="s1">&#39;test&#39;</span><span class="p">,</span> <span class="n">sentiment</span><span class="p">,</span> <span class="s1">&#39;*.txt&#39;</span><span class="p">)</span>
        <span class="n">files</span> <span class="o">=</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
        
        <span class="n">files_read</span> <span class="o">=</span> <span class="mi">0</span>
        
        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Starting &#39;</span><span class="p">,</span> <span class="n">sentiment</span><span class="p">,</span> <span class="s1">&#39; files&#39;</span><span class="p">)</span>
        
        <span class="c1"># Iterate through the files and send them to the predictor</span>
        <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">files</span><span class="p">:</span>
            <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="k">as</span> <span class="n">review</span><span class="p">:</span>
                <span class="c1"># First, we store the ground truth (was the review positive or negative)</span>
                <span class="k">if</span> <span class="n">sentiment</span> <span class="o">==</span> <span class="s1">&#39;pos&#39;</span><span class="p">:</span>
                    <span class="n">ground</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
                <span class="k">else</span><span class="p">:</span>
                    <span class="n">ground</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
                <span class="c1"># Read in the review and convert to &#39;utf-8&#39; for transmission via HTTP</span>
                <span class="n">review_input</span> <span class="o">=</span> <span class="n">review</span><span class="o">.</span><span class="n">read</span><span class="p">()</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s1">&#39;utf-8&#39;</span><span class="p">)</span>
                <span class="c1"># Send the review to the predictor and store the results</span>
                <span class="n">results</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">predictor</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">review_input</span><span class="p">)))</span>
                
            <span class="c1"># Sending reviews to our endpoint one at a time takes a while so we</span>
            <span class="c1"># only send a small number of reviews</span>
            <span class="n">files_read</span> <span class="o">+=</span> <span class="mi">1</span>
            <span class="k">if</span> <span class="n">files_read</span> <span class="o">==</span> <span class="n">stop</span><span class="p">:</span>
                <span class="k">break</span>
            
    <span class="k">return</span> <span class="n">ground</span><span class="p">,</span> <span class="n">results</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ground</span><span class="p">,</span> <span class="n">results</span> <span class="o">=</span> <span class="n">test_reviews</span><span class="p">()</span>
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<pre>Starting  pos  files
Starting  neg  files
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">accuracy_score</span>
<span class="n">accuracy_score</span><span class="p">(</span><span class="n">ground</span><span class="p">,</span> <span class="n">results</span><span class="p">)</span>
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<pre>0.86</pre>
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<p>As an additional test, we can try sending the <code>test_review</code> that we looked at earlier.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">predictor</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test_review</span><span class="p">)</span>
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<pre>b&#39;1&#39;</pre>
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<p>Now that we know our endpoint is working as expected, we can set up the web page that will interact with it. If you don't have time to finish the project now, make sure to skip down to the end of this notebook and shut down your endpoint. You can deploy it again when you come back.</p>

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<h2 id="Step-7-(again):-Use-the-model-for-the-web-app">Step 7 (again): Use the model for the web app<a class="anchor-link" href="#Step-7-(again):-Use-the-model-for-the-web-app">&#182;</a></h2><blockquote><p><strong>TODO:</strong> This entire section and the next contain tasks for you to complete, mostly using the AWS console.</p>
</blockquote>
<p>So far we have been accessing our model endpoint by constructing a predictor object which uses the endpoint and then just using the predictor object to perform inference. What if we wanted to create a web app which accessed our model? The way things are set up currently makes that not possible since in order to access a SageMaker endpoint the app would first have to authenticate with AWS using an IAM role which included access to SageMaker endpoints. However, there is an easier way! We just need to use some additional AWS services.</p>
<p><img src="Web App Diagram.svg"></p>
<p>The diagram above gives an overview of how the various services will work together. On the far right is the model which we trained above and which is deployed using SageMaker. On the far left is our web app that collects a user's movie review, sends it off and expects a positive or negative sentiment in return.</p>
<p>In the middle is where some of the magic happens. We will construct a Lambda function, which you can think of as a straightforward Python function that can be executed whenever a specified event occurs. We will give this function permission to send and recieve data from a SageMaker endpoint.</p>
<p>Lastly, the method we will use to execute the Lambda function is a new endpoint that we will create using API Gateway. This endpoint will be a url that listens for data to be sent to it. Once it gets some data it will pass that data on to the Lambda function and then return whatever the Lambda function returns. Essentially it will act as an interface that lets our web app communicate with the Lambda function.</p>
<h3 id="Setting-up-a-Lambda-function">Setting up a Lambda function<a class="anchor-link" href="#Setting-up-a-Lambda-function">&#182;</a></h3><p>The first thing we are going to do is set up a Lambda function. This Lambda function will be executed whenever our public API has data sent to it. When it is executed it will receive the data, perform any sort of processing that is required, send the data (the review) to the SageMaker endpoint we've created and then return the result.</p>
<h4 id="Part-A:-Create-an-IAM-Role-for-the-Lambda-function">Part A: Create an IAM Role for the Lambda function<a class="anchor-link" href="#Part-A:-Create-an-IAM-Role-for-the-Lambda-function">&#182;</a></h4><p>Since we want the Lambda function to call a SageMaker endpoint, we need to make sure that it has permission to do so. To do this, we will construct a role that we can later give the Lambda function.</p>
<p>Using the AWS Console, navigate to the <strong>IAM</strong> page and click on <strong>Roles</strong>. Then, click on <strong>Create role</strong>. Make sure that the <strong>AWS service</strong> is the type of trusted entity selected and choose <strong>Lambda</strong> as the service that will use this role, then click <strong>Next: Permissions</strong>.</p>
<p>In the search box type <code>sagemaker</code> and select the check box next to the <strong>AmazonSageMakerFullAccess</strong> policy. Then, click on <strong>Next: Review</strong>.</p>
<p>Lastly, give this role a name. Make sure you use a name that you will remember later on, for example <code>LambdaSageMakerRole</code>. Then, click on <strong>Create role</strong>.</p>
<h4 id="Part-B:-Create-a-Lambda-function">Part B: Create a Lambda function<a class="anchor-link" href="#Part-B:-Create-a-Lambda-function">&#182;</a></h4><p>Now it is time to actually create the Lambda function.</p>
<p>Using the AWS Console, navigate to the AWS Lambda page and click on <strong>Create a function</strong>. When you get to the next page, make sure that <strong>Author from scratch</strong> is selected. Now, name your Lambda function, using a name that you will remember later on, for example <code>sentiment_analysis_func</code>. Make sure that the <strong>Python 3.6</strong> runtime is selected and then choose the role that you created in the previous part. Then, click on <strong>Create Function</strong>.</p>
<p>On the next page you will see some information about the Lambda function you've just created. If you scroll down you should see an editor in which you can write the code that will be executed when your Lambda function is triggered. In our example, we will use the code below.</p>
<div class="highlight"><pre><span></span><span class="c1"># We need to use the low-level library to interact with SageMaker since the SageMaker API</span>
<span class="c1"># is not available natively through Lambda.</span>
<span class="kn">import</span> <span class="nn">boto3</span>

<span class="k">def</span> <span class="nf">lambda_handler</span><span class="p">(</span><span class="n">event</span><span class="p">,</span> <span class="n">context</span><span class="p">):</span>

    <span class="c1"># The SageMaker runtime is what allows us to invoke the endpoint that we&#39;ve created.</span>
    <span class="n">runtime</span> <span class="o">=</span> <span class="n">boto3</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span><span class="o">.</span><span class="n">client</span><span class="p">(</span><span class="s1">&#39;sagemaker-runtime&#39;</span><span class="p">)</span>

    <span class="c1"># Now we use the SageMaker runtime to invoke our endpoint, sending the review we were given</span>
    <span class="n">response</span> <span class="o">=</span> <span class="n">runtime</span><span class="o">.</span><span class="n">invoke_endpoint</span><span class="p">(</span><span class="n">EndpointName</span> <span class="o">=</span> <span class="s1">&#39;**ENDPOINT NAME HERE**&#39;</span><span class="p">,</span>    <span class="c1"># The name of the endpoint we created</span>
                                       <span class="n">ContentType</span> <span class="o">=</span> <span class="s1">&#39;text/plain&#39;</span><span class="p">,</span>                 <span class="c1"># The data format that is expected</span>
                                       <span class="n">Body</span> <span class="o">=</span> <span class="n">event</span><span class="p">[</span><span class="s1">&#39;body&#39;</span><span class="p">])</span>                       <span class="c1"># The actual review</span>

    <span class="c1"># The response is an HTTP response whose body contains the result of our inference</span>
    <span class="n">result</span> <span class="o">=</span> <span class="n">response</span><span class="p">[</span><span class="s1">&#39;Body&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">read</span><span class="p">()</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">&#39;utf-8&#39;</span><span class="p">)</span>

    <span class="k">return</span> <span class="p">{</span>
        <span class="s1">&#39;statusCode&#39;</span> <span class="p">:</span> <span class="mi">200</span><span class="p">,</span>
        <span class="s1">&#39;headers&#39;</span> <span class="p">:</span> <span class="p">{</span> <span class="s1">&#39;Content-Type&#39;</span> <span class="p">:</span> <span class="s1">&#39;text/plain&#39;</span><span class="p">,</span> <span class="s1">&#39;Access-Control-Allow-Origin&#39;</span> <span class="p">:</span> <span class="s1">&#39;*&#39;</span> <span class="p">},</span>
        <span class="s1">&#39;body&#39;</span> <span class="p">:</span> <span class="n">result</span>
    <span class="p">}</span>
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<p>Once you have copy and pasted the code above into the Lambda code editor, replace the <code>**ENDPOINT NAME HERE**</code> portion with the name of the endpoint that we deployed earlier. You can determine the name of the endpoint using the code cell below.</p>

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<pre>&#39;sagemaker-pytorch-2019-05-24-11-39-11-682&#39;</pre>
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<p>Once you have added the endpoint name to the Lambda function, click on <strong>Save</strong>. Your Lambda function is now up and running. Next we need to create a way for our web app to execute the Lambda function.</p>
<h3 id="Setting-up-API-Gateway">Setting up API Gateway<a class="anchor-link" href="#Setting-up-API-Gateway">&#182;</a></h3><p>Now that our Lambda function is set up, it is time to create a new API using API Gateway that will trigger the Lambda function we have just created.</p>
<p>Using AWS Console, navigate to <strong>Amazon API Gateway</strong> and then click on <strong>Get started</strong>.</p>
<p>On the next page, make sure that <strong>New API</strong> is selected and give the new api a name, for example, <code>sentiment_analysis_api</code>. Then, click on <strong>Create API</strong>.</p>
<p>Now we have created an API, however it doesn't currently do anything. What we want it to do is to trigger the Lambda function that we created earlier.</p>
<p>Select the <strong>Actions</strong> dropdown menu and click <strong>Create Method</strong>. A new blank method will be created, select its dropdown menu and select <strong>POST</strong>, then click on the check mark beside it.</p>
<p>For the integration point, make sure that <strong>Lambda Function</strong> is selected and click on the <strong>Use Lambda Proxy integration</strong>. This option makes sure that the data that is sent to the API is then sent directly to the Lambda function with no processing. It also means that the return value must be a proper response object as it will also not be processed by API Gateway.</p>
<p>Type the name of the Lambda function you created earlier into the <strong>Lambda Function</strong> text entry box and then click on <strong>Save</strong>. Click on <strong>OK</strong> in the pop-up box that then appears, giving permission to API Gateway to invoke the Lambda function you created.</p>
<p>The last step in creating the API Gateway is to select the <strong>Actions</strong> dropdown and click on <strong>Deploy API</strong>. You will need to create a new Deployment stage and name it anything you like, for example <code>prod</code>.</p>
<p>You have now successfully set up a public API to access your SageMaker model. Make sure to copy or write down the URL provided to invoke your newly created public API as this will be needed in the next step. This URL can be found at the top of the page, highlighted in blue next to the text <strong>Invoke URL</strong>.</p>

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<h2 id="Step-4:-Deploying-our-web-app">Step 4: Deploying our web app<a class="anchor-link" href="#Step-4:-Deploying-our-web-app">&#182;</a></h2><p>Now that we have a publicly available API, we can start using it in a web app. For our purposes, we have provided a simple static html file which can make use of the public api you created earlier.</p>
<p>In the <code>website</code> folder there should be a file called <code>index.html</code>. Download the file to your computer and open that file up in a text editor of your choice. There should be a line which contains <strong>**REPLACE WITH PUBLIC API URL**</strong>. Replace this string with the url that you wrote down in the last step and then save the file.</p>
<p>Now, if you open <code>index.html</code> on your local computer, your browser will behave as a local web server and you can use the provided site to interact with your SageMaker model.</p>
<p>If you'd like to go further, you can host this html file anywhere you'd like, for example using github or hosting a static site on Amazon's S3. Once you have done this you can share the link with anyone you'd like and have them play with it too!</p>
<blockquote><p><strong>Important Note</strong> In order for the web app to communicate with the SageMaker endpoint, the endpoint has to actually be deployed and running. This means that you are paying for it. Make sure that the endpoint is running when you want to use the web app but that you shut it down when you don't need it, otherwise you will end up with a surprisingly large AWS bill.</p>
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<p><strong>TODO:</strong> Make sure that you include the edited <code>index.html</code> file in your project submission.</p>

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<p>Now that your web app is working, trying playing around with it and see how well it works.</p>
<p><strong>Question</strong>: Give an example of a review that you entered into your web app. What was the predicted sentiment of your example review?</p>

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<p><strong>Answer:</strong> They gave $300,000,000 for this movie, but it doesn't look like that big of a movie, right? WB really did something. They've disposed of movie's original composers. They've re-edited and rewrote the whole movie. They've brought a new guy, who doesn't care about the characters or bother to know them, as a writer and director to make the movie look more like a Disney movie. And with that guy, they added a bunch of reshoots which doesn't add too much to the movie other than garbageness and a bad CGI mouth. This version of Justice League is just wrong, doesn't add up, doesn't lead anywhere. Every storyline that we started, every future we were building up to gone with this movie. It just depressing.</p>
<p>The review was predicted correctly to be Negative.</p>

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<h3 id="Delete-the-endpoint">Delete the endpoint<a class="anchor-link" href="#Delete-the-endpoint">&#182;</a></h3><p>Remember to always shut down your endpoint if you are no longer using it. You are charged for the length of time that the endpoint is running so if you forget and leave it on you could end up with an unexpectedly large bill.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">predictor</span><span class="o">.</span><span class="n">delete_endpoint</span><span class="p">()</span>
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