From 21d1605956e71815a45508e29e868233c9b5caaf Mon Sep 17 00:00:00 2001 From: Fionn Malone Date: Sat, 11 May 2024 13:19:20 +0000 Subject: [PATCH 01/16] Small fixes to hubbard bloqs. --- .../chemistry/trotter/hubbard/__init__.py | 4 +- .../chemistry/trotter/hubbard/hopping.py | 14 +- .../chemistry/trotter/hubbard/hubbard.ipynb | 201 +++++++++--------- .../chemistry/trotter/hubbard/interaction.py | 13 +- .../chemistry/trotter/hubbard/trotter_step.py | 6 +- 5 files changed, 121 insertions(+), 117 deletions(-) diff --git a/qualtran/bloqs/chemistry/trotter/hubbard/__init__.py b/qualtran/bloqs/chemistry/trotter/hubbard/__init__.py index a66c476db..ab3efeb4b 100644 --- a/qualtran/bloqs/chemistry/trotter/hubbard/__init__.py +++ b/qualtran/bloqs/chemistry/trotter/hubbard/__init__.py @@ -35,9 +35,9 @@ where $z_{p\sigma} = (2 n_{p\sigma} - 1)$. -For Trotterization we assume the plaquette splitting from the +For Trotterization we assume the plaquette splitting from the [reference](https://arxiv.org/abs/2012.09238). -The plaquette splitting rewrites $H_h$ as a sum of $H_h^p$ and $H_h^g$ (for pink and gold +The plaquette splitting rewrites $H_h$ as a sum of $H_h^p$ and $H_h^g$ (for pink and gold respectively) which when combined tile the entire lattice. Each plaquette contains four sites and paritions the lattice such that each edge of the lattice belongs to a single plaquette. Each term within a grouping commutes so that the diff --git a/qualtran/bloqs/chemistry/trotter/hubbard/hopping.py b/qualtran/bloqs/chemistry/trotter/hubbard/hopping.py index 98ed7975f..dd2db9b28 100644 --- a/qualtran/bloqs/chemistry/trotter/hubbard/hopping.py +++ b/qualtran/bloqs/chemistry/trotter/hubbard/hopping.py @@ -41,10 +41,10 @@ class HoppingPlaquette(Bloq): $$ \sum_{i,j} [R_{\mathrm{plaq}}]_{i,j} a_{i\sigma}^\dagger a_{j\sigma} $$ - where the non-zero sub-bloq of R_{\mathrm{plaq}} is + where the non-zero sub-bloq of $R_{\mathrm{plaq}}$ is $$ - R_{\mathrm{plaq}} = + R_{\mathrm{plaq}} = \begin{bmatrix} 0 & 1 & 0 & 1 \\ 1 & 0 & 1 & 0 \\ @@ -55,7 +55,7 @@ class HoppingPlaquette(Bloq): Args: kappa: The scalar prefactor appearing in the definition of the unitary. - Usually a combination of the timestep and the hopping parameter $\tau$. + Usually a combination of the timestep and the hopping parameter $\tau$. eps: The precision of the single qubit rotations. Registers: @@ -78,7 +78,7 @@ def build_call_graph(self, ssa: 'SympySymbolAllocator') -> Set['BloqCountT']: # page 14, discussion after E13 # There are 4 flanking f-gates and a e^{iXX}e^{iYY} rotation, which can # be rotated to single rotation + cliffords. - return {(TwoBitFFFT(0, 1), 4), (Rz(self.kappa, eps=self.eps), 2)} + return {(TwoBitFFFT(0, 1, eps=self.eps), 4), (Rz(self.kappa, eps=self.eps), 2)} @frozen @@ -116,7 +116,7 @@ class HoppingTile(Bloq): pink: bool = True def __attrs_post_init__(self): - if self.length % 2 != 0: + if isinstance(self.length, int) and self.length % 2 != 0: raise ValueError('Only even length lattices are supported') def short_name(self) -> str: @@ -129,7 +129,9 @@ def signature(self) -> Signature: def build_call_graph(self, ssa: 'SympySymbolAllocator') -> Set['BloqCountT']: # Page 5, text after Eq. 22. There are L^2 / 4 plaquettes of a given colour and x2 for spin. - return {(HoppingPlaquette(kappa=self.tau * self.angle, eps=self.eps), self.length**2 // 2)} + return { + (HoppingPlaquette(kappa=self.tau * self.angle, eps=self.eps), self.length**2 // 2) + } @bloq_example diff --git a/qualtran/bloqs/chemistry/trotter/hubbard/hubbard.ipynb b/qualtran/bloqs/chemistry/trotter/hubbard/hubbard.ipynb index 743cc71e3..d1be35176 100644 --- a/qualtran/bloqs/chemistry/trotter/hubbard/hubbard.ipynb +++ b/qualtran/bloqs/chemistry/trotter/hubbard/hubbard.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "1e593ef8", + "id": "e1362eaa", "metadata": { "cq.autogen": "title_cell" }, @@ -32,9 +32,9 @@ "where $z_{p\\sigma} = (2 n_{p\\sigma} - 1)$.\n", "\n", "\n", - "For Trotterization we assume the plaquette splitting from the \n", + "For Trotterization we assume the plaquette splitting from the\n", "[reference](https://arxiv.org/abs/2012.09238).\n", - "The plaquette splitting rewrites $H_h$ as a sum of $H_h^p$ and $H_h^g$ (for pink and gold \n", + "The plaquette splitting rewrites $H_h$ as a sum of $H_h^p$ and $H_h^g$ (for pink and gold\n", "respectively) which when combined tile the entire lattice. Each plaquette\n", "contains four sites and paritions the lattice such that each edge of the lattice\n", "belongs to a single plaquette. Each term within a grouping commutes so that the\n", @@ -48,7 +48,7 @@ { "cell_type": "code", "execution_count": null, - "id": "05a9568d", + "id": "f2f93e40", "metadata": { "cq.autogen": "top_imports" }, @@ -65,7 +65,7 @@ }, { "cell_type": "markdown", - "id": "f77ffe9c", + "id": "3e7aff24", "metadata": { "cq.autogen": "HoppingTile.bloq_doc.md" }, @@ -96,7 +96,7 @@ { "cell_type": "code", "execution_count": null, - "id": "85e250cb", + "id": "c9c2e7a7", "metadata": { "cq.autogen": "HoppingTile.bloq_doc.py" }, @@ -107,7 +107,7 @@ }, { "cell_type": "markdown", - "id": "b6d95ae1", + "id": "e9b0bec2", "metadata": { "cq.autogen": "HoppingTile.example_instances.md" }, @@ -118,7 +118,7 @@ { "cell_type": "code", "execution_count": null, - "id": "e48f160a", + "id": "b9ec98eb", "metadata": { "cq.autogen": "HoppingTile.hopping_tile" }, @@ -131,7 +131,7 @@ }, { "cell_type": "markdown", - "id": "672adf17", + "id": "9cbcd20d", "metadata": { "cq.autogen": "HoppingTile.graphical_signature.md" }, @@ -142,7 +142,7 @@ { "cell_type": "code", "execution_count": null, - "id": "467e12a7", + "id": "367dbd6d", "metadata": { "cq.autogen": "HoppingTile.graphical_signature.py" }, @@ -155,7 +155,7 @@ }, { "cell_type": "markdown", - "id": "7fbdb423", + "id": "4a336787", "metadata": { "cq.autogen": "HoppingTile.call_graph.md" }, @@ -166,7 +166,7 @@ { "cell_type": "code", "execution_count": null, - "id": "499da704", + "id": "a743762b", "metadata": { "cq.autogen": "HoppingTile.call_graph.py" }, @@ -180,47 +180,65 @@ }, { "cell_type": "markdown", - "id": "a6d226f9", + "id": "d6238e40", "metadata": { - "cq.autogen": "Interaction.bloq_doc.md" + "cq.autogen": "HoppingPlaquette.bloq_doc.md" }, "source": [ - "## `Interaction`\n", - "Bloq implementing the hubbard U part of the hamiltonian.\n", + "## `HoppingPlaquette`\n", + "A bloq implementing a single plaquette unitary.\n", "\n", - "Specifically:\n", + "The bloq implements\n", "$$\n", - " U_I = e^{i t H_I}\n", + " e^{i \\kappa R_\\mathrm{plaq}}\n", + "$$\n", + "where $\\tau R^{k\\sigma}_\\mathrm{plaq} = H_h^{x(k,\\sigma)}$, i.e. R is\n", + "non-zero only in the subploq relevant for the particular indexed plaquette.\n", + "\n", + "The plaquette operator is given by\n", + "$$\n", + " \\sum_{i,j} [R_{\\mathrm{plaq}}]_{i,j} a_{i\\sigma}^\\dagger a_{j\\sigma}\n", + "$$\n", + "where the non-zero sub-bloq of $R_{\\mathrm{plaq}}$ is\n", + "\n", + "$$\n", + " R_{\\mathrm{plaq}} =\n", + " \\begin{bmatrix}\n", + " 0 & 1 & 0 & 1 \\\\\n", + " 1 & 0 & 1 & 0 \\\\\n", + " 0 & 1 & 0 & 1 \\\\\n", + " 1 & 0 & 1 & 0\n", + " \\end{bmatrix}\n", "$$\n", - "which can be implemented using equal angle single-qubit Z rotations.\n", "\n", "#### Parameters\n", - " - `length`: Lattice length L. \n", + " - `kappa`: The scalar prefactor appearing in the definition of the unitary. Usually a combination of the timestep and the hopping parameter $\\tau$.\n", + " - `eps`: The precision of the single qubit rotations. \n", "\n", "#### Registers\n", - " - `system`: The system register of size 2 `length`. \n", + " - `qubits`: A register of four qubits this unitary should act on. \n", "\n", "#### References\n", - " - [Early fault-tolerant simulations of the Hubbard model](https://arxiv.org/abs/2012.09238). Eq. 6 page 2 and page 13 paragraph 1.\n" + " - [Early fault-tolerant simulations of the Hubbard model](https://arxiv.org/abs/2012.09238). page 13 Eq. E4 and E5 (Appendix E)\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "f59ebbcf", + "id": "e092c287", "metadata": { - "cq.autogen": "Interaction.bloq_doc.py" + "cq.autogen": "HoppingPlaquette.bloq_doc.py" }, "outputs": [], "source": [ - "from qualtran.bloqs.chemistry.trotter.hubbard.interaction import Interaction" + "from qualtran.bloqs.chemistry.trotter.hubbard.hopping import HoppingPlaquette" ] }, { "cell_type": "markdown", - "id": "abbc8d48", + "id": "4016b7a7", "metadata": { - "cq.autogen": "Interaction.example_instances.md" + "cq.autogen": "HoppingPlaquette.example_instances.md" }, "source": [ "### Example Instances" @@ -229,22 +247,22 @@ { "cell_type": "code", "execution_count": null, - "id": "28c8162e", + "id": "03cd543d", "metadata": { - "cq.autogen": "Interaction.interaction" + "cq.autogen": "HoppingPlaquette.plaquette" }, "outputs": [], "source": [ "length = 8\n", - "angle = 0.5\n", - "interaction = Interaction(length, angle)" + "angle = 0.15\n", + "plaquette = HoppingPlaquette(length, angle)" ] }, { "cell_type": "markdown", - "id": "1eb36835", + "id": "da09068f", "metadata": { - "cq.autogen": "Interaction.graphical_signature.md" + "cq.autogen": "HoppingPlaquette.graphical_signature.md" }, "source": [ "#### Graphical Signature" @@ -253,22 +271,22 @@ { "cell_type": "code", "execution_count": null, - "id": "8a6eba22", + "id": "d433f4ed", "metadata": { - "cq.autogen": "Interaction.graphical_signature.py" + "cq.autogen": "HoppingPlaquette.graphical_signature.py" }, "outputs": [], "source": [ "from qualtran.drawing import show_bloqs\n", - "show_bloqs([interaction],\n", - " ['`interaction`'])" + "show_bloqs([plaquette],\n", + " ['`plaquette`'])" ] }, { "cell_type": "markdown", - "id": "e4cb2793", + "id": "7a3ebc1c", "metadata": { - "cq.autogen": "Interaction.call_graph.md" + "cq.autogen": "HoppingPlaquette.call_graph.md" }, "source": [ "### Call Graph" @@ -277,79 +295,64 @@ { "cell_type": "code", "execution_count": null, - "id": "4efb569f", + "id": "ba578fcc", "metadata": { - "cq.autogen": "Interaction.call_graph.py" + "cq.autogen": "HoppingPlaquette.call_graph.py" }, "outputs": [], "source": [ "from qualtran.resource_counting.generalizers import ignore_split_join\n", - "interaction_g, interaction_sigma = interaction.call_graph(max_depth=1, generalizer=ignore_split_join)\n", - "show_call_graph(interaction_g)\n", - "show_counts_sigma(interaction_sigma)" + "plaquette_g, plaquette_sigma = plaquette.call_graph(max_depth=1, generalizer=ignore_split_join)\n", + "show_call_graph(plaquette_g)\n", + "show_counts_sigma(plaquette_sigma)" ] }, { "cell_type": "markdown", - "id": "2b193a28", + "id": "b99592ea", "metadata": { - "cq.autogen": "HoppingPlaquette.bloq_doc.md" + "cq.autogen": "Interaction.bloq_doc.md" }, "source": [ - "## `HoppingPlaquette`\n", - "A bloq implementing a single plaquette unitary.\n", - "\n", - "The bloq implements\n", - "$$\n", - " e^{i \\kappa R_\\mathrm{plaq}}\n", - "$$\n", - "where $\\tau R^{k\\sigma}_\\mathrm{plaq} = H_h^{x(k,\\sigma)}$, i.e. R is\n", - "non-zero only in the subploq relevant for the particular indexed plaquette.\n", - "\n", - "The plaquette operator is given by\n", - "$$\n", - " \\sum_{i,j} [R_{\\mathrm{plaq}}]_{i,j} a_{i\\sigma}^\\dagger a_{j\\sigma}\n", - "$$\n", - "where the non-zero sub-bloq of R_{\\mathrm{plaq}} is\n", + "## `Interaction`\n", + "Bloq implementing the hubbard U part of the hamiltonian.\n", "\n", + "Specifically:\n", "$$\n", - " R_{\\mathrm{plaq}} =\n", - " \\begin{bmatrix}\n", - " 0 & 1 & 0 & 1 \\\\\n", - " 1 & 0 & 1 & 0 \\\\\n", - " 0 & 1 & 0 & 1 \\\\\n", - " 1 & 0 & 1 & 0\n", - " \\end{bmatrix}\n", + " U_I = e^{i t H_I}\n", "$$\n", + "which can be implemented using equal angle single-qubit Z rotations.\n", "\n", "#### Parameters\n", - " - `kappa`: The scalar prefactor appearing in the definition of the unitary. Usually a combination of the timestep and the hopping parameter $\\tau$.\n", + " - `length`: Lattice length $L$.\n", + " - `angle`: The prefactor scaling the Hopping hamiltonian in the unitary (`t` above). This should contain any relevant prefactors including the time step and any splitting coefficients.\n", + " - `hubb_u`: The hubbard $U$ parameter.\n", " - `eps`: The precision of the single qubit rotations. \n", "\n", "#### Registers\n", - " - `qubits`: A register of four qubits this unitary should act on. \n", + " - `system`: The system register of size 2 `length`. \n", "\n", "#### References\n", - " - [Early fault-tolerant simulations of the Hubbard model](https://arxiv.org/abs/2012.09238). page 13 Eq. E4 and E5 (Appendix E)\n" + " - [Early fault-tolerant simulations of the Hubbard model](https://arxiv.org/abs/2012.09238). Eq. 6 page 2 and page 13 paragraph 1.\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "a59efbf4", + "id": "99538cc0", "metadata": { - "cq.autogen": "HoppingPlaquette.bloq_doc.py" + "cq.autogen": "Interaction.bloq_doc.py" }, "outputs": [], "source": [ - "from qualtran.bloqs.chemistry.trotter.hubbard.hopping import HoppingPlaquette" + "from qualtran.bloqs.chemistry.trotter.hubbard.interaction import Interaction" ] }, { "cell_type": "markdown", - "id": "69c95c9f", + "id": "a0237960", "metadata": { - "cq.autogen": "HoppingPlaquette.example_instances.md" + "cq.autogen": "Interaction.example_instances.md" }, "source": [ "### Example Instances" @@ -358,22 +361,23 @@ { "cell_type": "code", "execution_count": null, - "id": "9a0fe775", + "id": "f8c459ce", "metadata": { - "cq.autogen": "HoppingPlaquette.plaquette" + "cq.autogen": "Interaction.interaction" }, "outputs": [], "source": [ "length = 8\n", - "angle = 0.15\n", - "plaquette = HoppingPlaquette(length, angle)" + "angle = 0.5\n", + "hubb_u = 4.0\n", + "interaction = Interaction(length, angle, hubb_u)" ] }, { "cell_type": "markdown", - "id": "920fcb61", + "id": "3514c727", "metadata": { - "cq.autogen": "HoppingPlaquette.graphical_signature.md" + "cq.autogen": "Interaction.graphical_signature.md" }, "source": [ "#### Graphical Signature" @@ -382,22 +386,22 @@ { "cell_type": "code", "execution_count": null, - "id": "e59cefe6", + "id": "79203ad1", "metadata": { - "cq.autogen": "HoppingPlaquette.graphical_signature.py" + "cq.autogen": "Interaction.graphical_signature.py" }, "outputs": [], "source": [ "from qualtran.drawing import show_bloqs\n", - "show_bloqs([plaquette],\n", - " ['`plaquette`'])" + "show_bloqs([interaction],\n", + " ['`interaction`'])" ] }, { "cell_type": "markdown", - "id": "c834968c", + "id": "d0499611", "metadata": { - "cq.autogen": "HoppingPlaquette.call_graph.md" + "cq.autogen": "Interaction.call_graph.md" }, "source": [ "### Call Graph" @@ -406,16 +410,16 @@ { "cell_type": "code", "execution_count": null, - "id": "db248fc5", + "id": "7fcbb401", "metadata": { - "cq.autogen": "HoppingPlaquette.call_graph.py" + "cq.autogen": "Interaction.call_graph.py" }, "outputs": [], "source": [ "from qualtran.resource_counting.generalizers import ignore_split_join\n", - "plaquette_g, plaquette_sigma = plaquette.call_graph(max_depth=1, generalizer=ignore_split_join)\n", - "show_call_graph(plaquette_g)\n", - "show_counts_sigma(plaquette_sigma)" + "interaction_g, interaction_sigma = interaction.call_graph(max_depth=1, generalizer=ignore_split_join)\n", + "show_call_graph(interaction_g)\n", + "show_counts_sigma(interaction_sigma)" ] } ], @@ -426,16 +430,7 @@ "name": "python3" }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" + "name": "python" } }, "nbformat": 4, diff --git a/qualtran/bloqs/chemistry/trotter/hubbard/interaction.py b/qualtran/bloqs/chemistry/trotter/hubbard/interaction.py index 3ad62e67c..ff7768a79 100644 --- a/qualtran/bloqs/chemistry/trotter/hubbard/interaction.py +++ b/qualtran/bloqs/chemistry/trotter/hubbard/interaction.py @@ -37,7 +37,12 @@ class Interaction(Bloq): which can be implemented using equal angle single-qubit Z rotations. Args: - length: Lattice length L. + length: Lattice length $L$. + angle: The prefactor scaling the Hopping hamiltonian in the unitary (`t` above). + This should contain any relevant prefactors including the time step + and any splitting coefficients. + hubb_u: The hubbard $U$ parameter. + eps: The precision of the single qubit rotations. Registers: system: The system register of size 2 `length`. @@ -49,6 +54,7 @@ class Interaction(Bloq): length: Union[int, sympy.Expr] angle: Union[float, sympy.Expr] + hubb_u: Union[float, sympy.Expr] eps: Union[float, sympy.Expr] = 1e-9 @cached_property @@ -57,14 +63,15 @@ def signature(self) -> Signature: def build_call_graph(self, ssa: 'SympySymbolAllocator') -> Set['BloqCountT']: # Page 13 paragraph 1. - return {(Rz(angle=self.angle, eps=self.eps), self.length**2)} + return {(Rz(angle=self.angle * self.hubb_u, eps=self.eps), self.length**2)} @bloq_example def _interaction() -> Interaction: length = 8 angle = 0.5 - interaction = Interaction(length, angle) + hubb_u = 4.0 + interaction = Interaction(length, angle, hubb_u) return interaction diff --git a/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step.py b/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step.py index 43e3f6da9..bf5a5677e 100644 --- a/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step.py +++ b/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step.py @@ -46,9 +46,9 @@ def build_plaq_unitary_second_order_suzuki( coeffs = (0.5, 0.5, 1.0, 0.5, 0.5) # Build the basic bloqs which make up the 2nd order PlAQ unitary. # The pink and gold "tiles". - pink = HoppingTile(length=length, angle=0, eps=eps, pink=True) - gold = HoppingTile(length=length, angle=0, eps=eps, pink=False) - interaction = Interaction(length=length, angle=0, eps=eps) + pink = HoppingTile(length=length, angle=0, eps=eps, pink=True, tau=hubb_t) + gold = HoppingTile(length=length, angle=0, eps=eps, pink=False, tau=hubb_t) + interaction = Interaction(length=length, angle=0, eps=eps, hubb_u=hubb_u) unitary = TrotterizedUnitary( (interaction, pink, gold), indices=indices, coeffs=coeffs, timestep=timestep ) From 0aae692d6a1bd3e3236cac519ddf3a381cc1deeb Mon Sep 17 00:00:00 2001 From: Fionn Malone Date: Sat, 11 May 2024 13:22:16 +0000 Subject: [PATCH 02/16] Add cost notebook. --- .../hubbard/qpe_cost_optimization.ipynb | 574 ++++++++++++++++++ 1 file changed, 574 insertions(+) create mode 100644 qualtran/bloqs/chemistry/trotter/hubbard/qpe_cost_optimization.ipynb diff --git a/qualtran/bloqs/chemistry/trotter/hubbard/qpe_cost_optimization.ipynb b/qualtran/bloqs/chemistry/trotter/hubbard/qpe_cost_optimization.ipynb new file mode 100644 index 000000000..5641925e4 --- /dev/null +++ b/qualtran/bloqs/chemistry/trotter/hubbard/qpe_cost_optimization.ipynb @@ -0,0 +1,574 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Optimizing T Counts Given an Error Budget" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Quantum algorithms typically only allow us to estimate properties to within some additive error $\\epsilon$. For quantum phase estimation via Trotterization there are at least three sources of error [[1]](https://arxiv.org/abs/1902.10673): Trotter errors ($\\Delta_{TS}$), circuit synthesis errors ($\\Delta_{HT}$), and phase estimation errors ($\\Delta_{PE}$). Here we will focus on the Hubbard model but many features are similar for different Hamiltonian types." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Trotter Errors\n", + "\n", + "Given a $p$-th order product formula $S_p(t)$ for the unitary implementing $e^{-i t H}$ we have that \n", + "$$\n", + "\\Delta_U \\equiv \\lVert S_p(t) - e^{-i t H}\\rVert_{W_\\eta} = \\xi(\\eta, u, \\tau) t^{p+1}\n", + "$$\n", + "for some constant $\\xi$ which depends on the parameters of the system. The constant $\\xi$ can be either computed (through direct evaluation and extrapolating from small system sizes) or bounded using complicated commutator expressions.\n", + "\n", + "Ref. [[1]](https://arxiv.org/abs/1902.10673) showed that\n", + "\n", + "$$\n", + "\\Delta_{TS} t \\equiv |E - E_{TS}|t \\le \\arctan\\left(\\Delta_U \\frac{\\sqrt{4-\\Delta_U^2}}{2-\\Delta_U^2}\\right) = \\Delta_U + \\frac{\\Delta_U^3}{24} + \\mathcal{O}(\\Delta_U^5),\n", + "$$\n", + "so that as long as $\\Delta_U \\gg \\frac{\\Delta_U^3}{24}$ we can estimate \n", + "\n", + "$$\n", + "\\Delta_{TS} = \\Delta_U / t \\approx \\xi(\\eta, u, \\tau) t^{p} \n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Circuit Synthesis Errors\n", + "\n", + "Circuit synthesis errors account for the approximate implementation of single qubit $R_z(\\theta)$ rotations when compiled to Clifford+$T$ gates. A single qubit rotation gate can be synthesized to $\\epsilon_R$ error using\n", + "$$\n", + "T_\\mathrm{synth} \\approx 1.15 \\log_2(1/\\epsilon_R) + 9.2\n", + "$$\n", + "$T$ gates. Assuming these errors add at most linearly to the estimated phase then the cost is\n", + "\n", + "$$\n", + "N_{HT} = 1.15 \\log_2 \\left(\\frac{N_R}{\\Delta_{HT} t}\\right) + 9.2\n", + "$$\n", + "$T$ gates per single qubit rotation, where $N_R$ is the number of rotations per Trotter step." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. QPE Errors\n", + "\n", + "Phase estimation errors $\\Delta_{PE}$ arise due to not computing enough bits of the phase. Adaptive phase estimation allows one to reach a RMS error of $\\Delta_{PE} t$ using\n", + "\n", + "$$\n", + "N_{PE} \\approx \\frac{0.76 \\pi}{\\Delta_{PE} t}\n", + "$$\n", + "repetitions of the simulation circuit." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimizing the T counts\n", + "\n", + "We assume $\\epsilon \\ge \\Delta_{PE} + \\Delta_{TS} + \\Delta_{HT}$. There is some freedom for the relative weighting of these terms and we can minimize the $T$ count which this constraint. The total $T$ count comes from the Trotter step costs and the number of QPE repetitions $N_{PE}$\n", + "\n", + "$$\n", + "N_{\\mathrm{tot}} = N_{PE} (N_{R} N_{HT} + N_T)\n", + "$$\n", + "where $N_T$ is the number of \"direct\" $T$ gates (i.e. those that appear in the circuit before synthesis).\n", + "\n", + "If we write $t = \\left(\\frac{\\Delta_{TS}}{\\xi}\\right)^{1/p}$, then\n", + "$$\n", + "N_\\mathrm{tot} \\approx \\frac{0.76 \\pi \\xi^{1/p}}{\\Delta_{PE}\\Delta_{TS}^{1/p}}\\left(N_R \\left[\\log\\left(\\frac{N_R \\xi^{1/p}}{\\Delta_{HT}\\Delta_{TS}^{1/p}}\\right) + 9.2\\right] + N_T\\right)\n", + "$$\n", + "\n", + "Ref. [[1]](https://arxiv.org/abs/1902.10673) minimized this expression numerically. Qualtran should be able to produce this expression and perform the optimization. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cost analysis \n", + "\n", + "Let us first reproduce the results from [[2]](https://arxiv.org/abs/2012.09238) for the PLAQ Hubbard splitting through direct minimization. We will then see how Qualtran can help with this analysis." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Dict, Union, Tuple\n", + "\n", + "import numpy as np\n", + "import sympy\n", + "\n", + "from qualtran.resource_counting.t_counts_from_sigma import _get_all_rotation_types\n", + "from qualtran.resource_counting.generalizers import PHI\n", + "from qualtran.cirq_interop.t_complexity_protocol import TComplexity\n", + "from qualtran import Bloq\n", + "from qualtran.bloqs.basic_gates import TGate, Rz\n", + "from qualtran.bloqs.util_bloqs import ArbitraryClifford\n", + "\n", + "\n", + "def catch_rotations(bloq) -> Bloq:\n", + " \"\"\"Generalizer to catch rotations.\"\"\"\n", + " if isinstance(bloq, Rz):\n", + " if isinstance(bloq.angle, float) and abs(bloq.angle) < 1e-12:\n", + " return ArbitraryClifford(1)\n", + " else:\n", + " return Rz(angle=PHI, eps=bloq.eps)\n", + " return bloq\n", + "\n", + "\n", + "def t_and_rot_counts_from_sigma(sigma: Dict['Bloq', Union[int, 'sympy.Expr']]) -> Tuple[int, int]:\n", + " rotation_types = _get_all_rotation_types()\n", + " ret = sigma.get(TGate(), 0)\n", + " n_rot = 0\n", + " for bloq, counts in sigma.items():\n", + " if isinstance(bloq, rotation_types):\n", + " n_rot += counts\n", + " return ret, n_rot\n", + "\n", + "\n", + "def timestep_from_params(delta_ts: float, xi: float, prod_ord: int) -> float:\n", + " \"\"\"Get the timestep from the product formula spectral norm error.\n", + "\n", + " Args\n", + " delta_ts: The allowed Suzuki-Trotter error.\n", + " xi: The constant factor for the Trotter-Suzuki spectral norm error.\n", + " prod_ord: The product formula order.\n", + " Returns:\n", + " timestep: The computed timestep.\n", + " \"\"\"\n", + " return (delta_ts / xi) ** (1.0 / prod_ord)\n", + "\n", + "\n", + "def get_single_rot_eps(n_rot: int, delta_ht: float, timestep: float) -> int:\n", + " \"\"\"Get the precision required for single qubit rotations given n_rot of them.\n", + "\n", + " Args:\n", + " delta_ht: The allowed circuit synthesis error.\n", + " n_rot: The number of rotations in the circuit.\n", + " timestep: The timestep for trotterization.\n", + "\n", + " Returns:\n", + " eps: the precision for single qubit rotations\n", + " \"\"\"\n", + " return (delta_ht * timestep) / n_rot\n", + "\n", + "\n", + "def qpe_steps(delta_pe: float, timestep: float) -> int:\n", + " \"\"\"Get the number of QPE steps from the RMS error and timestep.\n", + "\n", + " Args:\n", + " delta_pe: The desired adaptive phase estimation RMS error.\n", + " timestep: The timestep value.\n", + "\n", + " Returns:\n", + " n_qpe: The number of QPE steps.\n", + " \"\"\"\n", + " return (0.76 * np.pi) / (delta_pe * timestep)\n", + "\n", + "\n", + "def rotation_cost(n_rot: int, delta_ht: float, timestep: float) -> int:\n", + " \"\"\"Get the rotation costs.\n", + "\n", + " Args:\n", + " n_rot: The number of rotations in the circuit.\n", + " delta_ht: The allowed circuit synthesis error.\n", + " timestep: The timestep value.\n", + " \"\"\"\n", + " rot_cost = TComplexity(rotations=n_rot).t_incl_rotations(\n", + " get_single_rot_eps(n_rot, delta_ht, timestep)\n", + " )\n", + " return rot_cost\n", + "\n", + "\n", + "def qpe_t_count(\n", + " delta_pe: float,\n", + " delta_ts: float,\n", + " delta_ht: float,\n", + " n_rot: int,\n", + " n_t: int,\n", + " xi: float,\n", + " prod_ord: int,\n", + ") -> int:\n", + " \"\"\"Compute the total number of T gates used for Trotterized QPE.\n", + "\n", + " Args:\n", + " delta_pe: The allowed phase estimation error.\n", + " delta_ts: The allowed Suzuki-Trotter error.\n", + " delta_ht: The allowed circuit synthesis error.\n", + " n_rot: The number of rotations in the circuit.\n", + " n_t: The number of direct T gates (before synthesis).\n", + " xi: The constant factor for the Trotter-Suzuki spectral norm error.\n", + " prod_ord: The product formula order.\n", + "\n", + " Returns:\n", + " tot_t_cost: The total number of T gates.\n", + " \"\"\"\n", + " timestep = timestep_from_params(delta_ts, xi, prod_ord)\n", + " rot_cost = rotation_cost(n_rot, delta_ht, timestep)\n", + " n_qpe = qpe_steps(delta_pe, timestep)\n", + " tot_t_cost = n_qpe * (rot_cost + n_t)\n", + " return tot_t_cost" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get some system parameters from Ref. [2]\n", + "# 8x8 lattice\n", + "length = 8\n", + "hubb_u = 4\n", + "\n", + "# From Ref[2] table 1.\n", + "xi_bound = 5.3e2 \n", + "# Fig 2. from Ref[2] uses this extensive size error (we're targeting some energy per lattice site)\n", + "epsilon = 0.0051 * length**2\n", + "# Arbitrary splitting of error for comparison purposes\n", + "delta_ts = 0.5 * epsilon \n", + "delta_pe = 0.45 * epsilon \n", + "delta_ht = 0.05 * epsilon \n", + "# using 2nd order Suzuki (Strang) splitting\n", + "prod_ord = 2\n", + "timestep = timestep_from_params(delta_ts, xi_bound, prod_ord)\n", + "print(f\"Computed timestep: {timestep:4.3e}\")\n", + "print(f\"Sum Error budget terms: {delta_ts + delta_ht + delta_pe}\")\n", + "print(f\"Expected Error budget: {epsilon}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's first check the fixed costs from the Trotter step match our expectations " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from qualtran.bloqs.chemistry.trotter.hubbard.trotter_step import build_plaq_unitary_second_order_suzuki\n", + "\n", + "trotter_step = build_plaq_unitary_second_order_suzuki(length, hubb_u, timestep, eps=1e-10)\n", + "n_t, n_rot = t_and_rot_counts_from_sigma(trotter_step.call_graph(generalizer=catch_rotations)[1])\n", + "print(f\"N_T = {n_t} vs {(3*length**2 // 2)*8}\")\n", + "print(f\"N_rot = {n_rot} vs {(3 * length**2 + 2*length**2)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's look at the total cost and the error we incurred with our default parameter choices." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import attrs\n", + "from qualtran.drawing import show_call_graph\n", + "# get appropriate epsilon given our input parameters now we know the number of rotations\n", + "eps_single_rot = get_single_rot_eps(n_rot, delta_ht, timestep)\n", + "print(f\"Adjusted eps_single_rot: {eps_single_rot}\")\n", + "tot_t_count = qpe_t_count(delta_pe, delta_ts, delta_ht, n_rot, n_t, xi_bound, prod_ord)\n", + "# This doesn't really matter right now because the cost is determined directly\n", + "# from the formula assuming we used an appropriate delta_ht.\n", + "# But let's show the call graph anyway to check the parameters all all what we expect.\n", + "updated_eps_bloqs = tuple(attrs.evolve(b, eps=eps_single_rot) for b in trotter_step.bloqs)\n", + "trotter_step = attrs.evolve(trotter_step, bloqs=updated_eps_bloqs)\n", + "trotter_step_g, _ = trotter_step.call_graph(generalizer=catch_rotations)\n", + "show_call_graph(trotter_step_g)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tot_t_count = qpe_t_count(delta_pe, delta_ts, delta_ht, n_rot, n_t, xi_bound, prod_ord)\n", + "print(f\"N_{{T_tot}} = {tot_t_count:4.3e} T gates.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Visualization\n", + "\n", + "It's helpful at this point to do some visualization of the error dependencies." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The $T$ count varies slowly with $\\Delta_{HT}$ so we let's pick a value an look at the dependence of the $T$ counts on just $\\Delta_{TS}$ and $\\Delta_{PE}$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "from matplotlib import cm\n", + "\n", + "X = np.logspace(-2, -0.5, 20)\n", + "Y = np.logspace(-2, -0.5, 20)\n", + "X, Y = np.meshgrid(X, Y)\n", + "results = []\n", + "delta_ht = 1e-5\n", + "for x, y in zip(X, Y):\n", + " for xval, yval in zip(x, y):\n", + " results.append(np.log10(qpe_t_count(xval, yval, delta_ht, n_rot, n_t, xi_bound, prod_ord)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(subplot_kw={\"projection\": \"3d\"})\n", + "surf = ax.plot_surface(X, Y, np.array(results).reshape(20, 20), cmap=cm.coolwarm,\n", + " linewidth=0, antialiased=False)\n", + "ax.set_xlabel(r'$\\Delta_{TS}$')\n", + "ax.set_ylabel(r'$\\Delta_{PE}$')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "surf = ax.contour(\n", + " X, Y, np.array(results).reshape(20, 20), 25, cmap=cm.coolwarm, \n", + ")\n", + "cs = ax.contour(X, Y, X + Y, 10, colors=\"k\", linestyles=\"solid\")\n", + "ax.clabel(cs, inline=True, fontsize=10)\n", + "clb = fig.colorbar(surf, shrink=0.5, aspect=5)\n", + "clb.ax.set_title(r'$\\log(T_\\mathrm{tot}$)')\n", + "cs = ax.contour(X, Y, X+Y, levels=[epsilon], linestyles='dotted')\n", + "ax.clabel(cs, inline=True, fontsize=10)\n", + "ax.set_xlabel(r'$\\Delta_{TS}$')\n", + "ax.set_ylabel(r'$\\Delta_{PE}$')\n", + "ax.set_title(f'Desired $\\epsilon$ = 0.0051 $L^2$ = {epsilon}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot the cost function a long the line $\\Delta_{HT} + \\Delta_{PE} + \\Delta_{TS} = \\epsilon$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "delta_ht_vals = np.array([1e-4, 1e-3, 1e-2, 1e-1]) * epsilon\n", + "fig, ax = plt.subplots()\n", + "for dht in delta_ht_vals:\n", + " delta_ts_vals = np.linspace(0.05*epsilon , epsilon - dht, 100)[:-1]\n", + " t_vals = []\n", + " for dts in delta_ts_vals:\n", + " t_vals.append(qpe_t_count(epsilon - dts - dht, dts, dht, n_rot, n_t, xi_bound, prod_ord))\n", + " ax.plot(delta_ts_vals, t_vals, label=fr'$\\Delta_{{HT}} / \\epsilon$ = {dht/epsilon:4.3e}')\n", + "plt.yscale('log')\n", + "plt.ylabel('$T$ count')\n", + "plt.legend()\n", + "plt.title(r'$\\Delta_{PE} + \\Delta_{TS} + \\Delta_{HT} = \\epsilon$')\n", + "plt.xlabel(r'$\\Delta_{TS}/\\epsilon$')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can perform a very naive two step constrained optimization along this line to find a minimum which looks to be around $9\\times10^6$ T gates." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.optimize import minimize, bisect, newton\n", + "def objective(delta_ts, delta_ht, n_rot, n_t, xi_bound, prod_ord):\n", + " t_counts = qpe_t_count(epsilon - delta_ts - delta_ht, delta_ts, delta_ht, n_rot, n_t, xi_bound, prod_ord)\n", + " return t_counts\n", + "\n", + "def inner_objective(delta_ht, n_rot, n_t, xi_bound, prod_ord):\n", + " min_delta_ts = minimize(objective, x0=0.7*(epsilon-delta_ht), bounds=[(1e-3*epsilon, 0.9*(epsilon-delta_ht))], args=(delta_ht, n_rot, n_t, xi_bound, prod_ord)).x[0]\n", + " return objective(min_delta_ts, delta_ht, n_rot, n_t, xi_bound, prod_ord), min_delta_ts\n", + "\n", + "def minimize_linesearch(n_rot, n_t, xi_bound, prod_ord):\n", + " res_min = np.inf\n", + " dht_min = epsilon\n", + " for dht in np.linspace(5e-3*epsilon, 1e-1*epsilon, 100):\n", + " res, _ = inner_objective(dht, n_rot, n_t, xi_bound, prod_ord)\n", + " if res < res_min:\n", + " res_min = res\n", + " dht_min = dht\n", + " t_opt, delta_ts_opt = inner_objective(dht_min, n_rot, n_t, xi_bound, prod_ord)\n", + " return dht_min, delta_ts_opt, epsilon - delta_ts_opt - dht_min, t_opt\n", + "\n", + "delta_ht_opt, delta_ts_opt, delta_pe_opt, t_opt = minimize_linesearch(n_rot, n_t, xi_bound, prod_ord)\n", + "print(rf\"\\Delta_{{HT}} = {delta_ht_opt}\")\n", + "print(rf\"\\Delta_{{TS}} = {delta_ts_opt}\")\n", + "print(rf\"\\Delta_{{PE}} = {delta_pe_opt}\")\n", + "print(rf\"T_{{opt}} = {t_opt:4.3e}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Symbolic T counts\n", + "\n", + "We can avoid doing any manipulation ourselves using sympy to represent the error expression." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s_eps_r, s_length, s_hubb_u, s_timestep, s_tau = sympy.symbols(r'\\epsilon_{R}, L, u, t, \\tau')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First let's check the Bloq counts look correct for the Trotter step, there are two sources rotations from the interaction and hopping bloq and some direct T gates from the `TwoBitFFFT` gate." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from qualtran.resource_counting.t_counts_from_sigma import t_counts_from_sigma\n", + "\n", + "s_trotter_step = build_plaq_unitary_second_order_suzuki(\n", + " s_length, s_hubb_u, s_timestep, eps=s_eps_r, hubb_t=s_tau\n", + ")\n", + "t_counts = t_counts_from_sigma(s_trotter_step.call_graph(generalizer=catch_rotations)[1])\n", + "t_counts" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# check the symbolic counts match the expected counts\n", + "t_counts_orig = t_counts_from_sigma(trotter_step.call_graph(generalizer=catch_rotations)[1])\n", + "# for some reason substituting both at once leads to a precision error\n", + "t_counts = t_counts.evalf(subs={s_eps_r: eps_single_rot})\n", + "t_counts_symb = t_counts.evalf(subs={s_length: length})\n", + "assert t_counts_orig == t_counts_symb" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's reproduce the expression for the total cost" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s_delta_ht, s_delta_ts, s_delta_pe, s_p, s_xi = sympy.symbols(\n", + " '\\Delta_{HT}, \\Delta_{TS}, \\Delta_{PE}, p, xi'\n", + ")\n", + "s_n_rot, s_n_t, s_n_pe = sympy.symbols('N_R, N_T, N_PE')\n", + "s_timestep = (s_delta_ts / s_xi) ** (1 / s_p)\n", + "s_eps_r = (s_delta_ht * s_timestep) / s_n_rot\n", + "s_n_pe = 0.76 * sympy.pi / (s_delta_pe * s_timestep)\n", + "s_trotter_step = build_plaq_unitary_second_order_suzuki(\n", + " s_length, s_hubb_u, s_timestep, eps=s_eps_r, hubb_t=s_tau\n", + ")\n", + "# just use this cost in lieu of a QPE bloq\n", + "t_counts = s_n_pe * t_counts_from_sigma(s_trotter_step.call_graph(generalizer=catch_rotations)[1])\n", + "t_counts" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "symb_t_count = t_counts.evalf(\n", + " subs={\n", + " s_length: length,\n", + " s_delta_ht: delta_ht,\n", + " s_delta_pe: delta_pe,\n", + " s_delta_ts: delta_ts,\n", + " s_xi: xi_bound,\n", + " s_n_rot: n_rot,\n", + " }\n", + ")\n", + "symb_t_count = symb_t_count.evalf(subs={s_p: prod_ord})\n", + "tot_t_count = qpe_t_count(delta_pe, delta_ts, delta_ht, n_rot, n_t, xi_bound, prod_ord)\n", + "assert int(symb_t_count) == int(tot_t_count)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "qualtran", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From e7ee9d32050fe3e39f4ffcaecdb63bfb11163247 Mon Sep 17 00:00:00 2001 From: Fionn Malone Date: Sat, 11 May 2024 13:31:26 +0000 Subject: [PATCH 03/16] Fix eps type. --- qualtran/bloqs/qft/two_bit_ffft.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/qualtran/bloqs/qft/two_bit_ffft.py b/qualtran/bloqs/qft/two_bit_ffft.py index eadb983db..26b7bf889 100644 --- a/qualtran/bloqs/qft/two_bit_ffft.py +++ b/qualtran/bloqs/qft/two_bit_ffft.py @@ -12,9 +12,10 @@ # See the License for the specific language governing permissions and # limitations under the License. from functools import cached_property -from typing import Any, Dict, Set, TYPE_CHECKING +from typing import Any, Dict, Set, TYPE_CHECKING, Union import numpy as np +import sympy from attrs import frozen from numpy.typing import NDArray @@ -73,7 +74,7 @@ class TwoBitFFFT(Bloq): k: int n: int - eps: float = 1e-10 + eps: Union[float, sympy.Expr] = 1e-10 is_adjoint: bool = False def __attrs_post_init__(self): From e2b95bc25a5b3268bfe9e3e9a74b49c61569f493 Mon Sep 17 00:00:00 2001 From: Fionn Malone Date: Sat, 11 May 2024 14:03:23 +0000 Subject: [PATCH 04/16] Add hamming weight phasing bloqs. --- .../chemistry/trotter/hubbard/hopping.py | 78 +++++++++++++++++++ .../chemistry/trotter/hubbard/hopping_test.py | 24 +++++- .../chemistry/trotter/hubbard/interaction.py | 59 ++++++++++++++ .../trotter/hubbard/interaction_test.py | 24 +++++- 4 files changed, 180 insertions(+), 5 deletions(-) diff --git a/qualtran/bloqs/chemistry/trotter/hubbard/hopping.py b/qualtran/bloqs/chemistry/trotter/hubbard/hopping.py index dd2db9b28..2b3661df5 100644 --- a/qualtran/bloqs/chemistry/trotter/hubbard/hopping.py +++ b/qualtran/bloqs/chemistry/trotter/hubbard/hopping.py @@ -21,6 +21,7 @@ from qualtran import Bloq, bloq_example, BloqDocSpec, QAny, QBit, Register, Signature from qualtran.bloqs.basic_gates import Rz from qualtran.bloqs.qft.two_bit_ffft import TwoBitFFFT +from qualtran.bloqs.rotations.hamming_weight_phasing import HammingWeightPhasing if TYPE_CHECKING: from qualtran.resource_counting import BloqCountT, SympySymbolAllocator @@ -134,6 +135,68 @@ def build_call_graph(self, ssa: 'SympySymbolAllocator') -> Set['BloqCountT']: } +@frozen +class HoppingTileHWP(HoppingTile): + r"""Bloq implementing a "tile" of the one-body hopping unitary using Hamming weight phasing. + + Implements the unitary + $$ + e^{i H_h^{x}} = \prod_{k\sigma} e^{i t H_h^{x(k,\sigma)}} + $$ + for a particular choise of of plaquette hamiltonian $H_h^x$, where $x$ = pink or gold. + + Each plaquette Hamiltonian can be split into $L^2/4$ commuting terms. Each + term can be implemented using the 4-qubit HoppingPlaquette above. The + HoppingPlaquette bloq contains 2 arbitrary rotations which are flanked by Clifford operations. + All of the rotations within a HoppingTile have the same angle so we can use + HammingWeightPhaseing to reduce the number of T gates that need to be + synthesized. Accounting for spin there are then $2 \times 2 \times L^2/4$ + arbitrary rotations in each Tile, but only $L^2/2$ of them can be applied + at the same time due to the $e^{iXX} e^{iYY}$ circuit not permitting parallel $R_z$ gates. + + Unlike in the HoppingTile implementation where we can neatly factor + everything into sub-bloqs, here we would need to apply any clifford and F + gates first in parallel then bulk apply the rotations in parallel using + HammingWeightPhasing and then apply another layer of clifford and F gates. + + Args: + length: Lattice side length L. + angle: The prefactor scaling the Hopping hamiltonian in the unitary (`t` above). + This should contain any relevant prefactors including the time step + and any splitting coefficients. + tau: The Hopping hamiltonian parameter. Typically the hubbard model is + defined relative to $\tau$ so it's defaulted to 1. + eps: The precision of the single qubit rotations. + pink: The colour of the plaquette. + + Registers: + system: The system register of size 2 `length`. + + References: + [Early fault-tolerant simulations of the Hubbard model]( + https://arxiv.org/abs/2012.09238) see Eq. 21 and App E. + """ + + def short_name(self) -> str: + l = 'p' if self.pink else 'g' + return f'H_h^{l}(HWP)' + + def build_call_graph(self, ssa: 'SympySymbolAllocator') -> Set['BloqCountT']: + # Page 5, text after Eq. 22. There are L^2 / 4 plaquettes of a given colour and x2 for spin. + # Each plaquette contributes 4 TwoBitFFFT gates and two arbitrary rotations. + # We use Hamming weight phasing to apply all 2 * L^2/4 (two for spin + # here) for both of these rotations. + return { + (TwoBitFFFT(0, 1, self.eps), 4 * self.length**2 // 2), + ( + HammingWeightPhasing( + 2 * self.length**2 // 4, self.tau * self.angle, eps=self.eps + ), + 2, + ), + } + + @bloq_example def _hopping_tile() -> HoppingTile: length = 8 @@ -162,3 +225,18 @@ def _plaquette() -> HoppingPlaquette: import_line='from qualtran.bloqs.chemistry.trotter.hubbard.hopping import HoppingPlaquette', examples=(_plaquette,), ) + + +@bloq_example +def _hopping_tile_hwp() -> HoppingTileHWP: + length = 8 + angle = 0.15 + hopping_tile_hwp = HoppingTileHWP(length, angle) + return hopping_tile_hwp + + +_HOPPING_TILE_HWP_DOC = BloqDocSpec( + bloq_cls=HoppingTileHWP, + import_line='from qualtran.bloqs.chemistry.trotter.hubbard.hopping import HoppingTileHWP', + examples=(_hopping_tile_hwp,), +) diff --git a/qualtran/bloqs/chemistry/trotter/hubbard/hopping_test.py b/qualtran/bloqs/chemistry/trotter/hubbard/hopping_test.py index c7f729458..6ca949b97 100644 --- a/qualtran/bloqs/chemistry/trotter/hubbard/hopping_test.py +++ b/qualtran/bloqs/chemistry/trotter/hubbard/hopping_test.py @@ -12,8 +12,12 @@ # See the License for the specific language governing permissions and # limitations under the License. from qualtran import Bloq -from qualtran.bloqs.basic_gates import Rz, TGate -from qualtran.bloqs.chemistry.trotter.hubbard.hopping import _hopping_tile, _plaquette +from qualtran.bloqs.basic_gates import Rz, TGate, ZPowGate +from qualtran.bloqs.chemistry.trotter.hubbard.hopping import ( + _hopping_tile, + _hopping_tile_hwp, + _plaquette, +) from qualtran.bloqs.util_bloqs import ArbitraryClifford from qualtran.resource_counting.generalizers import PHI @@ -27,8 +31,10 @@ def test_hopping_plaquette(bloq_autotester): def catch_rotations(bloq) -> Bloq: - if isinstance(bloq, Rz): - if abs(float(bloq.angle)) < 1e-12: + if isinstance(bloq, (Rz, ZPowGate)): + if isinstance(bloq, ZPowGate): + return Rz(angle=PHI) + elif abs(bloq.angle) < 1e-12: return ArbitraryClifford(1) else: return Rz(angle=PHI) @@ -40,3 +46,13 @@ def test_hopping_tile_t_counts(): _, counts = bloq.call_graph(generalizer=catch_rotations) assert counts[TGate()] == 8 * bloq.length**2 // 2 assert counts[Rz(PHI)] == 2 * bloq.length**2 // 2 + + +def test_hopping_tile_hwp_t_counts(): + bloq = _hopping_tile_hwp() + _, counts = bloq.call_graph(generalizer=catch_rotations) + n_rot_par = bloq.length**2 // 2 + assert counts[Rz(PHI)] == 2 * n_rot_par.bit_length() + assert counts[TGate()] == 8 * bloq.length**2 // 2 + 2 * 4 * ( + n_rot_par - n_rot_par.bit_count() + ) diff --git a/qualtran/bloqs/chemistry/trotter/hubbard/interaction.py b/qualtran/bloqs/chemistry/trotter/hubbard/interaction.py index ff7768a79..3c11a960f 100644 --- a/qualtran/bloqs/chemistry/trotter/hubbard/interaction.py +++ b/qualtran/bloqs/chemistry/trotter/hubbard/interaction.py @@ -21,6 +21,7 @@ from qualtran import Bloq, bloq_example, BloqDocSpec, QAny, Register, Signature from qualtran.bloqs.basic_gates import Rz +from qualtran.bloqs.rotations.hamming_weight_phasing import HammingWeightPhasing if TYPE_CHECKING: from qualtran.resource_counting import BloqCountT, SympySymbolAllocator @@ -65,6 +66,49 @@ def build_call_graph(self, ssa: 'SympySymbolAllocator') -> Set['BloqCountT']: # Page 13 paragraph 1. return {(Rz(angle=self.angle * self.hubb_u, eps=self.eps), self.length**2)} +@frozen +class InteractionHWP(Bloq): + r"""Bloq implementing the hubbard U part of the hamiltonian using Hamming weight phasing. + + Specifically: + $$ + U_I = e^{i t H_I} + $$ + which can be implemented using equal angle single-qubit Z rotations. + + Each interaction term can be implemented using a e^{iZZ} gate, which + decomposes into a single Rz gate flanked by cliffords. There are L^2 + equal angle rotations in total all of which may be applied in parallel using HWP. + + Args: + length: Lattice length L. + angle: The rotation angle for unitary. + hubb_u: The hubbard U parameter. + eps: The precision for single qubit rotations. + + Registers: + system: The system register of size 2 `length`. + + References: + [Early fault-tolerant simulations of the Hubbard model]( + https://arxiv.org/abs/2012.09238) Eq. page 13 paragraph 1, and page + 14 paragraph 3 right column. The apply 2 batches of $L^2/2$ rotations. + """ + + length: Union[int, sympy.Expr] + angle: Union[float, sympy.Expr] + hubb_u: Union[float, sympy.Expr] + eps: Union[float, sympy.Expr] = 1e-9 + + @cached_property + def signature(self) -> Signature: + return Signature([Register('system', QAny(self.length), shape=(2,))]) + + def build_call_graph(self, ssa: 'SympySymbolAllocator') -> Set['BloqCountT']: + return { + (HammingWeightPhasing(self.length**2 // 2, self.angle * self.hubb_u, eps=self.eps), 2) + } + @bloq_example def _interaction() -> Interaction: @@ -80,3 +124,18 @@ def _interaction() -> Interaction: import_line='from qualtran.bloqs.chemistry.trotter.hubbard.interaction import Interaction', examples=(_interaction,), ) + +@bloq_example +def _interaction_hwp() -> InteractionHWP: + length = 8 + angle = 0.5 + hubb_u = 4.0 + interaction = InteractionHWP(length, angle, hubb_u) + return interaction + + +_INTERACTION_HWP_DOC = BloqDocSpec( + bloq_cls=InteractionHWP, + import_line='from qualtran.bloqs.chemistry.trotter.hubbard.interaction import InteractionHWP', + examples=(_interaction_hwp,), +) \ No newline at end of file diff --git a/qualtran/bloqs/chemistry/trotter/hubbard/interaction_test.py b/qualtran/bloqs/chemistry/trotter/hubbard/interaction_test.py index 199b9aea2..43b0479b5 100644 --- a/qualtran/bloqs/chemistry/trotter/hubbard/interaction_test.py +++ b/qualtran/bloqs/chemistry/trotter/hubbard/interaction_test.py @@ -11,8 +11,30 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from qualtran.bloqs.chemistry.trotter.hubbard.interaction import _interaction +from qualtran.bloqs.basic_gates import Rz, TGate +from qualtran.bloqs.chemistry.trotter.hubbard.hopping_test import catch_rotations +from qualtran.bloqs.chemistry.trotter.hubbard.interaction import _interaction, _interaction_hwp +from qualtran.resource_counting.generalizers import PHI def test_hopping_tile(bloq_autotester): bloq_autotester(_interaction) + + +def test_interaction_hwp(bloq_autotester): + bloq_autotester(_interaction_hwp) + + +def test_interaction_hwp_bloq_counts(): + bloq = _interaction_hwp() + _, counts = bloq.call_graph(generalizer=catch_rotations) + n_rot_par = bloq.length**2 // 2 + assert counts[Rz(PHI)] == 2 * n_rot_par.bit_length() + assert counts[TGate()] == 2 * 4 * (n_rot_par - n_rot_par.bit_count()) + + +def test_interaction_bloq_counts(): + bloq = _interaction() + _, counts = bloq.call_graph(generalizer=catch_rotations) + n_rot = bloq.length**2 + assert counts[Rz(PHI)] == n_rot From aef141ac4d3f4d10e095f18c652e660f965bce09 Mon Sep 17 00:00:00 2001 From: Fionn Malone Date: Sat, 11 May 2024 14:32:04 +0000 Subject: [PATCH 05/16] Add costs to notebook. --- .../hubbard/qpe_cost_optimization.ipynb | 53 +++++++++++++++++++ 1 file changed, 53 insertions(+) diff --git a/qualtran/bloqs/chemistry/trotter/hubbard/qpe_cost_optimization.ipynb b/qualtran/bloqs/chemistry/trotter/hubbard/qpe_cost_optimization.ipynb index 5641925e4..5b1e1c706 100644 --- a/qualtran/bloqs/chemistry/trotter/hubbard/qpe_cost_optimization.ipynb +++ b/qualtran/bloqs/chemistry/trotter/hubbard/qpe_cost_optimization.ipynb @@ -446,6 +446,59 @@ "print(rf\"T_{{opt}} = {t_opt:4.3e}\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using Hamming Weight Phasing " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can compare this cost to that found using Hamming weight phasing for the equal angle rotations. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from qualtran.bloqs.chemistry.trotter.hubbard.trotter_step import build_plaq_hwp_unitary_second_order_suzuki\n", + "trotter_step = build_plaq_hwp_unitary_second_order_suzuki(length, hubb_u, timestep, eps=1e-10)\n", + "n_t, n_rot = t_and_rot_counts_from_sigma(trotter_step.call_graph(generalizer=catch_rotations)[1])\n", + "print(f\"N_T(HWP) = {n_t} vs {(3*length**2 // 2)*8}\")\n", + "print(f\"N_rot(HWP) = {n_rot} vs {(3 * length**2 + 2*length**2)}\")\n", + "delta_ht_opt, delta_ts_opt, delta_pe_opt, t_opt = minimize_linesearch(n_rot, n_t, xi_bound, prod_ord)\n", + "print(rf\"T_{{OPT}}(HWP) = {t_opt:4.3e}\")\n", + "# The reference counts Toffolis as 2 T gates, we count them as 4.\n", + "print(rf\"Reference value = {1.7e6 + 4 * 1.8e5:4.3e}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our value is slightly higher as we included all terms in the Trotter step. The reference only counts one layer of interaction gates. Let's correct for that." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "trotter_step = build_plaq_hwp_unitary_second_order_suzuki(length, hubb_u, timestep, eps=1e-10, strip_layer=True)\n", + "n_t, n_rot = t_and_rot_counts_from_sigma(trotter_step.call_graph(generalizer=catch_rotations)[1])\n", + "print(f\"N_T(HWP) = {n_t}\")\n", + "print(f\"N_rot(HWP) = {n_rot}\")\n", + "delta_ht_opt, delta_ts_opt, delta_pe_opt, t_opt = minimize_linesearch(n_rot, n_t, xi_bound, prod_ord)\n", + "print(rf\"T_{{OPT}}(HWP) = {t_opt:4.3e}\")\n", + "print(rf\"Reference value = {1.7e6 + 4 * 1.8e5:4.3e}\")" + ] + }, { "cell_type": "markdown", "metadata": {}, From 15e1d5e0711f9862d0a272d50c0b0ca0428b423b Mon Sep 17 00:00:00 2001 From: Fionn Malone Date: Sat, 11 May 2024 14:33:46 +0000 Subject: [PATCH 06/16] Add costs to notebook. --- .../hubbard/qpe_cost_optimization.ipynb | 396 ++++++++++++++++-- 1 file changed, 364 insertions(+), 32 deletions(-) diff --git a/qualtran/bloqs/chemistry/trotter/hubbard/qpe_cost_optimization.ipynb b/qualtran/bloqs/chemistry/trotter/hubbard/qpe_cost_optimization.ipynb index 5b1e1c706..f543c502f 100644 --- a/qualtran/bloqs/chemistry/trotter/hubbard/qpe_cost_optimization.ipynb +++ b/qualtran/bloqs/chemistry/trotter/hubbard/qpe_cost_optimization.ipynb @@ -102,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -225,9 +225,19 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computed timestep: 1.755e-02\n", + "Sum Error budget terms: 0.3264\n", + "Expected Error budget: 0.3264\n" + ] + } + ], "source": [ "# Get some system parameters from Ref. [2]\n", "# 8x8 lattice\n", @@ -259,9 +269,18 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N_T = 768 vs 768\n", + "N_rot = 320 vs 320\n" + ] + } + ], "source": [ "from qualtran.bloqs.chemistry.trotter.hubbard.trotter_step import build_plaq_unitary_second_order_suzuki\n", "\n", @@ -280,9 +299,228 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Adjusted eps_single_rot: 8.949367006180983e-07\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "counts\n", + "\n", + "\n", + "\n", + "b0\n", + "\n", + "TrotterizedUnitary\n", + "bloqs=(Interac ..., indices=(0, 1, 2 ..., coeffs=(0.5, 0. ..., timestep=0.017547 ...\n", + "\n", + "\n", + "\n", + "b1\n", + "\n", + "Interaction\n", + "length=8, angle=0.017547 ..., hubb_u=4, eps=8.949367 ...\n", + "\n", + "\n", + "\n", + "b0->b1\n", + "\n", + "\n", + "2\n", + "\n", + "\n", + "\n", + "b2\n", + "\n", + "HoppingTile\n", + "length=8, angle=0.017547 ..., tau=1.0, eps=8.949367 ..., pink=True\n", + "\n", + "\n", + "\n", + "b0->b2\n", + "\n", + "\n", + "2\n", + "\n", + "\n", + "\n", + "b3\n", + "\n", + "HoppingTile\n", + "length=8, angle=0.035095 ..., tau=1.0, eps=8.949367 ..., pink=False\n", + "\n", + "\n", + "\n", + "b0->b3\n", + "\n", + "\n", + "1\n", + "\n", + "\n", + "\n", + "b7\n", + "\n", + "Rz\n", + "angle=\\phi, eps=8.949367 ...\n", + "\n", + "\n", + "\n", + "b1->b7\n", + "\n", + "\n", + "64\n", + "\n", + "\n", + "\n", + "b4\n", + "\n", + "HoppingPlaquette\n", + "kappa=0.017547 ..., eps=8.949367 ...\n", + "\n", + "\n", + "\n", + "b2->b4\n", + "\n", + "\n", + "32\n", + "\n", + "\n", + "\n", + "b5\n", + "\n", + "HoppingPlaquette\n", + "kappa=0.035095 ..., eps=8.949367 ...\n", + "\n", + "\n", + "\n", + "b3->b5\n", + "\n", + "\n", + "32\n", + "\n", + "\n", + "\n", + "b6\n", + "\n", + "TwoBitFFFT\n", + "k=0, n=1, eps=8.949367 ..., is_adjoint=False\n", + "\n", + "\n", + "\n", + "b4->b6\n", + "\n", + "\n", + "4\n", + "\n", + "\n", + "\n", + "b4->b7\n", + "\n", + "\n", + "2\n", + "\n", + "\n", + "\n", + "b5->b6\n", + "\n", + "\n", + "4\n", + "\n", + "\n", + "\n", + "b5->b7\n", + "\n", + "\n", + "2\n", + "\n", + "\n", + "\n", + "b8\n", + "\n", + "CNOT\n", + "\n", + "\n", + "\n", + "b6->b8\n", + "\n", + "\n", + "3\n", + "\n", + "\n", + "\n", + "b9\n", + "\n", + "Hadamard\n", + "\n", + "\n", + "\n", + "b6->b9\n", + "\n", + "\n", + "6\n", + "\n", + "\n", + "\n", + "b10\n", + "\n", + "S\n", + "is_adjoint=False\n", + "\n", + "\n", + "\n", + "b6->b10\n", + "\n", + "\n", + "3\n", + "\n", + "\n", + "\n", + "b11\n", + "\n", + "ArbitraryClifford\n", + "n=1\n", + "\n", + "\n", + "\n", + "b6->b11\n", + "\n", + "\n", + "1\n", + "\n", + "\n", + "\n", + "b12\n", + "\n", + "T\n", + "is_adjoint=False\n", + "\n", + "\n", + "\n", + "b6->b12\n", + "\n", + "\n", + "2\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import attrs\n", "from qualtran.drawing import show_call_graph\n", @@ -301,9 +539,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N_{T_tot} = 1.049e+07 T gates.\n" + ] + } + ], "source": [ "tot_t_count = qpe_t_count(delta_pe, delta_ts, delta_ht, n_rot, n_t, xi_bound, prod_ord)\n", "print(f\"N_{{T_tot}} = {tot_t_count:4.3e} T gates.\")" @@ -327,7 +573,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -346,9 +592,30 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0.5, '$\\\\Delta_{PE}$')" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots(subplot_kw={\"projection\": \"3d\"})\n", "surf = ax.plot_surface(X, Y, np.array(results).reshape(20, 20), cmap=cm.coolwarm,\n", @@ -359,9 +626,30 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Desired $\\\\epsilon$ = 0.0051 $L^2$ = 0.3264')" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots()\n", "surf = ax.contour(\n", @@ -387,9 +675,30 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, '$\\\\Delta_{TS}/\\\\epsilon$')" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "delta_ht_vals = np.array([1e-4, 1e-3, 1e-2, 1e-1]) * epsilon\n", "fig, ax = plt.subplots()\n", @@ -415,9 +724,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\Delta_{HT} = 0.012907636363636364\n", + "\\Delta_{TS} = 0.11015760326314578\n", + "\\Delta_{PE} = 0.20333476037321788\n", + "T_{opt} = 9.226e+06\n" + ] + } + ], "source": [ "from scipy.optimize import minimize, bisect, newton\n", "def objective(delta_ts, delta_ht, n_rot, n_t, xi_bound, prod_ord):\n", @@ -462,9 +782,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "ename": "ImportError", + "evalue": "cannot import name 'build_plaq_hwp_unitary_second_order_suzuki' from 'qualtran.bloqs.chemistry.trotter.hubbard.trotter_step' (/usr/local/google/home/fmalone/projects/qualtran/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step.py)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[26], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mqualtran\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbloqs\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mchemistry\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtrotter\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhubbard\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtrotter_step\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m build_plaq_hwp_unitary_second_order_suzuki\n\u001b[1;32m 2\u001b[0m trotter_step \u001b[38;5;241m=\u001b[39m build_plaq_hwp_unitary_second_order_suzuki(length, hubb_u, timestep, eps\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-10\u001b[39m)\n\u001b[1;32m 3\u001b[0m n_t, n_rot \u001b[38;5;241m=\u001b[39m t_and_rot_counts_from_sigma(trotter_step\u001b[38;5;241m.\u001b[39mcall_graph(generalizer\u001b[38;5;241m=\u001b[39mcatch_rotations)[\u001b[38;5;241m1\u001b[39m])\n", + "\u001b[0;31mImportError\u001b[0m: cannot import name 'build_plaq_hwp_unitary_second_order_suzuki' from 'qualtran.bloqs.chemistry.trotter.hubbard.trotter_step' (/usr/local/google/home/fmalone/projects/qualtran/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step.py)" + ] + } + ], "source": [ "from qualtran.bloqs.chemistry.trotter.hubbard.trotter_step import build_plaq_hwp_unitary_second_order_suzuki\n", "trotter_step = build_plaq_hwp_unitary_second_order_suzuki(length, hubb_u, timestep, eps=1e-10)\n", @@ -605,7 +937,7 @@ ], "metadata": { "kernelspec": { - "display_name": "qualtran", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -619,9 +951,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.11.8" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From 1b7f3764667d59bed563b2fcb0de401eaa867c5b Mon Sep 17 00:00:00 2001 From: Fionn Malone Date: Sat, 11 May 2024 14:35:31 +0000 Subject: [PATCH 07/16] Add hwp trotter factory. --- .../chemistry/trotter/hubbard/trotter_step.py | 43 +++++++++++++++++++ 1 file changed, 43 insertions(+) diff --git a/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step.py b/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step.py index bf5a5677e..922edb387 100644 --- a/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step.py +++ b/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step.py @@ -53,3 +53,46 @@ def build_plaq_unitary_second_order_suzuki( (interaction, pink, gold), indices=indices, coeffs=coeffs, timestep=timestep ) return unitary + + +def build_plaq_hwp_unitary_second_order_suzuki( + length: int, + hubb_u: float, + timestep: float, + hubb_t: float = 1.0, + eps: float = 1e-9, + strip_layer: bool = False, +) -> TrotterizedUnitary: + """Build second order Suzuki-Trotter unitary for the square lattice Hubbard model. + + This variant uses Hamming weight phasing for the rotations. + + Args: + length: box length + hubb_u: Hubbard u. + timestep: The time step for the unitary. + hubb_t: Hubbard t. Default = 1. + eps: The precision for single-qubit rotations. + strip_layer: Whether to strip one application of the interaction term + which is a common optimization if multiple trotter step are merged. + + Returns: + unitary: The trotterized approximation to the unitary e^{-i t H}. + """ + # Build the basic bloqs which make up the 2nd order PlAQ unitary. + # The pink and gold "tiles". + pink = HoppingTileHWP(length=length, angle=0, eps=eps, pink=True, tau=hubb_t) + gold = HoppingTileHWP(length=length, angle=0, eps=eps, pink=False, tau=hubb_t) + interaction = InteractionHWP(length=length, angle=0, eps=eps, hubb_u=hubb_u) + if strip_layer: + # H_p H_g H_p H_I + indices = (1, 2, 1, 0) + coeffs = (0.5, 1, 0.5, 1) + else: + # Trotter splitting parameters when H = H_I + H_h^p + H_h^g + indices = (0, 1, 2, 1, 0) + coeffs = (0.5, 0.5, 1.0, 0.5, 0.5) + unitary = TrotterizedUnitary( + (interaction, pink, gold), indices=indices, coeffs=coeffs, timestep=timestep + ) + return unitary From 0b2577cf818f65c0343078196eff2d752c400a37 Mon Sep 17 00:00:00 2001 From: Fionn Malone Date: Sat, 11 May 2024 14:51:43 +0000 Subject: [PATCH 08/16] Fix up notebook. --- .../hubbard/qpe_cost_optimization.ipynb | 411 ++---------------- .../chemistry/trotter/hubbard/trotter_step.py | 24 +- 2 files changed, 58 insertions(+), 377 deletions(-) diff --git a/qualtran/bloqs/chemistry/trotter/hubbard/qpe_cost_optimization.ipynb b/qualtran/bloqs/chemistry/trotter/hubbard/qpe_cost_optimization.ipynb index f543c502f..bb382b6af 100644 --- a/qualtran/bloqs/chemistry/trotter/hubbard/qpe_cost_optimization.ipynb +++ b/qualtran/bloqs/chemistry/trotter/hubbard/qpe_cost_optimization.ipynb @@ -102,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -225,19 +225,9 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Computed timestep: 1.755e-02\n", - "Sum Error budget terms: 0.3264\n", - "Expected Error budget: 0.3264\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Get some system parameters from Ref. [2]\n", "# 8x8 lattice\n", @@ -269,18 +259,9 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "N_T = 768 vs 768\n", - "N_rot = 320 vs 320\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from qualtran.bloqs.chemistry.trotter.hubbard.trotter_step import build_plaq_unitary_second_order_suzuki\n", "\n", @@ -299,228 +280,9 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Adjusted eps_single_rot: 8.949367006180983e-07\n" - ] - }, - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "counts\n", - "\n", - "\n", - "\n", - "b0\n", - "\n", - "TrotterizedUnitary\n", - "bloqs=(Interac ..., indices=(0, 1, 2 ..., coeffs=(0.5, 0. ..., timestep=0.017547 ...\n", - "\n", - "\n", - "\n", - "b1\n", - "\n", - "Interaction\n", - "length=8, angle=0.017547 ..., hubb_u=4, eps=8.949367 ...\n", - "\n", - "\n", - "\n", - "b0->b1\n", - "\n", - "\n", - "2\n", - "\n", - "\n", - "\n", - "b2\n", - "\n", - "HoppingTile\n", - "length=8, angle=0.017547 ..., tau=1.0, eps=8.949367 ..., pink=True\n", - "\n", - "\n", - "\n", - "b0->b2\n", - "\n", - "\n", - "2\n", - "\n", - "\n", - "\n", - "b3\n", - "\n", - "HoppingTile\n", - "length=8, angle=0.035095 ..., tau=1.0, eps=8.949367 ..., pink=False\n", - "\n", - "\n", - "\n", - "b0->b3\n", - "\n", - "\n", - "1\n", - "\n", - "\n", - "\n", - "b7\n", - "\n", - "Rz\n", - "angle=\\phi, eps=8.949367 ...\n", - "\n", - "\n", - "\n", - "b1->b7\n", - "\n", - "\n", - "64\n", - "\n", - "\n", - "\n", - "b4\n", - "\n", - "HoppingPlaquette\n", - "kappa=0.017547 ..., eps=8.949367 ...\n", - "\n", - "\n", - "\n", - "b2->b4\n", - "\n", - "\n", - "32\n", - "\n", - "\n", - "\n", - "b5\n", - "\n", - "HoppingPlaquette\n", - "kappa=0.035095 ..., eps=8.949367 ...\n", - "\n", - "\n", - "\n", - "b3->b5\n", - "\n", - "\n", - "32\n", - "\n", - "\n", - "\n", - "b6\n", - "\n", - "TwoBitFFFT\n", - "k=0, n=1, eps=8.949367 ..., is_adjoint=False\n", - "\n", - "\n", - "\n", - "b4->b6\n", - "\n", - "\n", - "4\n", - "\n", - "\n", - "\n", - "b4->b7\n", - "\n", - "\n", - "2\n", - "\n", - "\n", - "\n", - "b5->b6\n", - "\n", - "\n", - "4\n", - "\n", - "\n", - "\n", - "b5->b7\n", - "\n", - "\n", - "2\n", - "\n", - "\n", - "\n", - "b8\n", - "\n", - "CNOT\n", - "\n", - "\n", - "\n", - "b6->b8\n", - "\n", - "\n", - "3\n", - "\n", - "\n", - "\n", - "b9\n", - "\n", - "Hadamard\n", - "\n", - "\n", - "\n", - "b6->b9\n", - "\n", - "\n", - "6\n", - "\n", - "\n", - "\n", - "b10\n", - "\n", - "S\n", - "is_adjoint=False\n", - "\n", - "\n", - "\n", - "b6->b10\n", - "\n", - "\n", - "3\n", - "\n", - "\n", - "\n", - "b11\n", - "\n", - "ArbitraryClifford\n", - "n=1\n", - "\n", - "\n", - "\n", - "b6->b11\n", - "\n", - "\n", - "1\n", - "\n", - "\n", - "\n", - "b12\n", - "\n", - "T\n", - "is_adjoint=False\n", - "\n", - "\n", - "\n", - "b6->b12\n", - "\n", - "\n", - "2\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "import attrs\n", "from qualtran.drawing import show_call_graph\n", @@ -539,17 +301,9 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "N_{T_tot} = 1.049e+07 T gates.\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "tot_t_count = qpe_t_count(delta_pe, delta_ts, delta_ht, n_rot, n_t, xi_bound, prod_ord)\n", "print(f\"N_{{T_tot}} = {tot_t_count:4.3e} T gates.\")" @@ -573,7 +327,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -592,30 +346,9 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0.5, '$\\\\Delta_{PE}$')" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "fig, ax = plt.subplots(subplot_kw={\"projection\": \"3d\"})\n", "surf = ax.plot_surface(X, Y, np.array(results).reshape(20, 20), cmap=cm.coolwarm,\n", @@ -626,30 +359,9 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Desired $\\\\epsilon$ = 0.0051 $L^2$ = 0.3264')" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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fZs6cOUm8Zt988w179+6ldevWvH//Pok2FpCkhL9KlSr079+fP//8k/j4eOrXr4+vry/btm1jwoQJuvevXbt2KV6P1qOj7zkJCbMjR2vEJD4qtKXh2oeFhYWYN29esUmTJuLvv/8uhoaGptgnICBAHD58uOju7i4qlUoxb968YqNGjcRly5bpxsTExIjfffedWKlSJdHe3l60tbUVK1WqJC5evDjF3PrK0gMDA/Wu15C5RVEUT548KVarVk20sLAQixUrJv7xxx8GKy3v3btX/Pzzz0UbGxvR0tJSLF68uNijRw8xKioq3X1NSVRUlPjtt9+KefPmFS0tLcUaNWqIhw4dSjFOW2Y9ZcqUDO1v6Njff/9drFmzpuji4iIqFAoxX758Ys+ePcV79+7pPWbhwoUzVO49a9asNEvFX79+nfYblwMY8v7p+5w2bdokNm7cWHRzcxMVCoXo7OwsNm7cWNyzZ0+KOerXr5/m+5Kc2NhY8YcffhALFy4sKpVKsUSJEuK8efPSfS1SWbrEx4Qgihmo1ZSQkJCQkJCQ+IiQWktISEhISEhIfPJIBo+EhISEhITEJ49k8EhISEhISEh88kgGj4SEhISEhMQnj2TwSEhISEhISHzySAaPhISEhISExCePZPBISEhISEhIfPJISstpoFarefnyJfb29nq7EEtISEhISGgRRZGwsDDy589vsvYpEqZDMnjS4OXLl0n6CElISEhISKTHs2fPKFiwYE4vQyIZksGTBvb29gB80/hrhg//DPviZbEoXJ7wR/8SH/oO64Ilscyl6TPz/tkD4qLCcXBzx9rRRXeMd+/eERoSgqOTEy4uLinmuPvkHXHxKooWcMLGykLvOmLj1Fx5GAFAzVJ2aXqbFi9ezIQJEwBo1LI7c+b9RlE3S6Ne9/twkZO3RKwU0LxK5u5SRBE2+UOcCtpUB2fTNcnW8fydyO5zYGMJ/RphtDcuNjaWQYMGsXv3bgRBYP78+fTu3dugfd+HivyxPx6ZDMa0V2ChNB9P4KXbcWzzjaFAbhlfdbQx+fFfvnxJ27ZtuXv3Li4uLuzcudOkXcsD3sfz49IgBAF+HOJCbme5yY6dk/j5+dG1a1fCw8OpWrUqO3bsSHJuEEWRGSsCuPUgms8r2zKsq2saR/vvMH/+fCZPngxAnz59mDdvHnL5h+9EfLzI2Bn3efQsilpVHBg3pHC2e+ZDQ0Nxd3fXXTskzIyc7Wxh3oSEhGh6NV07K0b4bxdjHvwjiqIovrx0Xgy6fFyMDniqG/v+2QPx1a3LYkRQ0r5O7969Ex/cvy8Gvnmjd467T9+K1+69FkPDo1Ndh0qtFk/fDBVP3wwVY+JU6a571apVokwmEwHRu8UXYnR06sfWR3SsWtx6ViVuPasS41Vqo/bVx+4Lorj8mCg+DMj0ofQSG6cW5+5Ri3N2q8WQiIytNz4+Xhw4cKCu19Ds2bMN2k+tVotzt8eK36+JFe8+T/+zyU7CItXi1wvCxFHzw8S3wVmztsDAQLFGjRoiINrb24s+Pj4mPf6cdUFivx8CxPX7U/Zp+5i5ePGimCtXLhEQy5UrJz5//jzJ8w+fRYudvn4odhz9ULz3xLjf76fMihUrdOe2Tp06iTExMUmev/84Qmze74ro3fuK6HvufbavT3vNCAkJyfa5JdJHCjIagjYWK6rx3XKawQ1+58qZp4hqlW6IkDBGVKuT7arZrk62XYtcprkDUalTb2kmEwQUcs24uPj0W5/17duXRSu2olRacOTALlq1akV4eHi6+2mxUIAi4SVHxBi8W6o4JjgXQiIzfyx9KBUCrg6a/78Kytgx5HI5y5Yt47vvvgPg22+/ZdKkSYjptJoTBIFi+TSfzYNX5tWWzs5aoHgBzR3wtYfxWTJH7ty5OX78OF5eXoSFhdGsWTP27dtnsuM3r6v58vhdiSIsUv9v6GOkevXq+Pn5UbBgQW7evEndunW5f/++7vmiBS3xrKZxh67d+z7d7+F/hQEDBrBlyxaUSiXbtm2jTZs2RERE6J4vXtiGbq3dAFi07jnBoVnzvZf4OJEMHgMQhASjRaXCZ/NpoiNiuez/BFFUpxiTwuBJcKmmbvBo9lOl8rwWpdbgURl24mvTth0/LdyDtY0tx44do3Hjxrx//96gfQVBwNZK8/+IaIN2SRMnW82/IRFpj8sM+RIiAi8Ne4l6EQSBWbNmMX36dAB+/vlnRowYkepnp6V4Ps1n+PCV+V2QKxZPMHgeZN2J397engMHDtC2bVtiYmL44osvWL9+vUmOXaaIksL5FMTGw4kLUSY5prlQtmxZ/P39KVmyJE+ePMHDw4OrV6/qnu/awhmlUuDWg2gu3ciiu4WPkI4dO7J//35sbGw4fPgw3t7eBAV9uNPp2tqNou5WhISpWLTueQ6uVMLckAweQ0gwSgREJm35miE/tabvN3Uh0YVQ5+ERU/HwpHKHZoiHBzReDDDMwwNgZSFQpVZD5v15CGdnZ86fP0/9+vV59eqVQfvbJqT9mNLDE5yF5+z8zpp/X2XC4NEyfvx4lixZgiAILF68mF69ehEXF5fq+KIJHp6AIAiPMq878QrFNGl6j1+pCYnIOoPMysqK7du307t3b1QqFb169WLhwoWZPq4gCDovz/ELkcTEmdf7m1kKFy6Mn58flSpVIiAggAYNGnDmzBkAXJ0VtKyncV2u3xdEvIE3O/8FmjRpwrFjx3BycuLMmTM0aNCA169fA6BUyPhmYCFkMjh1IRi/i8E5u1gJs0EyeAxBm/imVqO0UNKynwdyuQxRrUIURS4fu5ZqSCu17Vp0BpEq7YuRhZEGj2VC8myJcjU5efIk+fLl499//8XDw4OHDx+mu/8HgyfzJ1knrcEToUlizgq0Hp43IZjkwvDll1+yceNGFAoFGzdu5IsvviAyUr/FZmsl6OZ/aGZhLSc7GYXzyhCBfx+q0h2fGRQKBatWrWLkyJEAjBw5kmnTpmU6HFOtrCWuzjLCo0RO//NpeXkA3Nzc8PX1pW7dugQHB9OkSRMOHz4MQLtGTtjbynj5Jo7j58JyeKXmRe3atTl58iRubm5cu3YNT09PHj9+DEDJIjZ0baUJbS1c+5yQMCm0JSEZPIaRKIcHPhgxqNWsmbKFcd4/seGXvxKGGJnDIzfQw5MwLtbAi7llQv2dSg1lyn2Gv78/xYoV4+HDh3h4ePDvv/+mub+tpWY+U3h4HGxAQFOpFRWb+ePpw9FGU6WlFiEg2DTH7Nq1K3v27MHKyor9+/fTrFkzQkJC9I4tZsZhrUrFNV+GrAxraZHJZPz+++/88MMPAEyePJlvvvkm3bBgWshlAk1ra6zmw2cj0/2tfIw4OTlx5MgRmjVrRmRkJK1bt2bbtm3YWsvo1FTjvtx6KIjIaPP7fuUkFStWxN/fnyJFinD//n08PDy4efMmAN3auFG4gBXBofEsXi+FtiQkg8cgdPk52nCVTJMXIapVWNtZA5C7gItuW2LST1o2MIdH5+Ex7ISnkAsoEio2Y+JEihUrhr+/P5999hmvXr2iXr16nD9/PtX9TRnSksvAXvM2EZRFeTyCIJBPG9bKYOKyPlq0aMGRI0dwcHDAz88PLy8v3rx5k2Jc4sRlc0sw1Ya17r1QERGd9WsTBIEpU6bw22+/ATBv3jwGDBhAfHzGDa66la2xsxEIDFLz9y0TfCnNEBsbG/bs2UOXLl2Ii4uja9eurFixgiZ17MnnqiA0XM2eE8E5vUyzo0SJEvj7+1OuXDlevHhBvXr1uHjxIhZKGd8O0oS2fM8Fc+bv4JxeqkQOIxk8hqDz6GiMmcT5Ol3GtmXJ5Vm0GNhAty3prpmv0gJNXBoMD2kBWCWEtaIT0k/y5cvHyZMnqVWrFkFBQTRq1Ihjx47p3deUScsALgn6O+8NLxYzGlMkLuvD09MTX19fXF1duXLlCp6enjx9+jTJmMJ5BBQyCI2Ed6GmnT+zuDrJyJ9LhloNNx5ln2t/1KhRrFmzBrlczurVq+ncuTPR0Rn7QlkqBRrX1Hh5Dp6ONDuj0lRYWFiwYcMGhgwZglqtZtCgQcybO5serTRf7n2+obwLlsIzySlQoACnTp2iZs2avHv3joYNG+Lj40OpojZ0apEHgPlrnhMaLr13/2Ukg8cQhARXScJJVuvx0SYtl6hcVGcExUbFsmbKFqIjYxLGCgm76r/z14W00glVKY0oS9diqdT8GxP7YR8XFxdd1VZERAQtW7Zk586dKfbVenjiVMbNmRq5EnS43mVhGkLixGVTXxCrVKmCv78/7u7u3L17Fw8PD+7cuaN7XqkQcM+j9fKYX9ihYjaGtRLTu3dvduzYgaWlJbt2aSQSwsIy9iVoWMMaCyU8eRXP7cepJ5F/7MjlcpYsWcL48eMBGDt2LLs2/kTpIhbExYlsPmhCF+YnRK5cuTh27BgNGzYkPDyc5s2bs2fPHnq2zUuh/JYEhcTzx4YXOb1MiRxEMngMQZe0nBCuShTS+jBEs+2PcTtY/9N2pnacjSiKSfqp6DV40vEAadElLRuRkJvcw6PFzs6Ov/76i/bt2xMbG0unTp1YtWpVkjEKuYBFQh6QKcJa2eHhcXMCmaBZb1gW5LaWKlWK06dPU7p0aZ49e4anpyeXL1/WPV88IaxlbonL8KE8/c5TVRIDODto27YtBw8exM7OjuPHjxslkZAYOxsZnlU0sdGDpz/tMm1BEJg+fTozZ84EYObMmTy8+COiWsXJi+E8fvFphvUyi729Pfv376ddu3bExMTQoUMHNm9er6naEuD4mSDOXdGfhyfx6SMZPAagNWZEPUnLujEJ2xp3q46zmyOdvm2DIAhJpM31GTWyDJSlG+q90Hl49JTyWlpasmXLFvr164daraZ///7MmzcvyRhT5vFoPTxBEZpE6qwgsQChqcNaWtzd3fHz86Nq1aoEBgbi5eXFqVOngA95PI9ei2aXWJsvl4zcjgJxKrj1NGurtfTh5eXF8ePHcXFx4cKFC9SrV4+XL18afZymtW2QCfDvg1ievv50vTxaxo4dy7JlyxAEgU0blvPw/Heo4mNZtzeLvuCfAFZWVmzbto0+ffqgUqno06cPRw/8SYfmmtDW76ufERYhhbb+i0gGjyEkN3D0aO5oDZ5SVd1Z+2ARVRpW0GwXhDTzeIwVHhSBeAOvVx88PPovvgqFgpUrVzJmzBgAxowZw+TJk3UGlSkNHltLjYKzKGrK07MKbR6PKROXk+Pq6oqPjw/16tUjNDSUpk2bsn//fvK5CFhbQEwcvHxnXgaPIAg5FtbSUrNmTfz8/MifPz83btzAw8ODBw8eGHWM3E5yapTXfDEPnfm0vTxaBg0axObNm1EqlVw5v5uzfw3h8o0g/rn933j9GUGhUPDnn38yatQoAL766iue3lxOgbwWvA+OZ+lGKbT1X0QyeAxBm7Oj8/Ak5PSk0lrCyuZDs86ggGDm9FrGq/tv9Bs8WkNGBHVa7SVkAvKEZcQZ6CLRavHEpHEjLAgCs2fPZtq0aQBMmzaNkSNHolarEyUuZ/7iLQiQKyGslZV5PFmVuJwcBwcHDh06RKtWrYiOjqZdu3Zs2bJZJ0Jobm0m4EMez41H8TkmYleuXDlOnz5N8eLFefToER4eHly/ft2oYzSro0levvBvDG+Ds99blRN07tyZvXv3Ym1tzatHJ/Hb1YdlWx6ZnSfRnJDJZMybN4+pU6cC8NPUH4gLWASoOeofxPl/pNDWfw3J4DEAQfZBeBD0t5FIrbXE4q9Xc/vMfVaN3YpalfLkLEsU8krPy2NhZKWWVUJIKzUPjxZBEJg4cSKLFi1CEAQWLVpE7969sZBpPAGm8PAAuCSEtbIyj0ebuBwYYly+U0awtrZm586d9OjRg/j4eHr06MFln6WAeebxFHKT4WgrEBMHd5/lnKFQpEgR/P39qVixIq9fv6Z+/fqcO3fO4P0L51NSrpgStQhHzv13vBzNmjXj6NGjODo68u7lJTYt6syuQ+mLiP6XEQSByZMnM3/+fADWr1lM6OMZqNXxzF/9nHAptPWfwqwMnkWLFlGkSBGsrKyoVasWFy5cSHXszp07qV69Ok5OTtja2lK5cmXWrVuXZIwoinz//ffky5cPa2trGjduzL1794xfWBo6PB+GfBiTOMdmxPz+VGpUjgFzu6LvEigIwofS9HQ8N0a3l1BqE53TzxECGDZsGOvXr0ehULBhwwa+GtiB2Ogokxk82eHhcUgkQPgmOOvm0aJUKlm7di3Dhw9HFEVm/TgCv30zePZGbXZtEGRmENbSkjdvXnx9falTp45OIuHo0aMG79+8jqZB26nLUYR/Qk1F06Nu3bqcPHkSJ+c8BAfeZECvJty5+yinl2X2jBw5knXr1iGXyzl3aht3z40j4G04yzYbn0cm8fFiNgbPli1bGDNmDFOmTOHy5ctUqlSJpk2b6hV5A0159cSJEzl79izXrl2jX79+9OvXTyfJDjBr1izmz5/PH3/8wfnz57G1taVp06bGa4Eky+ERkikvJ9lG0twex9wOfLNmCG5FcutCWhGhSe9KFQmxqvTCDMaqLSvkmqolSDuslZju3buze/durKysOHr4L34Z05LAd6EmKfNO7OHJKhmVxAKEWR3W0iKTyViwYAGTJk0CwGf79xzeNIHHr83vQqyt1rr+MD7HwyHOzs4cOXKEpk2bEhkZSatWrfRKJOijXDElhfIqiI0Dn0ufXruJtKhUqRKnT/th71SQ0PePqFvXg9u3b+f0ssyenj17smvXLiwtLXl6/wRXjo9k//FnXLxmZsJZElmG2Rg8c+fOZdCgQfTr149y5crxxx9/YGNjw59//ql3fIMGDfjiiy8oW7YsxYsXZ9SoUTqZcdB4d3777TcmTZpE27ZtqVixImvXruXly5fs3r1b7zFjYmIIDQ1N8gBS5PB8aDXxoWJKp81D6v201Go1D64+pk+JERxbf0r3vFxurNqyYRcqQRAMDmslpmXLlhw+fBgHBwduXjnJ98Ma8fxloMH7p4azrSaXJybedGEyfeTPhsTl5AiCwE8//cScOXMAOHtwLl+NGIxKTxgzJymWX46tlUZQ8uHLnF+bra0te/fupVOnTjqJhNR+84kRBEGXy3PsfCSxZuZNy2rKlS3Fmo3HsHcpwbu3L/Hw8OTvv//O6WWZPa1bt+bQoUPY29vz/vVFLh0ZwszF14mIzPnfgkTWYxYGT2xsLH///TeNGzfWbZPJZDRu3JizZ8+mu78oihw/fpw7d+5Qr149AB49esTr16+THNPR0ZFatWqleszp06fj6Oioe7i7uwMpy9B1ScvwQX1ZEFLN45ElSmg+stqXkLdhHFh+TOfxyaqQFiROXDbuglCvXj18fHxwdHbl0Z3LNPSqx7Nnz4w6RnLkMnDSRCKyJXE5KwQI02PMmDFMnbkcQZBxZO+fdOnShZgY89FMkcsEPiumDWuZx0newsKCTZs2MXDgQNRqNQMGDGDu3Lnp7lejvCW5HGWERYqcvmoiSfCPiHbNStFv9C6c3Srw7t1bvLy8OHnyZE4vy+xp0KABJ06cIFeuXIS+u8Ghzb35dcnl9HeU+OgxC4Pn7du3qFQq3Nzckmx3c3Pj9evXqe4XEhKCnZ0dFhYWtGzZkgULFtCkSRMA3X7GHHPChAmEhIToHroLvM57k+DRScWbk1pn9MRl6UPm9GbwrF5M3TNOt93QkJaFVm3ZCCGb1MQHDaFq1ar8vvYkudzcuX/vDnXr1uXu3bvGHygRubJZgDA0B6IdY74aQKcRm5ArLNixYwetW7cmPDwLX7CRVCz2IY9HbSYtGuRyOcuWLeO7774D4JtvvmHSpElpGqxJmoqeiUyzyvFTRBAEvuxegnrt1+Na8HPCwsJo1qwZf/31V04vzeypXr06fn5+5HHLT0TIQ2b+0I7dB/7J6WVJZDFmYfBkFHt7e/755x8uXrzIzz//zJgxY/D19c3w8SwtLXFwcEjyAJIYOKjVGjFBXZhLf+JyYrSVWOoE5eVO37bBTuvqAB5ffYxarTYgpJXQviIj7SUy6PIvW6Y0P/1xiiLFSvHs2TM8PDy4cuVKho4FHxSXs9LDo5QLuDpq/v8qB/TZbK0EvLy/oNuYPVjb2HL06FGaNGmSIXXhrKCUuxxLJYREiDwLMJ88I0EQmDVrFtOnTwfg559/ZsSIEWmqkHtWscbWWuBNkIrLt83Hk5ZdlC1uRZ3qbni0W0XZSk11EgkbNmzI6aWZPWXLluXC+TO4uhUhOvwl3To15vyFjJ/bJMwfszB4cufOjVwuJyAgIMn2gIAA8ubNm+p+MpmMEiVKULlyZb755hs6duyoO1lq9zP2mPonShTCEpM1EDXSw5Mc/13nmdriZ7b8bwMx6WQWZySklZ74YHrYWkLuvIVYuukkVapUITAwkAYNGuhypYwlVzaUpgPZnricnOL5ZBQr34gp8w7j7OzMuXPnqF+/Pq9evcqZBSVCqRAoV8Q8qrX0MX78eP744w8EQWDx4sX06tWLuDj9vw1LC4FGNT60m/hUm4qmRY9WzigtLClbfwGt23ZHpVLRs2dPFi1alNNLM3sKFy7MhfOncXYtTXTkO7y8GnDmzJmcXpZEFmEWBo+FhQXVqlXj+PHjum1qtZrjx49Tu3Ztg4+jVqt1+RJFixYlb968SY4ZGhrK+fPnjTom8KGXlmYSzb/6DJ5UcniENAyeqPBoTf6PTKa3bD0xugaiKiPaS1ikLz6YFrZWmv0t7fLg4+ODp6cnoaGheHt7c+DAAaOPpw1phUZBbBZea3MicTkx2jYTCpeanDx5knz58vHvv//i6enJo0c5X0ZcKaE8/eqDeLM0EoYMGcKmTZtQKBRs3LiRL774gqgo/fHJhjVtUCrg0ct47jz59NtNJKdAHgua1LFHJlNQuu4MRowYAcCIESOYNm2aWX6+5kSRwvnZs+8oTq6ViYoMpVHjJkmqfSU+HczC4AFNsufy5ctZs2YNt27dYujQoURERNCvXz9A03V5woQJuvHTp0/n6NGjPHz4kFu3bjFnzhzWrVtHz549AY17fPTo0UybNo29e/dy/fp1evfuTf78+WnXrp1RaxMEIYX2jra/ll61ZTFpMqgsFc8PQJNe9Zl2eDKdpnYlvRQErYdHFA3vR5WRKq3EJG4v4ejoyKFDh2jRogVRUVG0bduWzZs3G3U8KwuNTg5krZdHm7icHQKE+iiUR0Ahg9BIyOv+Gf7+/hQtWpQHDx5Qt25dbty4ke1rSkzZwnIUcngbIvL6vfmEtRLTpUsXnbrw/v37adasGSEhKdVxHWxleFTWeHn+K+0mktOpqTPWlgKPX8bTqc8vTJkyBYDJkyfz7bffSkZPOnjWcmfStC3kyl+H6KhIWrduzfbt23N6WRImxmwMni5dujB79my+//57KleuzD///MOhQ4d0ScdPnz5NEg6IiIhg2LBhlC9fnrp167Jjxw7Wr1/PwIEDdWPGjh3LyJEjGTx4MDVq1CA8PJxDhw5hZWVl/AKFpJVaej08GQhpAZStXQq5Qk68SiNauHvhQUL1JLnIE7eXMDCspa3Sio0jQwmqWoMnMlZT8WRjY8Pu3bvp1q0b8fHxdO/enaVLlxp1TF3iclYKEFpr1q4WISA46+ZJDaVCoJCbts2EmmLFiuHv789nn33Gq1evqFevHufPn8/+hSVgaSFQppDGaL9qJtVa+mjevLlOIuHUqVM0bNiQwMCUEglNa1sjCHDtXizPA8wvTJfVONrJ+aKxEwCbDgTxv4nf89tvvwEayY8BAwYQH//fe1+MYWiv4jTttIi8RbyJi4ujS5curFixIqeXJWFCzMbgAY0L9smTJ8TExHD+/Hlq1aqle87X15fVq1fr/p42bRr37t0jKiqK9+/fc+bMGbp06ZLkeIIgMHXqVF6/fk10dDTHjh2jVKlSGVucNo8nWQ5PEvHB1MrStUnLqRg8Cm0DUZWa7XP2seirP/nGawqxeuJQH8QHDeynpQABTdPRjISQrC00ET1R1Bg9oFEXXr9+PUOHDkUURb788ktmzJhh8DG1AoTvstDDIwhCkvL0nEAb1tK2mcifPz8nT56kVq1avH//nkaNGiUJuWY35qK6nB6enp74+vri6urK5cuX8fT05OnTp0nG5HFRUL1cQlPRs/9NL0+Leg7kcpLzNkjFwVOhjBo1itWrVyOXy1m1apXZSSSYGzbWcr4ZVJwKHr9QsGQH1Go1FhYWKcZJ3rKPF7MyeMyZFN4bve0l5EnHJNs3NQ+LtoGoSi1So3kVcuV3pnn/RlhoS6wSoTSyn5YgCFhkolJLEIQPYa1EUicymYxFixYxceJEQFPSP27cOINOBtlRmg7mkbgM8DhA1Kkau7i4cOzYMRo3bkxERAQtWrRg165dObK+8kUVyAR4+VbN2xDzDGtpqVKlCv7+/hQqVIg7d+7g4eHBnTt3kozRChGevx7N+xDz9VplFZYWMrq10HzpdxwLJjRcRZ8+fdi+fTsWFhbs3LmTli1bmpVEgrlRtbw9LRvmoeznE2nedR2du/RIMUYQBERRJCgohxIEJTKMZPAYSrIO6XobiKZWlp5GDg98UFoGKFimACv+nUf70S31js1UpVaswbskIXEeT2IEQWDatGnMnj0b0LTyGDw4fXXhxJVa6VTiZ4rEics5cVeW10XjIYuJgxdvP8xvZ2fHX3/9Rfv27YmNjaVjx45JvJfZha2VQIkCCWGt++bt5QEoVaoU/v7+lClThmfPnuHp6ZlEIqFofiVliypRqf9bTUUT41nNjiIFLIiKFtl+JBiAdu3acfDgQezs7Dh+/DiNGzc2G4kEc2Rg1/zkyWWByuIzVm1LWlWpVqtZtGgRXl5edOjQge7du6cpmyBhXkgGj6GkprasL2k5jRwefRdemSDowl4qlTqJRk9cbBzTe/7OzXMawT9dpVY2avF8MHj07//NN9+wcuVKZDIZK1asoFu3bsTGpm5d2Vtr+nyp1BCShcKAeZw0AoSRMZrk4exGJgi6sNaDZN3TLS0t2bJlC/3790etVtOvXz9dzkV2UrmkJqx18bZ5Vmslx93dnVOnTlG1alWdRIKfn5/u+eYJXp6Tl6OJiPrvXYhkMoHebTSW/pHTobwK1ITFGzZsyPHjx3FxceH8+fPUq1ePly+lxpn6sLWWM7q/RmV/z7G3XL/zwSPWvn17Fi9eTN68eRkyZAivXr2iRo0avHv3LqeWK2EEksFjAE+fPtVVZelCWHq8Oem1ltCMTzusFZ8sN2fzjN2c2OjPlLYziYqIxkLxoTTdUDKvxaPZP63+V/3792fLli0olUq2bdtGmzZtiIiI0DtWJoBLgk2XlYnLiQUIX+ZYebrms3/4KuV7r1AoWLFiBWPGjAHg66+/ZsqUKdlqeFQpqUCpgNfv1TwxIxHCtHB1dcXHx4d69eqlkEgoX9yCgm4KYmJFfP9jTUW1VChlTZWy1qjUsOGvD56cmjVrcurUKfLnz8+NGzfw9PTk4cOHObhS86V6BQea1XNBFGHuyqdEx6gZO3YsJ06c4Ndff2Xz5s106dKFZcuWERsbaxZSExLpIxk8BtCwYUOu3Us4MYjJOqbrrdJKGtIREun4pJq4rGsgmvRi1/Gb1nzeqhpj14zA2tZKF9IyTm054+0lAGwTitrSa/jZsWNH9u/fj42NDYcPH8bb2zvVOHd2JC5DorBWjuXxaN7754GiXg+bIAjMnj2badOmATB16lRGjRqVbW5ya0uByiU0Xp7zNz8eDRsHBwcOHTpEq1atiI6Opm3btmzatAlBEHRenqPno4zyhH5K9GztgiDA+WuR3H74IfmufPny+Pv7U7x4cR4+fIiHhwf//vtvDq7UfBncrQC5XZS8DIhlwtTtzJ49mwULFtC8eXPdmGLFinH79m2ePHmSgyuVMBTJ4DGAwMBAmg4Yzbnrtz+EsPQmLevP4dE0Fk3Q0EktjydRpVZirG2tmLpnHDWaVQE0Xou4mDji4o3pp6X5N9MhLQP6MzZp0oRjx47h5OTEmTNnaNCggd7eZdmduJxTBo+zvYCznaY8/nGA/vdfEAQmTpzIwoULAViwYAF9+vRJVV3Y1NQqp/mCXL4bn+HvSE5gbW3Nzp076dGjB/Hx8fTo0YMlS5ZQo7wlLg4yQiPUnPkPNhUFKJTPgoa1ND+ydfveJ/EaFi1aFD8/PypUqKCTSDh37lxOLdVssbWRM7qvO6IosnzRBFq07kznzp2T3MCuWLGC4sWLZ7z6VyJbkQweA6hVqxbBoeG0Gj2VI8d9gQ/hK/0enpTGiCzdSi1tA9GU+yb+gUUFh7O0048cX3bQ4PVbmqC9hGb/lB4ofdSuXZtTp06RN29erl27hqenJ48fP04yRpu4nJU9teCDhycw1Li8J1NSPH/qYa3EDB8+nPXr1yOXy1m/fj0dOnRIVV3Y1OvL7SgQE/dxJC8nRqlUsnbtWoYPH44oigwbNoxZM6fT5PMEIcKz/72molq6NHPG0kLg7uMYzl1NmsSWL18+Tp48Se3atQkKCqJx48YcO3Ysh1ZqvtSo5EC5Qi8RBDmxFl7I5JY64/H69escPXqU8uXL4+7unsMrlTAEyeAxgN27d+Pt+TlRMbG06zOIrVu3ppLDo78sHdIXH1QkKk1Pi/O7zxH44CX+a44QFmSYe0SbwxMTl7FqJQsFJFTDE2mgjEeFChV06sL379/Hw8ODmzdv6p53TvDwRMUafsyMYJ/DAoSQWI8nfa9cjx492L17N1ZWVuzbt4/mzZsTGhqapesTBIFaZTVeno8prKVFJpOxYMECJk+eDMDEiRM5tG0KNpYQ8E7FlTsZLE/8yHF2VNDGS5PEtmH/+xQGv7OzM0ePHsXb25uIiAhatmzJzp07c2KpZk2H5q5EhT8jNNKOtTtfIQgCr1+/1nUFGDx4ME5OTh9F0v9/HcngMQAbGxu2L55Dx0Z1iIuLo2vXrqzauBUwLKQF6Rs8Wg9P8pBWctoNb0azcV3pvfxbbBxs0xyrRVulpRYhLgPyJIIg6PJ4wo2IEBQvXhx/f3/Kly/PixcvqFevHhcvXgRAKQdHTapFloa1EgsQ5lTiclE3AQF4EwyhkemfFFu1asWhQ4ewt7fn5MmTqaoLm5IaZRUIAjx4qSYw+ONIXk6MVmR07ty5APw2bw63T32HWq3i4OmI/+zFqLWXI072cgLexnPkdErD2dbWlr1799KpUydiY2Pp1KkTq1atyoGVmi+F3fNSqlRZwoPvs/3AK67dCmby5MmcOnWKXr160bRpU0DzHZRK1M0byeAxEAsrS/78fhQDu3dCFEWGfzuB31dvNjikldZz8CGHR19IKzEyGdTt441rsXzEJlRqPbvzgvi41EMRcpmAMqGKPqM5GnYZMHjgg7pwzZo1effuHQ0bNsTHxwcAl2zK48npxGUbK4F8uTRenkfphLW01K9fH19fX3Lnzs3ff/9NvXr1eP78eZat0clOpms18TF6ebR8/fXXOomE4wfXcmrHEO49ieDe04/3NWUGa0sZXZo7AbD9SDARUSnveCwtLdm0aRMDBgxArVbTv39/5s2bl80rNV8KFSrEt9+M4M7F6Vw4NIiaVYvg5+dP9+7dGT9+PKC5kVWr1UkqciXMD+nTMRBBJkcul7Pw5+8ZN24cAD/MX87kGXN1d4+plaWDAe0ldCGttA0eQRASiQ+qefTvU0bVncQP7X8lOo3YkFUmK7XsEwyesGjjDaZcuXJx7NgxGjZsSHh4OM2bN2fPnj3ZlsejS1zOIQFCIJEej+F3gFWrVsXPzw93d3du375N3bp1uXfvXlYtkc8Tkpcv3o43KFfLXOnfvz/btm3DwsKCRzf2cWxTb3afyFoPmTnjVdOegm5KwiPV7DyasvkqgFwuZ/ny5Xz77beAppnz5MmT/7OeseT079+fCxeuULxcS0pU/ZqhY7czevRo3fOiKErGzkeA9AkZSkJVliCqmTFjBj9/r2mpMG/ZKr788ktUKpVhScvphrTSP8EkFh989zKI2KhYQt6Gpeo9gsyLD9pba+YMzWAOrb29Pfv376ddu3bExMTQoUMHfPavBbLew+Pm9EGAMCSHBHiLJ+qrZcxFpEyZMvj7+1OqVCmePn2Kh4cH//zzT5assXxRObZWEBIhcufpx92aoX379hqJBFtbXj7wZfb37bhx979p9MjlAr0SxAgPnArhzXv9dz2CIDBr1ix++eUXQNOvcOTIkVKYJoEqlcsyf+53FCjRlkOn4liyfDsLFy5EFEXkcs31Yd++fTm8Som0kAweQ9FWZSXk53z79Sh+mzQGQRBYtmwZ3bt3Jy6hG7EoplRUTq9KS2FgSAsS9dNSiVT3rsSs41P4ef8ErO2sU90ns+KDDgmHDstE0ZCVlRXbtm2jb9++qFQqxozow/Fd8wmOhPgsvL4q5AJ5EgQIcyqs5Z5HQCHXvH+B+m+yU6VQoUL4+flRuXJl3rx5Q4MGDfD39zf5GhVygeplNJbxuY84rKWlcePGnDh+HBtbJwKfX6KptxevXr1Kf8dPkCplrfmspBXxKti0P/VkNkEQmDBhAkuWLEEQBBYtWkSvXr2yTSLB3KlT1RGv2s7ExUUx7Ze5rF27jvv37wPw+vVr/vjjjxxeoURaSAaPgQjJdHcEmYw+7Vuyas40lEolW7du5Yv2HYiM1FgEKbR4DPXwpNJ+IjEWyfpplfu8FA5aJT/gzJ6LvHma9G7W0uJDpVZG0Ia0ouMyV96tUChYuXKlzh28edEo9qz5kffhWes6z+nEZaVcoHAe48NaWvLkyYOvry8eHh6EhITg7e3NwYOGSxMYyuflNCKE/z5SER718YczatWqxfbdJ7C2c+PFkxvUrev5n1TFFQRNywlBAP/LETx4mnZp5JdffsnGjRtRKBRs3LiR9u3bZ4tEwsfAsJ4FyOViT8nqP+LZdDQlS5YENIr8T58+zeHVSaSFZPAYSvJeWgkeny+aNmTv3r1YW1tz+PBhug4aTkhoyvBS+g1EE4QJRc0jLbQhLX1qyxcP/8PUTnMY7TmZt4nahFtprmMZ9vAoFYJOwDAsk1puMpmMuXPnMnXqVAD2rf2Bb78ZnaWu85xOXIbE5ekZ+wwcHR05fPgwzZs3JyoqijZt2rBlyxZTLpF8ueQUyiNDrYZLtz+Nu/rmjaswYvJB7JwK8ejRA+rWrcuNGzdyelnZTtGClnhW01QKrN37Pt0bq65du7Jnzx6srKz466+/skUi4WPAwU7ByD4FsbRx5ebzstx7pImT37t3jzdv3uTw6iTSQjJ4DCV5s9BEHp9mzZpx9OhRHB0duXj5Hzr0GcTrZK7z9HJ4ZIKmdBnSD2sp0+inVbhcQfIXd6Ni/XK45HXSbdd6eDKatAyJEpdNcKMnCAKTJ09mzKT5AKxbOZ9+/foRH581wnfaxOUcFSBM6Kv1+LWY4aRgGxsbdu/eTdeuXYmPj6dbt24sW7bMlMvUKS+fu/lxNBQ1hJ5flKNl/324uJXVqQtfuHAhp5eV7XRt4YxSKXDzQTSXbqSf0NaiRQuOHDmCg4MDJ0+exMvLK8slEj4GPKo7Ub+WE2o1zFnxlLh4NeXKldN5eyTME8ngMZDkysq6XloJoau6dety8uRJXHPn4sbtuzTwapikv0p6Bo8gCAZr8SiThbQSk8c9N7/5T+PblcOSVA18EB/M+AXMXpvHk4FKrdT4cthIBoxfh0wuZ+3atXTs2JHoaNO3A7C31vQEE0V4HWzywxuEmwvYWEJsvKa3VkaxsLBg/fr1fPnll4iiyJAhQ5g5c6bJ1lm1lAKlXNNQ9OlH0lA0PSqUsKBkiQI067OL0uVq8P79exo1asSJEydyemnZiquzgpb1HADYsC/IoCIJT09PfH19cXV15fLly3h6evLs2bOsXqrZM6xnQRztFTx6Hs2mvQGUKFECR0fHnF6WRBpIBo+haD06YrJu6YkMmEqVKrFv81oK5s/H/Qca1/mtW7cShqdt8ECi9hLphHYSl6XrwyGXPQqlQvf32h+2cvXoZc2xVRBvRKf1xGS2UksfLnbweeOejJy6C0tLS/bs2UOLFi0ICzNtrbogCOTP4b5aMkHIdFhLi1wuZ/Hixfzvf/8DYPz48YwbN84kHhlrS4FK2oaitz6NsJYgCDSrY4OljTONemylUaPGOomE3bt35/TyspV2jZywt5Xx4k0cx88Z9jurUqUK/v7+uLu7c+fOHerWrcvdu3ezeKXmjZODghG9CwKw+a8AAt7L2b9/fw6vSiItJIPHUJLn8CQKcSW+yBQvWpS9G1dRpnRpXrx4gaenJ5cuXTLI4FHIErR40jFILOQfqrTSw2/HOdZN3ca0jr8S9lITX47JYNTIlCEtLU62mpLxz2q1Zucejbqwj48PjRo14t27d6abiA+Jy69yKHEZoFhCWOtBJg0e0FzEf/75Z3799VcAZs2axZAhQ1CpMl/yVishefny3XhiP6KGomlR6zMrnB1kRMXZMOHnbbRv357Y2Fg6dOjAmjVrcnp52YattYxOTTXW/9ZDQURFG+bFK1WqFKdPn6Z06dI8e/YMDw8Prly5kpVLNXvq1XTCs4YjKtWH0JaE+SIZPAYiJM/hERK9dYkMHkEmI59bHo4eOkCNGjV06sJ+fn6a3Q3w8Bga0lKp0++9VbtNdRp296DvT91wK5oHgOjYzJWmh0ebTsBPLgPnhA4Z5ao04MSJE+TKlYuLFy9Sr149Xrx4YZJ54EPi8sv3OSdAqNXjefFWzPDnkJxvv/2W5cuXI5PJWL58Od26dSM2NnP9o4oXkJPLQSA6Fq4++LgaiqaGQi7QpJamn8mJSyo2bd5Mv379UKvV9O3bl99//z2HV5h9NKljTz5XBSHhavacMFwnwd3dHT8/P6pWrUpgYCANGjTQndv+q4zoXRAHOzkPn0az85CU32TOSAaPoSQLYekMIM1G3X+1uT25XJw5fvw4Xl5ehIWF0aZNG44fP45KpUr1YqswMKQll4G2gXp6CbgKpYLx676iy9i2ujyeyKiMJaPaWGq8MWoRIkzY8FNbUf8uHKpXr46fnx8FChTg5s2b1K1bV6dzkVnyOGrWHxWbcwKETnYCLvaa9/BJgOmMroEDB7JlyxaUSiXbtm2jTZs2REREZPh4MkFIlLz8aYS1AOpXs8LaUuDVWxU3HqpZsWIFX3/9NQCjR49mypQpn0yidloo5AI9WmnuAPb5hvAu2HCj1tXVFR8fH+rVq0doaChNmzblwIEDWbVUs8fJQcnwXprQ1tYDATm8Gom0kAweQxGSe3gE3VNJGojq2kuosLe358CBA7Rt25aYmBiGDhvG7t27Uz2hakvT0/PwCILwQW3ZgLCWkLBWS6WAOl7F733msey7dUaf2AVB+JC4bMKwVi5tT62EdIKyZcty+vRpSpQowZMnT/Dw8ODq1auZnkchF8jjpPn/yxwtTzddWCsxHTt25K+//sLGxobDhw/j7e1NUFDG43c1yigQgAcvPs6GovqwtpThVV3zJT54OhKZTMacOXOYNm0aAFOnTmXUqFH/CXXhmhVsKFPUktg4kS0HjfueODg4cOjQIVq1akVUVBRt27Zl8+bNWbRS86d+LSfqVnNE/XELlH/ySAaPAajev/qQwyNqdXgEvYnLydtLWFlZsX37dnr16oVKpeKbb79lwYIFeuf50EDUgPYSaVRqpYaVEp6c/5erhy+xZ9Ehnt42Plz0oaeW0bumSmIPj5bChQvj7+9PpUqVCAgIoEGDBpw5cybTc+V04jIkbjNh+ouqt7c3x44dw8nJiTNnztCgQQMCAjJ21+lsL6N0QkPRC59I8jJA41rWKORw/1kc957GIggCEydOZOHChQAsWLCAvn37fvLqwoIg0LutxsvjezGcxy+Mc9taW1uzc+dOunfvTnx8PN27d//PKg0LgsCI3gVxz2+V00uRSAPJ4DEAMehNCqVl0JPXg/6u6AqFgtWrV9OnTx9A4zr/6aefUnhYFInUltPDIp1KLX1YKgWK1q1Ep5mDmbx1DIXLFjR4Xy0fPDym805oPTzh0UmVoN3c3PD19aVu3boEBwfTuHFjDh8+nKm5zCFxuWhejeZSYAiERpo+fFK7dm1OnjyJm5sb165dw8PDg8ePH2foWNqGohduxaP+iBuKJsbJXk6dipoL06EzH2Kbw4cPZ/369cjlctatW5dlEgnmRMnCVtSubIsowvp9xv8olEol69atY9iwYYiiyPTp0wkPT9kc778QJnRxUvLbZEmHx5yRDB4DEFGlqNICdInLYpIcHnmKbaApS//xhx8Y9dVXAHz//fd88803SVznhiYtAygTxsYaUWKuzeGp2LYBtVtX120PCggmKsKwE3tWlKZbKsEu4cYoeSNRJycnjhw5QrNmzYiKiqJ169Zs3bo1w3NpE5dzUoDQ2lIgfy7TlKenRsWKFfH396dIkSLcv38fDw8Pbt68afRxPiv2oaHo7Y+8oWhimtaxQQCu3Inl1dsP+Ss9evRg9+7dWFlZsXfv3v+EunCPls7I5XD1ThT/3DY+uU0mk7Fw4UJ++eUXjhw5gp2dXYoxgiAgimKmQqwfAzKZkP4giRxDMngMQa1OqbRMKh4eIaWHR4tcLuerr75iVoJI3Lx58xgwYIBOXVie8GPJypAWJG0vERYUzjjvnxjv/RNhQem3LXfIgpAWaPR4IGlYS4uNjQ179uyhc+fOxMXF0bVrV5YvX56heeytBexyWIAQoFj+jPfVMpQSJUrg7+9PuXLlePHiBfXq1ePixYtGHUMhF6hWWvPF+VQ0eQDy5VZQuYwFkNTLA9CqVSsOHdJIJPj6+tKoUSPevn2bE8vMFtxyK2nmoREjXLf3fYZUwLVNR0uXLp3iObVazaJFi/Dy8qJDhw507979P5EjJWF+SAaPIajVH5SWE3dCNyCHJzFaLZ6hQ4eyZs0a5HI5q1evpnPnzsTExBgV0sqIwWOp1Pbg+tC1PeBJIG+fv+P1ozeEB6Vf1aNtyB4Tp7+XV0ZJnricHAsLCzZu3MjgwYMRRZHBgwfr9GeMRRfWMos8HjFL3f0FChTg1KlTSSQSfHx8jDpGrbIJDUUffhoNRbU0r6PRQzh7LZqgsKTeq/r16+Pj40Pu3Lm5dOkSnp6ePH/+PCeWmS10aOKErbWMp6/iOHUx/RsfY2jfvj2LFy8mb968DBkyhFevXum+jxIS2Ylk8BhCYg8PfEhcTsvgEfUYPHJ5wuHU9O7dmx07dmBpacmuXbto2bIlUVGRCc+nfxG0MKJKS7eP4kNxmTZXpkTlosw5OZUZhyeRr5hbusdQygWsNTfGJq3U0pe4nBy5XM4ff/zBuHHjABg7diwTJkww2mDQJi7nVOd0AHdXAaUcwqPgTXDWzpUrVy6OHz9Ow4YNderCe/fuNXj/Aq5y3PPIUKnh0p1Px8tTwl1JyUJK4lVw7HzKL3O1atXw8/OjYMGC3L59Gw8PD+7du5cDK8167G3ldGjiBMCmg0FEx5jGAzN27FhOnDjBr7/+yubNm+nSpQvLli0jNjb2P9m1XiJnkQweQ1AnyuGBRB3TDUta1pJcbblt27YcPHgQOzs7jh8/TrOm3oQEa67CBjcQNcLLIggClgkdJxL31Cr6WSGKViis+/ve5Yc8vpF6r5ysqNTSeniCI5KmSSVHEARmzJjBjBkzAJgxYwZDhw41Sl04sYcnp5IpFXKBwm5ZV62VHHt7e/bv30+7du2IiYmhffv2rFu3zuD9tZo85z+hhqIAzetohAh9L0URpeciX6ZMGfz9/SlZsiRPnjzB09PTJBIJ5kgzTwdcXRQEhajYf9JwMcLUOHHiBLNnz2bBggU0b95ct71YsWLcvn07Sa9BCYnsQDJ4DEAU1UmVldXJ+mklTlpOpMOTHG3ZeeKQlZeXFydOnMDFxYXz58/Tr3s73gS8Tre9hNbgiTVSylybuJxa1/Tnd18yodk0xtT/ngdXH+sdkxWVWvbWoJRr1KODDcibHDduHEuXLkUQBJYuXUqPHj0MVhfO46gRb4yK1RhYOYW2r9bd59ljQFhZWbFt2zb69OmDSqWid+/eqUokJEfbUPTVOzXP3nw6+RcVS1mQL7ecqBiRk3/rt+C1EgmVK1cmICCA+vXrc/r06WxeadajVAj0aKlxf+4+EUJwWMaT1EVRZOTIkXTv3p3OnTvrtMAAli9fTvHixSlVqlSm1ywhYQySwWMIapXmB5usKkvQU7lljIdHS40aNfDz8yN//vzcv3eHPl3bcO9+2q5zpS7fB6PKhS3T6ZrukNuefMXzkreIK3kTWlEkJysqtQThQ+Jy8kqt1Bg8eDCbN29GqVSyZcsW2rVrR2Rk+taSQi6QJ6GpcU6Wp5dx13yGjwNEImOyx+hRKBT8+eefjBo1CoCvvvqKqVOnpuu1sbEUqFhc4x78lJSXZQlNRQGOnItMtWAgT548+Pj44OHhQUhICE2aNOHQoUPZudRsoU4VW0oUsiQ6RmTroYz/OK5cuYJcLqdHjx5YW1vrvl/Xr1/n2LFjlC9fHnd3d1MtW0LCICSDxxC0Bkryqiy92jxp5PCk0UC0XLlynD59mkKFi/Li+VOaNGrI9evXU12SQg7aeyZj8nisEvJvolMzeFzsmXV0MtMPTcLWwUbvmKwIaQHk0ubxGNEovXPnzuzduxdra2sOHjyIt7c3wcHB6e5nDonLuRwE3Jw1bSayy8sDmu/hvHnzmDp1KgBTpkzh66+/Trdy5lNsKArweQUrnOxlBIepOXc99S+1k5MThw8fpnnz5kRFRdGmTRu2bNmSjSvNegRBoHcbzY/j+LkwngdkvCfb/fv3yZ07t+64r1+/Zs2aNdy6dYtBgwbh5OSUwtB+8eIFmzZt+uT1jyRyBsngMYRkIawPOTz6PDxaIyiNpOVU8k2KFCnC9t0HKFWmHAEBr6lfvz7nzp3TO1YQhExVasWkcZNubWeNY24H3d9+O85xZI2v7u/ETUTVJsznSKs0PS2aNWvG0aNHcXR05PTp03h5eaWrLmwOicsAZQtpvkM3n2RvmEgQBCZPnsz8+fMB+P333+nfv79OIkEfJQrKcUloKHrtE2koCppQTpNami/1oTORaX6nbWxs2L17N126dCEuLo5u3bqxbNmy7FpqtlC2uBU1KtigVmdMjBAgd+7clC9fnn///Zf4+Hji4uKYPHkyp06donfv3jRr1gz4oM8DEB0dzYEDB+jRowe5c+fmxIkTJntNEhIgGTyGkRAySq62rFd9OQ0dnrQ8PFry5svHn+t3UqPm5wQFBdG4cWOOHTumd6y2n5Yx5eH6tHjS4tH1J/zS/Td+7beIy8euAWBtocmBEUUwUK/QILQenvdhSRrQG0TdunU5efIkefLk4Z9//sHT0zPNpEith+dtiGnL641Fa/A8eCmmGmbMSkaOHMnatWuRy+WsWbMmTXVhmSBQs6xWk+fTMXgA6lezxtpS4GWgiuv30vZqWFhYsGHDBr788ktEUWTIkCHMTNDW+lTo0coZmQz+vhHJjfvGx64LFSrE0KFDGTZsGA0aNCBv3rz4+/vTvXt3XZWlWnfjqDmP+fn5MWvWLMaOHcvMmTNp0qQJkyZNMt2LkvjPIxk8BiCibRiazKOjL2k5Azk8iVHIZTg4OrFp6y68vb2JiIigZcuW7NixI8VYnYfHiJCWpS5p2bB9Cpd3p+3wZtTvXJtKXuWBhCaiWRDWcrbVhOmi4yAyA570SpUq4e/vT6FChbh37x4eHh7cvn1b71idACHwOge9PG5OmpL8eDXcf5EzhlevXr3YuXMnlpaW7Nmzh5YtWxIWpj+uWKuspqHovecq3oZ8OsnLNlYyGlRLaCp6Jv08MLlczuLFi5kwYQIA48ePZ9y4cZ9MBVuBPBY0qaO5A1m7532G2or079+fa9eu0b17d2bPns3p06cZPXq07vnEicwPHz5k69atPHjwgGnTpjF8+HB8fHyYO3cuQ4YMyfTrkZAAyeAxjOQ5PKLWw5NG0nJigcIEDDF4tJVcllbW7N27l06dOhEbG0vnzp35888/k4xVZqCfllWikJYhJ2eZTMaQOX2YsH4Ucrm2bYaIraVmTlNq8Sjk4JiQNpSaAGF6lCxZktOnT1OmTBmeP3+Op6cnf//9t96x2jYTOZm4LAjCh7DW05wzINq0aaOTSDhx4gSNGjXSKwznbC+jVEJD0fOfUPIyQOPPrZHL4O6TOB48T/+1CYLAL7/8wqxZswCYNWsWX375pVESCeZMp6bOWFsKPHwey+krGStnLFmyJMOGDaNfv364uLiwb98+Fi5ciCiKSQweZ2dnvL29admyJTVq1ODs2bPUq1ePwMBAXSd7CYnMIhk8hqBOauDoQlhC6iEtzRNJDQpdWXoaJ0R5QpgqXi1iaWnJpk2bGDhwIGq1mgEDBjB37lzdWKVCczzjcng+LC3WwKiEIAjIFR+EF9dP3c6+/y1GHR9v0tJ0+CBAaGillj4KFiyIn58f1apV4+3bt3h5eeHr65tinDk0EgUoW0jzmd97IRrUViSr8PLywsfHh1y5cnHx4kXq1avHixcvUozTKi9fvB1v0hyunMbZXk7thKaih88a3lPqu+++Y9myZQiCwLJly+jevbvBEgnmjKOdnHaNnADYdCAo073nIiMjmT9/PuvWrUsh4Ojs7EynTp3Ytm0btWrVYtq0aURERGBra4urq+sn4zmTyFkkg8cQxORVWsnK0vWEtDSbkxo2skTPpeblUSRrICqXy1m2bBnfffcdAN988w2TJk1CFEVdDo8xIS2ZIOjyeKJijT+JPL/7kg0/7+DSLj+enbuaZT21MmPwALqkx/r16xMWFkazZs3Yt29fkjH5EhKXXwflbDfnArkF7Kw1XrfHr3P2xF69enX8/PwoUKAAN2/exMPDg/v37ycZU6GYAisLCA4Xuf/80/BmaPGurXEx/n0rhsAgw1/boEGD2LJlC0qlkq1bt9K2bVuDJBLMnZb1HXB2kBP4Pp6jZzLXRNXGxoa1a9fy448/6jR4kleiWllZ0aFDB06fPp0kB0/rDZIMH4nMIBk8hpAsZydFDk/ikJYg6Po3JP9xCgYYPB+8QEmPOWvWLKZPnw7Azz//zIgRI1DINMc39s7LyiIhjycDBk/BUvn5Yed3dJvSjcIe1Uxfmm4igwfAwcGBgwcP0rp1a2JiYvjiiy/YsGGD7vk8jiATIDIGQnPw2iQTBEoX1Hzut57l/Am9bNmynD59mhIlSvD48WM8PDy4du2a7nmlQqBqKY2X58InlrxcMI+Cz4pbIIpw9LxxX4pOnTqxb98+bGxsOHTokMESCeaMpYWMTk2dANh+JJjI6MyFXfPly6er0Fq/fj3z58/X5YtpPd+xsbHI5XIcHDSVookrBwVBIC4ujsOHD3Pp0qVMrUXiv4dk8BjAhxCW1qOTtEqLZJ6cFKEv7XZBSDePRxvS0texePz48SxZsgRBEFi8eDEjvuxLfFycUR4eAOsEgycqg173z1tVo9ekLwCNVyIiIpZQY8Rz0kDr4QmO1IgqZhZra2t27NhBz549UalU9OzZk0WLFgEaAUJXMxAghA9hrTvP1GYRJtKqC1eqVEmnLnzmzBnd89pqrWsP4jNkOJsz3rU1yct+V6KNvsA3bdqUo0eP4uTkxOnTp2nQoEG6EgnmTsNa9uRzVRIWoWafT+ZbTmgpWbIkPj4+jBkzhqioKORyOYGBgaxevZpatWrpxikUCqKiovjrr7/o0qULw4YNo3379tSsWZPjx4+bbD0Snz6SwWMIuhBWUo2d1ErQM1Oarg1pxatSJj0DfPnll2zcuBGFQsH2rZuZ8FV3wsKNuxPVNv+MykQZtFKuCY2pVWqm91rA1/UmE/g8892PbSzBUqHJMTJV2welUsmaNWsYMWIEACNGjGDatGmIoqgLa+W0wVM0r4CFQpME/vKdeRgQbm5u+Pr6UqdOHYKDg2nSpAmHDx8GoLCbDFcngdh4uHr/0/LylC9mQYE8cmJiRU7+bXxWfp06dTh58iRubm5cvXoVDw+Pj7pvlFwu0D2h5cQ+38y1nEhMrVq1OHr0KOfPn8fT05P27dtTunRpHj16RK9evShYsCD+/v706dOHpk2bsmDBAqpUqcKkSZMoXrw4Xbp0wdHR0SRrkfhvIBk8hiAmD2ml3ksLPhhG+oyadD08icNeqdzpd+3alT179mBlZcWZk4f5asAXRrnOrZVaD0/mLqz21hD5Loi7F+7x6kEAL+69ytTxQBMNdM6gAGFayGQy5s+fz/fffw/A5MmT+eabb3Bz1HwOOVmaDhpvU8kCms/l9lPzMHhAoy585MgRmjZtSmRkJK1bt2b79u0IiTR5Ltz6tKq1BEHA+3NNLs+xC1EZSiSvWLEi/v7+FClShPv371O3bl1u3bpl6qVmG7Uq2lCikCUxsSLbj5jux1K0aFGuXbtGr169qFatGl9++SXr1q2jW7du/P333/Ts2ZPbt29z8OBBDh8+zPjx4/ntt99Qq9X07NmT6tWrm2wtEp8+ksFjCFrjREietJz0by2CLKHc0sj2EprnBW0KUJoNRFu0aMGhw4extXPgn0unadiwEYGBgYa8Gl0OT6YNHiuwy5OLkdunMmXnd1T2+ixTx9NiqsTl5AiCwI8//si8efMAmDdvHj9PHIhKFc+bEHK0Qgo+9Na6/cy89G1sbW11EglxcXF06dKFlStXUqOMRpPnwUv1J6XJA5p2Ew62MoJC1Vy6GZOhY5QoUQJ/f3/KlSvHixcv8PT0/GjzTgRBoEcrjZfn2JkwXr81rZE7atQoJk6cyC+//ELZsmUBTbudYcOGceXKFX7++WdAk/ezbds2hg0bRqNGjQApkVnCcCSDxxDUKo1uRHKPTqq5Ohn38ID+xGV91K9XjyXr9uPkkpsrVy7j6enJ06dP03051rqk5cydLLRNRBUuuanVoqpue3BgCLfOp938NC2yyuDRMnr0aFatWoVMJmPThtVsnNOVmJgYAjNXhJJpShYUkAkQGAJvQ83rJG5hYcGmTZsYNGgQarWagQMHsuKPuZRy13zXL97+tLw8SoVAo5qaXJ7DZyMz/DspUKAAp06dokaNGrx79y5ViYSPgc9KWlO5jDUqNWw+mPUuUWtra8aOHcvu3bvZuHEjlStX5n//+x99+/alTZs2WFlZpdDz+VhZvXo1giDw+PHjLJ9r1qxZlClTJt3eeVnBH3/8QaFChYiJydhNRGaRDB5DEcUUzUN1mjvJPDkyE6gtA8Qb8IX8rEIVlqw/TMGC7ty5cwcPDw/u3LmT5j7asnS1EVo8+rDTo7YcERrJ/5r/zHcNf9C1ojCWxC0msoq+ffuyY8cOLCws+OfMTlb81IqHz7PIwjIQawuBInm1YS3z85jI5XKWLl3K2LFjAY3+zMWDPyCK4ienyQPgVd0aCwU8eRXPnScZN+hy5crF8ePHadiwIeHh4TRr1oy9e/eacKXZR/cEL8/pyxE8fJb1Fy21Wk2LFi04deoUz549482bN+TKlYuCBQsCfBLGTnYSGhrKzJkzGTduHDKZjGfPniEIgkGPhw8fZnr+vn37Ehsby9KlS03waoxHMngMRVTrKUvX3yhUkKcUJNSiM3gMEB9MK6SlRakQKFSkJH8d8qV06dI8e/YMT09PLl++nOo+MlnmtHi0aNtLhEd/8BTJFXKc3JywtrcmT6HcGTqus63m3+g4Tcl4VtGuXTsOHDiAtY0t964dZ0D3Jrx/n4Pt04Ey7tpqLfM0HgRBYObMmcyYMQOAVctmcWr717wNjufhS/Mz0jKDnY2MupU1Xp4jRggR6sPe3p79+/fTrl07YmJiaN++PevWrTPFMrOVogUs8aiq+YFu3J/1Xh7t+XL27NkULFiQDh06sHjxYkJCQnLEQ/Gx8+effxIfH0+3bt0AsLS0ZN26dbqHthGul5dXku3r16+nWLFimZ7fysqKPn36MHfu3BwJRUoGj6GoVbpQVXLlZW3IS0uaVVry1MNdWgwNacGHBqJ58mnUhatWrUpgYCBeXl74+fmlup+1CfJ4bC01ScYq9YcSdysbS37c9R2/+f9EwVL5M3TcJC0mstjp0qhRIzbuOI6NvQt3/j1PvXr1ePnyZdZOmgbaPJ5ngSLhJlaxNiXjxo1j6dKlCILAP34rObx2IGeumaiszoxo8rk1AvDP3Vhevc1cNZqVlRXbtm2jT58+qFQqevfuzYIFC0yz0Gyka3Nn5HK4eieK63dN2FsmFY4cOcKSJUvo378/q1at4tKlS1J1VgZZtWqVLhwIkCdPHnr27Kl7VKlSBYCWLVsm2d6jRw+TraFz5848efIEHx8fkx3TUCSDx1DU6pRVWam0kUhNhwcMzOExIqSl7acVGy/i6uqKj48P9erVIzQ0FG9vbw4cOKB3P6tEeTwZRSYTsLPU/D9xWEtpoaRAiXy6vx9ee8LO3/YbdeyszuNJTLNGNRn+80kcXPJz48YNPDw8ePDgQdZPrAdHW4H8uQRE4M5z8zV4AAYPHsymTZtQKBTcvbydH77rRJCptATMhLy5FFQqrdFxOHou8xd3hULBn3/+yahRowD46quv+Omnnz6qxFu33Eq862hEATf89T7L1+7t7c3u3btp3749FhYWODg4EB8fn6YX+1PgypUrNG/eHAcHB+zs7GjUqBHnzp1LMc7X15fq1atjZWVF8eLFWbp0KT/88EOKcN+jR4+4du0ajRs3TnVOrcBohQoVTPtiElGtWjVcXFzYs2dPls2RGpLBYyhqVYqydF2VFiRrL5G5pGWFzIiQVrL2Eg4ODhw6dIhWrVoRHR1N27Zt2bRpU4r9TKHFA2Cn8fin2kQ09H0Y45v+xJIxq9n3xxGDj5udBo+VUqBsufKMmO5HoSLFefToER4eHvz7779ZP7ketGEtc6vW0keXLl3Ys2cPSgtrHt04glejpoSEmE6czhxomlCifvpqFGGRmf9MZDIZ8+bN48cffwTg+++/Z8yYMR9ViKZDEyesLAUePIvl3NWslylv2bIl7u7ugCZ8PmjQIOrUqcPWrVuzfO6c4MaNG3h6enL16lXGjh3L5MmTefToEQ0aNOD8+fO6cVeuXKFZs2a8e/eOH3/8kQEDBjB16lR2796d4pha4dCqVaumeE6L1uCpWLGiaV9QMqpWrcrp06ezdA59mJXBs2jRIooUKYKVlRW1atXiwoULqY5dvnw5np6eODs74+zsTOPGjVOM79u3b4rEK62subGIojpFWTqJLOjE3hyZiTw8KiM8PInbS1hbW7Nz5066d+9OfHw8PXr0YMmSJUn2M0VICxLn8eg/joOLPR3HtKZYpcI06FLH4ONmp8EDmr5audyKMn/NKSpUqMDr16+pV6+e3juqrEYb1nr4UiQmkwZpdtCiRQumL9iPhbUjVy9r1IXfvHmT08syGaUKKymcT0FcPPheMk0IRxAEvv/+e37//XcAfvvtNwYMGJCkjYI542gvp00DTVhp4/73mW4sagwqlYqoqCji4uLo2rUry5cvz7a5s4tJkyYRFxeHv78/kyZNYty4cZw+fRoLCwtd0QDAlClTkMvlnD59mnHjxvG///2PkydPcuPGjRTHvH37NqDRPkqNa9eu4erqSt68eU3/ohJRrFgxbt68maVz6MNsDJ4tW7YwZswYpkyZwuXLl6lUqRJNmzZN9cTp6+tLt27d8PHx4ezZs7i7u+Pt7Z2iu3OzZs149eqV7qHP22EQalWisnRtlZagv5+WXH8yMxhp8BiSw6MzeJKOVSqVrFu3jmHDhiGKIsOGDeOXX37RuZ9NZ/BojpOahweg83dtWXBuOvZaRUHSL4fXtZiISCFzlCVoFZfjFfnw9fXl888/JygoiMaNG3Ps2LGsX0Ai8jhpxBfj1fDgpfkbPAD9utan48gDWNu78s8//xgskfAxIAgCTROaih6/EGXSi/tXX33F2rVrkcvlrF69mk6dOhEdbeIGdVlEqwaOONrJeP02nr0ngrNtXqVSyYYNGxgyZAiiKDJ48GBmzZqVbfNnNSqViiNHjtCuXbskicL58uWje/fu+Pv7Exoaikql4tixY7Rr1478+T/kS5YoUYLmzZunOO67d+9QKBTY2dmleE7L9evXs9y7A+Ds7ExUVFS2N9g1G4Nn7ty5DBo0iH79+lGuXDn++OMPbGxs+PPPP/WO37BhA8OGDaNy5cqUKVOGFStWoFarU/RWsbS0JG/evLqHs7NzxhaoVqfolg6kSGQGE+TwJIS0DBHCUyYYR/r6aclkMhYuXMikSZMAmDhxImPHjkUURayUptLi0fybXhNRC0ul7v9n9lzk+7YziU6jBMvOCpRyTel8cDb8JhJ3Tnd2dubYsWM0adKEiIgIWrZsyc6dO7N+EQkIgkCZQuYpQpgaLg4y6nxehU6jDpMnbyHu3r1L3bp1dXeVHzvVy1ni7CAjNELNueumNUh69erFzp07sbS0ZPfu3bRs2VLXUNOcsbaS0addLkDTWPR5QCYSAo1ELpezZMkSxo8fD2iS6MePH/9R5UKlRmBgIJGRkZQuXTrFc2XLlkWtVutK9KOioihRokSKcfq2pcerV694+/Ztqvk7RYoUwd/f3+DjpTVe+zllt6yAWRg8sbGx/P3330mSqWQyGY0bN+bs2bMGHSMyMpK4uDhcXFySbPf19SVPnjyULl2aoUOH8u5d6v2eYmJiCA0NTfLQkShpOYnLQU97ieQ9txJjjA5PRkNaiREEgZ9++ok5c+YAmvLOQYMGoZRrjLHMavFoQ1oRMfobniYnMiyK2QMWc+6vv9k9X39CtWbd2RvWyu2gqQ6LiYegcI268L59++jYsSOxsbF06tQpVeM7KyibkMdz97lo0PtqDtQoo8A5T0l6jztKmTJleP78OZ6envz99985vbRMo5ALNKml8fIcyYQQYWq0adOGgwcPYmdnx4kTJ2jUqFGa5ypzwaOqLVXKWhOvgj+2vEWdjd9VQRCYPn06M2fOBGDmzJkMHTpU13VdIim5cuUiPj4+VWM6u/J3AIKCgrCxscHa2jrL50qMWRg8b9++RaVS4ebmlmS7m5sbr1+/NugY48aNI3/+/EmMpmbNmrF27VqOHz/OzJkzOXnyJM2bN0/1BzF9+nQcHR11D22SHACi6oPQoB5vTmIj6INRk7aHJ7WTpjEhLYuEpGW1mLbBMWbMGFauXIlMJmPlypV069oVmajxsGQmrGWp1BgKABEG3Pja2Fvz097xNOlTn07ftklzbHYaPDKZgFuyzumWlpZs3ryZAQMGoFarGTBgAHPnzs36xQDurgI2lppy/ycBH4fBU6m4AgslxMnzs26bL9WqVePt27d4eXlx8uTJnF5epqlX1QpLC4EXgSpuPDC9N8PLywsfHx9y5crFxYsXqVevXooQvbkhCAKDOuXGylLgzqMYjpzJfs/U2LFjWbZsGYIgsHTpUnr06EFsbPZ5m0yNq6srNjY2egVkb9++jUwmw93dnTx58mBlZcX9+/dTjNO3rUyZMoCmWksf169fB/QbPAMHDuTp06d4e3tjZ2fHhg0bgA/J1U5OTlSrVk2XiJzaeC2PHj3StRDJTszC4MksM2bMYPPmzezatUunLwCaJptt2rShQoUKtGvXjr/++ouLFy+mKu0+YcIEQkJCdI9nz57pnhMThbSSNAsVUooMGuLhSe15+BDSUqnFdO8kZTLQtu5KL7egf//+bNu2DQsLC3bs2MGkEe2JjorIlMEjCILOy5NeWEtL+TqlGbtqBHKF9v0UCdUjq5zdict5E5yDiTuny+Vyli9fzrfffgvAN998w6RJk7LcdS6TCZQuqK3W+jgMHksLgcolFADcD3DkxIkT1K9fn7CwMJo1a8Zff/2VwyvMHDZWMupV0XzZD5ugRF0f1atX59SpUxQoUICbN2/mqESCobg6K+jeUvPj2fDXewKDsj/xetCgQWzZsgWlUsmWLVto165dtueHmAq5XI63tzd79uxJ0moiICCAjRs34uHhgYODA3K5nMaNG7N79+4k2mH379/n4MGDKY5bu3ZtgFT7uV27dg25XE65cuVSPLdixQoKFSrEkSNHCA8P1xmVrVu3plOnTgQGBjJ27Fhat25NUFCQ3vGJuXz5MnXqGF7EYirMwuDJnTs3crmcgICAJNsDAgLSzRafPXs2M2bM4MiRI+m64ooVK0bu3Ln1Wr+guaN3cHBI8tChpywd9Ht40svh0cYtUwtZaT08mjFpX+wEQUCRrDQ9Ldq3b8/+/fuxtbXl/OljjB/SgtdvMqeYap9OaXp6bJm5myGVvuXJzWdJtudEpRYkNXhA8x7PmjWLX375BYCff/6ZkSNHZnkZceI8no8lN6FGGY3B88/9eKys7Tl48CCtW7cmOjqadu3apbjT+9hoXMsGQYAbD2J5HpA1F/Zy5crh7+9PiRIlePz4MR4eHrpwg7nStK49pYtYEh0jsnzb2xz5vnbq1Im9e/dibW3NwYMH8fb2Jjg4ONvXYQqmTZuGQqHAw8ODX375hVmzZlGnTh1iYmKSJGj/8MMPxMfHU7duXWbNmsX06dOpX78+n32WspFzsWLF+Oyzz1Itwrh27RolSpQwOMx0/vx51Go1X331FUqlki5dulC6dGkOHTqU5n5///0379+/p23btgbNY0rMwuCxsLCgWrVqSRKOtQnIWqtUH7NmzeKnn37i0KFDVK9ePd15nj9/zrt378iXL1+6Y1MgqvV2R0/RUJSkHh59P/x0O6YLAjKtUWRIWCudPJ7kaCuPHJ2cuXXtHF3befHq1SuD9tWHrlIrldL0tIiJiuHY+lO8ffGeKyeS6t5oi7oiYzInkGgoWoPnbWjK91IQBCZMmMDixYsRBIFFixbRq1cv4uKyrmlmsXwCSjmERGiSqT8GiheQ4+IgEB0L1x/GY21tzY4dO+jZsycqlYpevXqxePHinF5mhnF1llOtrEZt8/C5rPMgFClSBD8/PypWrMjr16+pX7++wfmMOYFMJvBl19wo5HDlVhSnr+SMAGWzZs04evQojo6OnD6tkUhIfiP9MVC+fHn8/Pz47LPPmD59Oj/++COFCxfGx8eHWrVq6cZVq1aNgwcP4uzszOTJk1m5ciVTp06lUaNGSaIdWvr378++ffuIikp6dxofH8+tW7eMyt95+fJl0rQPoHDhwukq1W/bto1ChQrRsGFDg+cyFWZh8IAmx2T58uWsWbOGW7duMXToUCIiIujXrx8AvXv3ZsKECbrxM2fOZPLkyfz5558UKVKE169f8/r1a8LDNe6A8PBwvvvuO86dO8fjx485fvw4bdu2pUSJEjRt2tT4BarVOmXlJCEtmb6QVqKwlZixxGVdPy0DkgBTK01Pi88//5zt+07gkjsvd2//i6enZ6qx3fQwNqSVGEtrS+b4/sioJYNpNyJpKaWF4oP3KDu8PPbWAnZWGtHs5F4eLUOHDmXDhg0oFAo2btzIF198keLkYSosFALF85tvM1F9yARB5+W5cFvjAVEqlaxZs4YRI0YgiiLDhw9n2rRpH43XKjnaEvVz16IJDsu6BNm8efNy8uRJ6tSpQ3BwMI0bN+bIEcPFO7Obgm4WdPB2AuDPne8IDc+Z5OG6dety8uRJ3NzcuHr1Kh4eHjx58iRH1mIoffv2RRRFihQpottWpUoVDh06RFhYGBEREZw4cUKvA6Bhw4ZcvnyZmJgY7t+/z4ABA7h//76uwWpi+vfvj4WFBRs3bkyyXaFQEBMTk6aQY/KKqvz58ydJ+wB4+vSprkReXwVWTEwMa9asYcyYMTnS+NVsDJ4uXbowe/Zsvv/+eypXrsw///zDoUOHdInMT58+TeKFWLJkCbGxsXTs2JF8+fLpHrNnzwY0cdBr167Rpk0bSpUqxYABA6hWrRp+fn5YWloav0C1KkW3dEjUMT1JqXraeTpGtZcwop+WISGtxFStXJG5q33IV7AIDx48oG7dunoFq9IjPbXl9HDM7UCrIU10f8fHxXPjjCZhL7vDWgUT+p0+e5v6mG7durF7926srKzYv38/zZo1yzJ14bIfWXk6QI0yGgmCu09VBIdr1i2TyZg/fz7ff/89AJMnT+bbb7/9KI2e4gWVlHBXolLDiYtZ20vKycmJI0eO0LRpUyIjI2nVqhXbt2/P0jkzQ9uGTrjnUxIWoWb17pyrMqtUqRJ+fn4ULlyY+/fvU7duXW7dupVj68lKkt9w3bt3jwMHDtCgQYMUYx0dHRk7diy//vqr0SH5PHnyJMkp0nqaFi5cSHx8PNu2bePWrVs6cd/k40HTy0upVPLll18aNbfJECVSJSQkRATEgB0LxLhnd0RVdIQY4b9djDi9Uzcm/NG/YtDl42LU6ydJ9n195x/x1a3LYlxMVIrjvnzxQnxw/74YGhqa6twPn78Xr917Lb4PjUx3nY8DosXTN0PFh69SzpUWkTFq8fA/MeKmo4/Ezz77TAREFxcX8dy5c0YdJy5eLW49qxK3nlWJMXFqo/ZNjkqlEn/p8ZvoregsHl13Urz0QBSXHxPFkzcydViDuf5YLc7ZrRY3nkz/dZw6dUp0cHAQAbFq1arimzdvTL6e8Ci1OGVtrPj9mljxfVjm3tvsZP72CHHU/DDxyMWYFM/NmzdPBERA7NevnxgXF5cDK8wcl25Gif1+CBBHzHwjRsdm/ecSExMjdurUSQREmUwmrlixIsvnzCh3H0eJnb5+KHYc/VC8fDMiR9fy/PlzsWzZsiIg5s6dW7x06VKWzqe9ZoSEhGTpPInJmzevOH78eHHZsmXixIkTRRcXF9HW1la8e/euSefZsWOHWKBAAdHR0VHcsGGDKIqiePXqVbFOnTqig4ODWKVKFfHUqVNpjs9pzMbDY/aIqg85POKH3Bxd+EpM6r7NrBaPLqSVgX5ahmKpBAFwcc3P4WO+1KpVi/fv39OoUaMUAo5poZALut5cGfXyaBHVInKlXFP95WKX7R6eQq6af18Hk25bB09PT3x8fHB1deXy5ctZoi5sayVQOM/H01tLS42yGi/PxVtxKbw4o0ePZtWqVchkMlatWkWXLl2IiUldhNIcqVLaEldnGRFRImf+yfqO4RYWFmzatIlBgwahVqsZOHCgzpttbpQsbEULT03Bx7Jtb4mKzrnvbYECBTh16hQ1atTQSSSkVqX7sdKsWTM2bdrEyJEjWbBgATVq1ODUqVOULFnSpPO0b9+e58+fExwcTPfu3QFNCfvp06cJCQnRnQPTGp/TSAaPoSTK4QE+NAtNxbAxVcd049pLGGfwyAQBrQCyjZ1GXbhx48ZERETQokULdu3aZfCxMpPHkxi5Qs63K4cx/+zP1GpRVWfwBGVTiwkHGwFHW00ezwsDPPJVq1bFz88Pd3d37ty5g4eHB3fv3jXpmkprm4k+/XjCP5VLKLBQwJtgkScBKT+4vn37smPHDiwsLNi5cyetWrXS5d99DMhkiYQIz0WhzobQnFwuZ+nSpbpeSt999x0TJ040y7Bg1xbOuLooeBukYtOBnM24z507N8ePH8fLy0snkbBv374cXZMpWbVqFY8fPyY6OpqQkBAOHTqUZoNQcyEsLIzRo0dTuHBhrK2tqVOnDhcvXkxzH19fX6pWrYqlpSUlSpRg9erVRs0pGTyGkjiHR/s3+nN4wMCO6WkogsoTxsQbo7ZspIcHkvbUsrOz46+//qJ9+/a6/ChDv1B2WoMnKvMnX5lMRqlqxQFN0rIqLIx/N+0hOCJ77hQLJeTxPE0jjycxpUuXxt/fn1KlSvHs2TM8PDy4cuWKydajbSb69I1IZAYq4XICKwuBisUTkpdv6a9ka9euHQcOHMDW1lZnbL9//z47l5kpPKpYYWMlEPBexdW72SN0JwgCM2fOZMaMGQD88ssvDB8+3Ow6rVtZyhjSSdN24pB/KHce52x/MHt7ew4cOECbNm2IiYnhiy++YP369Tm6pv86AwcO5OjRo6xbt47r16/j7e1N48aNUxXbfPToES1btsTLy4t//vmH0aNHM3DgQA4fPmzwnJLBYyhqdZLu6DoDR09ZumZzGh4eeerGkBaF3PCydG1IKzYDTQ0/GDyavy0tLdmyZQv9+vVDrVbTr18/fvvtt3SPY2+tOU64ic9rolqF3+TpXFuxnpUTskfDxV2buBxo+D6FChXCz8+PKlWqEBgYSIMGDfDz8zPJelzsBdycNWrad198HAYPQM2yGoPnyr34VL2P2vCps7Mz58+fp379+pmSSMhOrCxk1K+mydg/fDZ7Re7GjRvH0qVLEQSBJUuW0LNnzyyVSMgIlcrYUL+GHaIIf2x+m60d1fVhZWXFjh076NWrl04iYeHChTm6powSHxFpdg9jiIqKYseOHcyaNYt69epRokQJfvjhB0qUKMGSJUv07vPHH39QtGhR5syZQ9myZRkxYgQdO3Zk3rx5Bs+rMGqV/2FEUf2hO7pajahWIZA4V8fEOTwybUjL8LL0eJVGmdmYcj+rhNybxGrLCoWCFStW4OTkxLx58/j66695//49P/74Y6rHNlVIKzlyuZzaPZtyaNYbyrZqYNqDp4LW4AkMhagYEWtLw97PPHny4OPjQ5s2bTh16hTe3t7s2LGDFi1aZHpNZdxlBASpuf1MTeXiH8d9SomCcpzsBILDRf59FE+Vkkq942rVqqV7v/799188PDw4evRokk7R5kqjmtYcORvJ3SdxPHoZR9H8+l9jVjB48GAcHR3p2bMnmzZtIiQkhG3btmFjY5Nta0iPPm1duHIrkucBcew6FkznZhls3mwiFAoFq1evxsnJiQULFjBy5EiCgoKYNGlSjpRJZ5R9jpWIx7xufrzf/Z3iPbS0tNRbFR0fH49KpUqhFWRtbZ1qw9GzZ88maR0F0LRpU0aPHm3wGiWDx1C0Bo0gB9S6HJ7UQ1oJHh49YSujcngMCWnJBQQ0ZS+x8SKWSsN/uFoPT3Sy9hIymYw5c+aQK1cuJk2axE8//URwcDC//fZbkvYYWhKrLRtrdKVHg+71kZWviTJf9jSas7USyGUv8i5MU55eqoDh+zo6OnLo0CE6derE/v37adu2LWvXrqVbt26ZWlMZdxknr6m5/1IkLl7UGbnmjFaT5+ilOC7cSt3gAfjss884ffo0jRs35uHDh3h4eHDkyBG9irHmhIuDnJqfWXL2WgxHzkYypINjts7fpUsXHBwc6NChAwcOHNDlpzg6Zu86UsPeVk7/9rn4bW0gO48FU7uSLe75LHJ0TTKZjN9//51cuXLxww8/8P333xMUFMTs2bP1ntvMjejoaAaoHhGEeTVJtStYMEUe3pQpU/jhhx9SjLW3t6d27dr89NNPlC1bFjc3NzZt2sTZs2dT7fT++vVrvf02Q0NDiYqKMkgh2vw/XXNBa/AkbyWhC11lIIfHgJCWITo8giDoLoDGhrUS5/DoO+7EiRN1bt8FCxbQp08fva5zG0tNxE8tQqSJ0xlc7EBpbU1Qwm/pyc1n/K/lL4S+y7pGhdpqLUPzeBJjbW3Nrl276NatG/Hx8fTo0SNVN62h5HMBRxuIi4eHr8zrzi4ttJo8t5+qCEknB6to0aL4+/tToUIFXr16Rb169Th37lx2LDNTeH+u8ahcvBHD+5Dsvwg1b96cI0eO4OjoiJ+fHw0aNODNmzfZvo7UqFPZlmrlbVCpYMmWtwaJqWY1giAwZcoUXbh+3rx5DBgwgPj47O8DZiyxsbEEoeLRnbu8e/nKLB6P7twlPDycZ8+eJelHmVgsODnr1q1DFEUKFCiApaUl8+fPp1u3bllqdEoGj6EkGCfJQ1gfStWTnujSzOExJqRl4MlB214iNs645EWdwRNHqtUew4cPZ/369cjlctavX0+HDh1SiF3JBEGXuBxu4ipdbaVWeLTGE/VLj9+5ePAKS8asNu1EiSiUgTyexCiVStavX8/QoUMRRZFhw4bxyy+/ZLiiRhAESrt/fCKEeZxlFM0nQxTh7zvpX0zy5cuHr68vn3/+OUFBQbo2KOZM4XxKyhRRohbh2PmsL1HXh4eHB76+vuTJk4d//vknSyQSMoogCAzqmAtrS4F7T2I47B+a00vSMWrUKNasWYNcLmf16tV07tyZ6OicTbA2FJe8brjky2sej7waz0vyXpRpifwWL16ckydP6gylCxcuEBcXl2ooO2/evHr7bTo4OBjc/0syeAxFm5ScPISVjocns0rLagM6pgNYKDXjjfXwaLV4RBFi0rge9ejRQ6cuvG/fPpo3b05oaNITV1bl8VgqwTbhdxMcIfC/DaOo5l2JoXP7mnaiRBTIrXlfgiIyXnkmk8lYtGgREydOBGDixImMHTs2w0ZP2UIa4/TOcxG1GdwlG4rWy3PhVrxBr93FxYVjx47RpEkTIiIiaNmyJTt37szqZWYKbbuJk5ejiIrJGYO0cuXK+Pn5UahQIe7evYuHhwd37tzJkbUkJ5eTgp6tNR3VN+4PIvC9+SRY9+7dWyeRsGvXLlq1akVYWNZ5jyWSYmtrS758+QgKCuLw4cOpNhWtXbt2Cn24o0ePptlvMzmSwWMgYjIDR+vRSTeHR08vLblBHp4PORqGJC5bZDCkJRMELBNC6snzeJLTqlUrDh06hL29PSdPnqRRo0a8ffsh5vPB4DH9xdjFXvPv+3AoXM6dGYcm4ZDLXvd8VIRprSwrpYCbk+b/GfXygObudtq0aTqRuNmzZzNo0CBUaUgSpEZhNwErC4iIhmeBH4/BU7mkAqUcXr9X8+yNYcaAra0t+/bto0OHDsTGxtKpUydWrVqVxSvNOBVKWpA3l5yoGBG/KznnIShVqhSnT5+mTJkyOomEy5cv59h6EtO4tj1li1kSEyuydNs7s9IPatu2LQcPHsTOzo7jx4/TuHFj3r3LudYY/wUOHz7MoUOHePToEUePHsXLy4syZcro+mdOmDCB3r1768Z/+eWXPHz4kLFjx3L79m0WL17M1q1b+frrrw2eUzJ4DCV5CEtnACV4clJTWk4jaVkUxVSNHkEQdEaPIVo8Hwwe4+8urZWp5/Ekp379+vj4+JA7d24uXbqEp6cnz58/Bz6UpmdWbVkf2rDWOz3adOcPXKZ38RHcPGvau1n3TOTxJOebb75h5cqVyGQyVq5cSdeuXY1WF5bLBEoV0Koum8/FIj1sLAUqJGjyXLxteI6EpaUlmzdvpn///qjVavr3729UCWp2IhMEvBO8PEfPReZonkrBggU5deoU1apV4+3btzRo0IBTp07l2Hq0yGQCQ7rkRqkQuHo7ilOXzEtosmHDhhw/fhwXFxcuXLhA/fr10+38LZFxQkJCGD58OGXKlKF37954eHhw+PBhlEqNR/jVq1dJwrJFixZl//79HD16lEqVKjFnzhxWrFhhVDNwyeAxlFRCWELyJOYEhDS8OIm7qZtKbVkX0kqnHYI+kmvxpIe2CWvBggW5ffs2Hh4e3Lt3L8tCWpB6E1FRFNn5218Evwnh4MoTJp1TJ0AYmHp+kzH079+frVu3olQq2b59O23atCEiIsKoY5RJ1EzUnO6Q00PbQf3vu3HEGyGQqZVI+OabbwAYM2YMkydPNsvXXqeiFXY2Au9C1Fy+lbOtMlxdXTlx4gT169cnLCyMpk2bsn///hxdE0CBPBZ0TOiovnr3e0KysNt8RqhZsyZ+fn7kz5+fGzdu4OHhwYMHD3J6WXpRRUaZ1cNYOnfuzIMHD4iJieHVq1csXLgwSXXh6tWrU7QBadCgAVeuXCEmJoYHDx7Qt29fo+aUytINRUxclk4Kj0/y5OS0kpYFQUAmk6FWqw0KaxlSmm6ZwZAWpF2plRplypTB39+fJk2acO/ePTw9Pdn71yGgIpExmmTrxGG5zKJrMRGuyTfSVr0LgsAPu8ayY+5fdB3fzmTzAeR3AblMkywdHAHOdpk/ZocOHdi/fz/t2rXjyJEjNGnShP379+PsbJg+SYn8AgoZvA+DwBDI45T5NWUHpd3lONoKhESI3HikolIJw089giDw66+/4uLiwsSJE5k2bRpBQUHMnz/frMqILZQCDatbs/dUJIfPRlK9nGWOars4ODhw8OBBunTpwr59+2jXrh1r1qzJ8b5GbRo6cuafCJ68jGXVrneM7p0nR9eTnHLlyukkEh48eKCTSKhQoUJOLy0JFxp7YyuXpz8wG4jIQIg+JzCfs4W5kyKHJ71eWqknLWsOY3jisiF3xBYKbdKy8SEtfeKDhlC4cGH8/f2pXLkyAQEBeDduwP1/TwOmV1x2tNYYH/FqCE12M2Fta0XPyR1RKD9cRO/+nfm7MqVCIF+CHfI0E3k8yWnSpAnHjh3DycmJs2fPGqUubKkUKJrv42smKpMJVC+d0GritvEJq4Ig8L///Y/FixcjCAKLFi2id+/eZqcu7FXDBoUcHr6I58HznC9xtra2ZseOHfTs2ZP4+Hh69uzJ4sWLc3RNCrnA0C65EQQ4fSWCv29kr0q1IRQpUgR/f38qVqzI69evqV+//kchkSCRNpKHx0CSl6GnKEtXq5II7qXVPBQM1eIxIqSl0HqDNEnO2m7rhpCa+KAh5MmTB19fX1q1aoW/vz8/jmzKN79sp3bJ5jiaUPBVJgMnW3gXpglrpXXs7XP3sfTbtQye1YtO37bJ1LyFXOH5O40AYaWimTpUEmrXrq1TF75+/Tqenp4cPXqUokXTn6SMu4x7L1TcfipSz7xuOtOkRlklxy/HceuxirBINfY2xt9vDR06FCcnJ3r37s2GDRsICQlh69atBpelZjWOdjJqV7TC70o0h89GUsI95wUAlUola9aswcnJiYULFzJ8+HCCgoL43//+l2MeqOKFLGnVwJF9PiEs3/6WssULYmNlXvffefPm1Z3bzpw5Q6NGjdi9ezdNmjTJ6aUBUPPYERwcHHJ6GQCait18eXN6GeliXt8wcyaVsvTE+TgkqsgyiYfHiJCWXC6QYB9lXHwwDS2etHB0dOTw4cM0b96cmOgoZo5tx9ZtW40+TnrkSiWPJzlBASEAxMdl3s2qbTPx9K1p8ngSU6FCBfz9/SlatKjOdX7jxo109yvtrlHWfvFOJDTS/HJZUiOvi4xCbjLUBmrypEa3bt10Egl//fWXXomEnEQrRHj5Vgxv3ue8lwc055v58+fz/fffAzBp0iS+++67HM2F6tLMCbdcCt4Fq9j4l3k2jXV2dubIkSN4e3sTGRlJy5Yt2bFjR04vCwC5jbVZPT4GJIPHAEYv2kB8bILrPLnQYKIO6om9OYnL0vWdVIxqL2FgkmdGw1qGavGkhY2NDbt376Z56y6o4uP4dlg3li9fnrGDpUJqicvJGTSzJ78en2KSnJ68zqCUQ3QsvM2Ca2rx4sXx9/enfPnyvHz5knr16nHhwoU097G3Fijo+vGFtQBqlv2gyZMZWrZsyeHDh3FwcODkyZN4eXkRGGjCuGMmKJBHwWclLBCBozkkRKgPQRD48ccfdZVux48f15s0n11GkKWFjCGdNXcUh0+HceuheQr+2drasnfvXjp16kRcXBydO3fmzz//zOllSWQAyeAxgE0nztFt4gyio6MThaq0vbSERF6fDwaPLIkhlLkGooa0l4APYa2YDGjxZDSPJ8n8FhYsXrGeJl8MQRRFBg8ezKxZszJ8vOQYavAAVPb6TOeuV8Wr2Pn7fuLjjL/IymUCBXJp/m+K8nR95M+fn5MnT1KzZk3ev39Po0aNOHEi7YqzMu4JBs/Tj8fDA1ClpAK5DF6+U/M8MHMeuHr16uHj44OrqyuXL1/G09OTZ8+emWilmUMrROh/JZqIKPMySkePHs2WLVs4fPgwdnYpM/EFQUAURYKCgrJ8LRVKWeNVS7OGP7a8NVopPruwtLRk06ZNDBw4ELVazYABA5gzZ05OL0vCSCSDxwAsFHL2+V2kRYsWhGnL75J4c7SaO4l+rIli42m2l0gju13bT8uQkBZoqkTA+PYSAFaZyONJjJOtnIHfLaJD3/EAjBs3jvHjx5vkrlFbJRUWBbFG2C7zhy1nyder+bnbbxmaV9dXKwsdCLly5eLYsWM0bNiQ8PBwmjdvzu7du1Mdry1PfxwgZvozy05srQQqFEtIXs6klwegatWq+Pn54e7uzp07d6hbty53797N9HEzS7miSgq6KYiJEzlyzvyScjt37kyePCmro9RqNYsWLcLLy4sOHTrQvXv3NG/KTEHvNi442ct5+SaOHUeDs3SuzCCXy1m2bBnfffcdAN9++y2TJk0yS4kECf1IBo8BbJ48DHsba3x8fGjWcwjvQsKS6u7oSVAWBCHt9hLy1JuLasl4SCsDpek68UGjd02CnZXmtXcZ8jO/TJ8JwMyZMxk6dGiG1IWTrNECbBI8UUFGyNfU/aIWNvbWNOlVP0PzavV4nr81vLdZRrC3t9eVrMfGxtKhQwfWrFmjd2xuB4Hcjpok9XsvPq4Trk6T545xmjypUbp0afz9/SlVqpROXfjKlSuZPm5mEASBNvU0Xp4jZ6MICTdPz0Vy2rdvz+LFi8mbNy9Dhgzh1atX1KhRI0tVh+1s5AzooHGj7jkewpOXJu4+bEIEQWDWrFlMnz4dgJ9//pkRI0ZkuVGoD3V0lFk9PgakKi0DqPtZKQ7OnUjbifO4dO1fmg6fzP71yylarBKg8fCIkMTro9kuQ1SrTNBA1LiQVnZp8ehDIRewthCJioVBw7/DNbcLQ4YMYenSpQQHB7N27VosLCwyfHwXO4h8r9GhcTOwAKZm8yqse7goSSsKY3B1BCslRMdBQLBGnyersLKyYtu2bQwaNIjVq1fTt29fgoODGTVqVIqxZdxl+Ieouf1MTYWiH8+9S5nCcuxtBMIiRW49Uek8PpmhUKFC+Pn50axZM65cuUKDBg3Yv38/Hh4eJlhxxqhW1pKiBRQ8ehHPvlMR9GyRse9fdjF27FhOnDjB5s2badGiBaDxoLVv355Hjx6RK1euLJu7VkUbalSw4eL1SJZsCeTnUflNquNlasaPH4+zszNDhw5l8eLFBAcHs3r16mxdw80eHbBTmsclPDwD6QI5wcdzlsxhqpUszKlTpyiQ141bj5/j1XUA9+/fB9ISH0zdi2NMWbqxOTwZCWlZmyCHR4t9QsJ+eBQMHDiQLVu2oFQq2bJlC+3atSMyMuMu/sQ9tYwhsbETHhzBT13m8sbAGJUgCLpqrcz01TIUhULBypUrGT16NKDJufjhhx9SuM7LJuTx3HshmsRTkl3IE2nyXLxlOh2dPHny4OPjg6enJ6GhoXh7e3PgwAGTHd9YBEGgU2NNHPbk31EEmEnFlj5OnDjB7NmzWbBgAc2bN9dtL1asGLdv3+bJkydZOr8gCAzskAsbKxkPnsZy4JT5VN2lxpAhQ9i0aRMKhYKNGzfyxRdfZOrcJpH1mId5+DGgVlOuXDl8dm+iecfuPHj+UqfAWdQ6NfHB1LV4DKvS0lzQRFHTNV2Wzh1PRjumw4ccHpMYPFbwJkTbRFSgY8eO2Nvb0759ew4ePIi3tzd//fUXTk5ORh/bmMTl1Jg/fDmntp0l4PEbFpybbpAWibsr3HulSVyuVTrjcxuKTCZj7ty55MqVi8mTJ/Pjjz/y/v17fvvtN913J39uAXtrTU7T49ciJQqY7x1xcmqUUeBzJY4bj1WER4nYWZtm7Y6Ojhw6dIhOnTpx4MAB2rZty7p16+jatatJjm8sZYpY8FkJC/69H8sunwi+7JDzujzJEUWRkSNH0r17dzp37pzk97B8+XKKFy9OqVKlsnwdLo4KerVxZunWd2w+EETNz2xwy63M8nkzQ5cuXXBwcNApqLdv3z7b5i63YYd56fC4STo8nw4JGjtFChXiyKKfqFC6BAEBAdSvX58L//ybMCRlSEuzPWMeHlmiE48hYa3EIS1jE+l04oMZ1OJJjL1VQhPRRFWmTZs25ejRozg5OXH69GkaNGhAQECA0cdObPBkdJmDZvaiXJ3SjFk+1GDhNW0ez8v3EJdN3hRBEJg0aRILFiwAYMGCBfTt21enLiwTBEq7a75Htz6iZqIA+XPLcc8jQ6WGy3dNq5aslUjo1q0b8fHxdO/enT/++MOkcxhDx0a2CMCFf2N4/NK8lKEBrly5glwup0ePHlhbW+t+/9evX+fYsWOUL18ed3f3bFlLo8/tKV/Citg4kaXb3n4UCcHNmzfXSSScPXs22+aVWVmb1eNjQDJ4DEWrpCyTkzeXM0dWL6BOnToEBwfTps8QTpy9pCeHR3+oCwwzeARBMCqspTV4wPiLslViLZ5MnpO1Ia3kXdPr1KnDyZMncXNz4+rVq3h4eBjtKne0AZkAcaqMt69wLZiL3/x+oljFwrptsem8aGc7sLXSJAm/ymaNtBEjRrB+/Xrkcjnr1q2jY8eOREdrXry2PP3OMzXqj+DikBht8rIpqrWSo1QqWb9+PUOHDkUURYYOHcqMGTNMPo8hFMqrpFYFSwB2HDevDuFa7t+/T+7cGqteEARev37NmjVruHXrFoMGDcLJySlbjA9BEBjSOTdKpcD1u9H4XjDP9ys5np6e+Pr6Uq5cuZxeykeBSqVi8uTJFC1aFGtra4oXL85PP/2U5nesb9++mmKgZI/y5csbPK9k8BiDKOoqspzsbDhy5AjNmjUjMiqarqMmsmPPviTD0+qYLjMwIVmntmyAASMIAkpdHo9xJyfBRFo8gK5renh0Sm9RxYoV8ff3p0iRIty/f5+6dety69Ytg48tT2gxAZo2ExklsWfn+d2X9C8zivP7/05zvNbL8yyL9HjSokePHuzatQtLS0v27t1L8+bNCQsLo2heAQuFxrh8+e7jMniqllIil8HzQDUv35q++aBMJmPRokVMnDgRgAkTJjBu3Lgc8Rp84WWHXAY3HsZx46F5VSHlzp2b8uXL8++//xIfH09cXByTJ0/m1KlT9O7dm2bNmgFJfzPa9zCzlZf6yOeqpHNTJwDW7H1PUKj55j4lpkqVKpw+fTqnl/FRMHPmTJYsWcLChQu5desWM2fOZNasWTpvtj5+//13Xr16pXs8e/YMFxcXOnXqZPC8ksFjDGrVB2VltRpbW1v27NlDh1bNiYuPp9eQEaxcuVI3XGaAh0dU61di1qIrTc/OSi0jjaXk2FhqvDBqESJjUj5fokQJ/P39KVu2LC9evMDT05NLly4ZfPzcCWHrQBPlNe6Y9xcBTwJZ/9P2ND1uujYTOSTo27p1aw4dOoS9vT2+vr40bNiQ4KB3lCzwcYoQ2lkLlC+i+Y1cvJ01FzVBEJg2bRqzZ88GYNasWQwePDhLLtRp4eosx6u6xvW5/Vi4WXnjChUqxNChQxk2bBgNGjQgb968+Pv70717d8aNGwekvGnTGj/R0dHcvn2bZcuWsXDhQpOtqXUDR4oWsCAiUs2qnebZdkIfMln2XVLVMdFm9TCGM2fO0LZtW1q2bEmRIkXo2LEj3t7eaarMOzo6kjdvXt3j0qVLBAUF0a9fP4PnlZKWjUFUI2hVlRNyeiwsLFi9aB52Shlrdu1n4MCBBAUF8e233xqUwwOak4lcLk8xBhJr8Rhq8MiIQJ2hrukag0ckOpM3oIIgYGclEhqlyeOxtUo5pkCBApw6dYoWLVpw8eJFvLy82LdvHw0aNEj3+Hkc4O5LeGMig2f4/P7YOdnS/utWaZ6wtAKEr4MhJk7EUpn9ScINGjTgxIkTNGvWjEuXLlGvXj1+X34IyMftZ2oaV9X/PTJXapZVcu2hikt34mlVxyLLSpG/+eYbnJycGDx4MCtWrCAkJIR169ZhaWmZJfPpo1U9W/z/iebJq3gu3Yih5md6fhg5RP/+/XUNbK2trWnbti0uLh/0F5Lnuh08eJAbN26wdetWnJycuHnzJi9fvsTV1ZUuXbpkej1yucDQrrkZP+8lZ69GcPF6BDUq2Gb6uJ8ST0b1wt7CPJK6wxJaLyXvaWdpaan3N1anTh2WLVvG3bt3KVWqFFevXsXf35+5c+caPOfKlStp3LgxhQsXTn9wApKHxxjUap2HJ7HXRq5UMm/S13z95UAAvvvuO/73v//pWk6k5uHRnkTSLE1PuADEGyh4Z5HBkBaYvlILUubxJCZ37twcP34cLy8vwsPDadasGXv37k332HkSCl3ehmq8SJlFoVQwYHoPnLUHBp7efpHC8+ZgI+Boq4lsvsg6HbZ0qV69On5+fhQsWJBbt24xsEc9ggLuERgCb0PNx3NgCGULy7Gz/qDJk5UMGDBAJ5Gwbds22rRpo7eXVFbhYCujWR2NGOHOExHEZcALm5WULFmSYcOG0a9fP1xcXNi3bx8LFy7U5C4KAvHx8UyePJn27dszYsQIAgMDmTt3Ll999RWff/45nTp1omHDhiZbT9GClrRuoPlNLt/+jtDw7PXKSRiPu7s7jo6OuodWoDE548ePp2vXrpQpUwalUkmVKlUYPXo0PXr0MGiely9fcvDgQQYOHGjU+iQPjyHowljxuhye5K0lBEHgp/Ff41akBOPHj2f69Om8efWSH79NXYVTJpOhUqkMVFs2zGOj9TrEZMDDo1UxjowxkRZPEIRGaUrTUx1nb8+BAwfo2rUre/bsoX379qxatYpevXqluo+TLVgoNO0l3oWBq4krM/8+epXJrWfQdkRzBv/aK8ndbRFXuBoBDwOgWA5WYZYtWxZ/f3+aNGnCvXv/Z++sw6M4uyj+m5Vs3AgheCC4OwUSnODu7qVA0aJfS4ECRUopRYu7F/cQJAIUKO5OsOAR4pvd/f6Y7MaT3ehCc55nHpbdmXferMzc995zz3nIxtkN6Tr2EDeeVKZhpS8nyyOVipo8Z64p8b6upFyRzL0kderUCRsbG9q1a4eHhwdNmjTh8OHD2NnZZep5tXCvZcbpf8N5F6DioHcoHRom9rIyBoSFhbFo0SKCg4Np1qwZxYoV4+HDh8ybN4+mTZvy8OFDJBIJb9++Zdy4cXz8+JEZM2aQO3fuDJ1H56a2XLwZhv97JYs2v2fyt3mMWpAwK1H4z03G1Za+LQ8vXryIN6fkMqg7d+5ky5YtbN26lbJly3Lt2jVGjx5Nvnz56Nu3b6rn27BhA7a2trRr186geeZkePRBHIsIIQ6HRwtBGnORVqmYOHEiK1asQBAE1qzfwPcTfiIqIun6pn5+WoaJDypitHgi05DhMVeIF5LQDAh4bM3FsQL10OEyNTXl77//pk+fPqhUKvr06ZMieU0igJOt+Ph1JpT3X9x/jTIqmrd+7xIFo9og57F/1rlKJ4fChQvj4+NDpUqVCAp4y4bZjdl32Neo+CH6wK2iHEGABy9UmUJeTogmTZrg6emJra0t58+fp379+rx58ybTzwtgaiKhVwsxyDl6NowXb42TkGtubs7GjRuZMWMGxYoVA6BUqVLs3LmTK1eu0KRJE0JCQli3bh3379+ne/fu8VStDx8+zF9//cVvv/2WqMxhCBQmEn7o54iJXOD6/XB2ewSm90/7aiBRmBrVBmBtbR1vSy7gGT9+vC7LU758eXr37s2YMWOSzQjFhUajYe3atfTu3dtg1f6cgEcfaFcUcUjLmqTMQ9Xixevbb79l27ZtyOVy9h05Tq/BQ5NU4NTydlIiJOsCHj0zNqYxAU9EVBoyPAot4Zl0K/faxJTbg8L0CwxkMhnr1q1j5MiRAIwcOZJffvkl2WO19g6vM8HQud33zZl1+H9M2jwqEbeqoIOYXQqNhDeZbyadKvLkycPp06epU8eVyLAglv/SnI3bjmb3tAxCLmsJFV3ERcPpq1mjU1OrVi28vb1xcnLixo0buLm58ezZsyw5d9XSplQtrUClhnUHgjPVny09yJs3L+7u7gC6slbbtm3x8fFBJpORN29etmzZQuvWrfn22291x23ZsoVRo0axdOlSPDw8cHFxwcPDI83zKJzPhG87i7YWf3sEcvVujprxl46wsLBEfEmpVKqXJ5mXlxePHj1i4MCBBp83J+DRB7qsjir+4xgIUq1beuxzXbt2ZffOHZiamuJ52otmzZoRFBSUYNgY64kMzfDEdmkZutKXS8UWZ0h/lsfSVIwTVWoxONAHEomEhQsXMn36dACmTp3K2LFjk/wR5IupQLwJFM+R0ajRvDImilhC4KmtPoSHhCOTChTJIz73KGuSAqnC1tYWD4/jVPmmKdFR4Qzu246dO3dm97QMQv3K4nt95UE0QaFZY8RYvnx5fH19KVKkiE4i4c6dO1ly7h7NLTFTCDx7Hc2Jf4zfeFFb1tVoNBQpUoTJkyfj5OTEnTt3ePXqFQDR0eKC78KFC5QsWZK9e/dy4sQJxo8fz6hRo7hx40aaz1+vuhVNaluh0cCize95/8n4BBxzoD9at27NrFmzOHz4MM+ePWPv3r0sWLCA9u3b6/aZPHkyffr0SXTsmjVrqFmzJuXKlTP4vDkBjx4Q4gQ5cR/rsg8SMUpISE5u1qwpO9Ysw9rKCh8fH+rXr8+7d+90r+tjDiqXiedTRuuX6pfLBLSUk7QQly1isjzp5fFIBAEbkZ+pV1lLC0EQ+Pnnn/nzzz8BWLhwIQMHDtRdTLWwsxDFElVq0cYiM3Fw+XFm91rEhCYziIpU4hKnrGUsMDc3Z8eufZSt2ZnoaCXdunVj1apV2T0tveHsJKVIXlF52fdG1t3MXFxc8PHxoUyZMrx+/Ro3NzcuXbqU6ee1s5LS1V0sbe07HcK7gC+DkCsIAq9fv2bdunUULlyYadOm6RYkMpmMkJAQKlSogJ2dna4UNnz4cMLDw7l582a6zt2/fS5cCpoQEqbm9/XvjI70nQP9sXjxYjp16sSwYcMoXbo048aNY8iQIcyYMUO3j7+/P8+fP493XFBQELt3705TdgdyAh79ELeMJYlT4ohpTY/N8EQnOExGjSqV2LNpFY6Ojly7dg03Nzfdh6hPhkcu0wZFGtR6pL4FQYglLqfBRDQjeTzagCco1PCxRo4cyYYNG5BKpaxfv57OnTvr1IUBBCFzy1pxUaxKUazsLKjetBImCjlF8ojZq08hEBBiPBddl/wKBo3bSNUGg9FoNHz77bfMmzcvu6elN+pXEuvxZ28q08RBSyu0Egk1atTg06dPNGzYkNOnT2f6ed0qm1LKWU5UNGw8FJztnDB9cfPmTS5dukTbtm2ZMmWKzraja9euNGzYkD/++IOtW7cyadIkAHbv3o1Go8HSMn0EbblMYGw/RyzNJTx+EcW6vdnYKmkEUEdFGNVmCKysrFi4cCF+fn6Eh4fz+PFjZs6cGY+Ts379es6cORPvOBsbG8LCwhg8eHCa3rOcLi19IJGCivglLWL/LyRR5oJYpeWyJUvg4+2Ne9OmPHjwgDp16nDixAmc8oi1kZQyPBKJmLHRaCBapcJEkvpHZiqXEBGlIkKpwVCrwtgMj4EHJgEbc1HXJyiNJfc+ffpgY2ND165d2bdvHy1btmTfvn1YWYnO5/nsxG6p15+gatH0zzc5lK5ZnJU3F5Arr1hHU8gFCjpo8HsPj/yhevHMO7chEASByiXkfOq7hDyOdhzZMY+JEyfy6dMnZs/WzyQ1O1G+qBQHG4EPQRou3Y3GtULWaYzkypULT09P2rVrx6lTp2jevDk7duygbdu2mXZOQRDo29qKn5d/4s4TJb7XInCrbPyeRE2bNmXnzp26koJEIuH3339n//79eHh44OTkxIsXL+jatSunTp3ixYsX9OjRwyALgOTgaC9nZK/czF71lhPnPlPSWUG96lbpHvdLxPtfRhChMBIdnvT6EWURcjI8+kAb0KhUEOemoeXsaLu0NCpVvFWaECc4cnEpqlMXfvnyJW5ubtyISfGmlOER7SK0ZS1DO7WyN8Njm4aSVkK0bduWo0ePYmlpyalTp2jUqBEfP4orO22G512w6K2VmXDIZ68LGFQqFTdXrOWz33MeGwmPR4uKRSVIBIFqLWYy7RfRO2ru3Ll89913Wa4ubCgkEoF6FcUL+JlrUXplNDMSVlZWHD58mHbt2hEZGUnHjh3ZuHFjpp4zj72MdvVFhv8OjxCCvhCtGW2wo1KpUCqVOjHMunXrUqJECRo1asTvv/9O3rx5uXbtGr///ruuxJVeVC5tTscmtgCs3PURv9fGZdWRA+NFToZHDwhCbCu6IAhiAKRW6TI6sYGNRixzxewvCAKCRCLaR6hUFChQAG9vb5o3b86///5Lq1atWLliBW5166Z4frlUQpRSZUDAk/aSVlwOj7YzI63QlrRCI8WuL5k0bWM1aNBAt+q+dOkSdevWxcPDg3z58mNpKnp2vQ2EArnSPFWDsH32Ps5uOobi0D+YLVtEaIQpFqbGkT2xtRQo4iTw5I2Gem3HsdLJniFDhrBy5UoCAwPZtGmTwa2cWYkapeUcuRDFhyANt5+pKF80ay9Rpqam7Nq1i8GDB7N+/Xr69u1LYGCgrnswM+Bey5yLtyPx849my9EQhnU2NC+bfZBKpUilUmrXrs3JkydRKpXI5WLQGhYWxtOnT9NdykoKnZra8tAvkuv3w/l9/Vtmj8mPhdl/a/2e++fFRqPDYxocDIuNv1Hiv/UNSSukCRSTtbo72v/HyeRoVAnLWjE8nZh9terC9evX5/Pnz/Tr358TqbRsygwkLpumQ4vHzESUCVSpITKdEiEKuYBpTMY1rWUtLapXr463tzf58+fnzp07uLq68vjxI1231qsstNtpPcydMrVLUmtkf2Smpjx5m3Xn1geVXMTP/9ojNYMGDdKpC+/cuZO2bdsmKZFgLFCYCNQuJ35pTl/NnpW7TCZjzZo1jB49GoBRo0Yxffr0TOPYSCUC/dtYIRHg3zuRXLmXAfXkLEbz5s3x8/NjyJAhPH36lN27d+Pj40OePHn0ajU2FFKJwMheucllK8X/fTTLt7//YjhQGQWJialRbV8CcgIefSDE5+gk1N3RZX3iPKeFlpgct4PL2tqaI0eO0LJlSyIjIxn87bds3bo12dNrict6Z3hM0q7FI5EImGWg4nJGlLW0KFOmDL6+vhQrVoxnz57h6upK8Bux1dU/CzVxrO2t+MP7Fxp3qwWIPB5jKheVLiTKCwSEwPN3Gjp37syBAwcwMzPj2LFjuLu7ExgYmN3TTBZuFeRIJPDktZrnb7PnfZVIJCxYsIBffvkFgGnTpjFmzJhMu6kWcpLrbCc2Hf5MWETWtOZnFKpUqYKvry/37t2jffv29OvXj6CgIMaPH6/j3GU0rC2l/NAvD1IpXLgRxqEzGWSul4OvFjkBjz6QJiAlx1FW1kIXBCWT4Un4vJmZGbt27aJd27ZER0fTq1cvli1bluTptQFPtJ431fRo8UAcHk9EBnZqhWXMjcLZ2RkfHx8qVKjA27dv6d2xHo/vnOfDZ4jIQt6cRCLBJa/4+LFfGCNr/8Tx9Znf2aMPTOQCZQuLn+G1x+KNs1mzZpw4cQIbGxvOnj1L/fr1efvWyFJTMbC1lFCleNYKESYFQRCYMmUKixYtAiBfvnxJlngzKghqU8+CPPZSgkLU7DoRkiFjZiXy5s3LuXPnWL58Ob6+vqxbtw53d3dUKlW6hAdTQvHCCvq3E2vZmw994s5j49c0ykH2ISfg0QNCAv+shBkeiG1NT9ippWs9T8JAVKFQ8Ntvv9GnTx80Gg3Dhw9n1qxZiS6gMqlhpGW5VNCJQ6dJi8c0Y7R4AGwsxLHSW9KKCycnJ7y8vKhduzaBgYH8MaExt//1yHLl41xWoq+X3/FTPLj0iNUTNxMalHVmlCmhUjHxO3vbT0NUjF5JnTp18PLyIk+ePFy/fh1XV9csUxc2FFohwuuPogn4nL3ZjhEjRnDlyhUmTJiQ5OuCIKDRaAgISN8X0EQu0K+NmA3xuhLB/WdfJhm3Vq1aVKxYEUdHRzQaDUOHDqVp06bMmTMnU87nXscKt6oWqNXwx4b3BAQZp11HDrIfOQGPPkjYdi5JKsOj7dSK/2NLLsMDYpZAKpXy85Qp/PjjjwD89NNPjB8/Pl7QY2hJS9TiMb5OrYwsB4jqwh40bdqUyIgwFv/Uii3b/s6w8fWBIAi4OIFzm5bUHNCBWUf+h4XWUyObUchRwM4SIpVw73ns+16xYkV8fHwoXLgwjx49wtXVlbt372bjTJNGgdxSiheQotaA9/Xsb3mtXLlyks+r1WqWLl1KgwYN6NixIz169EgXZ6VkYRPqVxX5EOsOfk7TgsXY4ODgAIjKuRMnTszwsqAgCHzb2YGCTnICP6v4Y9N7VOm0xvkSoImKNKrtS0BOl5Y+SOifpcvmxAlukuDqAEgkyWd4xKGlaDQapvz0Ew4ODowZM4bff/+dgIAAVqxYgUwm07WlR8e0vevTOaWQC4RHkU4tngxwTTcVO/mjVRAWBRZJe8mlCRYWFhw4cID2nXtx5MAupoztSl6rVQwYMCDjTpIKiuWFy48F8nbohkuce+K7Fx9wyG+fyC8mqyARBCq6SDhzXc3Vx2oqFI2dR/HixTl79ixNmjTh7t27uLm5cezYMapVq5Ytc00ODSrLefhSxblbShpXNcHCzDg64eKiQ4cOPHz4kPLly9O+fXv++usvqlevjoeHB7lypa1tsFNjS649iOLdJxX7vULp3Ng4HdX1gSAI/Prrr9jZ2TFhwgTmzZvHp0+f+OuvvxL51KUHpgoJP/R3ZPKC19x9HMHWwwH0bmOfYeMbI4KW/ojG1Dg6LoMjvoxsZE6GRx8kKmnF6u5okSyHJwmfrbiIay8xevRo1q1bh0QiYe3atXTt2pXIyEhkMRkejQa9jQYzIsMTHkW6tVAkEgHrGC21zKj2mJiYsHPHNtxaDEajVjNw4EB+//33jD9RMshrL3a2RSrhVYzwq/+Tt4z45n/M67eEaGX2pdcrxQQ5T/01idSuterC1apV4+PHjzRo0CCRqml2o1RhKfkcJEQqwfOy8V1QJ0yYwKlTp/jtt9/Yvn07Xbt2ZeXKlURFRfH06dM0j2tuKqF3C7G0dfxcGH7+2Z/hSi/Gjx/PqlWrkEgkrF69mu7duxMZmbFZgfyOJgztlhuAA6eDuHDDOMrLOTAe5GR49IGQkLSclIGo1k8rQZeWHhkecSjx9X79+mFra0vXrl3Zs2cPrVq1Yu/evUglAiq1BmW0SmcomhLSo8WjkImd+Cq1GPRYpLPj0NZc5PAEhsWKBWYkLEyljP55BeZWdhzfMY9x48bx6dMnZs6cmenqwhJBoKiThtvPxW6tQrnh8fVnBL0P5vG1Z0SERmJpmz0/MzsrAec8As/earj+RE3d8vFX1A4ODpw6dYq2bdty+vRpmjVrxs6dO2nTpk22zDchJIJAq1omrDwYgfd1JXUryrGzMo412qlTp5g/fz7r1q2jefPmuueLFi3KvXv38PPzS1fGrHIpBdXKKPj3TiTrD37mp0F2SCXGl+EyBIMGDcLW1pYePXqwa9cugoKC2LNnDxYWGVcGrlXJglZ+1hw6E8zSbe8plNeEvLmNQ404o2EzfJbR6PAIwcEwzfi9+4zj6mHkiLWO0HpnJebrJGsvkUypS4ukDETbtWvHkSNHsLCwwNPTk8aNGxMaIrZcRuvJ49Fp8UQZnqERBCGDPbW0xOXMq6vntxfoNHguQ8aIxMhff/2V4cOHZ4oGSEIU03ZrvRF5Sq7tazLz0GTmHJ+CpW32cnp0mjyP1UlyJ6ysrDhy5Aht2rQhMjKSDh06sGnTpqyeZrIoXVhKsfxSVGo4esE4sjwajYYRI0bQo0cPunTpEi+oXrVqFS4uLpQoUSLd5+nZ3AoLUwE//2g8zhuvdpIh6NSpE4cOHcLc3BwPDw/c3d3TTfZOiJ6t7ClVREF4hIb5694SmQZ5ji8BgonCqLYvATkBjz5IyOGRJObwJFe60mV4kilpJWcg2qhRI06dOoW9vT0XLlygd9c2vH/3Vm/xQZ0WTxoyPBDL48lQE9FMvGZrM0f1O05k+fK/EASB5cuX6wwM4yKjSZOFcoNMCp/D4X2Mc3s194o67y2Aa6dvEah9MQtRprCAXAYfg+Hlh6T/blNTU3bv3k3v3r1RqVT06dOHxYsXZ/FMk4YgCLSqLfIULt2Nxv9j9usdXb16FalUSs+ePTEzM9N9n27evImnpydly5alYMGC6T6PjaWErk1jHNXPhPL249fRfeTu7o6npye2tracO3eO+vXr8+ZNxnm0yKQCY/o6YmMp4bm/klW7Pv7nRAlzkDRyAh59kAyHJ8kurQzI8GhRo0YNvL29yZcvH/fv3aVvtzY8evxYrymnX4tH/DcjxQc/R4gWE5kBJ1uRHB0SAT36DmHbtm0ULlyYoUOHJto3o9qItZBLBZwdxcePkrhu3/C+w48tf2Vs3Z8JeBuYIefUFwq5QJlC4nfh6qPk33uZTMb69esZMWIEILrVz5gxwyhuFM5OUiq6SNEAh84bR5bn0aNHuu4jQRB48+YNGzZs4O7duwwePBhbW9sMee/qVDSlTFE5ymjYcOizUXweGYFatWrpJBJu3LiBm5tbhkok2NvIGNPXEUEAr39D8Dz/OcPGzkH6oVKpmDJlCkWKFMHMzAwXFxeDrjdnz55FJpNRqVIlg85rVAHP0qVLcXZ2xtTUlJo1a3Lx4sVk9121ahVubm7Y2dlhZ2dH48aNE+2v0Wj4+eefyZs3L2ZmZjRu3JiHDx8aPjFJAisJXRCTRJdWMm3phmZ4tChbtiy+vr4Udi7Cyxd+tGnhzq1bt1Kdcrq1eDIww6OQi7wggOBM0gWTS8Exppz9+hN07dqVe/fuUaRIkXj7ZXQbsRbFnMR/H/knfs3W0Qab3NYUKJkPK/us77jRlrVuP1OjjE7+85RIJPz5559MmzYNgJ9//pmxY8dmSVkwNbSopUAiwO2nKp68zt4sj4ODA2XLluXWrVtER0ejVCqZMmUK3t7e9OnTh2bNmgHEK3VpL+SGKnILgkCfVtaYyOHeMyXeVyIy7g/JZlSoUAFfX1+cnZ159OgRderU4c6dOxk2ftliZvRoKWZZ1+75yOPnX0brtL7QKKOMajMEc+fOZfny5SxZsoS7d+8yd+5c5s2bp1dmOTAwkD59+tCoUSOD3zOjIS3v2LGDsWPH8tdff1GzZk0WLlxI06ZNuX//Po6Ojon2P3PmDN27d6d27dqYmpoyd+5c3N3duX37Nvnz5wdg3rx5LFq0iA0bNlCkSBGmTJlC06ZNuXPnDqam+jNxBW3kkKIOT8pt6Zpkbhq6DE8KF8IiRYpw5NgJ2rVtzcP7d6lbty5Hjx6lZs2ayc85RosnPEpNhFKNqYlhsW1GtqYLgoCdpYY3gfApBDLrnp/PHt4GwesAKJWfJD/jzGgjBijiJGaYPgRDUKhGJ7gIUKhUfhadm4V1Litk8qz/yTk7CdhYiF1y915oKF8kefKrIAhMnToVW1tbRo8ezcKFCwkMDGTVqlXIZNl3uchjJ6FmGRnnb0dz8FwkIzuaZTohPTkUKlSIoUOHMmzYMNasWcPdu3dxdHRkyJAhOv8ttVodT5JAO9eIiAhevHiBt7c3UVFRfP/996mez9FOSvsGluzwCGHniRAqlDDBzirjWrqzE8WKFcPX1xd3d3fu3Lmjk0ioXr16hozftqEND55FculWGL+vf8fcH/JhZfF1vHdhm+YgMzMO7kxYuBhMBgfHt/dQKBQoFInneO7cOdq2bUvLli0BUUF/27ZtKSY5tPjuu+/o0aMHUqmUffv2GTRPo8nwLFiwgMGDB9O/f3/KlCnDX3/9hbm5OWvXrk1y/y1btjBs2DAqVapEqVKlWL16NWq1mpMnTwLiimrhwoX89NNPtG3blgoVKrBx40Zev35t8JtEgpZzIYkMj460nDDDE6eklVS6LlaJOeVVdIECBVi7eQ+VKlcjICCARo0a4enpmeIx6enU0pKWo6JBmQFlqFyW4ngfQzIvJa81En39SWzhT4jMaiMGMDMRyB8TLz1OoqzlkD8XJnE0M/b8eZhbvlkj+CcRBCoWjSUv64NRo0axYcMGpFIp69evp3PnzkREZG92oVkNE+QyeOqv5vbT7M3yDBgwgBs3btCjRw/mz5/P2bNndcEOkCgYO3r0KPPnz6dBgwaMHDmSX375hZEjR7Jjxw69ztekphlF8skIj9Sw5ciXZzuRErQSCdWrV+fTp080bNiQ06czxqZFEASG93Agj4OM9wHRLN7yPt1SGzlIHgULFsTGxka3zZ49O8n9ateuzcmTJ3nw4AEA169fx9fXN17HY1JYt24dT548YerUqWman1FkeKKiorh8+TKTJ0/WPSeRSGjcuDHnz5/Xa4ywsDCUSiX29iJ79enTp7x584bGjRvr9rGxsaFmzZqcP3+ebt26JRojMjIynjaELlrVR2lZmjSHRxLXSV2tjrWgiIE+GR4Q1ZZtbO1YvWkXk8d8y4kTJ2jZsiXbtm2jQ4cOSR4javGo0tSpJZMKKGSiY3pYpEbXaZVWaLM6nzLxWu1oI7bTRyghIDR+Jimz24hBLGu9/CCWtaq4JL/f2X0XWT5mPQozE1bdWkDeInnSdV59UMlFgvdNNY/9NQSHabDW4/Ps06cP1tbWdO3alX379tGyZUv27duXaWaQqcHGUkLdinJOXlZy6HwUZZylSLKxVbt48eIUL15c9/+DBw/i5+fH8OHDEQSB6Ohopk+fzu3bt7l+/TqdOnViwYIFBAYGsn79eqRSKQ0bNtTrXBKJQL821vyy8hNX7kXy750IqpX5Mhyq9UGuXLk4efIk7dq149SpUzRv3pwdO3bQtm3bdI9tYSZlXD9H/venP1fvhrPHM5BO7napH2jkMO89CXMjaUuPDg6GMb/z4sWLeK3ySWV3ACZNmkRwcDClSpVCKpWiUqmYNWsWPXv2TPYcDx8+ZNKkSfj4+KQ522wUGZ4PHz6gUqnIkyf+hT9Pnjx6s/cnTpxIvnz5dAGO9jhDxpw9e3a86FTXaZFAdyelDE9i81CJWOsgaeKyvhkerdqyicKM/QcO0LFjR6KioujcuTPr1q1L8pj0ZHggYy0mtMFHSAREZpJcvlQikpdBLGtpkVVtxFoz0VcfITyFILOqe0VqtKhM+1EtsyTYAchlLVAot4BGAzee6P99aNeuHUePHsXS0pJTp07RuHFjPn78mIkzTRmNqppgroA3n9Rcumc8XUthYWEsWrSITZs28TimseDhw4fMmzeP6OhoHj58yNy5cylevDg7duzg48ePjBgxgty5RaG8yMjIVLl5BfPIaF5H7ADYcjSE0PDs51ZlJKysrDh8+DDt2rUjMjKSjh07snHjxgwZ2zm/gsGdxBTszmOBXL/35bf5C3ITo9oArK2t423JBTw7d+5ky5YtbN26lStXrrBhwwbmz5/Phg0bktxfpVLRo0cPpk+fnq5rtVEEPOnFnDlz2L59O3v37jWIm5MQkydPJigoSLe9ePECAEEnPBhzgUkiw0MywoOQcmu6NsOjVietkxK7n4D2Ni2Vytm+fTsDBgxArVYzYMAA/vjjj0THmOpa09MWYOh4PBngmm4iE7CK+WgyM8ujLWv5f4p9LsvaiM0FcluDBniSQpxuaq5g+t4JDJjVXfdcVnTfaA1Fk9PkSQ4NGzbk5MmT2Nvbc/HiRerVq8fr168za5opwlwh0LiaeHE9eiFKZ4ya3TA3N2fjxo3MmDGDYsWKAVCqVCl27tzJlStXaNKkCSEhIaxbt4779+/TvXt3XF1ddcdPmjSJgQMH0qVLF4KCkpcvaF3XAqdcoqP6Do+vq7QFIu9u165d9O3bF5VKRd++fXVu9elFgxpWNPrGCo0G/tz8nvcBxhMw/9cwfvx4Jk2aRLdu3Shfvjy9e/dmzJgxyZbAPn/+zL///sv333+PTCZDJpPxyy+/cP36dWQyGadOndLrvEYR8Dg4OCCVSnn79m2859++fYuTk1OKx86fP585c+bg4eFBhQoVdM9rjzNkTIVCkShCBeLo8Ig/kFhn9NQzPHH3TynDAylneQRB0FlMKKNVyGQyVq9ezQ8//ADA2LFjmTJlSrwbWXrsJQDMTbUZnjQdngixZa1M5PHE6PH4B8bGp5C2NuLXr19z6NAhgyTwtVmepLq14kIml+kyTWq1mrl9F7N9zl69z5MWlC0sIJOKWkGvPxr2GdSoUQMfHx/y5cvH7du3qVOnji6TkdVwqyDH1lIgMESD7w3jsV3Imzcv7u7uADrPu7Zt2+pS8Hnz5mXLli20bt2ab7/9Foj9zXfv3p2ZM2cSHR1NmTJluHDhQpLnkMtiHdV9r0Vw54lxtOlnJGQyGWvXrmXUqFGAyCebPn16hiwKBnSwp0gBEz6Hqlmw/l2KXYs5yDyEhYUl8hmUSqXJ3gOtra25efMm165d023fffcdJUuW5Nq1ayk28MSFUQQ8JiYmVK1aVUc4BnQE5Fq1aiV73Lx585gxY0aSxodFihTByckp3pjBwcFcuHAhxTGThE6HJ36GJ56XVpyyV8IfZkoZHkEQdB986jwerYmoWnfsb7/9xqxZswCYOXMmI0eO1H1p0qvFk5GdWgD2OuJyhgyXJHJZgYlMJFtrz5OWNmK1Ws2jR4/YvHkzFSpUwNvbW6/za9vT/d6j98X04pGrnNzsw7op2/G7+1L/P9ZAmJoIlNZq8jw2/DMtU6YMZ8+excXFhWfPnuHq6srNmzczepqpQi4TaF5TzPJ4/huVYd/PjIT2u6TRaChSpAiTJ0/GycmJO3fu8OrVK0D8vWt/+zVq1KBJkybs2bOHDh068McffyR78S9RyIQG1USDug2HgjOtRJydkEgk/PHHH/zyyy8ATJs2jdGjR6dbIsFELuGHfo5YmEl49DySDfuzrzz7X0br1q2ZNWsWhw8f5tmzZ+zdu5cFCxbQvn173T6TJ0+mT58+gPh9KFeuXLzN0dERU1NTypUrp7c9iVEEPCBmKFatWqVbdQ8dOpTQ0FD69+8PiATKuKTmuXPnMmXKFNauXYuzszNv3rzhzZs3hISIdzlBEBg9ejQzZ87kwIED3Lx5kz59+pAvXz7atWtn2OQSkpaTzPDEIVElFB9MpmVdC61rsD7EZQBlHHsJQRD43//+x9KlSxEEgSVLltCnTx+USmW6tXh0HJ4oTYasrnLFcF0/hWReCUciQF5b8fHrmLJW3Dbi+vXr4+TkhK+vLz169GDixIlA4uyaRCKhVq1azJkzh379+tGyZUuWLFmS6vlz24CVmegO//y9fnP+plVVvv2tDxPWf0/h0gX0/VPTBK0mz62n6jSJQDo7O+Pr60uFChV48+YN9erV459//snoaaaK6qVkONlLCIuEk0ZoLKqFIAi8fv2adevWUbhwYaZNm4YgCERFRcVzC4/72y9fvjwXLlzgzZs3yf5OOjW2wM5awvsANftPf50mmYIgMGXKFF1Ja9GiRfTv35/o6PSVovLkkjOip8idOu77GZ/LX2ZpUBMdZVSbIVi8eDGdOnVi2LBhlC5dmnHjxjFkyBBmzJih28ff35/nz59n6HtmFF1aIArFvX//np9//pk3b95QqVIljh07piMdP3/+PF4KbPny5URFRdGpU6d440ydOlUnnDZhwgRCQ0P59ttvCQwMxNXVlWPHjhnM8xGSU1rWaMTOK4kkNguEGNhou7YgLjE5+YBHqVSmGvDIYsZJyl5i2LBh2Nra0rdvX7Zs2UJQUBA7d+5MlxaPmQkIiImtSCXE6apOE2zMxIBEqRLJy1Zm6RsvOeSzB78PInG5orP43IABA3Bzc+PEiROYmZnRtm1bXUcfJM7uSCQS5HI5zs7OutX5xo0b6dixI3nz5k323IIgUCyvhqtPRNVll+R3jYfOP7SO9/+QwFAU5ibITTLW+LCok4C1OQSHwf2XGsoWNrzLycnJiTNnztCyZUvOnz9Po0aN2LdvH02aNMnQuaYEiUQ0Fl19OMZYtIIcG0ujWb/Fw82bN7l06RJDhw5lxIgRqFQq3W9eLhc/X23wExERwfXr15HL5eTLly/ZMc0UEnq3tGLRtiCO/xNG9XIKiuT7Ok0yR4wYgZ2dHf369WPjxo0EBQWxffv2dPE1q5Y1p0MTG/acCGLFzg845zOhYN50XuCyGJEH/iLS3Dg69SLDDJOssLKyYuHChSxcuDDZfdavX5/iGNOmTdPd6/WFUV0hvv/+e/z8/IiMjOTChQvx6nJnzpyJ9wY8e/YMjUaTaIv7BgiCwC+//MKbN2+IiIjA09MzbQzvOErLGo0mNsMDuiyPIAiJ9Hp080iB3wPpy/DERY8ePdi3bx+mpqYcOnSI5s2bo4wQJdXTYqAnEQS0ulYZ0aklkQjYxfB4MrOspSUuvwkUHd+1KF68OMOGDaN///7Y29tz8OBBlixZouNbxM5TQkBAQDyyZOHChfHx8eHDhw+pnt8lpqz15A1pKiWGh4Qzufksfm47l/DQjNW+kUjiaPI8Snt5wM7OjhMnTuDu7k5YWBgtW7Zk9+7dGTVNvVC2iJQieSUoo+HYRePN8jRt2pSdO3fqbDu0v3epVEpwcDBXrlxhx44d/O9//6NRo0asXbuW5cuXAylnQiuVUFCjnAKNBtYd+Jxpti3GgF69erF3714UCgX79++nRYsWfP6cPruILs3sKF/clMgoDfPXvyM84uvqestBYhhNhseoEZdcpVGDIEHMfWjQqFQIMe+iIJWJAoPqpMUHU8rwgP4BT3QK+7Vs2ZLjx4/TunVrvLy8GNC9BXOX7yYyV/KrxZRgoRAIi9QQGqnRlaTSg1yW8PGzSFx2zp05Giq2FmJ2KjwK3gVBHA9PHbRtxMHBwbi7uycKhF++fMnkyZP5+++/GT16NDNmzKBhw4aYmaWelsqfS7TTCI8Sy2oFDBRwfnb7Jc9uPkeukPHh5UcKlsxv2ACpoJKLBJ9bah691vA5XIOVWdo+BwsLCw4cOEDv3r3ZtWsXXbp0YdWqVQwYMCBD55scBEGgdW0Fi3aHc+FONPUrq8ljZ1RrOB3KlSune3zixAl+/fVXlEolT548wcTEBEtLS/LkyUP79u2ZN28ederUSRSIJ4Uezay4/TiKl2+jOX4ujJZu+nEZvkS0bt2aY8eO0bp1a06fPk2jRo04evRomhXSpRKBUX0cmfj7K16/U7J8+wfG9M2dbQrehkLR5jsURqLDowgOBqZk9zRShXFeHYwNkrgZHZX4gzCgU0uib4Ynldq0lrScXIZHi7p163L69Gly587N7ZtXGd6nGU/90lYLNf8CicuCEEd1ORl/UG0bcVxdh9u3b+teL1++PNeuXePjx4/s27ePb775hh9//FHXcnzt2jX+/vtvVqxYkUhOXSoRKBojr/M4lW6tpFC6ZnHmnpjCrCM/ZniwA+BgI1DAQUBtoCZPUlAoFGzbto1BgwahVqsZOHAgv//+ewbNNHUUzSelbBEpag0cOf9leCX9+++/eHl5YW5ujpeXFw8fPuTq1aucPHmScePGUadOHSCxWnNSsLaQ0D3GUX2/Vyj+H77uVuv69etz+vRpcuXKxaVLl6hbt66OBJ4W2FhKGdPXEakEzl8P5Yh3cOoHGQkEmYlRbV8CcgIefRA34NEGLUmqLScgNyd4Pr2kZZk0ti09NdJvlSpV8PHxIX+Bgvg9eUD3do10Mt6GICNNREHM8IDo65SZKXhtVuf1pxT2yZtX16H1999/s3z5cl2aXK1WU7x4cb755hs0Gg3Lly+nXr16gChQqSXcbdy4kcKFC3PmzJl4Y+va09+kjaBdplZJSteMVfF9cf8Vb569M3ic5FDJRfxcDdXkSQpSqZSVK1cyfvx4AMaNG8dPP/2UZc7erWqZIADXH6t49iZ7LSf0weTJk9m/fz9eXl7MmzcPqVSKXC5PMxm3VgVTyrmYEK2CDQc/p6mM+iWhWrVq4rUtf37u3LlDnTp1ePToUZrHK+lsSt+2Ip9v04FP3Hvy9Ri05iA+cgIePRCPn5NAiydpteX4F66U2tLBkJJWzPga9PKDKVmyJB4nvSjoXAz/1y9wc3Pj2rVrqR4XFxmd4TEzAVO5KM4XmInNJflj+MjvgkGpx32kSJEinDx5kh9++IHw8HAkEgkqlQq5XK4rY0VHR7N8+XJ++uknRo0aha+vL2fPnmXAgAFMmzaN8PBYK3hnR1H5OShULOGlB++ev2dikxmMcZvCi/tpX83GRTlnCTIJvAuML9KYVgiCwLx583TCYbNmzeL777/PEqf1vLmkVC8tLkAOnYvKskArPWjdujVPnjzh4sWLFCtWjNu3b6dZLl90VLdCIRd48FyJ1+Wv/4ZdunRpzp49S7FixfDz88PV1ZUbN26kebxmbtbUqWyBSg0LNr4j8LPxB845MBw5AY++0JmDJp/hIRk/rYxqS5dIBKQxfeZKlX43kmJFC7N803FKlK7Iu3fvqFevHr6+vnodC7EZnvAo/YKs1CAIgk6AMDPLWlZmYGkqBodvAlPfv2rVqhw9epTz589Tr149Zs6cSaNGjfD19dWpMP/zzz+MGzeOadOmMWLECF0prGXLljx48AB/f3/dzdZEJlBI7HxNVYQwNUhkUsytzTC3NsM6I4hUgJlCoGTB2CxPRmHSpEn89ddfCILAsmXL6N27N0pl5osDNq9pgkwKj16puPf8y7hZ5c+fn+vXr+Pm5saGDRuIihKJ16dOnWLZsmUGjeVgK6VDI5G/s+tECJ+Cv4z3ID0oXLgwvr6+VKxYkbdv31KvXj3OnTuXprEEQWBIVwfy55ETEKRi4cZ3qL5iEvh/FTkBj77QtpnrleFJwOHJINIygEzH49HvgiaXCuRyyM3i9YdwdaurI+keOXJEr+NNZBDDlSYsgxphtM7pmam4DLGqy8nxeBLC2dmZmzdv0rp1a/z8/LCzs2PUqFFMmSKS8caOHYubm5vu/9rP69GjR0RERJA/f/54vItiMWWtpNzTDYFDPnsWeP3CnONTsHHIOJJi5RiriZtp1ORJDkOGDGHr1q3IZDK2bt1K+/bt42W/MgN2VhLcKoht2QfPRX1RZZ0NGzYwdepUTExMePbsGW3btmX48OHMnDnToGxVo+pmFM0vIyJKw+bDn7+ITFd6kSdPHs6cOUOdOnUIDAykSZMmHD9+PE1jmSkkjOvviMJE4PajCHYc0/PCkU3QRCuNavsSkNOlpScEqQwNpMLhSdpPK9W29JhUtkaj0WnAJAe5TEJkFESnQlzWnVsQUMglWFrZsHPPIQb3687hw4dp27YtmzZtStI1PuHx5gqB4HCxU8vSNP0dDFmR4QGRuPzgtf4BjxZTpkyJp5ECotnd/fv38fHxAcTylkwm49mzZ2zfvp2uXbsmKt9oictvA0lXNxSQKLNz6fg1lBFKaretnuYxi+YVsDSDkHB4+EqjU2HOCHTr1g1ra2s6duzI4cOHadasGQcOHMDGxibDzpEQjauacP62ktcf1Fx5EE21kl+OLo1WKbZw4cK6LOKUKVP49OkT8+fPT/GaoIVEItC/jTXTVnzi2oMoLt2JpEZZ49BpyUzY2tri4eFBx44ddV1cW7ZsoXPnzgaPVSCPCUO7ObBw43v2egZRvLCC6uWMs/Mt8sw2Ii0ySczMQESGZu6CJqOQk+HRFwb4aWFghkcQBF1mIFUeTxzisr7QWkxIpAr27t1Ljx49iI6OpkePHqxYsSLV4zPeYkL8NzwqZVfx9ELbqfXxM0QYuADRBjvaz+Pdu3cULVqUAgVEJWSZTIZGo2Hp0qWEh4fTqFGjRC3rFqaCLsuUlm6t5PD01nOmd/iN6Z3mc/3M7dQPSAbSOJo8F+9nPNemRYsWeHh4YG1tjbe3Nw0bNuT9ez3lp9MACzOBRlXEbpEj/0R9kbo0giAwdepUnSDbH3/8wcCBA/UmNOd3lNHSTXRU33r0MyFh/w1tGXNzc/bv30/Xrl1RKpV069aN1atXp2msOpUtaVFXzKQu2fKBNx++jOxFDlJHToZHX0gTZHRS8NNKjsMTT5k57uuCgEQqRRUdrSPKJgddSUtPDg9oTURVRCo1yOUmbNq0CVtbW5YtW8Z3331HQEAAkyZNSvZ4ncVEBrimA8ikAjbmGoLCRJsJLcE4o2GuEDV5AkPBPwCKOBo+hrbcmCtXLlQqVTzPlnnz5uHj40ODBg0SKX5r4eIkdoo9fgOViqbpz0iEQqXyU7dLLQLfBVOmdhqENOOgRkkJ5+6oeeKv4c0nDU72GatB4ubmxpkzZ2jatClXr17l/PnztGnTJt4++ujN6Iu6leT43FTyKVjD2VtK6lX8MtplE2LUqFHY2dkxYMAA1q9fT1BQENu2bUOhUKR6bEtXC/69E8nr9yp2eIQwsJ1xaLVkNkxMTNiyZQu2trasWLGCwYMHExAQoOseNAS9Wtvz6HkkD55F8vv6d8wcmReFgUr1mQ1F/e5GpsMzIrunkSqM6xM0ZkgSZHSSyPDEBkEJS1qxb3NyWR6ZoeKDepa0QDSNBIiIcU2XSCQsWbKEH3/8ERDbZCdOnJhszd8i5hqbkSaNsWWtTObx6NGerg+qVKlCcHAwgwYNYv369QwePJjffvuNunXr6hzrk4KWx/PiA0RkkMmjVCZl3JphTNs9Lt3WE7aWAmViSlnn72YO0bVy5cr4+vqyYcOGRMEOiAG/RqMhICD9nAmFXKBpDTHI8bgYRUQmZhAzG3369GH37t0oFGJmtmXLljqvwJQglwn0a22NAJy9HsGtx1+GPlFGQCqVsnz5ct0CbsKECUyePNlgPpNcJjC2ryPWlhKevYpizR7jMxkVZHKj2r4E5AQ8ekJIkOERDMnwCEKqPB6Jga3phpW0xI85UhnfdHTmzJnMnz8fELMV3377bZLnN89gLR6IQ1xOZ8t2ajCUuJwcSpYsyblz5/D392f16tVcvnxZd2F1cHBI9jg7S7ErTa2Bp2/TN4e4kEgkmMQxN9vz52GWjlqbpjbw2mW15GUNwWGZEyCUKFGC3r17J3perVazdOlSGjRoQMeOHenRo0e6W9m/KS0jt61AaAScvmK8lhP6oG3bthw9ehRLS0tOnjxJ48aN+fQp9ei9WEE5DWuIJdaNhz4TkQZrmS8VgiAwe/Zs5syZA8CcOXMYNmyYXk0hcZHLVsbo3o4IApy+EMLJfzL5YpWDTEdOwKMvEnB4UlRaTiKLo83ypNteQsfhMbSkBZFJZBh++OEH1qxZg0QiYfXq1XTv3p3IyPgrQm3Ao1SBMjpjeTyfQjPPOR1E53QBCAqD0HQudPPly4enp6eufb1z5846A9KUbtK6bq0M5PHExYv7r1jxwwb2LT7KPwcvG3x8AQcJhXILqNSZw+VJCR06dGDZsmU4OTkxZMgQ/P39qV69Oh8/pn1FLZUKtKwlpiVPX1Py+QvnsTRo0ICTJ09ib2/PhQsXqFu3Lq9fv071uI6NLMhlI+FDoJp9X6mjekqYOHEiK1euRBAE/vrrL3r16qVr/dcX5UuY0a25mCZes/sjT1/+d7JlmQmVSsWUKVMoUqQIZmZmuLi4MGPGjBTvBf7+/vTo0YMSJUogkUgYPXq0wefNCXj0hT4ZHl3nVmKCoSSj1JZ1flr6K+SaxpCWo6I1SWrpDBgwgJ07d2JiYsKuXbto06YNoaGxF0iZVEARk7HMqCyPtRnIpKK5Z1BYhgyZJBRydB5g6S1raWFlZRWPS6FSqejRowc///xzkp+JVnX56dvMUZcuWDI/49d/T5dxbajVplqaxtBmef69r04yMM4MTJgwgVOnTvHbb7/pOt1WrlxJVFQUT58+TdfYFV2kFHKUEKU0bmNRfVGjRg18fHzIly8ft2/fxtXVlcePH6d4jKmJhD6txC//iQvhPHrx3yPfDh48mO3btyOXy9m+fTvt2rUjLMywC067RjZULWOGMlrD/HXvCDISUUKNKtqoNkMwd+5cli9fzpIlS7h79y5z585l3rx5LF68ONljIiMjyZ07Nz/99BMVK1ZM03uWQ1rWE7EdWClkeKTJl620wVB61ZZlUkmMbamY5TGRS1PcXzxGQCKIZZUIpRpzReJjOnbsyKFDh2jfvj0eHh40adKEw4cPY2cnrm4sFQKRStFs0jYDujQFQSCXpYa3QfDhMxkyZnLIby+e4+VHKJ4348f38PBgx44dAAQEBPDnn3/GayN2shVFEEMi4MlbKJE2H9cU0bhX3Xj/V6lURIVHYWapX9tqyQIC9lZiifGfu2rqVUj9e5UenDp1ivnz57Nu3TqaN2+ue75o0aLcu3cPPz8/qlVLW/AGMcaidUxYujeCczejqV5KhbNT5v5NmY0yZcpw9uxZGjduzOPHj3F1dcXDw4Py5csne0z5YgpqVTDl/I0Ilv8dxNRv7bG2+G+tc7t06YKNjQ3t27fn6NGjNG3alIMHD2Jra6vX8RKJwPc9czNpwWvefoxm1oo3TB2eFwuz7H0fwy8dRm5hnq1z0CI8VAwiE/oKKhSKJIn2586do23btrRs2RIQNdC2bdvGxYsXkz2Hs7Mzf/75JwBr165N0zz/W9/89EAan5AsSOXx/i8+l7QOD8TJ8CQTCWtl5VMzEBUEAbncMB6PIAiYxXQYpFTLb9KkCZ6entjZ2XH+/Hnq16/Pmzeiap61uZglCspAjkdua3HMt0GZm1HQKh4//yBmlDIazZs3Z9myZQiCwJIlS+jTp088dWFBECgtijVzJ20ergZBo9GwcMhKxjeaTrCevhYSiUDDSuL36uxtdYZ15CU3vxEjRtCjRw+6dOkSr0Nr1apVuLi4JHKvTwuKF5BRraSon7X9ZOQX2aaeEM7Ozvj6+lKhQgXevHlDvXr1+Oeff1I8plcLS/LkkhIQrGbVnqAMUUz/0tC0aVNOnDiBjY0Nvr6+NGjQgHfv9PemszSX8uMQJ2wsJTx9FcXcNW+J/A/xovRFwYIFsbGx0W1aq5mEqF27NidPntT5O16/fh1fX994i5/MQE6GR1/EBDioYm5k0sTlK13AEx2dqNVWIk05w6Pt0tJHb8NEJiVKqSJKqUJf3SkzhYTQSHWqujfffPMNXl5euLu7c+PGDdzc3MQLhV0hQE1QBvIh8tjArRfwPjhjW5MTwtFa9PAKjxLb0wvkyvhzDB06FFtbW/r06cOWLVsIDg5mx44dOm2esgXh0kN4+k5s77fIAAHH5PDu+QfO7b9ESEAI9y4+okbzynodV9ZZ4Oxt0VvL+6aa5tUzJyNy9epVpFIpPXv2xMzMTPfZ37x5E09PT8qWLauz80gv2rspuPdcxZtPajz/VdKs5pfZph4XTk5OnDlzhlatWnHu3DkaNWrEvn37aNKkSZL7mykkDO9iw8zVn7j9RMl+r1DaN7DM4llnP+rUqYOXlxdNmzbl2rVrumtboUKF9Do+b245Pw5xYupSf+4+juCPje8Z198RmTTzfsspwax6S8yMpC1dGZPZefHiBdZx5pScjMKkSZMIDg6mVKlSSKVSVCoVs2bNomfPnpk6z5wMj54QEgQ4uuBGpUy8DxrQxA8MdOKDyWR4tGrLKlXqTujaDE+UAZ1apjEZnvDI1AOW8uXL4+vrS5EiRXj06BF16tTh9bO7gEj8VWbQStnWQuTxKFWZayQqCFA4ppHKL/N07+jevTv79u3D1NSUgwcP0rx5c12K195KwMlO9Pa69zLz5gCQp3BuFnj/wsRNI/UOdgAkgkCTKuJ369J9NQGZKBnw6NEjXXebIAi8efOGDRs2cPfuXQYPHoytrW2i38Hr1685dOhQIlJ9SrAwE+hQVwxyTvwbxZtPX8eq3M7ODg8PD9zd3QkLC6NVq1bs2bMn2f0LOMro20q8ER30DuPGw/8m+bZixYr4+PhQuHBhHjx4QJ06dbh3757exxcpoGDyICfkcoHLt8NYvv1DtmXMBKnMqDYAa2vreFtyAc/OnTvZsmULW7du5cqVK2zYsIH58+ezYcOGTH3PcgIefZGgpJVUhken1UMSWjypZXgS2EukBJMY4rJSqX/Ao09JKy5cXFzw9fWlbNmyvH79mkYN6/Ls/r8AGda6LBEEcscsBt4Gp7xvelE4pqzl90EMOjILLVu25Pjx41hbW+Pl5RVPXbhsTNLi9ovM7UwDKFy6AA27u+r+HxoUyrPbL1I9ziWfhKJOYsfW6WuZQ850cHCgbNmy3Lp1i+joaJRKJVOmTMHb25s+ffrQrFkzgHgZP7VazaNHj9i8eTMVKlTA29tb7/NVLi6jrLMUlRq2n4z4ako6FhYWHDhwgM6dOxMVFUXnzp1T5DbUqmBKg2pixnHVnmA+BBoH+TarUbx4cXx9fSldujQvX77Ezc2Ny5f1724s7WLKD30dkUjA+98QNuz/9J/wLctIjB8/nkmTJtGtWzfKly9P7969GTNmTLIlsIxCTsCjL5LN8MQpaQlCks9DbEkrOQ6PIAg6omtqPJ60ZHi0AU+4AXXnfPny4eXlRY0aNfj06RNjBjTl2sXTGcrjyRPD43mXyTyevHYgl0JYpEhgzkzUrVuX06dPkzt3bi5fvkzdunV58eIFJfODVAIfguF9UObOIS4iwiL5qfUcxrhN4c75+6nu37iK+F258UTDm4CM/1wKFSrE0KFDGTZsGPXr18fJyQlfX1969OjBxIkTgcRt/hKJhFq1ajFnzhz69etHy5YtWbJkiV7nEwSBzg0UKOTw7I0a35tfT7eSQqFg27ZtDBo0CLVazcCBA1mwYEGy+3drakmR/DJCIzQs3RmUYTITXxoKFCiAt7c31apV48OHDzRo0AAvLy+9j69a1pzve4irqCPewew+EZhJM/06ERYWlsgfTiqVpluDKzXkBDx6ImFJK8kMD0kHQhAb8KhSaN/TZnmiU+nUMokRH4xS6v/l0Ja0lCqNQeTNXLly4enpScOGDQkLDeGn79uyb98+vY9PDY4xXpIfPpOpK2+ZNJa7k5llLS2qVKmCj48PBQsW5N69e7i6uvL82UNcnMTX9Ui2ZBhUSpFTptFoMDFLncOS30FC2cICGsDzSuZkAQYMGMCNGzfo0aMH8+fP5+zZs/F0NRJmd0D0N3N2dmby5MksWrSI3bt34++vn7iRraWE1nXE9Pqh81F8Cv46Slsg3ihWrlyps1D44Ycf+Omnn5LMOshlAsM622BpJuDnH83Wo/9dMT0HBwdOnjxJ/fr1+fz5M82aNePQoUN6H+9W1ZIB7UUdrh1HAznum8lp6q8IrVu3ZtasWRw+fJhnz56xd+9eFixYQPv27XX7TJ48mT59+sQ77tq1a1y7do2QkBDev3/PtWvXuHPnjt7nzQl49EWCDixtlxbq+JybZAOemGBGHZ386lLL40mNuGwSp0tL31SqTCogjyHXGZLlAVF35vDhw7Rs1RZlVCTjh3fNsFqrtRkoZGL3VGa7p+vKWlkQ8ICozuzr60uJEiV4/vw5bm5uEHwNEHk8qiwqrVjYWDDn+BTmn55GsUpF9DqmUWUpEkF0UX/2JnOCg+LFizNs2DD69++Pvb09Bw8eZMmSJYkJ/xIJAQEBLFq0SPdc4cKF8fHx4cOHD3qfr3Y5GUXzito8O09HflVlCEEQmDdvnq4kMGvWLL7//vskV8y5bKR820G0nvC6EsHZa1+G03VmwNramqNHj9KmTRsiIiJo164dW7Zs0fv45nVt6NLUFoA1ez5y/lomX8TiQKNSGdVmCBYvXkynTp0YNmwYpUuXZty4cQwZMoQZM2bo9vH39+f58/htrZUrV6Zy5cpcvnyZrVu3UrlyZVq0aKH3eXO6tPSFNqMTnSDDA2KWJ8ZLJLUMj1qVuINLC52fVioBT1q0eEDs1lCGqYiIUmNlZlgHjqmpKXt276J150F4HNhIv379CAoKYuTIkQaNkxCCIOBoo+HFR3gXjI7TkxkomEskMAeEimKHNlkgYVGoUCF8fHxo1qwZV69epWeH+nw75RB5i7vy9G2sCnNmw9RcES/YefXInzvnHtCkT70k989lLVC1uIRLD9ScuKJmUHMh07roQExxL1q0iODgYNzd3RO1pb98+ZLJkyfz999/M3r0aGbMmEHDhg3jmbmmBokg0LWhKfO2hXHvuYrLD6KpVvLL8ADSF5MmTcLOzo6hQ4eybNkyAgMDWb9+fSJD4nLFFLStb8G+M6FsPPyZgk4yCjl9Xe+FvjA1NWX37t0MGDCATZs20bt3b4KCghg2bJhex3dqasvnMDVHfYJZuVP/ADy9CLrli8YyEwXMDEBwiGFdJ1ZWVixcuJCFCxcmu8/69esTPZfeRUpOhkcPaNSq2IyOtitLkIh3T+J3aiUiN8dAm+HROqYnBameJa20aPFAHB6PHp1aScHERM7UeStp31N0xR01ahTTp09P95fQMYt4PAq5yOWBrMvyADg6OnL69Gnc3NwIDg5m8ZSm3L18lNtZoMmTFII/fWaS+wzm9VuCx4Yzye5Xr6IEuQxeftBw70Xmfjbm5uZs3LiR6dOn64Kd27dv614vX748165d4+PHj+zbt49atWrxv//9j6JFYy3onz17xqtXrwgPTz5jkcdeojMX3esdSUj415Pl0WLIkCFs27YNmUzG1q1bad++fZLvSau65pQrZoIyGpbtCiYs4usp8xkKmUzG+vXrGTFiBBqNhuHDhzNz5ky9rm2CINCvnT1uVS0yRecrBxmHnAyPHlDdvwylxfZenfCgIIjBTbQyaS2ehAGPRIogSNBo1KhV0bo29bjQV3wQ0qbFY5oG4nJC2FtKGTLuNwrmc2DRb1OZNm0anz594o8//khEQtMXWh7PxxDReiEzdS0KO4gWE37voULhTDtNItjY2HDs2DE6d+7MkSNHWPtrW6LCN9GkUledV1lWwcrOknpdauO79yLVm1VKfj8zgVqlJXjfVON5RUWJAgJSSebNNW/evOTNK6a8/v77b86cOcPs2bOxsrJCrVZTvHhxvvnmG5RKJcuWLdMdt2PHDjw8PNi1axdVq1bl7du3/P7778mKmDWqIufaw2hef1Sz1zuS3k1NM+1vyi507doVa2trOnbsyOHDh2nWrBkHDhzAxsZGt49EEPi2vTXTV37i3ScVa/YH830Xm0zN5BkzJBIJf/75J/b29kyfPp0pU6YQEBDA/PnzU31PJBKBYd1zk8c2kv3Ls2a+NuVc42neZCeE4C+Dv5ST4dEDGlV0bAkrnpVECmrLSflppcLjMUR8MKs6tRLCxlwsbXQfPFnne7Jo0SL69++v17yTgoUCzE3EdvHM7qDS8njeBolChFkJc3Nz9u3bR/fu3VGrotn0ew9m/bYiayeBGKwPmtOLpRdnY5fHNsV965SVYK4QO8uuPc66bEiRIkU4efIkP/zwA+Hh4UgkElQqFXK5HDMzMx035cyZM/Tv3x8rKysOHTrEpk2b6NWrF126dOG3335LcmypVKBbIwWCAJcfRHP7adq+t8aO5s2b6yQSvL2940kkaGFpLmFYZxtkUrh6L4pj5zLR2O4LgCAITJs2TVdqWbBgAQMHDtTr2iaTCjSva5PqfhkFQSo1qu1LQE7Aow9U0fG6tHRpzpTUlpM0EI3l8SSFuOKDqUHbqZVWLZ60lqFszMUxPodrGDZsOJs2bUIqlbJx40Y6duxIRESEwWOKPB7xcWaXtSxNwSHGTPR51pXbdZDL5WzatIlOPYag0Wj4dcpQ5syZk/UTQSQza3H11E3m9V+CMip+MG5qIlC3vPiZn7muIiqL2pirVq2qc6WvV68eM2fOpFGjRvj6+pIvXz4kEgmvXr1i0KBBREREEBYWxoEDB7CysuJ///sfy5cvJywsLNkbVaE8UupXEhcsu85EEpGKAvmXCjc3N86cOUPu3Lm5cuUKbm5uiYigRfLL6d5M/FH8fTKU+8++fLPV9GLUqFGsX78eqVTKunXr6Nq1q0GClzkwTuQEPPogboZHE6uinJLactIZHvECq07mIiwzQG3ZRC5+dIZkeBQmAgKiiWhab1xmJqKejUYDweEaevXqxd69e1EoFBw4cIAWLVrw+bPhaRodjycLMqNZ3a2VEFKplA1rl9G402RAbL+cOHFitnUNhQaHMaPLAk5s8GL3gsRtudVLSrC1gOAwuHAv60gKzs7O3Lx5k9atW+Pn54ednR2jRo1i6tSpAAwbNozXr1/Tq1cvatWqxdOnT6lVqxZ+fn507NiRkSNH6n5TSaFZTRNyWQsEhmg4dO7rvclXrlwZX19fChUqxP3793F1deX+/fh6TPWrmlKrgikaDSz/O5hAI3EEz0707duXv//+GxMTE/bs2UOrVq0ICcm6LqwcZDxyAh49oFFHx3ppQeJOrQzK8MTlwKQqPqjT4tH/wiQRBBQmaWtN10IQBGwSGIm2bt2aY8eOYWVlxenTp2nUqBEfP340aFxthicgNO3BmL7QBjyvPom2FtkBc4WEEeNn0arfPADmzZvHt99+q1d2L6NhYW3O5M0jqdWmGh1Gt0z0ukwaayzqe0tNWGTWBmZTpkxh2bJl7N27l8GDBwNiKeuff/5h6tSprF27lv79+7N69Wry5MmDr68vZmZm2NnZpTiuQi7QpYGozXP2ppInr7/em3yJEiXw9fWlVKlSvHjxAjc3N65evap7XRAE+rSyooCjlOBQNcv/Dv4qzFbTi3bt2nH06FEsLS3x9PSkcePGfPr0KbunlYM0Is0BT2hoJpofGRkuXL+NIJGInVmg69QSJEmoLaeDwyMIgv7ig7ouLcPKU+nt1AKwsRDHiKu4XL9+fU6dOkWuXLm4dOkSdevW5dWrVwbMS8Aqhjv6PpOzPHYWYGUmav+8Miwuy1CULwwN2o2jx8hVSCQSVq9eTffu3YmKyvpsQ/Vmlfll30RMTGOFCSPDY1P45YsI5LGFiCgx6MlqaNuqtdydiIgIZDIZrVq10v1m7OzsePnypUFCZCULyahRWnRU33Eq4qu+yRcsWBBvb2+qVq3K+/fvqV+/Pj4+PrrXFXKBYV1sMDURePhcye6TOdkMgIYNG3Ly5Ens7e25cOEC9erV01vwMjOhVquMavsSkOYurbp16+r8R44dO6bzv/ka0WHiTP4uVoUm5jKIjkKjjkaANGR4Yi7aKagtS2UyoqOjUyXJpVmLx0RCACq9PbWSgq0uwxN/jGrVquHj44O7uzt37tyhTp06eHp6UqxYMb3GdbSBzxEijye/feZ1imjNRG+9EMtazo6ZdqoU4ewI9pZQteFAKhSzYcoPPdm1axfBwcHs3r3bII2ZjMbB5cf59CaQvtO7AmIXSuMqUracUnHhrpqapSTYWGR9N482C2piYoKFhQW5cuXSvXb48GEcHBxwdnY2aMx2rgru+ql4G6DB41IULb5J2vDwa0Du3Lk5deoUrVu3xtvbG3d3d3bv3q0Tb3PKJWNgOyuW7gzm+PlwihWUU7X019fFZihq1Kihe79u3bqFq6srJ06ciCeLkNV4/+gWEZbG4Xr/+Qsp9aU5wxNXwfN///tfvNcaNWqU9hkZIaKU0bRv355tp/4Rn0jJT0uPDE9K5Sp9xQfjavGkyTU9HQGPtbm2LJa4/FS6dGl8fX0pXrw4fn5+uLq6cv36db3GdbTJeh7P8w+QyfYtyUIQBKrGxIL2JTtx4MBBzM3NOX78OO7u7gQEBGTLvCLDI3n10J9eP3eK93zx/AKF8whEq+H09exd0ZUqVQpTU1PGjx/PgwcPWL16NStWrEAulxukvApgbirQsZ4Y5HheVvL6w5exWk0rrK2tOXbsGK1atSIiIoK2bduybds23etVS5vSrJaoyrlm32fefPw6u9gMRdmyZfH19cXFxYUnT57g6urKrVu3sntaOTAAac7wxNUlSFhS+dpqnF0a1GLn6fMM+HUZgYE9GFUjhueQUIwQfTk8ethL6NmpFaVUiZ1aemrxZERrulwqYKGA0EixrJXbOv5KXyv736xZM65du0b9+vU5fPgwtWvXTnFcxxhJieBwiIjSYGqSeRmEPDZgKocIJbwJgnwp0z0yDaULgO8d+BwOzlXd8fT0pEWLFpw7d4769etz/PhxnJycsnROCjMF3y3ol+h5QRBoUkXC6qMqrj3WULuMBjvzaCLDohCE+F1fmY18+fJx/PhxOnbsSJ8+fbh9+zZ9+vRhwoQJ5M+f3+DxKrpIKVdEyq2nKrafimR0JzMkmag5lN0wMzNjz5499O/fny1bttCzZ08CAwMZOnQoAB0bW/DklZIHz5Us3RnETwPtdfy//zKKFCmCj48PTZs25ebNm9StW5ejR49Ss2bNLJ9L7mLljEaHx/Rr1+F5//49+/bt4+nTp4lEmb424apFw3vrLBTGLtvKjLnzRXsIXXATG5zo2tfVKjSa+EGFVMfhSX+GB9KnxROp1KTLrFPbnh4YmnTglCdPHk6fPo2rqyuBgYE0btyY48ePpzimiUzANuaemdlZHokECjqIj7OrWwtEQrDW8eHyY/jmm2/w8vIiT5483LhxAzc3N549e5Z9E0yAgrkllC4koNHA4qlHmN3zT4ZWncCPrWbjs/ufLJ1L3rx5OXfuHJs3b+bu3bssXboUV1dXQDQZNEQiQRAEOtVXYGoCz9+q8b7+9TiqJwe5XM7GjRsZPnw4Go2GYcOG8euvv6LRaJBKBL7rZI21hYRX71RsPBz8VXmPpQd58+bFy8uLWrVqERAQQKNGjfD09MzyeUgkUqPavgSkOeAZO3YsBw8epFu3bjx58oRatWrRt29fZs2aZXCHjrFDgpqFCxfy06AeAEyb+ztjx45FnYDEDHECHkhkqKbl8GjUybedy/Q0EIW0afHIZQKymGmHpVOAEOITlxPC1taW48eP07x5c8LDw2ndujW7du1KcVxtliez9XgAnOO0p2fntbxiEZBK4G2g2DlWoUIFfH19cXZ25tGjR9SpU8cgIm5mo1ElKc+2b+biym3IrGzoOKYVbh2+YW6fxZzbfynL51OsWDEKFCig+//ly5epW7cuzZs3J9iAlaetpYQ2MY7qR/6J4mPQ1+8TIJFIWLx4MVOmTAHgxx9/ZMKECWg0GmytpAztbI1EgPM3Ijlz2XCNra8VdnZ2nDhxAnd3d0JDQ2nZsiV79uzJ7mnlIBWkOeAZM2YMa9as4cKFCwQGBrJ161Y6deqERCLRrbK+FmhU0QiCwJQhvfjtu24ALFy4kG/H/Uh0tCo+h0eQiOkDEpe14qpRJpflMUh8UKvFY0DAIwgC5qbiPNLjnaMlrAaHaVJc+WnVhbt27YpSqaRbt26sXr062f21ejxvsyBDmt9eDDRCIuBTNnLuzBUCZQqKjy8/Ev8tVqwYvr6+lClThtevX1O3bl0uXcr6YCIpHJi3jbenTuDSdyC5Wnen7fBmYtDT6Rtun72X3dMjLExUCz5z5gyNGjUyyFH9m7IyXPJLiIr++hzVk4MgCPzyyy8sWLAAgPnz5zN48GBUKhUlC5vQsZGYdt127DNPX339mS99YWFhwYEDB+jcuTNRUVF07tyZzZs3Z/e0vgg4OzsjCEKibfjw4ckes3DhQkqWLImZmRkFCxZkzJgxBgvdpjng8fT0xNXVlVq1ajFz5kwKFChA69atmTx5Mlu2bEnrsMYJbeAilfN9u8asXTBbVBfe8Te9f/6diLD4cuxJtauDeGFJjccTN8OT2sVWp8VjQEkLRA0YgNDItJMzLU0FJBKIVotcnpRgYmLCli1bGDJkCGq1msGDBycr+5/bWuyiCouE0IjMvdnIpFDAXnycnWUtgCou4r+P30BAiPh358+fH29vb2rUqMHHjx9p2LAhp0+fzsZZwolNXuyYu4+hiweT182V158VPHglzvfzpxD87r7M1vlBrLqwg4MD//77L25ubrx8qd+8JIJAt4amyKVw/4WKS/f+O4TdMWPGsHbtWiQSCWvWrKFbt25ERkbSrLY5lUuZEK2CZbuCCAn7+jNf+kKhULBt2zYGDhyIWq1O8Yadg1hcunQJf39/3XbixAkAOnfunOT+W7duZdKkSUydOpW7d++yZs0aduzYkahhKjWkOeAZPnw448ePZ/HixTx9+pTp06endSjjhzbbEhOs9G7fUqfAecD7Ih2+nxRPXViQ6aHFk5y9REwWSJOCq7oWadXisTAVP/awdGjxSAQBG7Ok29OTglQqZfny5UyaNAmACRMmMHny5ETzlkkF7GM6LbMiy5Pdqsta5LISKJJHfHzlSZznc+XC09OThg0bEhISQvPmzdm/f3+2zDHgbSD7lxylw6gWNO1Zm29Kid8jzysqrp2+jd+dl9TtVCtb5pYQVapUwcfHh4IFC3Lv3j3q1KnDw4cP9To2t62EpjVFPaJ9PpF8/g/d4Pv378+uXbswMTHh77//pk2bNoSFhTGwrTWO9lI+BqlZuSc4Xfy/rw1SqZRVq1Yxbty4LD2vWq02qs0Q5M6dGycnJ9126NAhXFxcqFevXpL7nzt3jjp16tCjRw+cnZ1xd3ene/fuXLx40aDzpjngMTc3p23btlSrVo2VK1dmC2kry5CgDR1VNO3atePQ39uxNDPl9MWrNG7cWMddSi7DA3G0eJIpaUkkEp3WSGqdWjKpBC0/XBmt/xfOXJH+khbE4fGE6nfxEwSB2bNnM3fuXADmzJnDsGHDEpXv8mQhj6eQAwiITu2fwzP9dCmiakyW5/ZzCI/j7WRlZcXhw4dp164dkZGRdOzYkY0bN2b5/EKDw3nr94EK9ctiaq7AtZwEUxPwu/uaXX95kadwbopWzEIL+lRQqlQpfH19KVGiBM+fP8fV1ZVr167pdWyDSnLyO0gIi4TdXl+v7URS6NChA4cPH8bCwgIPDw+aNGlCZHgQw7vYYCKDW4+jOOj93zYZTQhBEJg3bx5HjhzJsnP6PXvGs6dPjWLzi2msCA4Ojrfp4z8WFRXF5s2bGTBgQLINT7Vr1+by5cu6AOfJkyccOXLEYAmKdHVp7dq1ixs3bhAdHZ0t6rBZBo1azLYkaDlv1LAhh/+cir21FRcvXqRevXq8fv0aQaZ1UU9ctkpNbRliszypEZcFQUiTxYS2pKVUaYgyIFBKCG2nVkrE5aQwYcIEVq5ciSAI/PXXXwwZMiTe6442AhqNhnfBiSUPMhqmJqA1DPfLBjPRuCjoIJb0olVw41n810xNTdm1axd9+/ZFpVLRt29fFi1alKXzUymjsXGwIn8xsU3eTCFQytwff08Pbp97QOO+DSimbTkzEhQqVAgfHx8qVarEu3fvqF+/Pr6+vqkep3VUlwhw7VE0t578d0pbAI0bN+bkyZPY2dnpDFxl6vf0biWajB7wCuXWoxwzzbgQBIE6depk9zSyFQULFsTGxka3zZ49O9Vj9u3bR2BgIP369Ut2nx49evDLL7/g6uqKXC7HxcWF+vXrG1zSSrMOzw8//MCxY8eYP38+9+7dIzIyki5dulC+fHnKly9Pu3bt0jq0cSKugaiO0yOjWpniHF86g7YT53H79m3q1KnDgfXLKGitQJNEUJOanxaIPB6lUqlXa7pOi8cAHo9UImBqIhARpSEsQo2JZdriXi1x+XOEhmiVBplUfzmCwYMHY2try4ABA+jWrVu81+wtxdJWRJSG568DKZzfPk3z0xeFc8ObQLGsVa5gpp4qRYhChBqOXYFrT6CqS/z3VCaTsXbtWmxtbfnzzz8ZNWoUAQEB/Pzzz1kiBeFUxBEzKzNWT9pC1wltCXgbhO+SY4S/isCxQROsq4vNChqNJt58nt1+wZMbfjTsnj3NDI6Ojpw5c4ZWrVrh6+uLu7s7e/bsSVUdvqCjlPqV5Zy6omTXmUhc8ksxU3xdkhspoWbNmjp14Zs3b+Lm5saJEyeoVyUXXlciWLEnmKnf2uNg+2W0JH9tKOzsbDQ6PNpuyBcvXsSbk0KRumr5mjVraN68Ofny5Ut2nzNnzvDrr7+ybNkyatasyaNHjxg1ahQzZszQdRjqgwzp0goKCuLu3bu4ubkhk8n4+++/0zqs8UKlREggNKgtcZUpUgAfb29cXFx49uwZTbr05daDx2ni8ICB4oNywzM8ABYxZa3QdPB4TOUCipi3JDjc8ExM586defbsGY0bN473vIAGn4PLmD68Ed26dqJHjx4G14gNgZbH8yZQFCLMTpTMDxamIhH8fhJWZBKJhD/++INffvkFgGnTpjF69OhMfX+0UJgp+O3kVEICQ1kyci2ze/5JnsIOtB7djryN3fG+oSYsQhUv2IlWRvPJP4ATG88wtt7PREVmzxtsY2MTTyKhTZs27NixI9XjmtU0wcFGIChUw8Fz/72MRrly5fD19aVo0aI8fvwYV1dXKhZ6TuG8MkLDNSzfFYQyk81+c5A0tPQHY9lAVPGOu6UW8Pj5+eHp6cmgQYNS3G/KlCn07t2bQYMGUb58edq3b8+vv/7K7NmzDbr2pdst/fPnz6xcuZJu3boxevRoJk+e/HW25sXJ8GjidG1pUaRQQXx8fChfvjxv372n1eCx/HMhMaFKy+HJCHsJSEenlo64nD4ZfX30eFJCXC8kLTp06MDBncuxzZWH5h0H4+/vT/Xq1TNN38naDOwsRS2eF9lc1pJKBCrHESJMqqQnCAJTpkzRlbQWLVpE//799dJuSi9MzRX84T2DmYcms/LG70xY/z29vqtJbhuRE3b+bvz9ZXIZFeqVYeSywVg7WDGs6gRCg7LHeFgrkdCtWzeUSiXdu3dn5cqVKR5jIhPo2lC8aJ+7Fc3jV1+37URSKFq0KL6+vpQrV47Xr1/TqGE9arncx8JU4OnraLYf/zJ8lHJgfFi3bh2Ojo60bNkyxf3CwsJ0QZUWcRt89EWaAx5vb2/69u2Lk5MTP/74Yzzhr68RmiRKWnEd1DUqpU6B85tqVQn6HEKrngN07XZaxGZ4kl/pGiQ+GKPFY4j4IMTJ8KSbuKzl8WRMhmHChAmcOnWKWb/OY/SMbVRw7cqKFSuIiori6dOnGXKOpOBsBKrLWlRwFlvmPwTD8xTmM2LECDZu3ChKJGzcSKdOnQzWpUgrHPLZU6BEPqIiotgwZRulFC8QJBLO31HzOU7wq9FokMll5Cmcm0mbRpK7YC4Orci+BgcTExM2b97Md999h0ajYciQIToSfXIoXkBGrbLib3L7qYj/ZEZDe22rWbMmnz59omM7dyrkFc2jT/8bzvHzOSTmHBgGtVrNunXr6Nu3r+6ep0WfPn2YPHmy7v+tW7dm+fLlbN++nadPn3LixAmmTJlC69atdYGPPjAo4Hnz5g1z5syhePHitGjRgujoaHbt2oW/v//X3ZYOoIqO16WlQ4Ln7OzsOLJ3Jw2/qUZYeAQtW7Zk9+7dsbtrOTwpBDOGiA+mV4snPFKNOh3EYEM7tVLCqVOnmD9/PosXL6Zz++bIpaBUgV2eoty7dw8/P790nyM5aMtaLz+JpOHshKmJQLlC4uPLj1Pet3fv3uzZsweFQsH+/ftp0aJFPImEzMa5/Zc4uuYUQffuUDC3QFS0hjM3YoPfP79byfmD/yKRSDA1V6BWqXl2+3mWzS8pSKVSli1bpiM8Tpo0iYkTJ6a4UmxdR4G1hcD7QA3HL37FDRopwN7eHk9PTxo3bkxISAjfD25LfvkpAHZ4hOBzNZvbHP9jUKs1RrUZCk9PT54/f86AAQMSvfb8+XP8/f11///pp5/44Ycf+OmnnyhTpgwDBw6kadOmrFixwqBz6k1abt26NSdPnqRBgwZMmzaNdu3aYWERaxb4tflnJUJSJS1EHo8mOirec5bWtmxdOIPvps5n3/GTdOnShVWrVjFgwAAkMR1calV0InKnFjI9u7QgsRaPvp+DQi4glYBKLQY9FqZpIx5amwkIQGR0+gw/NRoNI0aMoEePHnTp0gWJREJuazWvA2Dp8lW4uLhQokSJNI2tD3JZoTNEfR0gtqtnJ6q4wLWn8OwdfAzWkMs6+fe1TZs2HD16lDZt2nD69GkaNWrE0aNHkywXZjTqd62DhY05ZWqX5EO4hJV7PnPloSW1ykhwsBZwLJybXzrNp9+M7gS9D+L9y0/UalNdd7wh39mMhCAIzJo1Czs7O8aPH8+8efMICAhg+fLlSa4YzRUCneopWHskglNXlFQuLiN/7v8eWdfS0pJDhw7Ro0cP9uzZw69TejJk7FIiLDuw/uBnLMwkVCmVOlE1B+nH3afvsbQyDruPkDQsstzd3ZNdZJw5cybe/2UyGVOnTmXq1KlpmZ4Oemd4Dh8+TIcOHZg+fTo9e/aMF+z8FyCWtBK7o+syPOo4QZBMhsLEhDVzf2bQoEGo1WoGDhzI77//ruvSIgVhwbgZntTqk2nV4hEEIVaPJx3EZZlUwNJUnEBgGnk8AFevXkUqldKzZ0/MzMzQaDQ4Wgs8f3STM6c9KVu2LAULZl4LlSDEZnmeGUFZy9ZCoFhe8XFqWR6ABg0acPr0aXLlysWlS5eoW7cur14lwXrOBFRvVhkLa3Nkn98S7nOIiIAATl0Vv7s9/teB9iNbcP7gJd6/+oR73/pUa1oJgE9vAji43IPH159lyTyTwrhx41i1ahUSiYRVq1bRvXv3ZCU2KrjIqOgiRa2B7aciUf1HxfcUCgU7duygf//+qNVqls8fSviLdWg08NffQdx9+t/MgOXA+KF3hufcuXOsWbOGhg0bkjdvXnr27EnPnj1xcXHJzPkZD1TRCAqzmMdx3dHlaEiY9REDI4lGzcqVK7Gzs+O3335j3LhxBAQE8H3vTqDRoFYpkSSxmoy7wlSpVInqm3EhCAImMimRShVRSpUu46MPzBUSPoerCI1Uk1vvoxLDxkLgc4SGoDA1TrZp58E/evQIBwcxtSIIAppwf84c2cjjh/cYv2Q+tra2iTICV69e5fXr1zRp0gQTE5N0/BViwHPnpcibUZcCSTYnLau6wCN/uPsS6pTWYGGa8oSqVauGj48PTZo04c6dO9SpUwdPT0+KFSuWJfMNCQzj892bRFgX4LatK3U+asjvIGDnZEdUeBTj1w3HRBFL9D+79yJeu87x94KD9JvRLdva1gcNGoStrS09evRg165dBAcHs3v37iQXdR3rKXjwMowX79R4XVPSsEr6vnNfKmQyGWvWrMHOzo4FCxawfc0kmnX8hFO5sSzaHsSEvrYUySdPfaAcpBmli+Q2orZ00+yegl7Q++70zTffsGrVKvz9/Zk4cSIeHh6UKFGCb775hsWLF/P27dvMnGf2I15JK4kMT5yAR1u2QqMGjZp58+bpBJhmzZrF/2bMFeW4kylZCYKgt/gggFyeNh6PzmIiIns7tQAcHBwoW7Yst27dIjo6GqVSybxfp3D/ug91m/emhquomRI32AkNDeWPP/7gzz//pGrVqnh7e6fr78hrCyYysTX9TUC6hsoQ5LMHJzux7KhvEqR06dKcPXuWYsWK4efnh6urKzdu3MjUeWpRspoLHUc04+nGNbzz8eKIbyhv/d7z5ulbCpbKj0wujZexbD20KVN3j6PH/zqwePhqvHadz5J5JoVOnTpx6NAhzM3NOX78OO7u7gQGBibaz9pCQltXsWRz9EIUH/4DjurJQRAE5s+fz8yZMwE4tnseD8/9TESEij+2BOL/4b8l1pjVkEgEo9q+BBi8HLewsGDAgAH4+vpy584d6taty6+//ppIS+WrQ1LCg8Rq8cQLgiRSRMMCdOKDkyZN4q+//kIQBNZt2c73E6cQGZE8yU+b1dFXfBDS3qmVnpIWxHZqBYdp0kyALlSoEEOHDmXYsGHUr18fJycnzp49S4t23WnXewLvgjSJ9BYsLCxYsmQJixcvplu3brRv3z5dlgsSCRRxFB8/fJPmYTIMgiDo7CauPxWVsfVB4cKF8fX1pWLFirx9+5Z69epx7ty5TJxpLJoNaMiIv4byYv9ujv/0O0OrT+aG1x1K1SiORCLRBazawMfa3opmAxrSeqg7XjvPospGxri7uzuenp7Y2tpy7tw56tevn+RCrmZpGcULSFFGw45T/w1H9eQgCAI//vgjS5YsAeDsiZVc9RhF8Oco5m8K5GPQf6+NPwfGi3Tp8JQsWZJ58+bx8uVL9uzZk2ov/ZcMTYIuLd1FLqkgSBBi7SXiqC0PGTKEbdu2IZPJ2HvoKJ279SAsLOl2TkPEB9Oa4dF2akVFawzi/ySEhQJkElBrICQNAoRaDBgwgBs3btCjRw/mz5/P2bNnGTlqNCAaiSYkt2o0GqytrSlZsiQ//vgjQ4cO5fDhw3p1tyWH4jG8mafvIMoIFqjF84o6QeFRcMeA5qY8efJw5swZ6tSpQ2BgIE2aNOH48eOZN9E4aNGnDt0WTyRP/UYUad+Ob3/vS/uRoudNVKRSV5ZURsX+NswszXhx7zWR4VHZGkDUqlULLy8v8uTJw/Xr13F1deVZjE+QFoIgavPIZfDwpYqzN43gi5LNGD58OJs3b0YqlXL9n52c2zuQ9x/D+G1DIO8CcoKeHBgH0i08CCLnpF27dhw4cCBd4yxduhRnZ2dMTU2pWbNmik6ot2/fpmPHjjg7OyMIAgsXLky0z7Rp08TgI85WqlSptE0uLmlZ+39i+TqaBJkYQdeNFV9vp2vXrmxbvwZTU1OOn/CkWbNmBAUFJTqdIVo8ijSqLUulgq6rKiQdejyCIGAbYzMRkM729OLFizNs2DD69++Pvb09V3wPcmzXUj4EaxIFIIIgoFKpUCrF97hw4cIcO3aMgIC016Py2ICNudiafv91ev6SjIFEIlA1hoLzz30M0oCxtbXl+PHjNGvWjLCwMFq3bs2uXbsyaabx0b5tYQq6foNlnaZE5Cune95EIdcFrnITOWGfw7l/6RHH1p2icNkCmFuZZXvHZ4UKFfD19cXZ2ZlHjx7h6urKnTt34u3jYCOhZS2Rv7PXJ5Lnb3Nu6j179mTfvn2Ympry8NZxzuzowUv/QH5dE4CffzZLmOcgB2RQwJMR2LFjB2PHjmXq1KlcuXKFihUr0rRpU969e5fk/mFhYRQtWpQ5c+bg5OSU7Lhly5bF399ft+ljHJgkopWx2RyI7dTS8nVU8TsTtDwejTJxx0Kzpu5sW7UEaysrfHx8aNCgQaK/U64NeJSpXyi0ROXIqGiDV8dWZuKxn8PTd8G2j/Hj+vg54zgNYWFhrFi+mLMem/F/8RD/JOIYqVSKXC7n3bt3LFq0iObNm+Pg4JDmLIEgQPkYDZzbLyALHBtSRfnCYG0utsxffWLYsRYWFuzfv58uXbqgVCrp1q0bq1evzpyJxj2vqUCz6uJ369TlKBaN2cSs7n/wU+vZjK3/M6Pq/MiwahMYVG4Mv3T+ndwFcjF21dBMn5e+KFasGL6+vpQpU4ZXr15Rt25dLl26FG+fehXllC8qRaWGdUcjCE1HdvNrQatWrTh27BhWVla8eHSW09s68e7de+auD+TOk5zurYyESq0xqu1LQJrNQzMaCxYsYPDgwfTv3x+Av/76i8OHD7N27VomTZqUaP/q1atTvbqo55HU61rIZLIUA6K4iIyMjGdnrzVEA7E0JQiCmOVRKcX/K1LK8Iirv6Rc0SUyOd9Uq8L+HZvo0ncwV69e1RnzFSok3m1lcnFc/TI8MXwftYZolVonRqgPLE2lvA+KJiTdAU9shket0SDJgFW6ubk5GzduZL/nVXIVKsGrAA1W0k8EBATw5MkTnj17xvPnzzl16hR+fn4UKVKEMWPGAMTjihiaMSjmBP8+hpAIsUW9aJ50/ynpgkwqUKeUhqNX4OJDKO+swcwAvSMTExO2bt2Kra0tK1euZPDgwQQEBDB+/PhMnDVULCpw44nA/SdRnNjoRWRgMEPm9wFArpCTK58dEqmEXPnsKFE1ttvz5UN/nt99SeDbIHLls6Nmy6qZOs/kkD9/fry9vWnevDmXLl2iYcOGHDx4kPr16wPid6xHY1N+3xHGhyANmzwi+La16RdD4Mws1KtXj9OnT9OsWTNe+13Dc3NbGnTbyR9bNAxqb03Ncl9GR4+x49+HIVik0fg5oxEa8mXYixjFuxUVFcXly5fjEZ8lEgmNGzfm/Pn0dW48fPiQfPnyUbRoUXr27Mnz58kTIWbPnh3P2j6e7kuijE6Mgag2k5MgwyPIxYBHE514VSONCWbKlSqBr68vhQoV4sGDB7i6unL//n3xNDEZHqUeGR6JRNCVtSIMJJ5oMzwh4alr/qQ8joBMKnYUBaejWysh8ubNS7cOzQGY+8s4SpYsSceOHenVqxcbNmzg7du3NG7cmI0bN+Lj40PNmjXjHS8IAkqlkuPHj/Pvv//qdU6ZFErHOKXcfC56bGU3ShWA3NYir+jiA8OPl0ql/PXXX0ycOBEQLTwmT56cqXwZQRBoXUuKmZU5FX79ndwu+bn/7yM6jmlFm2FNqdOuBrVaV9MFOwFvAzmy+iTfVRrHvsVHObLak80zdzOm7pRs4/XkypWLkydP0rBhQ0JCQmjWrFm80r2ZQmBAC1PkMrj3XIXHpZzSDUDVqlXx8fGhQIECvPd/iOfmNnx6/4QVu4M58U+ODUUOsgdGkeH58OEDKpWKPHniL6Xz5MnDvXv30jxuzZo1Wb9+PSVLltTZX7i5uXHr1i2srKwS7T958mTGjh2r+39wcHBs0BMdG+BoImP/rwuAEmRiJLLkAx5tuUulVFKiRGl8fX1xd3fn3r17uLm5cezYMSpWrAiIfiNqtTqRcVpCmCpkRCpVRERGY2Wuv9KpuakEiQDRalEp2UyRttWpIAjYWwq8C9LwKUSDbQbqUtqYg7kCXErXwHPfCvLmzcvp06ext7dPcn+NRkNERAQnT55k06ZNWFtbs3XrVsLDwzlx4gSNGjVK9ZxlCsANP3gfDG+DwMk24/6etEAQBFzLaNj7j6jAXLmooHpQGwAA/sdJREFUBmtzwz4rQRCYM2cOdnZ2TJo0iTlz5hAQEMDSpUsN8qMxBHaWAo0qSTj2rzmlp/zKozk/MqbuFH49+iNmFrEr/ZDAUDw3ebNl1m6qN69MrymdcKnoTPCnz8zosoCx9X5m7omf4+n4ZBWsrKw4fPgw3bt3Z9++fXTo0IF169bRu3dvAPI5SOlcX8FWz0iOX4yisJOE0oWN4tKarShVqhS+vr40adKEhw8fcnJzW+p33c6242UJ/KymU2OLbOdrfcmoVtwSa2vL7J4GAMHBRlD71wNGkeHJLDRv3pzOnTtToUIFmjZtypEjRwgMDGTnzp1J7q9QKBLZ22uhazuXxu++ii1pJcjwaEtaSXB4pNqskFqFRq2mYMGCeHt7U6VKFd6/f0+DBg04e/asLsjRp6xlaiJeYA3N8EgEQafH8zmdejxaHs+nkIz98guCQH47qN24C9uP3uLRo0c0bNiQR48eAbEtzmFhYZw9e5b+/fvTtGlTFi9eTOXKlfnpp59wcXGha9eu2NjY6HVOMxOxtAVilscY4OwIBRzELNr5tK8DmDhxIitXrkQQBFasWEHPnj2TVRfOCNQsJSG/g4BSI6PJknmEBIbyc5s5RCujdVIDl0/cYOO0nchNZIR9Dmde3yWsmrAJa3srJm4cQZ7CuXn7LGk+X1bA1NSUXbt20adPH1QqFX369GHx4sW612uUllO7nAwNsMkjgoAM5LJ9ydBKJFSqVImggHd4bm7P2+cXOXoujDX7PxOtp9RCDhJDKhGMavsSYBQBj4ODA1KpNJHmxdu3b/Xm3+gDW1tbSpQoobtRGoTo+CWs2JKWSbzXtZDIE7elayFIpGj9IFQxr+fOnZvTp09Tt25dgoODadq0qU5ITx/ismnMyjci0vAWWcs4Za30IFdMwBMYmvEktnx2MZwc88I8ePiQihUrUqJECQ4ePKhbJV6/fp3evXtz9+5djh49yvHjx5k0aRILFy5ErVbTq1cvqlWrpvc5y8Uk9/zeQ5ARZOEFQcCttPj4zgv4EJz293jw4MFs374duVzOjh07aNeuXbISCemFRCLQtpYUiQD3XmgYfXA+PX7siFolZi4jwyNZPmYdlRqW4w/fmfy4dTRjV32H776LeO08h72TLd/+1puCJfNnyvz0hUwmY926dYwcORKAkSNH8ssvv+gC7vZuCgo6SgiLEEnMOTdzEY6Ojpw+fRpXV1dCQ4I4ubULrx+d4tz1CBZtDyIiKic4zEHWwCgCHhMTE6pWrcrJkyd1z6nVak6ePEmtWrUy7DwhISE8fvyYvHnzGn5wdNIZHmSxGZ64PAMhhZKWIAhIkyA1W1tbc+zYMVq1akVERAQDBw3iwMGDKA3I8ERGRRss/pdRnVrmClDIRD2ewAxwT48LB2tRBTkqGj5+hg0bNrBlyxauX79ORIRooFelShW+++47rl69yqxZswDYvHkzu3btYtiwYbpSlr58EDtLKBDjv3n7RYb+OWlGXnuB4nlBA/jeTd9YXbp04cCBA5iZmXH06FGaNm2apLpwRiCPnYBbefFyc+SCipJ1ymFiKv4G/jl4GYlUwqC5vShQPC/WuawoWb0Y+Vzy8PrxWyQSCfZOdpkyL0MhkUhYuHAh06dPB2Dq1KmMHTsWtVqNXCbQr7kp5gp4/lbNPp+criQttBIJzZs3JzIinJM7+vD87j5uPYritw2BBIfmBD3/JWjlZBJuw4cPT3L/9evXJ9rX1NRw8rtRBDwAY8eOZdWqVWzYsIG7d+8ydOhQQkNDdV1bffr0YfLkybr9o6KiuHbtGteuXSMqKopXr15x7dq1eNmbcePG4eXlxbNnzzh37hzt27dHKpXSvXt3g+enSY60LI/hy2jUoI7jsRWnpJXUDVbH40mQATIzM2PPnj307NmT6Ohoxo4dy8qVK1Odn1wmQSIR0ABRBpa1tBmesAh1ujIzgiBgb5U5ZS2JIJDXVnz86pM4x+7duzN58mTdF1+hUDBhwgT27dvH1q1bqVSpEv/73//o168fbdq0wdTU1OCuLW2L+oPXouWEMaBOaTFB+OQNvPqYvsCyWbNmnDhxAhsbG3x9fWnQoEGm2cTULS8ht43Y/ebxb+xvRWGuQCqTYpmA+PX05nMiw2O7Jo1F0VgQBH7++Wf+/PNPABYuXMjAgQOJjo4ml7WEXu6mCIDvTSX/3jeSL40RwNzcnH379tG1a1eilUpO7fyOZzc28/R1NLPXBvA+R6DwP4NLly7Fk4s5ceIEAJ07d072GGtr63jH+Pn5GXxeowl4unbtyvz58/n555+pVKkS165d49ixYzoi8/Pnz/H399ft//r1aypXrkzlypXx9/dn/vz5VK5cmUGDBun2efnyJd27d6dkyZJ06dKFXLly8c8//5A7dxqsMhNwdnQZnzjlqbjZHC1pOWEgpIW2Uyspjo9cLmfjxo0MHjQIjUbDhAkT+PXXX1O84AuCoMvyhBsY8ChkAnKpGCyFppvHI74Xn0Iy/uaU314c+3VA7M0vIdlWrVbTokULvL29efHiBe/evSNXrlwUKCC2XRlKksxnB/aWIqn7ftaYj6cKeyuBcjGBmM+d9AcCderUwcvLC0dHR65du4abm1uaLiapQSYVaFNLigBcfazh8WsxKLayt0StUuN356Xub5k/YBk2DtbU7RSb4RUEgahIJVGRSq573eb8wX+54pk1PmFJYeTIkWzcuBGpVMr69evp3LkzERERlHGW4V5d/H3vPBXJy/c5N3ItTExM2LJlC9999x0ajYbTe37g8eWlvP2k4te1OQKFhiBapTGqzRDkzp0bJycn3Xbo0CFcXFyoV69esscIghDvmIRNTvrAqFoJvv/+e77//vskXztz5ky8/zs7O6d6od++fXtGTS3WOkLXhh4TAAkCyBSgjABlFCjMxeelUtGcSa1GHa1EKo3/VieX4dG9LpGw4I8/UJiasmTJEn788UcCAgKYN29esjdtU4WMsAilyONJ3ISWLARBwMpMyqeQaELC1Vib639sQojEZRXBYeKPQCbNODJbHhvRwTw0EoLDxe6thNASvefPn0+BAgUoV64cy5YtY8CAAVhZWaXa7ZYQWiFCrztw+yWUKwRSI1gm1Copuqi//iRmelzSUKWNi4oVK8brqHF1deXEiRNpVyZPBoUcJVQvqeHifTUH/1ExrI1A2dolaTeiBbO6/UGZWiW4f+kRNg7WtB/ZgqIVCnPn/H1ePXrDs5vPuXr6FvZOtnwOCCX4QzCvHr5h+ZV5uFR0ztB56ovevXtjY2NDly5d2LdvHy1btmTfvn00rWHJs7dq7j9X8df+CEZ1MiO3rRF8cYwAUqmUZcuWYWdnx+zZs/E++AvRkYGUqPU/5q4PZEQ3G0oX+W+60BsCrztKLCyNI0AMDRHnEVe7DsSsu0KRctdwVFQUmzdvZuzYsSkuSENCQihcuDBqtZoqVarw66+/UrZsWYPmmfML1BMJu7LikpST09xJqTVd26mVlDChFnK5nDGjR/PTjz8C4k188ODByXpFpbVTC8DSLKZTK508HjMTATMTkWOSXpuJhJBJBfLYio9ffUp+Pw8PD5YvX86AAQNYt24d//77r97dWUmhaB4wN4GwSHiSOdUeg2FpJlClqPjY9y5pNm2Ni+LFi+Pr60vp0qV5+fIlbm5uXL58Od3jJkTjKhJszCEgBE5fE7M8nX9ozY/bx1CnfU3aj2zJrMOTaTqgAae3n2Vy81lcPnGdAiXz0WFUS35YPZTvfu9LRGgk9brUyrZgR4s2bdpw9OhRLC0tOXXqFI0bNyYg4BP9mplSILeEkHANy/aFE5jBZd4vGYIg8OuvvzJv3jwAznks4o7XRMIiolmwOZCLtyKyeYY5SAsKFiwYT8tu9uzZqR6zb98+AgMD6devX7L7lCxZkrVr17J//342b96MWq2mdu3avHz50qD5GVWGx6iRiKQcJ+CRmaAhmdb0qIgk7SWkqWR4IFZ8sH///hR2dmbIkCGsWbOGwMBAtmzZkihyNlPEBDxp6NTKKOIyiN1aLz+p+fRZTW7rjI2p89sJ+AdoeB2goUyBpFcD7u7u7Nu3jwoVKmBiYoI8pnxoaHZHC6kEyhQU1ZdvPhfb1Y1BPqRacbj+TCRx33kO5Qqnf8wCBQro1IX//fdfGjRowMGDB1NMNRsKhVyg1TdStpxScf6umnLOAvkdJFRpVD7RvoVK58exkANB74NpPlAknT+88oR5fRfjVMSRH7eJytr6aFVlJho0aMCpU6do3rw5Fy9epF69enh4eDCkjROLdofzPlDDX/sjGNHBDAszI/jyGAnGjx+PnZ0d3377LRfPbCA6MphyjRaxYncwQaFqmtRMR7r5K0e9MnKsrbNelyopBAeL83jx4kU8OZfUsjsAa9asoXnz5uTLly/ZfWrVqhWvgal27dqULl2aFStWMGPGDL3nmZPh0Rcq0eU5IWkZ4nRkKZPR4klBfDClDI9UKtVdxPv06cOuXbswMTFh9+7dtG7dmpAEct4KE63DuppolWGrSUtTreO6hihl+laiWh7Px0zg8eSLadYJCIWwyOTHb9mypU40UpsmnT9/Pj/99FOaOC+l8ouO8J9CRA6RMcBULlCzhPj43D1QZlAbtIODAydPnqR+/fp8/vyZZs2acejQoQwZW4sSBSSULyKg0cD+86pkyfIuFZ35afsY/J+8Y1zDabx65M+CwX9hbm3ObyenAiKHKTuDHS2qV6+Ot7c3+fPn5/bt29SpU4d3/k8Z2tYMGwuBN5/UrDgYTkSUcZCvjQWDBg1ix44dyOVyrpzfy7Wj/VFGhbHtWAi7PEOMhqxubJBJBaPagEQ6dqkFPH5+fnh6esbj3uoDuVxO5cqVDZaYyf6rxJcCjQbUqjhCg3ECFZ0WT4KSljxpUUKIz+FJ6Qcti2Mi2qFDBw4fPoyFhQUnTpygSZMmfPoUW9uRSiQ6I1FDszxSqYC5ImMECO1i9HhCIjREGeDurQ8UcgGHGH7Si4/6H3fjxg3Gjx/PrFmzGDFihE7wTl+YyqFEzALklpEIEQJUKgJWZmLn0/WnGTeutbU1R44coXXr1kRERNCuXTu2bNmScScAmleXYq6AtwFw9nbyn0fhMgWZc/wnAt8FMbz6JCLDo1h6cQ4yuQy1Wm1Uar1lypTB19eXYsWK8ezZM1xdXXnld5uh7cywMBXb1dceztHoSYjOnTtz8OBBzM3NuXHZk8sHexAZHsTRszkChV8z1q1bh6OjIy1btjToOJVKxc2bNw2WmMkJeAxBtDLpDE8yHJ6UDES1XVpoNGiS6OLSQmsiqtXiady4MZ6entjZ2fHPP/9Qr169eN1rsTwew8lssQKE6cvwKOQClqaZ161V2EEc+9l7jd6rvwoVKrB8+XIEQWDp0qX07t1bL5+yuCgbI0T44qOYYTIGyKQCtUqKjy88gAhlxr3fZmZm7N69m169eqFSqejVqxdLly7NsPHjOqp7XVfzISj5uZuYyrHJbU1kWCQqZWwwbwyZnYRwdnbGx8eHChUq8ObNG+rWrcvT+xcY0sYMhRwevFSx6XgE6i/EYTqr0LRpU06cOIGtrS13bp7n4t6ORIa+49z1CBZvDyIyJzP2VUGtVrNu3Tr69u2rW9hrkVCG5pdffsHDw4MnT55w5coVevXqhZ+fn8GZIeO7WhgjtBdVlTJWaTk6iZJWcqTlJDg8giBBEtO5pUrhxhs3w6PFN998g5eXF05OTty6dQtXV1eePHkCxAl40sLjMc0Y4jLEbU/PeKJmwVxit1ZwuGGBx3fffcfWrVuRyWRs3bqV9u3bEx4ervfxNuZQOEbRwJiyPGUKQS4riFTCpYcZO7ZcLmfDhg2MGDECEDspZ86cmWFlhgpFBIrnF4hWi6WtpMjXwR8/M7vnn7y8/5o/z82ica96XPe6nSHnzyw4OTnh5eVF7dq1CQwMpHHjxty/cYqBLU2RSuD6YxU7T0fmlGsSoHbt2nh5eZEnTx4ePbiJz852RH5+wc1HUczbEJAjUBgH2d2Gnp62dABPT0+eP3/OgAEDEr2WUIYmICCAwYMHU7p0aVq0aEFwcDDnzp2jTJkyBp0zh7SsD2ICE020MrHSMnE5PJHxDhNkyZe0QCxrqVXRMRwfsyT3kWsDngRqy+XLl+fs2bM0btyYJ0+e4OrqioeHBwWdiwFp7dSKyfBEqAwW6EsIe0sJzz+IxOWMhlwmUMBew/OPYpZHG1zpg27dumFtbU3Hjh05fPiwzv1a3y6u8oVEq4lHb6Cai+i5ld2QCAKupTXsvwhXn0DlIhosM5AYK5FI+PPPP7G3t2f69OlMmTKFgIAA5s+fn+5ykiAItKopZemBaJ6/0/DvAzU1SsbXVjKzMkWQSPhl/0RKVHWhSPlCyE2Mg6yZEmxtbfHw8KBjx44cP36cli1bsm3bNvo2a8u6oxH8cycac1OBNnX0N/v9L6BChQo6iYRnzx5zcktbGvfYwVOKM3ttAGN72ZLbLnPMbr8kHLyiwdzCOALmsDR05Lq7uycb8CeUofnjjz/4448/0jK1eMjJ8OiDuFmdJEpayJP209KWupIjJsd2aiUfnOhKWklkgYoWLYqvry/lypXD39+funXrcuO62EYcERVt8OrRXCHRSgcRFpm+QMUuJggJiyJTSJqFc4vjv/iIwerQLVq0wMPDA2tra7y9vWnYsCHv37/X69g8NpDbWjTwvGNYR2SmoqgT5LOHaBWcv5/x4wuCwLRp01i4cCEACxYs0KkLpxe2lgKNKouXIs8raoISXDzlJnLmnfiZktWLiRYOX0Cwo4WFhQUHDhygc+fOKJVKunTpwr9eG+naUAxyTl1R4nk5x4IiIYoVK4avry9lypTh7ZtXHFnfFmXQDZ1A4fM3xqE/k4MvCzkZHj0gyOSgUoklLa0OjypalwWJLWnFz/CkpMMjvq7t1Er+giePE/AklXXJly8fXl5etGjRggsXLtC8aVP+XL6eGrXciFSqdCUuvf5OQcDSVEpwmIqQcBUWpmlfRcmlAjbmAkFhGj6GqMlvn7Ersjw2YnYlPAr8A2I9r/SFm5sbZ86coWnTply5cgU3Nzc8PDwoVKhQiscJgig+ePqWKPxXsTDIjGCxKQgCbmU07PAVy21VXTTYW2U8mXfUqFHY2toycOBA1q1bR1BQEFu3btWr/TQl1Cgp4dYzDS/eazh0QUWPBtIks0dJcXZePvRn8fermbjhe6Px3IoLExMTtm3bhq2tLatWrWLgwIHMnx9AmwYjOHA2ikPnorBQCNQq9+UEclmB/Pnz6yQSLl26xL7VHWg/aDNBfMOcdTkCha2rCFhbGwdhPzjYOOaRGnIyPPpAoi1NRcdmeCCRY3pCZ/S4z2s0iTMmWuJySlo82oBHo9EkKzhob2+Pp6cnjRo1IjQ0lKGDenLS40g69XjSX4rKTJsJQRAo7CA+fvYhbeNXrlwZHx8fChYsyP3793F1deX+/dTTI0Vyg6Wp6K316E2aTp0pyJ9LoKiT2FB4Np3Goimhb9++/P3335iYmLBnzx5atWqVSCLBUEgkou2EVAIPXmq4/Uy/z1Sj0TCv72KunLjBkhFr0jWHzIRUKmXFihVMmDABEH3+Tu6eTsMq4oJk55lIrj1Kf7bsa0OuXLk4efIkDRo0IOTzZ3Yu64I64CQRURr+2BLIxdv/XYHC7G5DT6ot3diRE/DoAy2DXKWM550VK0YYs8pQJnRMjw2OEgZDECfDkwJpWRAEHXE5pa4iS0tLDh8+TPv27VFGRfHDiEFs2LA+tb8sEazMMqY1HbQ2EyJxOTPImdqy1puAtJfNSpYsia+vLyVKlODFixe4ublx5cqVFI+RSGI7tm49FwMMY4FraRCAh/7g/ynzJtauXTuOHDmChYUFnp6eNG7cOJ5EQlrgaBvHUf2SirCI1OcvCAKTNo2kerNKjFg6OF3nz2wIgsDcuXOZM2cOAL/++itHN4+hZmkJGg1sOh7B/ec5QU9CWFlZceTIEdq2bUtkZCSbl/RG/WEf0SpY8XcwnhfCsnuKOfhCkBPw6IG4dhKCICQiLmu5OqCJ364uCHGIy0m0puuhtgwgNxHHV0alXOtXKBTs3LmT7j16oVarGT1iqI5zoS+0xOXwSHW6tS9sLQQEQeweCssEmoK1mYC9pWhj8dwATZ6EKFSoED4+PlSuXJn379/ToEEDvL29UzymZD6QSyEwDF6m49wZDQdrgTIxwZjP3cx1GG/UqBGnTp3C3t6eCxcuJJJISAvcyklwtIXQCDj2r35Bdz4XJ3498iN2jrHE8/AQ/bvvshoTJ05kxYoVCILAX3/9xYE1gyjnrEGlhjVHInj2JsdsNCFMTU35+++/6dOnDyqVig1LhqB6sxENsPVYCH/nCBTmQA/kBDz6QJZMgBPDvREkUjHzQwpaPEm0puujtgzxeTypTlUmY8XKVfTuPwSAMWPG8PPPP+t9MTCRSVDIxcxJep3TpRIBW/OYslYmdGsBOOeO1eRJDxwdHTl9+jR169YlODiYpk2bcuTIkWT3N5GJ6ssg2k0YE2qVEu0wXn6AZ+8y91w1atTA29ubfPnyJZJISAviOqpff6Lh0SvDvzdXPG/Qq8hwrp2+leZ5ZDa+/fZbtm3bhlwuZ/v2bexZ3o0ijpFEKWHlgXD8P+YEPQkhk8lYt24dI0eOBGDjX+OJerEYjUbDkbNhrM0RKMxBKsgJePRBQu2dJMjIydlLpGwgGhMMqaKT5PhoYWJAwANgbmrCuMnT+H7MJABmzJjBqFGj9FYX1tpMZIivllVsWSszoNXkCQqDwHSaldrY2HDs2DFatmxJREQEbdu2Zdu2bcnuX7agWN18HSD6WRkLrM0FKhURH/veydwsD0DZsmXx9fXFxcVFJ5Fw61bag42CuSXULC1+bw7+oyLSQDHF/UuPEfzxM0dWe6Z5DlmBrl27cuDAAczMzDh69Ai7lrTH0fozYZHw1/4IPgbnaM4khEQiYeHChUyfPh2ALWtmEvpwJoJGzdn/mEChUmVc25eAnC4tPSBI4nB4IGkrCbkJRIUnspeIbU1PQnxQGsMH0mhQR0cjlSfdcWBIhgdAKhUtJr4dNpoiBZ34YexoFi9eTEBAAGvXrtWNlxyszKR8/Byd4cTl9Gr7JAUTmUA+Ow0vP4lZnkoW6RvfzMyMvXv30q9fP7Zu3UrPnj0JDAxk6NChifa1NIUijqKD+s3nUL9suk6doahRAm75wftguPcSShfM3PMVKVIEHx8fmjZtys2bN6lbty5Hjx6lZs2aaRqvYSUJ956rCQyFU9fUNK+ufyvcj9vHsGv+ATqPa5Omc2clmjVrhoeHB61ateKsrw8hIS1p/e0egkIdWL4vnFGdzLAyz1mXxoUgCPz888/Y2toyatQodm1dQpsOQeSpOIebj2DexgBG97D96t+3rT5gZpHdsxARbiTK86nh6/5GZBRkcYQHSTqbk5zackocHkEQ9OLx6Dg8ypR9t+JC247ep/9gNm/ejFQqZfPmzXTs2DFVdeFYiwlVurMD1uYCUom4AvisBwk1LdCWtfw+kCFy/XK5nE2bNjFs2DA0Gg3Dhg1j9uzZSb4X5WO62B+/FXknxgIzE4HqxcXHZ++RJan+vHnz4uXlRa1atQgICKBRo0Z4eqYty6KQC7T+Rvwe/nNXzYOX+gffJgo5PX/siIkiNrC/f8kwk8GshKurK2fOnMHR0ZHr166yfUFTpMqXfAjSsHx/RIomuf9ljBw5kg0bNiCVSjmwZxN+54ehkEXy9FU0v64N4EPgF5J2yEGWISfDow902jsxQYk8cYYn1ZJWEhwe8XU5KmWUyPFJJlyXSkVNEo1Gg1KpxMQkde0JU4Wcz2FRRERF07NnT2xsbHQGfc2bN+fAgQNYW1sneayFqQQB0X07UqnB1CTtWROJIGBnIfDhs4ZPIRqskxaUThfy2IrmnhFK8A+E/PbpH1MikbBkyRLs7e2ZOXMm//vf/wgICGDu3LnxslS5rcHJFt4EikKE1Yul/9wZhcpFReXl4DC48QyquGT+Oe3s7Dhx4gTt27fnxIkTOnXhDh06GDxWsfwSqpfUcOm+mj2+Koa0EnSClobgwLLjLP5+Nb1/7kyfaV0MPj4rUKlSJXx8fGjSpAmPHj1gxwJ32gzdz2uKs/pgON+1NcNE/mW0/mYl+vTpg42NDV27dsXj2H5cwz5Tpfka3n40Z9aaAMb0tKGQ09epb9TDDZK5hGc5goNhZHZPQg/kZHj0QEL/rKSyOUIyjumxBqJJBzz6ZHgEQTC4rJXQU6tVq1YcO3YMKysrvLy8aNiwIR8+fEh6ThIBc1Ot43kGtqdnEnFZEkeTxy+d5OW4EASBGTNm8PvvvwPw22+/MXjw4ER6SOVisjx3X4HSiLqK5TKBWqXExxceYDAXJq2wsLDg4MGDdOrUiaioKDp37sy6devSNFazahLyOwiER8FOLxXKNGSqPgeIGkHG3sVTokQJzp49S6lSpXj16gU7/nAn8M01nvirWX80AlUOITdJtG3blqNHj2JpaYmv9yl8/+6CvXkQQSFq5q4P5O7Tr1PJWi41ru1LQE7Aow8SlLTi6u7okIxjuiSZ53Wv69upFaespQ/MFLGu6doLfb169Th9+jQODg5cvnwZNzc3Xr5M2h8hVoAwAwKeGMXfgFBNkuaQGQGtJs/rwIy/sY8dO5Y1a9YgkUhYs2YN3bp1IzIyVlW7kANYm0FUNDxIX1d2hqNcIbCzEBWpLz/OuvMqFAq2b9/OwIEDUavVDBgwIE1eODKpQJe6UswV8PqjhqMXDQ+ae/7Ykd/PTDfa7E5cFChQAB8fH6r+n73zDo+i/N7+55nZml5IQgu9VwEBkYQeOoiAAtIEVIpiwYp+sWLHgooNKSJSRUB6JyQU6b3XUBJCes9md+f9Y5IlgZRNSFl/b+7r2utKZmfmeWZ3duaec+5zn1atiImOYsV3vYm4EsrpaxYWbU0v77CeBzp37sz27dvx9vbm8KEDrJ//OJXcokhNzzQoPOlA+eZylBnKCY89kHKmtEQuJCavlJawmQsWPcID2YTLBXjxZEGnlbP00JiySehbtWpFSEgIVatW5ezZs7Rv354LF+5vsW0jPCkPTnhcDQKtrPafetBKqrzg7iTwdFaP96p9bbEKhbFjx7J8+XJ0Oh1//fUX/fv3JzlZVepJ4m6U50SY2s/KUSBJgvaZDYUPXiy5zz83yLLM7Nmzee211wCVOE6bNq3QkRYPF8GgALVU/dAFK6EnC/8BN+vQyJaKtFgs/P3tOkxpjvnkX6FCBbZv307Hjh1JSkpk1Y+Pc/X0Rg6dN/P7pjQyzOWkJze0bt2aXbt2UaVKFc6cPsXi7/tSzfMWZgv8vCKBJZvKy9YdBTVq1FB96u55Pf/887muP3v2bAIDA/H09MTT05Nu3bqxf//+Qo9bTnjsgNDeIzzWZvYNytYdXWQuu7djumRbbsr1Qp9VmWXJgxBlIUu3Y7KT8AghMGaKNlPScpKpBg0asHv3burVq0dYWBgBAQEcPXo0xzpuTlmd0x/cgFAIga+7eqrdiim5UtvafuoN7UKEUiJPwgMHDmTdunU4OzuzefNmgoKCiI2NBaBeJXDSQ1KamtpyJNStBFUrqERs67HSTe0IIfjiiy/45JNPAJg+fTqTJ0+22yIhC3WqSAS1Us+hLYetHLpQ9PPo5ym/89OU+bw74AuHTXO5ubmxYcMG+vXrR3p6Gut+G8b5Q8s4dtHCz/+kklouZM4VjRo1IjQ0lDp16nDlyhV++bQXjSpeAWDzvlQ+nVcuZnYEHDhwgPDwcNtry5YtADzxxBO5rr9z506GDRvGjh072Lt3L/7+/nTv3p2bNwt3sS0nPPYgS8OTSUps5MZ0N0yaF+HJ7sKcq9ty5naFITz2XqSdDJmEJ/3+cbPchR966CEiIyPp1KkToaGhtvf1WglDpkgyoRiiPJW91FPtdvyDE6i8UK0C6LVq+ubGg3U5yBPdunVj69ateHp6snfvXjp27EhERAQaGVpmet8cvaqmtxwFQgiCmqtmhGF34PT10h9/6tSp/PjjjwghmDVrFqNGjbI7PZuF9o1l2je+689z+lrRSE/AwLa4eDjTa2yXYrdJKE4YjUZWrFjBiBEjMJvNbFzwDKf3/Malm1a+X5FKfAl5W/3XUaNGDUJCQmjWrBkRERF8MrUXHRucxdkguHLTzPu/xHDkbHrBO3JwZJgVh3oVBj4+PlSsWNH2Wrt2LbVr16Zjx465rv/nn38yadIkHnroIRo0aMBvv/2G1Wpl27ZthRq3vErLDtwrSBa5RngMQC6ER0gIjQ7FbMKakW7T9GQhq4Go1ZyBYrUicukGDWpKq7CVWlkRntS03G8svr6+7Ny5k759+xIaGkr37t35+++/6dmzJwDuzhrS4jKITzHj5fpgp4qHk8BJp7aYiIy3UrmYu6eDKrau4wenbiicD1fw96ZEbmiPPPIIwcHBdO/enRMnThAQEMCWLVuoV70mJ8JUE8Tj1+DhUqiKsheeLoJHGyiEnIadJ6GGr4KzoXRv9hMnTsTDw4NRo0bx559/Eh8fz7JlyzAa7S/dC2opkZqucPiiwl8hFoZroXblwj23Ne/YmAWXfsDV06Wwh1Dq0Gq1/P7773h4ePDDDz+wZckrZKTFonR+jW//SmVCfyN+XuXPrfeiYsWKtmvbnj17mDS2F78vXMHZ6DZcvmnm+6XxdH/EyOBuLv+Zxpf34pdNYHAq61moSMtsZ5aQkJBjuV6vR6/X57utyWRi4cKFTJkyxe7rdUpKChkZGXh5Fa4kt/yXYg/urcrKjdxkI0H3RmCkPKI/AELW2EiOJZf3besJUei0VlaEJzXdnGeKx93dnU2bNtGrVy9SU1Pp378/S5cuVd9zVklJfPKDR3iEEDaSczO2JNNaqqYmNhmiStD9uGnTpuzevZuaNWty6dIlAgICOHPmFK0zSc7JMEhxsIfIVrXB113tbbbjRNnMYdiwYaxatQqDwcDatWvp1avXfRfJ/CCE6s/TqJrAYoUlOy3cuFP48yk72UlOSOHDJ2Zw44KDKc4zIUkS3333He+++y4AO1d9yKGN/yMmwcp3K1LKe2/lAU9PTzZv3kyPHj1ISUnhqSH9qee+ne6PqAR7875UPitPcRUr/P39cXd3t70+/fTTArdZtWoVcXFxPP3003aP8+abb1K5cmW6detWqPkJxVGT2A6AhIQE3N3dib15DUPoMpA1GAa9jJKeSvrORQDou49BCAnFaiV170oAjG363o0CAUmXjmNOiMLoXx99hSr3jRN19SzmtFQ8qtTE4OqR53zuREaSmJiIh6enXcxWURTOXo3CbLFSq4onzsa8o0Imk4lRo0axdOlSW1PDMWOf4cAFVZj7cF1ndJoH48epJoWQM2q0KbChFuMD+Pvkh0OXrVyOhMqe0L5+yXL6W7du0aNHD06ePImXlxfr12/gttSGyARoWAXaNyjR4QuNyHiFP4NVcXf/NlCnUtk83YaEhNC3b18SEhJo2bIlGzduxMfHx+7tzRaFP7dbuByuYNTB2J4afD2KdixfjfuRjfN2UKOJP78cnYGUR5TVEfDtt9/yyiuvANCm0yjaPjYTvV7D0z0NNK5ZHrDPDSaTiREjRrB8+XIkSWL27Nm0eHQ4c1YnkJKm4GQQjBvgRov6+Uci7EHWPSM+Pj5Pn7PiGiMqOq7ExigsEhISqODtwfXr13PMyZ4IT48ePdDpdKxZs8ausT777DO++OILdu7cSbNmzQo1T8f9ZTsQbCktixnFar0bzYG7uh5Juttk9F7hsk5d35pHBEejUyNGZlP+IQFbhCfdvtCBEOKujiePtFb2ff/5559MmDABRVEYP348X3/1JU569RQpDh2PUSdsrSZulWCUp16lrDEgMbVk+XzlypUJDg6mbdu2xMTE0K1bV9JvbQfg7C01veVI8HUXPJxpjrjteOl589yLwMBAdu7ciY+PD4cPHyYwMJDr1+0XF2lkwdBOMlUzPXoWbDETm1S0Yxn7yVM07dCQ1+c979BkB+Dll19m3rx5SJLE/p0LCF32NKmp6cxZl8a/pwunifr/BTqdjsWLF/Pss89itVoZN24c29f9wPvjvahZRUNKmsL3S+L/c1VcWo1wqBeoYvvsr4LIzrVr19i6dSvPPPOMXcc8Y8YMPvvsMzZv3lxosgPlhMc+ZNfdmDNUciNnkZvswuXMVJcpp+eDzYsnT8KTKVw25e8Vocs8eexNaUE2HU8uwuV7IcsyP/74I1OnTgXgrbfe4qev1U7rxZHWAqjsmVWt9eBtK/KCq1FQyUP9+0JEyV/AvLy82Lp1K127diUpKYkRT/bi+vFVKAocLEXvG3vxSH3wcFZbYYScLrt5tGjRgtDQUPz9/Tl37hwBAQGcP3/e7u31WsHwLjI+7pCYqpKepCIQXE8/D77a8QH1Wt0VXZns+L2UFZ5++mlWrFiBTqfjyN7VBC98krS0JBZvS2fLAfuLGv5/gizL/PLLL7zxxhsAvPbaa3w74z3eetqjPMVVhpg3bx6+vr706dOnwHW/+OILPvroIzZu3MjDDz9cpPHKCY8dELIGpEyRbSZpETr7S9OzllvziODImfuyN8JjNpvtLuu1N8Jjm6sQfPLJJ3zxxRcA/Drra754/yViEopHkOLrLiFLaiVVSXrCZEV5rt4pnSiGi4sL69at4/HHH8dkMvHx64PZs3kBVyLVBp6OBK0sCHpI/fv4VbgeVXY3yCx34fr16xMWFkZgYOB9Fgn5wckgGBWkwcMFYhLhj61m0orQLTu7WDL88m3GNniJ4GV7Cr2f0sKAAQNYv349zs7OnDyyne3zB5CWHMO6fSb+3mUqNyjMBUIIPv/8c5uu5OOPP+bllybzZJAzk4e442QQXL5p5oNfYjh6zsEEeP8HYbVamTdvHqNHj0ajyZmOHTVqlO3BG+Dzzz9n2rRpzJ07lxo1ahAREUFERARJSUmFGrOc8NiLe8mMLZpjvxdPQSktSwGER5ZlZFklXvamtYwG9UTKMFvJKIQj3uuvv87s2bMRQvDP8vm89dIYEouhO6ZGFvhlefKUYFrLxw08nFSzw8uRJTZMDuj1epYtW8bTTz+NxWJh3hej2fr3TByxb6V/BUHT6urfW45SpJYNxTYXf39CQkJo2bIlkZGRdOzYMYdFQkFwcxKM6qbB2QARsbBouwXTA5jz/fPjJm5fu8OiT/7G4kgukvega9eubN++HS8vL86d+pfNc3qTEh9ByPEMFmxKLzcozANvvfUWP//8M0IIfvzxR0aOHEmT2pItxZWcpvDdkniWbnbsFJcpQ3GoV2GxdetWwsLCGDt27H3vhYWFER5+t4jgp59+wmQyMXjwYCpVqmR7zZgxo1BjlouW80F2AZp+93KUxFh0nYci+VTFdHAD1uhbaJt0QK6itqU2XTqCOeIymqoN0FVvbNuPJTWJxLP7EbIG92Yd7hvHarEQeeE4AL51myLJeYsPw8PDSU1JoUKFCri5u9t1HBfCokkzmalW0R13F0NhPgKWL1/OU8OHY87IoHPX7qxZ/TfOzrk3ObUXMUlWDl4yI0vQqbEWWSoZ4ey1KIX9FxUMWujdQpTYOPfCarXy2muv2Vop9Bv5HrO+fg//Co5V/pqeoTB/u5raalELOjct2/nFx8fTv39/du3ahdFo5K+//qJ37952bx8eozBvk5n0DKhXRTC0s1yk79xisbDww7/oMz6ICpWLoRNtCePkyZN0796d8PBwqvjXose4Vbh61aROFZlxfQwY9Y513jkKli5davM46tOnD8uWLUOnN/LX1iQ270sFoHZVDeMHuVPBwz4bjdIULb/5cxR6o2OIltNTE/h8QoUSPe7iQHmEx17kFeHJ1YsnZyTEFvmxmFGs9z8xSrJs66lVUFpLn5nWSi+MjseQvx9PfnjiiSeY88ffGIxO7Ni2me7du9vchYsKT2eBUadGXyLjSy7K4+8FRp3aRf167n1SSwSSJPHVV18xffp0ANb88QETnn8Ji8WxjOL0WtWQENSu6udulu2zj7u7Oxs3bqRPnz6kpqby2GOPsWTJEru3r+Slano0Mpy/qbBqt6VIvdtkWWb0B0NykJ1rZ244rDamSZMm7N69m1q1anHz+mXW/NiDhMjTXLxpKTcozAdDhgzhn3/+wWg0sm7dOnr27ElyUgJDe7jyQmaK69KN8hTX/yWUR3jyQXa2bjiyCevta2jb9kau3oiM03uwXD+DXKs52rqqgCoj/DIZl48ge1VC3/BR234URSH+WDAoVlwbtUPW32+0FhN2AVNKEu6VqmF0985zTkmJiURGRqI3GKhS5f4S99wQE5/CzTuJOBu11KpS+CfW+GQzS1bv5PWJT5CYEEezZs3YtGkTFStWLPS+snAxwszl21a8XQStamuLvJ+CcPaWwokwBXcnCGoqSt1Z99uZs3jl5RcAGDB4JMsWzbH1RXMUhJxWOHBB7Xg8vOPdZq9lhYyMDJ5++mkWLVpkSztMmDDB7u3P37CyeIcFqwJtG0j0ai090Pd+LPgUb/f6mKBRnZg8a5wtrexoCA8Pp0ePHpw4cQJ3D08GTfob14oP4+kqmPCYET/P8ufb3BAaGkrfvn2Jj4+nRYsWbNq0CR8fH+7EWvj5r3iu3FJt03u2c2JgV+d8jQpLM8JzJ8qxytJ9KniUR3j+z+DedhK5uS3r8hAtC5Gv+SAUQricVamVfr/BYV5wMqhRodQ0c5GeUl2MMs1atuWH39fj6+fH8ePHCQwM5OrVq4XeVxYqe6o3jegkpUgiU3tRy1dtqRCfApHxJTZMnnj5peeZ/vVCJElm1V9/MGjQYNLSHKtzc/sG4F8BMiyw5gAPpH8pDmi1Wv744w8mTZqEoihMnDiRTz/91O5zt15Vicfbq+fXv2et7Dz+YBGO8MuRZKSbiQ6PQXFgMXClSpXYuXMnjzzyCPFxsSyd2Zf4GzuJTVT47q9yg8K8EBAQYLNIOHLkCIGBgYSFheHjKTN1rCdBbdUH1I17U/h8fizR8Y7xOeq0wqFe/wWUEx47cbedRKbvTi7kJi/Rcvb1H9SLJys6oCgKZrN9DZv0OhlJCKyKQnoRmjzJksDNKFOnfhP+XrOdGjVqcPHiRQICAjh9umh1zU56gadzyXvy6DSCmr7q3+fDy+Zm9cbk4bz88Uq0OgNr1vxDr169SEwsQRvoQkKSBL1bgbMeohNVEXNZB34lSeKHH37gf//7HwBvv/02b775pt3zalZLoncb9fK285iVfWeKfpPqOaYzn236H/9b8goarWOb+2VZJAQFBZGcnMyibwcRd3UNyWnw48pUTl91oCZvDoSHHnqI0NBQqlWrZrNIOHfuHBpZMKynmuIy6tUU1/s/x3D0fHmK67+IcsJjL2xkRn06z72fVlYU6P4fg5RpXlhQaXpBXjxFaTEhhLDpeOwtT78XWb203H1rEBoaSqNGjbh58yYdOnTgwIEDRdpnVkPRW7El58kDULeiSqwi4iE+pfRv5FoNjBvRj5c+3YjByZWdO3fSpUsXoqJKUVhUAJwNgr6t1bYc527C0StlPSP1vP3oo4/46quvAPjyyy957rnnsFjsIy9tG8h0aq6eYxsOWDl2uejEumW3ZuiNd03Uti8OJSkuucj7K0k4OzuzZs0aBg8ejMlk4o9vRxB7YREmM/y2No1/zziux1BZol69eoSGhtKgQQOuX79OYGAghw8fBqBlA71axVU5s4prcTzLNic5dBVXOe5HOeGxE3d9d+7pp2W6X7SM1YxiyfkkJeWR7sqCJltKq6Cbf/a0lr1wyixPz61zuj3wzCQ8CSkWfP0qsWvXLlq3bk10dDRdunRhx44dhd6nn7uEJKk9p0qSiLgYBFnSpbKK8jSoDK0f6chrM3bg4VWBgwcP0qFDB27cuFEm88kNVbwFHTKLC4NPwq0Yx7iYT5kyhTlz5iBJEr/99hvDhg0j3c5zv1MzibYN1Mvcqt0Wzt148Gjipvk7+HT4TF7t/B6pxWDVUBLQ6/UsWbKEcePGYbVaWfD9eKJP/4RVgcVb09lysNygMDf4+/uza9cuWrVqxZ07d+jcuTO7du0CsKW4ujloiqscBaOc8NiLezQ84p6IDwCyBoSUuTwP88G8NDxZESNFwWouuA0EFM5x2UmfFeEpWkjboJVwzmwzEZtkwdvbm23bttG5c2eSkpLo1asXq1evLtQ+S8uTB6B+phFhWBQlqhnKC5KkNu+sXq8Vb3wTQpWqVTlz5gwBAQFcuHCh1OeTF1rUgnqVwarA2gOQku4YN8WxY8eqZcM6HcuXL6d///4kJxccYRFC0LO1RLNaAqsCy4ItXL39YOda3Za18PRzp3WPhzA6F87moTQhyzKzZ8/m1VdfBWDhz28QcfgTFEVh3d5Mg8Jy0nMffHx82L59Ox07diQhIYEePXqwbt06QL1mPdXTleefLPsUV3qG4lCv/wLKq7TyQXbFvXPMdTL2b0Dyq4Gu42CsyfGYQv8CWYuh2yjbNqkH1qOYUtE364zserciyhQXScqVk8hObrjWz90W+87l01hM6Xj610bvnLfSPSUlhYjwcLRaLf7Vqtl1LBlmC2evqimURrV8kIvQMyjsTjo3okx4uWho4K8+5aSlpTF06FBWr16NLMvMnTuXUaNGFbCnu4hOtHLoshmNDB0blZwnD8C2k1ZiktSmnk38S5/rKwqsPqB2cfcS13hrQhAXLlzAz8+PTZs20bx581KfU25Iz1BYtAtik6CaDwxsB1IpV7flhS1btvD444+TnJxMu3btWLduHZ6engVuZ7EqLNlp4fwNBb0WxnTXUMm76McUExGLp59HqVf9FQWKovDpp5/yzjvvAPD4kIn4t/sMIUk8VEfDiO76fCuP/n9FamoqTz75JGvXrkWj0bBgwQKGDRtme/9OrIWf/ornarYqrm6tLXh7lWy1UtZ9acLnNx3Kh+fnN6uUV2n9n8F9Gp7MJztLRg5vnVwjP4CkUwmCNR+Njr3C5ayGbBkZGXa3mNBqZLSZ3c5Tixjl8c5Ma8Ulm7FkVqsYDAb++usvRo0ahcViYfTo0Xz//fd279PLRWDQgtkCdxJKJ8pz6Ta2+ZcmhIDWmY0746jOhs0hPPTQQ9y+fZuOHTuye/fuUp9TbtBrBf1ag0aGsDuw92xZz+gugoKC2Lp1K56enuzdu5eOHTsSERFR4HayJHiyg0x1P0F6BvyxzUx0QtHPAa+KnjayY7VamfXSXM4fcsDGaahRrrfffpsff/wRIQQrl/7EhW0TQMng6EUzP69OI9VBInmOBKPRyN9//83w4cMxm80MHz6cn376yfa+j6fM1DE5U1zf/PlgHmXlKFk4dsmBgyAjIyMXDU82L5WMdNA7qX/rDJBMDjEzgJRJZhSzCcVqQUj3e3ncFS7b12LCYrFgMpkwGOwLqzsZtMQnpZOSloGLk67gDe7dXi+h1wrSMxTik814uaqfgUajYd68eXh4ePDdd9/x4osvEhsby7Rp0wp8AhZCUNlT4nKklVsxVira6WhaFFT2Aie9qhm6egdq+5XYUHmiipc6j1sxcD3Zjx07dtCvXz9CQ0MJCgri77//pmfPnqU/sXtQwU0Q9JDChkPw73mo5KlQq6JjRAEeeeQRgoOD6d69OydOnCAgIIAtW7ZQs2bNfLfTagRPdZaZv9lMeIzabHRsTw3uzg92XH9/u45V329g28Jd/HF5Fs7uD+ZEXlKYOHEiHh4ejBo1ig1rFpOWksBD/edx8aaR7/9OZXx/A+7O5c/A2aHValmwYAEeHh7MmjWLSZMmERsby9SpUxFC7RL+VE9X6lfXMnd1Ildulp6e58Nxzri5Oca5lpBg4ec3y3oWBaP87LYDfbt0ID5FJSG2CI+Q7ndfJp8GotkakOYV5bkrXC5YCFkkHU9WpVYRhctCCLxcVI4ck3iPKFuS+Pbbb/nggw8AeO+993jllVfsikBV8lI/l6jEks0FS0JQL/OmfT5cKTPRZpvMptwXI8Aie7Bp0yZ69epFamoq/fv3Z9myZWUyr3vRsKqgeSaH2HC4bCrc8kLTpk3ZvXs3NWvW5NKlS3ZbJBh0ghFdNXi7Qlyy2mw0Je3Bjqv3s914qEsTJs0c67BkJwvDhg1j1apVGAwGdmxbx96lg9GJRG5FWZn5Vyq3S1hL91+EJEl8//33TJs2DYB33nmHN954I8f1o1VDA++N96JVw8I/SBYVeq1wqNd/AeWExw7sO36aLv0HEhmfdDfCQ7bS9GwEJa/SdCFEgWkte5uIQjbCU4hKLaP+bouJot7ss8rTY5LuLyUXQvDuu+8yc+ZMAGbOnMnYsWML9Aty1gs8nErekweghq/qKJyUBuFxJTpUnqjgphoiAhy4BE5OTqxatYohQ4aQkZHB0KFDmT17dtlM7h50bAwVPSE9A9bux6HKcGvVqkVoaChNmjTh1q1bBAYG2mWR4GJUO6y7OcGdeFi43fJARNvJ1cjnm6fRbcTdPnmO3HS0T58+bN68GTc3N/bt2cXmOf0wiihiEsoNCvOCEIIPP/yQr7/+GoAZM2bw7LPP5rBI8PWUGTfAvv6G5SgblBMeO1DBw42jx0/Q9b1ZhN2+c7fkvBARHgBJrxKavAiPLaWVYUIpIDJiK00vTE8tvRYBmC1WMsxFIxZuTjIaSb3xJaTmfmF88cUXWbBgAbIs8/vvvzN4cMHuwnc9eawlGnnRysJGNsqqRB3Uii0h4Ea0mt7S6XT8+eefTJgwAUVReO655/jiiy/KbH5Z0MiCvg+DQQe342HHibKeUU5UrlyZ4OBg2rZtS0xMjN0WCR4ugpHdNBh1cDNKYckOywOROSlbEUBqchpTOr3Hqu83FHl/JY3AwEB27NiBj48Px44e4u8feuEq3So3KCwAr7zyCnPnzkWSJObMmcOQIUPstkgoR07cvHmTESNG4O3tjdFopGnTphw8eDDfbWbNmkXDhg0xGo3Ur1+fBQsWFGrMcsJjB9Z8+jbVq1fnYngUXd6dxdmTamfz3MhNXg1E4a6Ox5qemus4kqxBZF44zXmUr2che0rLXoIgSQKDPtOPp4gGhEIImyfPvWmt7Bg5ciR///03er2e1atX06dPn3zdhf08JCQByWkKCaklS0TqVBQIAXcSIDa5bEiPu5PqzQOw97zaSFWWZX788UemTp0KwJtvvslbb71V5n4pbk6qEzPAiWtwKsxxojxw1124a9euhbJI8PUQjOgmo9PA5QiFv0IsxSJm37YwhNN7zrHg/aXE3SmDfiZ2omXLloSEhODv78/5c2f5/YsgPOXLNoPC/eUGhblizJgxLF++HJ1Ox4oVK+jXrx9JSUllPa3/FGJjY2nfvj1arZYNGzZw+vRpvvrqq3wrLn/66SemTp3K+++/z6lTp/jggw94/vnnWbNmjd3jlhMeO1DLx5PQ0FDqV/HjZnQ8Hbp259ChQ7n308ovwqPLP8IjhLA7rZVFeKxWq90tJiCbjqeIhAfIoePJ72bcv39/NmzYgIuLC9u3b6dr165ER0fnuq5WFvhmevLcjCnZtJaTXuCf6Rhw7lYZRnlqgUELsclw+LK6TAjBJ598YovufP7550yYMMFud+GSQg1fwaMN1L+3HoMb0Y5FelxcXFi3bh2PP/446enpDBo0yK6nv6oVJIZ1lpElOBOm8Hfog6W3APo8142nPxrK9HVv4+Hj2CmO+vXrExoaSr169bgeFsaPHwRRQT6JVYFFW9NZHZpOehn4Vjk6Bg4cyLp163B2dmbLli0EBQURExNTqnNINykO9SoMPv/8c/z9/Zk3bx5t2rShZs2adO/endq1a+e5zR9//MH48eMZMmQItWrVYujQoTz33HN8/vnndo9bXqVlBxRTGlWrVmXbF2/Qf9o3HL58g86dO7Pyh89o76dHMd2N2IisaizT/VEcObOSy5KWkudYss5ARloKGWmpGFw98lwvq8WEyWQiPS3N7g7cTgYd0fGpJKWkA652bXMvPFw0SJLq1xKXbMHTJe/TqHPnzuzYsYOePXty4MABOnTowObNm3Pt9F7FSyIiTq3WqumrYNSVnBCuXmVBWLTC9Wio4aNQ0aP0RXcGHbSrBztOwbFrYNRBk0xbpddffx1PT0/Gjx/Pr7/+SlxcHH/88YeN6JYF2taD8Fi4chtW7IGeLRXqV3EcsaJer2fZsmU899xzzJs3j9GjRxMbG8tLL72U73a1KkkMDoRluyycvKoQHmPmiQ4aKnkV7diEEAx/Z1COZXduRONV0QNZ43id1qtVq0ZISAi9evXi8OHDfDm1B1M/XUmEpQ07jmRw5IKZgR30NK0l/yd8h0oL3bp1Y9u2bfTq1Yt9+/bRsWNHVqxYUWrjv/JVFDqDY6TTTGlq9D4hISHHcr1eb7NRyY5//vmHHj168MQTTxAcHEyVKlWYNGkSzz77bJ5jpKen31eRbDQa2b9/PxkZGXbdA8sjPHZAyUxBVfD1ZcO08XRs15bExET6PjeF9f8eQ8lGYIQhs0rDnIFyj2OybFTfs6Yn56nR0WWuk5FasIus0aiKoFNTc0+R5QZXJx0CSM+wkFrEai1ZEvh5qCfXreiCNUQPP/wwISEhVKlShdOnTxMQEMDFixfvW8/LReDpojriXixh4aSns6BORfXvQ5cVMspIjFu7IrTMrITadwHO3br73jPPPMPSpUvRarUsW7aMxx57jJSUvMlySUMIVc9Tu6Kaglt3EA5eLLtqt9yg0Wj47bffeOWVVwB4+eWXef/99wucY6PqEk8Hybg5QXQCzF5vZt+Z4unxFhl2h5fav8NHQ77GlGa/5q404evry/bt2+nQoQMJCQl8+FpfGrjsxMtNEJekMHd9GrPXphEVX17FlR1t27Zl165dVKpUiZMnT9KjR4+ynlKZwt/fH3d3d9vr008/zXW9y5cv89NPP1G3bl02bdrExIkTefHFF/n999/z3HePHj347bffOHToEIqicPDgQX777TcyMjLs70uolCNPxMfHK4AS/vO7iqIoSnrI30rq0i+VhJP7lH79+imAIkuSMu/9V3Nsl7zvHyU59C/FkhSbY7nValXijgUrsYe3KebkhFzHNKUmK+FnDisR544qVqs13/klJycrly5eVK5dvVrgutlxLTxWOX4hQrkZGW/3NvcizWRRdp9OUHafTlASU8x2bXPlyhWlTp06CqD4+fkpx44du2+d+GSLsulourLpaLoSn2wp8vzsQYbZqqw7bFGW7bUoBy+V7Fj5wWpVlH3nFWX2VvV1MTzn+5s2bVKcnJwUQGnfvr0SGxtbJvPMgsVqVbYdsypfrVJf245ZFUshzr/SgNVqVaZPn64ACqBMnjxZsVgK/o6TUq3Kn9sylHd/Nynv/m5S/tyWoSSnPtix7d94ROmlH6qMafiSEh+V++/eUZCSkqL06dNHARSNRqMsXLhYWbsnTZnyQ6Ly0neJymuzEpVN+9OVDLNjfd9ljUuXLim1atWynW/x8UW/thaErPtS5J04JS3d6hCvyDtxCqBcv35diY+Pt73S0tJyPQatVqu0a9cux7LJkycrjzzySJ7HnZKSoowZM0bRaDSKLMtK5cqVlTfeeEMBlIiICLs+u/IIjx1Q0lNRFAWRmZIyYmXFihUMf3IQFquVMe9/xaxZs2zrS5lRHmtaziiNEALJ6AKAJTV3kZtGb0QICcVqLdCPJyu8ZzabMWfYH63xdFUjQ3GJaUXupaPXSvi4qamsmzH2PbXWqFGDkJAQmjVrZnMX3rNnT4513JwkKnmop+X58JLtoq6RBQ/XUkP0lyMhMr5sIhVCQJs60CAzy7fzNFy7c/f97t27s3XrVjw8PNi9ezedOnXi9u3bZTJXUP2MOjdVS9ZB7ay+Zj9kmB0n0iOE4J133uGHH34A4Pvvv2fMmDEFWyQYBMM6y/RqLSFLcO6Gwk9rzQ/Uf6t1j4f4dOP/+GzT/3DzLloaubRgNBpZuXIlTz31FGazmZEjn+LG8fm88ZQTdavKZFhg/T4Tny9K4VxYeSVXFrIsEh5//PFSG1OvEw71AnBzc8vxyi2dBVCpUiUaNWqUY1nDhg0JCwvL83iNRiNz584lJSWFq1evEhYWRo0aNXB1dcXHx8euz8yhCM+sWbOoUaMGBoOBtm3bsn///jzXPXXqFIMGDaJGjRoIIfj2228feJ95wmpVhcmZREZJT0ar1TJ/7hwm9O8CwAsvvMD06dNVYpRJapRcSI1cAOERQqA1qsSqoLSWJEk20lOYtJaLkw6tRsJiVUhMLnoOuLK3qieJTjCTZrLvhlCxYkWCg4N59NFHiYuLIygoiE2bNuVYp04lGUlATJJCVGLJ3kR93e+WqR+8rJSZz4wQ0L4+1Kmo9tzafhJuZtNAtmvXjuDgYPz8/Dh27BgBAQFcu3atTOaqzlfQqo6a4pIluBQBf+1xnGajWXj++edZuHAhsiyzYMECBg0aVKBFghCCRxrKPNtLg7cbJKTA/M0WdhyzYC1iFVfzTo3x9a9g+/9Y8CmibpWuyNVeaLVa/vjjDyZOnIiiKEyYMIF5v3zBpAEGRvbQ4+YkuBOn8NPqNH7fmEZ8UnmaC9Sb+Pz588t6Gv8JtG/fnnPnzuVYdv78eapXr17gtlqtlqpVqyLLMkuWLKFv3745bCHyg8MQnqVLlzJlyhTee+89Dh8+TPPmzenRoweRkZG5rp+SkkKtWrX47LPPqFixYrHsMz8oaSkIg5Ptb1DJy9cThzH1qb4ATJs2jddeew1hUEmNNfX+MuyCCA+AtjA6Hid1TimFIDxCCDwyozwxCfZvdy+cDTIezqoI85adUR4ADw8PNm/eTI8ePUhJSeHIkSM53jfqBP7eWY7IlhLv6NysusBJB8npcOJ62d2whYAODaG6j6qR2XIMIuKyzbNZM0JDQ6lRowYXL16kffv2nDlzpszmC1CvimDwo6DXqoLmJSEQm+RYpGf48OGsXLkSg8HAP//8Q+/evfO1SMhCJW/B+D4aHqotUBTYeczK/C0W4h/QyuD03nP8r8+nvBI4jTs3cq9aLGtIksSsWbNsDUenTp3KW2+9Rcu6GqaOcKJDcy1CwJELZj5emMLaPekkl7CdRDn+7+CVV15h3759fPLJJ1y8eJFFixbx66+/8vzzz9vWmTp1ao5G1OfPn2fhwoVcuHCB/fv3M3ToUE6ePMknn3xi97gOQ3i+/vprnn32WcaMGUOjRo34+eefcXJyYu7cubmu37p1a7788kuGDh2aZ9issPvMD0p6ii2llUV4hCQj9EbeHTWArz+dbhvzubc+wGy2oORKeNSQtiU1Mc90jTYzkmQqhHA5LTW1UOkfTzc1MpSUYsKUUXSBcFaUJzIuo1Bmhs7Ozvzzzz8sXLiQN9+8vwlLrYoaNJJCRGQMt0q4TF0rC1plprYuRsCdB2gq+aCQJOjSBKp6gdkKm45CVLbChzp16hAaGkqjRo24efMmgYGBBZp1lTSqeAuGBYKbk9quYUkI3IpxrJtfv3792LhxI66uruzYsYMuXbrYJXTUawWPt9cwMED167l2W+GnNWbOXi/6OelVyROvSh7416+Mh6/jdpYWQjB9+nRmzJgBwBdffMFzzz2HTmNlYAc9rw4xUr2ihCkDth7K4MPfk9mwL93honzlcDy0bt2alStXsnjxYpo0acJHH33Et99+y/Dhw23rhIeH50hxWSwWvvrqK5o3b05QUBBpaWns2bOHGjVq2D2uUEpSJGEnTCYTTk5O/PXXXwwYMMC2fPTo0cTFxRVoIlajRg1efvllXn755QfaZ3p6eg7XzISEBPz9/bn1zav4DBqP0OvJ2LkM4eqJvtc4dZs9q1ASo9G2COKP9dsZN24cVquVxzq2Zd5Hr+MRMDBHKaditRB/bBeg4Nb4UZs3T3ZYzRlEXjwJgG/dpkhy3mXfiqJw7epVrFYrlatUsbuRKMDlmzEkp2bg5+WMr5eL3dvdO/7xKykkp1vxr6DD3yd38lkYWK1WfvrpJ/5YtIy0DIkKPn6sX/UnOm3JlvQevGTlyh1wMUBQU4FGLrsSXLMFNh5VIzx6LfRtCZ7ZvqLo6Gh69erFgQMHcHV15Z9//qFTp05lNFsVyWkKq/6F23Fqmqt3K6hb2bHKmA8dOkTPnj2JioqiYcOGbNmyJVeLhNwQnaCaE97K9CBq20AiqJWEtgjnSeztOAwuBozO9v9eyxJz587l2WefxWq18sQTT7Bw4UJ0Oh2KonDyioUN+0zcilZJoEEHnVvo6PiQFkMJWks4IhISEnB3dyc+Ph43t5Ihs1lj3I6MLbExCouEhAT8fD1L9LiLAw7hwxMVFYXFYsHPL2f7aj8/P86ePVtq+/z0009tzS/vQ1oKwl11q7u3DF1JjEZJT+Hpp5/Gw8ODIUOGsDr4Xwa+8gH/bO6Iq9fd3L2QZCSDE9a0ZCypSbkSHkmjRdbqsWSkk5Gagt4l7xNICIHBaCQlOZnU1NRCER5PVyPJqRnEJqTh4+lcJI8NIQRVvHWcv5VGeGwGlb11yNKDXeQGDhzIhQsXaNKkKQ3b9mPl4tm0bNWG4B2b8fb2fqB954fm1QUR8QpJaXDgksIjdSkz3xGNDN2bw4YjqiP0hiPQt5UaRQHw9vZm27ZtDBgwgO3bt9OzZ0+WL19Ov379ymS+oAp+n2yvsPag6tWz5gB0bqrQopbj3PRatWpFSEgIQUFBnDlzhvbt27N161bq1KlT4LbeboJxPWW2HrGy97SVf89auXbbyhMdNFRwL9wxevp55Pj/nx83UaVuRVoFNS/UfkoLY8eOxd3dnWHDhrF8+XISEhJYsWIFzs7ONK2loXFNmeOXLGz810REjJUN/5oIPmaiSwsdgc20NlFrOYoPz70XhlbvGCL4jPSCU8SOAIdJaTkCpk6dSnx8vO11/fp123vZU1pkpNv6ad3V9ajppwEDBrB+/XqcjQZ2HjpBtx4973PgtE/Ho+63MGmtwgiXAdxdDEhCYDJbSH4A52VvNw16rcBsUbgT/2B29G+88Qbbt2/nyy+/ZOnSJUwYO5SXp80iOTWdcxcuP9C+C4JWI3ikrtp24kYMnA8v0eEKhE4DPR4CT2dIMcH6I5CcTW/r6urKunXrGDBgAOnp6Tz++OMsXLiwzOYL6mf4WBtoVkP9f8cJCD7pWF49DRo0IDQ0lLp163Lt2jUCAgI4duyYXdtqZEHPh2WGd5Fx0kNELPyyzszRS0XvAXdw8zG+f+E3/tf3U66duVGkfZQGBg0axLp163BycmLTpk10796d2NhYQK3ce6iOhjeGGRnVQ4+vhyAlDdbuNfHRghR2HDZhekAH63KU40HhEBGeChUqIMvyfaW2t2/fzlOQXBL7zMsVEjKjOjqDKrKwWiEtBZzdEPpMo8H0u8Ska9eubJg7kwHjX2X/wUM2d+HKldXmSbLRhYzY2/kSHp3RmbSE2EIZEKalpmK1Wu1XrEsCD1cDMQmpxCak4mIsmouvEILKXjqu3E7nVrQJPw9tkSIj27dvZ8aMGcybN49evXoB4OcuUb9eba5fPcf+41d49JHWRZqjvajgKnioOhy5qnA8TMHDGfwK+fRenDBooVcLWHsIElJV0tO3lerKDKo1wfLlyxk3bhwLFixg5MiRxMXF8cILL5TZnCVJ0LWZgpsRQs/AoUuQmKo6M5dlmjA7qlevTkhICD179uTo0aN06tSJdevW8eijj9q1fb2qEhP7Cf4OtXAlQmHlbguXwgV928rotYU7xmYdG9HhiXZUqOxFtQb2pdfKCkFBQWzdupXevXuzZ88eOnXqxKZNm2zXVEkStKyn5aE6Gg6dN7Npv4moeIXVu01sP5JBt1ZaHm2iRatxjPPgv4xfP6jmMOmjhIQEVv9U1rMoGA4R4dHpdLRq1Ypt27bZllmtVrZt20a7du0cYp9KWop6E88SLmcRnHsqt7LQtk1bNv3wEZX9fDh16hQBAQFcvqxGKO5GePIOA9oqtdKSC3xy1Gq1NlvtwjrxerqpZCk+KQ1LAR3a84OvhxaNDGkZCtH5NBXNC4qiMHnyZJ566imefPJJG2ESQrBv01wqVa2Fk1cdktJK/imxth/UyLR12HdBIbmMRZhOeujdUtUWxaeo6a3sJtkajYZ58+bx4osvAjB58mQ++uijMo2qCCFoU0/QqyVIAs7fUttRpDpQXyY/Pz927NhBQEAAcXFxdOvW7T6LhPzg5iQY1U2my0Nq49vjlxV+Xmu2aXzshU6v5e1FLzH+q1G2896RImL3ol27duzatYuKFSty/PhxAgMDuXr1ao51JEnQuoGWqSOcGNZVj5ebIDFFYWWIiekLUgg9nlFmFhD/V2DQSw71+i/AYWY5ZcoUZs+eze+//86ZM2eYOHEiycnJjBkzBoBRo0bZukiDKko+evQoR48exWQycfPmTY4ePZqjZUFB+ywMlPTMyqwsL560e/+/x2TQ6EKjWv5sWzCLWrVqceXKFQICAjhx4oStUsuanoqSR1PIwhgQCiFwclbnkZxccEQoO4x6DXqdjKJAfGL+4+QHWRJU9FTDDrei7e/gnoUjR44gyzLDhw/HaDTatj9x4gR7QrZTr34jKvhV5UJ4yZudCSFoWVPg6QwmM+w9rxRLF+0HgYtBjfQYdRCTpAqaTdk+CkmS+Pbbb20atHfffZcpU6ZgfQASWxxo6C8Y2A70GtVXaEkID1zWXZzw8PBg06ZN9OrVi9TUVPr168fy5cvt3l6SBB2byYzpIePuDDGJ8NsGM3tOF85OQZZlW2RWURS+ee4X5v1vscMSn6ZNmxIaGkrNmjVtFgmnT5++bz1ZErRtpOXtEU482VmPh4sgPlnhr+B0Pv4jhb0nM7CUE59ylBIchvAMGTKEGTNm8O677/LQQw9x9OhRNm7caBMdh4WFER5+V1Rx69YtWrRoQYsWLQgPD2fGjBm0aNGCZ555xu59FgpZBOfe0vRMzx0lLSnHxUnKJDXVvV0JDQ2ladOmhIeH07FjR/YfOozQqOTAkvbgBoSglnkDpCQnF+omJ4SwOS/HJBSd8ABU9NQiCUhKs5KQUvhS94sXL1KhQgXbvCIiImxkdfKk53B18yAy3kpMNqOz1NRUTp06RUYhnKbtgSwJ2tUT6DSZ3cyvlL0Oxd1JJT16jSpk3nJMrebKghCCd999l5kzZwLw7bffMm7cuALdhUsa1XwEQwLB1QixSbA4BCJiHecm5+TkxKpVqxgyZAgZGRkMHTqU3377rVD7qOYrMbGvhobVBBYrbDpoZdF2C8lFiEge23mKDXO2seSzlVw8cqXQ25cWateuTWhoKI0bN+bWrVt06NCBAwcO5LquRhY82kTL/0Y5MaijDndnQWyiwtId6XyyMIV/T2eU+UNFOf7vwyHK0h0VWeV/t755FQ//mhgHPY9p/wasV0+haRqIpmFbFIuZ9K1qwzN956cQOpU8WNNTSTu4HgBjuwHExSfQp08f9u7di7OzM4tnzSCwWT2M/vXRV8g9b5945xbJ0bcxunvhXil/B0pFUQi7dg2LxULFihVtER97kGG2cPaq6klSt5o3Bl3RpV2XwtO4HZeBp4tMQ38nu7cLCwtj0KBBTJo0iZEjR6IoCpMmTeLYsWMMGjSIN998k9M3zNyItuJmFLStq87x4MGDfPbZZ8TGxvLxxx8XOQWaFyLjFYLPqD+RljUEtSuWvfbgTgKsPwwZFvD3hm7N1DLw7Pjjjz8YM2YMFouFxx9/nEWLFhWqgq8kkJSqsPJfuBOvVqH1fRhqOcDnmQWLxcLzzz/PL7/8Aqi+M6+//nqh9qEoCgfPW9l4wIrZqpK8Pm1lGviLQuna1v6yBY1WpufYLoUavywQHR1N79692b9/Py4uLvzzzz907tw5321MZoW9JzPYeiiDxBT191XBXdCzjY6W9TRID1jpWVYo3bL0GIfS8Pj5ejl8WXo54ckH2QmPu48fTsNfJ+NYMJZzB5DrtkTbQr0Ype1cDOkp6Nr2R/JQxR+KopC67x+wmjG0CEJyciM5OZnHH3+cLVu2oNNp+e3jdxg0ZChO/vVzHT8tMZ64m5eRdQZ8ajUscL5Rd+6QkJCg9hbx9S3UsV4LjyMhOZ0KHk5UqlD0UsdUk5Ujl9SI1EO1nHDS2++dM3fuXJ5//nlatWrFmTNn8PX1Zfz48TZ/pdR0C3vOW7BYoWk1mUqeMnFxcRw8eJDvvvuOtWvXEhwcTGBgYJHnnxvO3VIFzEJAp0aCCq5lfzGOiIUNR1VH5pq+0LmxqqfPjtWrVzNkyBDS09Pp2rUrK1euxNW1bMtYTRkKaw6ovcIE0LU5NKtR9p9nFhRF4e233+azzz4D4K233uKTTz4ptAj/dqzC8l1m7sSr/9etIujdRsariOdOckIKskbG4PTgPlclgcTERJtFgl6vZ+nSpTz22GMFbmfKUAg9kcG2QyZbBaKvp0p8HqqrQSojW4iiojQJT5ehIWh0RfNPK26YTUlsXxLo8ITHYVJajo68NDwAIjN9ld1ZWQiB5JSp1clc7uzszJo1axg8eDAmUwZPv/khvy/8M88xs1JaFlMaVkvBaQnnbDqewvLYLOfluMS0B0rdGHUSXq6ZTUWj7W83AarXx/Hjx3nqqaeYMWMGu3fvzmEmadBJ1PRVCdSFCAsmUwYeHh507dqVqKgoevfuXSJRjHqVVOdjRVH1PI4gvK3oCUHNVEHwlUgIOavOLzsee+wxNmzYgIuLC9u2baNbt25ER5dtKwOdVjDgEWhcTW0rvfUYhJ4u+3RhFoQQfPrpp3z++ecAfPbZZ0ycOBFLHlq7vODnqbal6NBUbUJ64abCrH/MBB+3FFqsm56azrT+n/F2r49Jji+cRq+0cK9FwqBBg1iwYEGB2+m0gi4tdbw72pm+7XQ4GSAyVmHBpnS+WJTKsYvmEm8tU47/f+AQZen/CWSYUCxmm+9O9jJ0YXRBibt9XysJYXSBpNgcy/V6PUuWLOG5ceOY+/vvTHr7I1L1HkyZMuW+IWWNFlmrw5JhKtCAEMBgNCJJElarlbS0NFu5uj1wddKjkSXMFiuJyem4uRSdOFTx1hGTaCYq3kw1Hyt6rf28um7dutStW9f2/5o1a7h27RrPP/88Qgiq+0hcj7KQmq4QFiNRpyJMmDCB5ORkpk2bRuvWxV+2LoSgdW1ISFVISIW9FxQ6NaTMw+5VvdU2FNtOwoVw0MrQrp7akysLnTt3Zvv27fTq1Yv9+/fTsWPHHBYJZQFZEnR/SC1b33sO9l9QS+57tFAe2LSyuPDGG2/g6enJ+PHj+eWXX4iLi2PBggXodPZbN2g1gq4tZJrXklj3r4XLEQrbj1o5dslKn7YytSvb97u4cT6cS0evAhAZFkXNpvanq0sTWRYJzz77LPPnz2f06NHEx8czefLkArfV6wTdHtYR0ExL8NEMdhxRDQznbUijcgWJXm11NKkpl5kRqCNi8XeNHSaakpCQgN+Ssp5FwSiP8NiDrFLRtJS7HdMLiPDAXeGyNSXnclmWmT13Di+MGgLAq6++yrRp03J9ys0qT7fHgPBBqrXUhqIqyYl5gGotAFejjJuTjAKEF6Kp6L1ISUnhu+++448//uDSpUuAerOsX0WD1Wrlym0rs37+jfXr1zN+/HibbqCwT+P2QCMLHq0n0MoQnQhHrznGU2cNX7XhKMDpG3Dw0v3rtG7dml27dlGlShWbRULW51lWEELQroGgRws1SnX2Bvy9V7U1cBQ8++yzLF26FK1Wy9KlSxkwYEChbR9A1aaMCpIZHCjjYlTPnwVbLSzfZSYhpeDjrd28Bl/t+ICP106lZtOCu0mXJTQaDXPmzLFFZl988UU+/PBDuyN4Bp2gRxsd7z7tTI/WWvRauBVlZc66NL5elsrpq2aHiQaWNQx62aFe/wWUEx57oM+MdqSn3lelBSCccic8d4nQ/ZVYkiTz8dQpTHtB7ck1ffp0Jk+efF+FVWE6p0O2tFZSUhHSWmpEKDE5nQzzg5GGyl7qk/DtuKL7bTg5ObFgwQI+/PDDHNb/Pq4Klbw0XL5wkukfTWfgwIEMGDAAg8GA1WpFltUf36FDh1iyZAm//PKLXd2xC4KrUdCmjkp+L92Gq3cc48JbtxIENFD/PnYNMoMBOdCoUSNCQ0OpU6dODouEskbjamqKS6eB61GwNAQSHajr9hNPPMGaNWswGo1s2LCBHj16EBcXV+j9CCFoWlNi8mMa2jaQEAJOXlX4YbWZvWcsBVYo1WlRkyYBd3V8ty5FcP3czULPozQgSRJff/01H374IQDvvfcer7zySqGqR530gl6P6Hl3tDPdWmnRaeF6pJVf16Tx7V+pnAsrJz7lKDzKCY8dELq7JMeW0jKlolhVUpAXsbFFePLojK5xcmXK2KeY+emHCCGYNWsWo0aNylFirXNyyRwuCcWOC4bRaEQIgcViydEI1R4YdBqcDKqBYdwDRnk8XWSMOgmLVSU9RUWlSpXo0aOH7f/9+/cjyzINq2j44ZPJVKvVkK79RtpSNFleJps3b+bFF19kypQpLF26lDp16hTKVC4vVPYUNKqqkp5DlxVikxzjotugCrTJ5IQHL8Gp6/evU6NGDUJCQmjWrBkRERF06NCBvXv3lu5Ec0ENX8GTAeBsUKMfi3bBnXjH+FwBevTowZYtW3B3dyc0NJTOnTvf5+BuLww6Vbw8vreGqhUE6Rmw8YCVX9eZuX7HPkIQdSuGN4M+ZEqHd7l8/FqR5lHSEEIwbdo0vvvuOwBmzpzJmDFjCm2R4GwU9H1UJT6dW2jRauBahJWfVqfx/YpULt4s/mhuOUoHN2/eZMSIEXh7e2M0GmnatCkHDx7Md5v09HTeeecdqlevjl6vp0aNGsydO9fuMcsJjx0QBjXyobaXMN4VSaSrvaukTOdkJTUJRbl70RKZy7FkQMb95CPLcXnsE/35888/0Wg0/PnnnwwcONDWF0ujM6jd0hXFrrSWJEk4OamkLKWQaS0Az8y0VmxC6gM9QQkhqOytRnnCY0zFIjy8efMmjz76KCNHjmTShGewZiTT58nnMPo2IylNwWq928/o+PHjpKWlsXr1arZv386bb77JpEmT2L179wPPo1EVqOQJVgX2nFcwmR3j5tysOrSoqf6997yq67kXFStWJDg4mEcffdTmLrxly5bSnWgu8HUXPNUBvF3VfmFLQyHMQSJoAO3btyc4OBg/Pz+OHj1KYGAg164VnWxU8haM6yXT7xEZo06tupuzwcLafy2kFSCK12hlXL1ccHJ3wtPPvchzKA1MnjyZP/74A1mWWbBgAYMHDyYtrfAPUy5GwWMBeqaNcqLjQ6qr++VwKz/8ncpPq1IJu11OfP5LiI2NpX379mi1WjZs2MDp06f56quv8PT0zHe7J598km3btjFnzhzOnTvH4sWLqV8/9yrn3FBOeOyA0GcSnvQUhCRB1v9Z7sp6Z5UEKVYbCQK1M3pWCsyaS1orexPRYcOGsXr1agwGA2vXrqVXr14kJCQghEDnrEaKTCn2pWWci6jjAXB3NSAEpGdYSE1/MMM6HzcNWllgMitEJzy4+V2VKlU4c+YMp0+fZt68eXTt3IHuQUEoChy7nIoQqteJ1WqlTp06+Pv720TMkydPxtnZmaNHjz7wPIQQtKktcNarTT3/veg4VUYta0ITf/XvXWfU8u974eHhwebNm+nRowcpKSn06dOHFStWlO5Ec4GrUTUorFpBdZFeuQ/O3XSMzxWgefPmhIaGUr16dS5cuEBAQABnz54t8v4kIXi4nsTkARqa1xIowIFzVn5YbebUtbybkXr4uPPl9vf5ctt793Vdd0SMGDGClStXotfrWb16Nb179y5yitnNWeLxQD3/G+VEQFMtsgTnrlv4elkqc9elEh79/w/xSU2zONSrMPj888/x9/dn3rx5tGnThpo1a9K9e3dq166d5zYbN24kODiY9evX061bN2rUqEG7du1o37693eOW+/Dkgyy/g8g1v+N08xzatj3QPdSB9C1/oMTeRhvwOHJl9QtK37UUJTUJXZs+SJ53m5OmnQrBGheJrnZLNBVr5ti/1ZxBwokQANyaBiJptISEhNC3b18SEhJo2bIlGzduxFkrkRARhtbojHf1egXO22KxcC2zt01Vf/9CVZYAXI+IJy4pDS93I1V8HqwK4EZUOmF3TDjrJZrVdCq2KovJkycza9Ysvvv+R+q0G4Ek62hQWebbT1/n2LFj3L59m9OnT/P666/z+eefs2HDBt5++21GjRrFK6+8gqIoDzyXuGSFbScVrAo0rno31VXWUBSV7FwIVw0Jez6kRqTuhclkYsSIESxfvhxJkpg9ezZjx44t9fneC7NFYcNhuHBL/b9TE2hRC4ep0Ll58yZBQUGcOXOGChUqsHHjRlq1avXA+70SYWXNXgvRmVygXhVB77Yyni4FH/fRHSeJvR1P56H2X/xLGzt37qR///4kJibSunVrNmzYgLe39wPtMzreysb9Jg6eM6MoqrdTq/oaerbVUcG99J/nS9OH5+Guy5A1jlGxZzEnc3Dbk1y/fj3HcefVkLtRo0b06NGDGzduEBwcTJUqVZg0aRLPPvtsnmNMmjSJ8+fP8/DDD/PHH3/g7OxM//79+eijj+yvSFbKkSfi4+MVQIncvFxJ+vltJX3vBkVRFCU95G8ldemXSsbFo7Z10/9dp6Ru/E0x37yQYx/pl44oyaF/KelXjuc6RtyJUCX28DYlIzHWtuzQoUOKj4+PAij169dXLl+8oISfOayEnzmsWMxmu+Z+6+ZN5dLFi0psTEwhj1pREpLTlOMXIpRTl24rFou10NtnhynDquw9k6DsPp2gxCVlPNC+7sUff/yhBAUFKUfP3VY2HU1X3vx4ruLq6qrMnz9fOXTokLJhwwbFx8dHadGiheLv768MGTJEOXDgQLHO4cptq7Jsr0VZtteiRMQ+2GdVnLBYFGXTUUWZvVVR5u9QlDsJua9nNpuVZ555RkG1xVFmzJhRqvPMCxarVdl6zKp8tUp9rT1gVdJMjvP53rlzR3n44YcVQHF1dVV27txZLPs1ma3KtiNm5YM/TMq7v5uUj/40KaEnzYo5n9/h9fO3lH6uI5Qg6Qnl3/WHi2UeJYUDBw4o3t7eCqA0atRIuXHjRrHsNzzaosxdn6K89F2i8tJ3icorPyQqS7enKrGJlmLZv73IumfEx8eX2BipqamKVu9l+806ysvFxeW+Ze+9916ux6DX6xW9Xq9MnTpVOXz4sPLLL78oBoNBmT9/fp7H3aNHD0Wv1yt9+vRR/v33X2XdunVK9erVlaefftruz648wpMPspj0neA1GM/sRdOgFfqOA8k4tAXLpWPIjR5B2yQAgIyTu7DcvICmTks0tVvY9pERfpGMy8eQvSqhb/jofWMkXTqGOSEaY9V66H2q2pafO3eOoKAgrl+/jr+/P8vm/kSNqpXxrFoLvUvBefv4+Hiio6LQGwxUqZJ764q8oCgK565FkWG2Uq2iO+4P4MkDRW83YQ/S09PR6XTsPZvKZ+9PIezSCYK3rcfdXf2MVq1axcyZM5kzZw4+Pj4l4jR88LKVK5FqpVFQU4GT3jEiEWaL2mQ0Ig4MWuj3sNqP614oisKbb77Jl19+CcDbb7/N9OnTyzyioigKhy/BrtNq1MrDGfq2VvU+joCEhAQee+wxdu7cafOg6du3b7Hs+068wpp9Fq7dVi/PFT2hXzuZqhXuj1pYrVZmvTiXsLM3+XjtVHSGwkV0SxtnzpwhKCiImzdvUr16dbZu3ZqjCvNBcD3Swvp9Js5cU1MsGhkCmmrp9rAOF2PJnzelEeEBiI1LxmQquuVHScCgl+67ZuQV4dHpdDz88MPs2bPHtuzFF1/kwIEDeRZSdO/enZCQECIiImzX97///pvBgweTnJxsV5Sn3HjQDuTVMJRsImJhKKhSK/cmobLRBXNCNJZ73q9fvz6hoaEEBQVx/vx5+g0dzaJfv6etl69dhMfZ2ZnoqCjS09Iwm81oNPZ/1VmePHdiU4hNSH1gwlPZW8ftuAxikywkp1lwNhSfZ0PWj6l5TSPVazfk/OnDRCUKMn8PyLJMeHg4RqOxxNoqtKghiE1WiEtWTQk7Nyp7U0JQL/bdm8O6w2r104Yj0K+VWg2VHUIIvvjiC7y8vJg6dSqffPIJsbGx/PDDD7aqt7KAEIJWdaCSl8K6gxCXDIt3QeemCk2rl32Ky83NjQ0bNjBkyBD++ecfBgwYwO+//87w4cMfeN8+7oIx3WWOXlLYdNBCRCz8tt5C6/oKXVtIGHR3j12SJF74fhwZ6Rk5yI5SDGnbkkDDhg3ZvXs33bp14+LFiwQEBLB582aaNWv2wPv295UZ39/IpZsW1u1L5/ItKzuPZrD3VAYdH9LSuYUOo4M8kDwIPD2cAcdIZxUFlSpVolGjRjmWNWzYMF8tYaVKlahSpYqN7GRtoygKN27cyGFYmxfKRct2wCZaTssUJNuqsrK1kshWqZVj2xzd1O8vO80uXL4X1apVIyQkhBYtWhAVHc2g0c8RvGOHXXPWaDQ2MlAUs7SsDuqJKaYH9uQx6iS8M9tNhN0pmacSZ4Ogd/dOJCfG8fwLk7l+M4Jdu3axc+dOLBYLWq22RMYF1Qzx0bqqKWFMEhxzEFNCUKNOPR9SIztJabD+CKTl8RW89dZb/Pzzzwgh+OmnnxgxYkSxd6EvCip7CUZ0gpp+au+wrcdg/SFIdwCTQoPBwIoVKxg5ciQWi4URI0Ywa9asYtm3EIIWdXKKmvdnippP3yNqFkLkIDtrft7M18/+XCImnMWB6tWrExoaSvPmzbl9+zYdO3bM8bT/oKhdRWbyQCPj+xvw95VIz4DNBzL46Pdkth4yYXKAc+f/Z7Rv355z587lWHb+/HmqV8/bWLN9+/bcunWLpKSkHNtIkkTVqlXz3C47ygmPHchepQUgnO4nNyIXEqRu66R2dVSUHGaFWbARnrTc+1/5+vqyY8cOAgMDSUxK4snRz7B2zRq75p3lulyU8nS9ToNRXzyePADVfFTyFZtkJiHlwSu2ckOPDs2Y9cdWrl0+S7du3Rk0aBDbtm3jo48+okKFCoUyPissnA2CtpmmhBdvQ1iU41xQjTro1QKc9RCfoqa5THl8BePHj2fx4sVoNBoWL15cZHfh4oZRJxjQFgIbqQWR527Cn8FqN/uyhkajYf78+bYWCi+88ALTp08vtso9Z4NgYICG0UEyXq6QmApLgy0s3mEhLhcfqNvX7vDjS3PZOHc7wcvK3mcpL/j5+bFz507at29PXFwcQUFBxeKVlQUhBA2ra5jypJExvQz4eQpS0mHtHhPTF6QQctxUZFPUcjwYXnnlFfbt28cnn3zCxYsXWbRoEb/++ivPP/+8bZ2pU6cyatQo2/9PPfUU3t7ejBkzhtOnT7Nr1y5ef/11xo4da7douZzw2INMHx6yCE8u0Ryb+WBack4vHiHuRnlS7y/FlPRGEBJYLVjTc7+xuLu7s2nTJoI6dyQtPZ3HBw5kyZKCG5c4Z/rxpKamFulmn9VQNPYBG4oCGPUSfh4qgboWaSqRMm5JEnRsVZWZf4Tw9EufMXfhCpYsWcLQoUMBiImJ4fTp08U+bhYqeQoaZsqlDl5WiLejbUBpwcUAPVuAXgtRibD1uKrxyQ1ZKRqj0cj69evp2bMn8fHxpTvhXCCEoHVdwZAA9XiyUlzHr5a9LYAkScycOZP33nsPgGnTpvHqq68WK8muVUliUn8NHZupDUnP3VAbku45ndOp2a+6D28vfoWBL/Vx6KotuGuR0LNnT1JSUujXrx/Lly8v1jGEEDSvo+HNp5x4qpsebzdBQorCimATH/+Rwr9nMgp0ui5H8aJ169asXLmSxYsX06RJEz766CO+/fbbHOng8PBwwsLCbP+7uLiwZcsW4uLiePjhhxk+fDj9+vWzmVvag3LRcj7IEqDF3riGdu0vIMk4PfMBZKSTvuoHAPQDX0JotChWK+lb54OioO8w5K7pIJB+dh+W6JtoazRDW+X+PGPi+UNYkuNxqtYQnXelPOcTfeMKz06YxMp1GxFC8OOPPzJhwoQ811cUhethYZjNZnz9/HBxcclz3dxgsVg5c/UOigJ1qnphNDxYWig9w8qRS8lYFWhQ1Wjrql7cOHfLzLU7VgxaeLS+Fo0sSEhIoGvXrly6dIkNGzbQtm3bEhlbURR2nVGITABXA3RtKtDKjqMZuJMA6w9DhgWq+0DXJmoAMjdkt0ho0aIFGzduxNfXt3QnnAdSTQobD8OVTMPj+lUgqLnafbusMXPmTFsvqaeffprZs2cXSkNnDyLjVFFzWKR6+a7kBf0ekamSi6gZVGFzalIazm7FWzRQXDCZTIwcOZJly5YhSRK//PILzzzzTImMZbYo7DttZvMBEwnJ6ufn6yno3VZPszoy0gPonkpLtFyOoqE8wmMHbO0krBbIMIFWD7J6AcsyHxSSlEOvk2P7rIhQWu5mWxpnVYRlTknIdx7O7p788MV0nh4+BEVRmDhxIp9++mmeT7dCCJsJYVHSWrIs4eacmYpKTC1g7YKh10pUyuyxdS0yvcSeymv7qe61aRlwIVwNY1gsFjQaDbGxsXTt2pWtW7eWyNhCCB6pKzDqIDENDl4q++hDdvi4QVAz1Z/n2h0IPatWQOWGwMBAdu7ciY+PD0eOHCEwMDDHE1dZIrcU18Jgx2hJ8dJLLzF//nxkWWb+/Pk8+eSTRXIXzg++HoIxPWT6t1PP9fAYmL3Bwvr99zs1K4rCD5Pn8krgNKLDY4t1HsUFnU7HokWLeO6557BarTz77LO2qsHihkYWBDTV8r+RTvRvr8PJAJGxCvM3pvH10vIGpf+XUU547IFGq75QCY4Q4m4Exw4dT4GVWk7qk4AlOf+0gc7JBUmS+OSdN5j61luAWkL85ptv5vkDtel4UlKK9CPOEi/HJaYVS3uIKt46NBKkmqxExpeMlkcjCxpVVQnp9WgrcclWPD092bp1K0FBQSQnJ9OnTx9WrlxZIuPrtYJ2dQVCwI0YuBhRIsMUGZW9oHMT1aTtfDjsv5g36WnRogWhoaFUq1aN8+fPExAQcJ/YsKyQleJ6sv3dFNciB0lxjR49mr/++gudTsfKlSvp27dvsTSwzQ5JCFrVlXjhMQ3NagoUBf49a2XWP2bOhN1NpcXejmP3qv1cPXmdM/vOF+scihOyLPPzzz/z5ptvAvDGG28wderUEvsudVpBl5Y63h3tTM82OvRauHFHbVD63YpULpX36fo/h3LCYweyExybbidXHU8elVp5LM+CxjmT8KQm2xqS5gZJ1qAxqG7F0956nRkzZgDw5Zdf8uyzz+ZakWEwGJAkCavVSlpq4aM0Lk46NLKExaqQmFy4ZqS5QSMLqlRQo0bX76SXWO7c21Wisqd6ep+6bsZqVXB2dmbNmjUMGjQIk8nE4MGDmT9/fgmNL2heXQ2NHwtTiEp0rCfGGj4QmNl8+0SY2nA0r/tKvXr1CA0NpUGDBly/fp2AgAAOHz5cepMtAFW8BSM73V/FVdaVOAMGDGDDhg04Ozuzbds2unXrRnR0dLGP42IUDArUMKqbKmpOSIElOy0s3mEmPlnBq6In34Z8xBu/v0DA4yWTyi0uCCH47LPP+OyzzwD47LPPmDhxYolWmxl0gp5tdUwb7UyXllq0MlwJt/L936n8tLq8T9f/JZQTHjshjGqkRMn03sktmpNn1/Qsjx5TKorl/qiG0OoRGh2gYCmgX5Y+s0IsPTmRV199lTlz5iBJEnPmzGHo0KH3dUgXQtiiPMlFqLbJ8uSB4qnWAqjkqUWnUXtsRcSWXNlz/coyOg0kp8PlSPWipdfrWbJkCWPHjsVqtTJmzBi+/fbbEhm/jh/4e6tEYu95hTQHK4WtVxnaZkrKjl2DkDOQl8bW39+fXbt20bJlS6KioujUqRO7du0qvckWAKM+9yqusk5xdenShe3bt+Pl5cX+/fvp2LEjt27dKpGxaleWmNRPQ4emEpKAs9cVflhtZu9pC741fOk2ooNt3ZTEVC4evVIi8ygOvPnmm/zyyy8IIfjll18YPnx4iRvtuRgF/dvf7dMlSXAu7P/PPl3/V1FOeOyEMGQRHpXM5F6plUdpulYHGt1969veF+Kujic5fx1P9kaiiqIwduxYli1bhk6n46+//qJ///73NQ3NruN5kLRWQnI6ZvODV51IksA/s0z9ZlR6iZWGajWCBlXU1NaVSCuJqercNRoNv/32G6+++iqglki+++67xR46F0LwcC2Bq1HVE/17oexTLfeiaTUIaHA3vbUln+otHx8fduzYQYcOHUhMTKRHjx6sW7euVOebH+5NccU6SIqrTZs27Nq1i8qVK3Pq1Cnat2/PpUuXSmQsrUbQtYXMxH4aqvkITGbYeNDK7PVmbkWrn4EpzcR7j3/BK4HTOLL9RInMozjw3HPPsXjxYrRaLUuXLi01iwR3F4nBnfS8PcKJ1g00CAHHL1v4YlEqCzenERVfcvYW5ShZlBMeO3Gv987dUvNcStNzITV3dTy5R3DkrLRWSv46Hq3RGRBYzRlYMtRozqBBg1i7di1OTk5s3ryZoKAgYmPvihONRiNCCMxmc5Gekgx6DUa9ShxiEh5cvAzg667BqJMwW+FmdMk9ufm5C3zcVH3D6RsW241PCMGXX37J9OnTAfjoo4948cUXi92rRyMLHq0nkCWITICT1x2L8AA0qALdMoXM16PVKq68zAnd3NzYuHEjffv2JS0tjQEDBrBo0aLSnXAByEpx1fC9m+LacLhsU1yNGzcmNDSU2rVrc/XqVQICAjhxouTIhq+HYExPmX6PyBgyRc2/rjez4YCFlFQLkiTU6K+rnU0XywjZLRI2bNhA9+7diYuLK5WxK7hLDA8y8OZTTjSvLaMAB8+Z+WRhCst2pBGXVE58/msoJzx24i7ByUxp5WY+mI+rckE6nizhckERHkmS0RrVqjFT8t19BQUFsXXrVjw8PNi7dy8dO3YkIiIicxsJY6Ynz73RH3vhndmEKTo+pVjEy0IIqvuqUZ7wGBOmjJK5eAghaFhVg0aC+BSFsKicHknvvPMOs2bNQgjBDz/8wKhRo4rdXdjNKGhdS9XznL0Ft2Idj/RU91HNCfUalZitOaQa3OUGo9HI33//zfDhwzGbzYwYMYIff/yxdCdcAIx6weOPQEBmiuvsjbJPcdWsWZOQkBCaNm1KREQEHTt2ZN++fSU2niQED9eTmPyYhqaZouZ9Z6zM2aZhxKw3+XrXh9RvXTw9rEoSPXv2ZMuWLbi7u7N79246d+7M7du3S238il4SY3obeXWIkQbVZKxW2HPSzMcLUlgdmk5SquP9nsuRO8oJj524X8OT2ZcpO4ExOKlXV0WBe1yVC4rwaJwyo0MZ6Vgz8hcH67OltbKjXbt27Nq1i4oVK3LixAkCAgK4ckXN02eZEBalPB3A3dWARpYwW6zEF5OWx9NFxtUoY1UgLKrkojwGraBeZbV/14UICynpOS9QkyZNYuHChWg0Gv78808GDhxIahEE3vnBv4KgTkX17/0XFRId8CJZ0QP6trrryLzmIETmEXDUarUsWLCA559/HkVReP755/n4448dKmUnhKBNLimuw5eUYiHtRUGlSpUIDg6mXbt2NouELVu2lOiYLkbB4EANI7vJeLqooua/9krsj/EnMk79HCKuRrLu15Kdx4Ogffv2BAcH4+vry9GjRwkMDOTatWulOgd/X5kJjxmZPNBIrUoSGRbYcURtV/HP7nRiE8sjPo6OcsJjJ0QWIckkGdn1OnfTJHe9eKz3eOpImREca3Jc7vuXNbY2E+ak3NfJgi5zLunJiffdYJo2bUpoaCg1a9bk0qVLBAQEcOrUKZtw2WQyFSmtJQlBBQ+VNEXGJhdjlEfVNkXGZZCQUnKiwCpeEp7OAqsVjl0z31cd9tRTT7Fq1SoMBgNr166lV69eJCTkH20rLJpXE3i7qKZ/u84o9xEvR4CnC/R/GDydIcUEaw/BybDcK7gkSeL777/nf//7HwD/+9//eP311x2K9MD9Ka6dJ1WH5ttxZTNPT09PtmzZQvfu3UlJSaFPnz75Nk0sLtSpLPF8fw2BTVRR87kbCj/+Y2bxpgRe7/YR3074lX9+LL7WDsWN5s2b2ywSLly4QEBAAGfPni31edSuIjN5kNqnq6qP2qdr++EMPvo9hT+3FK/fUjmKF+WExw5YYqOyEZ7Mm6DRVY3mWK05vXgyxcfKvYTH1UtdnpqIYs6dcGhcPAEwJ+ZvDqY1OiNkDYrVginl/hRZ7dq1CQ0NpVGjRty6dYsOHTpw6NAhnDKjPEX1A/FyN6KRJUwZFmKLScvj5qTB113VB10KT8NaQmXqQgiaVtOglSExVeFsLh4bffr0YdOmTbi5uREcHEznzp25c+dOsc1BkgSP1he4GFQyseus4hANMO+FswH6PQw1fcGqwL4LaiuK9FwyfUIIPvroI77++msAvvrqK5555hnM5pLxWCoqslJcXZupabvbcbAoGIJPKpjMpf8dODs7888//zB48GAyMjJ48sknmTt3bomPq9UIurWUmdRPQ6PqakPSMxEG9C064FbZl6bdHy7xOTwI6taty+7du2nQoAE3btwgMDCQQ4cOlfo8svp0vTrEyDN9DdSrqkaqj19yrPO+HDlRTnjsgOnkPkSmqFhJTkBRFNVZ+V4SRDbCc4+JoNDqbZVe1jwIjcbVPsIjhMDgos4nPSn3nEPlypXZtWsXbdq0ISYmhq5du3Io0zslKfH+yJA9kCUJXy/1GCJjkrEUk8C3up8BrSxINVm5UYICZoNO0Ky6Sq5uxli5GXM/6enQoQM7duzAx8eHw4cP06FDB65fv158c9AKOjTMdGJOhZCzChkO2MBQp4EuTeDReiAJuBYFK/fnneJ65ZVXbBYJc+fOZciQIfdZJJQ1hBA0rykY3VUtyVeAQ5fg9+1wKaL0v4Msi4Rx48ZhtVoZN24cX331VamM7eMhGNJRw/g+GupWkajSbwANpn3Cwn/d2HTQQnKa452TWahatSohISG0atWKqKgoOnfuzM6dO8tkLkIImtTUMOlxI28MMxLQ7MHa75SjZFFOeOyAkpJkIzyYM8Ckhi2F0/3kxrYsl2oryUWN8liTYnIdR+PiAQisplSspvwjKHoXDwDSEuPyJC/e3t5s3bqVLl26kJSUxKBBg9i6bRsWi6XIGhUvNyM6rYzZYiUqrnhKRLWyoGbFrDJ1E8lpJZfa8naVqFNR1fOcuWEhIfV+0tayZUtCQkLw9/fn7NmzBAQEcP588TnUOutV0qPTqLqSXWccM9IjBDTyV1NcrkZISss/xTV27FiWL1+OTqfj77//pl+/fiQl5S7SL0u4GAR9WwsGPAJuRpV4rv4X/tlf+toqWZaZPXs2r732GgCvvfYa77zzTqmlBSt7C0Z20zC2h0yt6s6YLbDntJX3Pz3Jiz2/JiHBsUhrFipUqMD27dvp2LEjiYmJ9OzZkzVr1pTpnCpXkOn3qL5M51CO/FFOeOyANSUJodGCXi3htOl4skV9spBXhAfuprUsibkTHiFrkDOjRhkFRHn0zq4IIWE1Z2BOz5u8uLq6sm7dOgYMGEB6ejqTJk3i75Uri5zWEkLg56VqjaJiUzBbiifK4+2qwctFg4Ka2irJC35NX4kKrgKrAseumnONsNSvX5/Q0FDq1atHWFgYgYGBHD16tNjm4GYUBDYQaGWISYIdpxSSHVDTA1DBDR5vo7oz21JcJ3JPcQ0cOJB169bh7OzMli1bCAoKIiYm9/O9rFHLTzC6CzxcRyV3F8PVaM+Ry6UrahZC8MUXX/DJJ58A8Mknn/DCCy8Uu0VCfqjuJzG2h8yIrjK+ziZOffcdZzbv5dUxKwg9aSmTtF9BcHNzY8OGDfTr14/09HQef/xx/vzzz7KeVjkcGOWExw7YvHfuITi5kRspa1lqIso9F6wswmNNjMnzhm53WkuSbCaEaYn5e/cYDAaWL1/O008/jcVi4fXXX+fHWbOKbNfu7qLHoNdgVRQiY4pW9XUvhFCjPLIESWlWwkvQgTlLz2PQQaoJTobl3iywWrVqhISE0KJFCyIjI+nYsSOhoaHFNg8vF0GXJncbjW4/qRCf4ng3FlBTXF2bQrusFNedvFNc3bp1Y9u2bXh6erJv3z46duxIeHh46U/aDmg1gg6NBSM6QkVPMJlhxwlV1BxZiiXsQgimTp3KTz/9hBCCH3/8kZEjRxa7RUJBc6hbRWLSQCcm/PwSfq1a4NezP1sOW5m50sy/Zy0lZhJaVBiNRlasWMGIESOwWCyMGDGCWbNmlfW0yuGgKCc8dsCa6Xdzt9Iqi/BkEqDsAmW9k9pJXVHubyLq7A5CArPJ1mX9XmQnPAVFOQyuKrlKL6CqC1R34Tlz5vDSSy8BoNPr76tCsjeqIoSgkrca5YmJT8GUUTwpKL1WsnnzhEWmk2YquSdcrUbwUHXVRfVOgsLVO7mP5evry44dOwgMDCQhIYHu3buzYcOGYpuHm1HQpbHALdONeccphTsJjnVTyYIQ0NhfFTS7GvJPcbVt25Zdu3ZRqVIlTp48SUBAAJcvXy6bidsBH3fB0EDo0kwld7fjVN+e4JMKGaUY3ZgwYQKLFi1Co9GwaNEiHn/88VJxF84OIQT9hzRhwb9TGdzFCQ8XSEqFNSGpfL/azJGL1hLrgVcUaLVafv/9dyZPngzACy+8wPTp0x2uWrAcZY9ywmMHskrP70Z41MfaXDU8QiCcstaLy7EfIclIzh6AGuXJDZpMUqSYTVjT87/Q6V0y21Gkp2E2FZxrlySJb775hi1btjBs6FAS4uNzhM2FECiKksOlOS+4OOlxMepQgNsxxafT8PPQ4uakVjxciijZ1Jabk0TDKpn+POEWYvJwTnV3d2fjxo307t2b1NRU+vfvz9KlS4ttHk56QefGggqud0vWb0Q77sXaxw0G2JHiatKkCaGhodSqVYvLly/bLBIcFZIQPFRT8HSXTFGzooqa52+Hy6Uoah46dCirV6/GYDCwbt06evbsSXx8/lHckoAkCR6qrRoX+l0P5vj/3iD8Yjir9lj4cY2ZU1etZeZndC8kSWLmzJm89957AEybNo1XX321nPSUIwfKCY89sFjAlIbITCEpyfdoeFIScvywRGZ5uZJLWsqW1spDuCwk+W5frQLSWpKsQZfVTDSPaq379i8EXbt2RZZlLBaLTVRqtVqZNWsWnTt3ZtCgQTz11FMFaggqZkZ54hLTSM1N0FEECCGoXdGAEBCfbOFOfMmWeVbxuttV/fg1c54NPp2cnFi1ahXDhg3DbDYzbNgwfvnll2Kbh06jCpmreKokYu8FhYtlUDlkL/Ta3FNcd+6xLqpVqxahoaE0adKE8PBwOnTowL///ls2k7YTLsZMUXNbVaydmAqr/oU1B5RSc9Xt3bs3mzdvxs3NjZCQELp06VKsFgmFgWIxc2jRWlLv3ME9bB9GHUTFw7JdFn5dZ+bcDccgPkII3n//fVsz4G+++YZx48Y5nEVCOcoO5YTHTliTE+9PYRld1RTVPV48+ZGa7DqevGCvjgfuRnkK0vFkhxACd3d1u/g4tcpr4MCB/Pjjj1SsWJHx48cTHh5O69atiY6OznM/RoMWdxc1BRURXXxRHqNewr+Cakh49XYapmJoWJoX1NYTMi4GtdHi8avmPC/eWq2WhQsXMnHiRBRFYcKECXz22WfFNhdZErSrJ6jlq/5/5KrCyetWh31KzS3FteYgnLyeM8WV5S7ctm1bm0XCtm3bym7idqJWRTXa0ypT1HzhlhrtOXqldETNgYGB7Ny502aREBgYSFhYWImPey+0Oi0zdnzAmOnDePv7J3h5oIZOzSR0GrVH16LtFmatNrP/nKVM+5Vl4aWXXmL+/PlIksS8efMc0iKhHGWDcsJjJ5SUxGypqkwNT15ePFkmg7mQGimTzFiT41CsuWtfbIQnqWAdTxbhyUhNwmqx/0nG1c0NIQQZGRlMmTKF7du38+WXX7JkyRKGDBnCr7/+islksrWmyAtZFVtJKSaSUorPQ6eytw5nvdpc9Mrtkr1YyZLgoRpqv624FIXzt/LWJEmSxKxZs3jnnXcAmDp1Km+++WaxkRIhBC1rChpXVXtvnbkJh0q5aqiwuC/Fdf7+FJeXlxdbt26lW7duJCcn07t3b1auXFl2k7YTWo2gY2PB8I7g56GKmrcfhyUhpdOXq0WLFoSGhuLv78+5c+cICAjg3LlzJT7uvfD0deeptweqHmA6Qcdmgsfq3KB9Ywm9FqISYN2/Vr5aYWbzIQtxSWV7vo4ePZoVK1bYLBL69OnjkBYJ5ShdlBMeO2HN5sWTsww9p64HsrkqJyeg3ENChN4ZtHpQlDzbTMhOriDJKBYzljyajWZBo9OjySyXtzetBar/h6ubG3v27mXmzJl8//339OrVy/Z+rVq1OHv2bIH9avQ6DV7u6vgR0UUzNMwNkhDUrmQAIDrBTExiyYalnfSCJtVUU8KwKCvXo/ImPUIIpk+fzowZMwD44osvGD9+fJGr3nLbf6OqglY1VdJz5Q7sOac4XIVMduSW4lp1T4rLxcWFtWvXMnDgQEwmE4MHD2b+/PllNufCwNddMKwDdG6qipojYmFhMOw6VfKi5nr16rF7927q16/P9evXCQwM5MiRIyU6Zn5QFIVfXl3A64FT0V3cx5RBGnq1lvByhTQT7D5l5duVZpbuNHPtdtlFKAcMGMD69etxdnZm27ZtdOvWzWEtEspROignPHZCSU64S25Sk2zRmbvC5WxXdp0RdAZAQUnKmZYSQiBlanzySmsJIWWaEJZcWgtUH4sPP/iA/v36MeCxxxBC2N6bPXs2tWvXpl69egXux9fTGUkIUtPNJCQXXzTGxShT2VtNbV2OSCvxG76vu0QtP/UnceamhfDY/AnMq6++ym+//YYkScyePZthw4YVqU9ZXqjlJ3i0nkASEB7nuAaFWbg3xZWYmeI6lS3FpdfrWbp0KWPGjMFqtTJmzBhmzpxZthO3E5IQtKilevfUraQe08GL8PsOuHy7ZL8Xf39/QkJCaNmyJXfu3KFTp06EhISU6Jh5wWq1EnM7DnOGhYx0Mwad4JGGMpMf0/BUZ5laFdXO7KfDFOZusvDLOjNHL1nLhLB37dqV7du34+Xlxb///uvQFgnlKHmUEx47oaQkqR3TJSmz5Pxeb56clVo2V+VcCItcKB1PwU8ktvL05ASshYgynDx5Eo1WS//HHiPdZLI9iZ04cYKtW7fSuHFj/P39C9yPViPbGovejk4q1ic6/wo6DFqByaxwLbLk8/C1/WT8vdWfxckwC1EJ+euHxo0bx9KlS9FqtSxfvpz+/fuTXMSO9LmhipegYyPVoDA606DQEZuOZse9Ka6952FbthRXlkXClClTAHj55Zd57733HFardC9cjYJ+bQSPZYqaE1Jg1T5Ye0AhqQRbMvj4+LBjxw46dOhgs0hYv359iY2XF2RZ5q0/JvPF1ncJGtXRtlySBPX9JUZ31zCpn4aWdQQaWdX5rNxt4ZsVZnYcs5Sa8DsLbdq0YdeuXVSuXPk/YZFQjpJDOeGxE9bkBLUbepZmJx/zQQDhmlWpdb/o1x7hsjZzHXNy3H0GhvdCozci69Q0WVpinB1HcxdXr17Fy9OTpMRELBYLERER/P7775w5c4Znn30WDw+P+25EqampnDp1KocpWgVPJ2RJkJ5hIaaYGouCqq/JSm3djssgPrlkU1tCCBpUkanoIaEAR6+aiU7M//MfPHgw69atw8nJiU2bNtG9e3e7SvvtRQVXtWw9y6Bw20mFKAf16snCvSmuq5lVXNczfw5CCGbMmMH06dMB+PDDD3nxxRdL1V34QVG7ohrtaVUbBHD+FszfBv+eK7mGpG5ubmzcuJG+ffuSlpbGY489xuLFi0tkrPwgyzItujS1/Z+anMaanzbZrhV+noLHHtUwZZCGri0kW2uSncesfL1CTXedDSu9qE/jxo0JDQ2ldu3aNouEkydPlsrY5XAclBMeO3EfwcnUy2T9b70ndSW5eqvLE3IhPJnRHyU9JU+vHcngjNDowGrFXICxoBACo7s6XkrsHbuflCtUqEDjxo25cuUKZrOZyMhIpk2bxq5duxg1ahQ9e/a07T8LiqJw8uRJ3n33XXr06MHevXuBrMaiqoA5IjqJDHPx9cNyd9bg56E25Tt/Mw1TRsneFIUQNPGXbe0nDl8xF5jeCgoKYuvWrXh4eLBnzx46depEREREsc3J3SmnQeHO0wpnbyoOHRXJrYpr01HYfhJS0tXP+Z133mHWrFkIIfjhhx8YPXp0qboLPyh0GkHHJoKnsomad5+FOVvg8KWS0V0ZjUb+/vtvhg8fjtlsZvjw4fz000/FPo69sFqtfPTEV3z3/G/8+vofOd5zNgg6NJV5ZZCGwYEyVSsILFY13bV4p4Wv/jKz9l8L1++UvNanZs2ahIaG0rRpU5tFwr59+0p0zHI4FsoJj52wZupj7kZuVIIj3CuoK6QkomQz/xMeam2xkhB1X4RGaLR3U17xuXtrCCHQZu47I491ssPJwxshJMzpqZhS7KtGqFatGhMnTuTtd97hqeHDady4MSEhITz11FO8+eab6vzunbsQ1K1bl4kTJ+Li4kL79u1tWgJvdyNGvQarVeHWnaL16soLNfz0OOklMiwK526mlXjVkpRZuVXRQ0JR4ESYhat38ic97dq1Y9euXVSsWJHjx48TGBjI1atXi21OTnpB1yaCahXUTt8nriuEnnNsXQ+oKa6BbaGJvxoJuXwb/toHZ26oOphJkyaxcOFCNBoNCxcuZNCgQUVubltW8PNQRc29WoGHs9qyZOdJmLsVDlxQSDMV73ek1WpZsGABzz//PIqiMGnSJD799NMyIcCSJBEw8BGcXI0EDGyb6zqyJGhaU+LZ3hom9NXQrpGEi1ElvgfOWfltg4XvVqkpr5jEkjuGihUrEhwcTLt27YiNjaVbt25s3bq1xMYrh2NBKI78iFjGSEhIwN3dnWvvP4u7jy/uk6Zj2r+ZjCPBaBq3RR/QH4C0tb9ASiK6zkORfKoCaiQkfftCMJvQPfIYUhYxyoTp2knMN84h+1RDX691ruNnxEeRfPk4QqvHrfGjOSItuc434jopcVHond3w9K9t93FeuHCBFX/9hSRJ9O/fnwYNG9reUxQlx7hmsxmNRoOiKLRv3x4vLy/ee+89WrdWjyE1PYOL19VUXbWK7ri7GOyeR0FINVk5fiUZixUqeWmp6Vd8+84LiqJw7paFsCiV+FX3kahXSc73u7h06RJBQUFcuXKFypUrs2XLFho1alSsc7p6Bw5fUbAqYNTBI3UFFVzzPz8cAVEJEHoWojL5sK87BDQALxdYt24dgwcPJi0tjY4dO/LPP//g5uZWthMuAixWhVNhsO+cGtUC0MhqtKtFLfAqxu9JURTeffddW2rw9ddf5/PPPy/wWlESiI9KwL2C/d+X1apwOULh2GUrZ8IUMrJlq/19BM1rCRrXkHDSF/+xJCcnM3DgQDZv3oxOp2Px4sUMHDjwgfebdc+Ij4//T567/9dRHuGxE0pSAorVmi3CE2d7T3L3AcAadzcSI4S4uzw+8r79ye6+me/lnYLSuHqq5ekZ6VhSC46YOHmp+0xPTsCcnmbHUamoW7cuU6ZMYfDgweh0OlasWMEPP/xwH9lRFAWNRi3dnjBhAsnJyTz//PM2smO1WjHqtfh4OgNw604ilmLqpg5g1EnUqaySnPCYDG7HlXzqQwhB/coydSupLSiu3bFyMsyCNZ9eQrVr1yYkJIRGjRpx69YtAgMDOXDgQLHOqaavGu1xMWRGE04pnL3l2CkuUDuv928Nj9QDraw2H125Hw5chB49+7Bx40ZcXV0JDg4uU3fhB4EsCZrVEIztBt0fUo/ZbIFjV1Xjwr/3Kly5XTzflRCCjz76iK+++gqAL7/8kmeffbbYLBIKg+xk586NaD5+6luS4vIW8EuSoE5liUEBGl5/QsPA9jK1KwmEgOt3FNb+a2XGcjNLdpo5U8x6H2dnZ/755x8GDx6MyWTiiSeeYN68ecW2/3I4JsoJjz0QAhQrSkrSXePAbNVXwkMlNso9qaestFZuaSvJ1VvtmWVKtVV83TesJKN1U1NfGXFRBU5To9PbStSTY+4nWflBp9fj4upKamoqM2fO5I8//uDSpUs51slKb82dO5f169czfvx4OnfuDIDFYkGSJBRFISEmHL1WxmyxEh5VvKktb1ctVTNL1S+FpxGdUDqkp6avTBN/GQGEx1k5csWc7wW4SpUq7Nq1izZt2hATE0OXLl3YsWNHsc7Lw1nQramgmndmiivsv5HikoSa3hr0CFT3UdNax67Bin+hdpOO7Ny5kwoVKnDo0CE6dOjA9evXy3rKRYJGFjSpLhjZCQY/CrUrqsuvRsLKffD7djh2pXh8fKZMmcKcOXOQJIk5c+YwdOjQMnMXVhSF6UO+ZueS3Xz93M92baPXCprXlhgVpAqdu7eSqOgJFiucCVNYstPCjOVm1uyzEBZZPHofvV7PkiVLGDduHFarlbFjx/LNN9888H7L4bgoJzx2QGT2q7ImxSFcPQA1wpP1o7sbyclJbLKWK3H3Ex4hy0huqtDYkksEKAvazH1kxEXa9SN3zozypCbEYDEXjgx4enpiNBr54vPPefvtt6lTp47tPYvFgizLnDhxgg8//JBBgwYxYMAADAY14iLLagRk2rRpjH/uOd5960USExOITUwjKaV4L7z+Pjp8s0TMt9KIK+HKrSxU9pJpUVODLEF0ksLBS+Z8yYW3tzdbt26lS5cuJCUl0atXL1avXl2sc9LKgjZ1BK1qqX49EXGw5YRCVAnqIIoLLgYIagbdmoGzXu1ZtfEoxOtasnlbCP7+/pw9e5aAgADOnz9f1tMtMoQQVPMRPNZWjfq0rKWaF8Ykwbbj8Otm1cAwIeXBvrOxY8eyfPlydDodf/31V7FbJNgLIQQv/fQc9VvXZsKMUYXe3s1J0L6xzMR+Wib109C+sVrllWqCg+etzNloYeZKM9uPWoh+wGpFWZaZPXs2r732GqASx2nTpjl8pLQcRUM54bEDUmbUREmMQ2QaAmI2QZpaYXU3whOV44diIzwp8TkEzVmQ8yBK2aF1rwCShDU9BUu29hV5rm90RmtwAkUhJbbgqFCObbVa3N3d8fX1pVXLlrZj2b9/v43QTJw4kcaNGzN69GgqV6583z769evHSy+9RFpqCo/3DOTIof3cjEzMNwVUWKgNRvV4uWpQFDh7I5XE1NIJ4Vdwk3i4tgatDAmpCvsvZuTri+Pq6sq6desYMGAA6enpDBo0iAULFhTrnIQQ1PqPprhA9esZ9IiqcRHApdtwIrYB8/8KpV69eoSFhREYGMjRo0fLeqoPDA9nQaemgue6q67N7s6qP9HBi2pl15oDCjeji/69DRw4kHXr1uHs7MzmzZsJCgoqVosEe1GrWXW+3/cpvtV8bMsyTIWPxvp5Crq3kpkySMOobjLNawl0GohNguDjVr5bZWb2ejP7z1pIKaIHkhCCL774gk8++QSA6dOnM3ny5P+URUI57EM54bEDWeaC1qR4hKy5W4qeWXIuXDxB1oA5AyVbCbnQGWz9t3JNa2XqeCz56HiErEGbuZ4pumCHUCGETcuTGnd/hVhB8PD0REgSJpOJ5KQkbt68yaOPPsrIkSMZN24cSUlJTJw4kRYtWuS6fdu2bRkwYACrVq1ixMgRzPnlOxKSkrgdU7x9bIQQ1KtswN1ZxmqFM9dTSUkvHdLj7iTRpq4Wo04lF/svZpCQkvfnbDAYWL58OaNHj8ZisTB69Gi+++67Yp9XVorLP1uKa/d/IMUFasSjXT1V3+PtqpZ3X06sxv++D6Fps4eIjIykU6dOhIaGlvVUiwU6reraPLYrDGiLrfLuwi1YGgp/BsPp60Ura8+qPPL09GTv3r1l5i6cXf93et95xjZ4iXMHL+WzRd6QJEHtyhIDM/U+gwJk6lRW9T43ohTW7bcy4y8zi3eYOXXNSkYhPzchBFOnTuXHH39ECMGsWbMYOXLkf8oioRwFo5zw2IGsCI+tND3T88aaqasRkoTITE/dp+PJivLkkraSXDxB0oDZdJ9xYXbovNXkvykuMs+Go9lhcPVA0mixWsykJhSud4wsy3h4eAAQExND5cqVOXPmDKdPn2bevHl07NiRoKAg9ZhyIWkWi8W2PKhbN86eOkFE+C2i4lJISSvei4ckCRpUNeJikDBbFE6HpZJWwh49WXDWC9rU0eKa2WX9wKX8DQo1Gg1z587lpZdeAtSOzh988EGxR2C0sqBtHbUPV1ZLiv9KigvUEvbHHoZH6qqVTemyL89+uJOmLQOIj4+ne/fubNiwoaynWWwQQlCromBwe8HIztCkOsiSKubeeBh+2wJ7zxXeXfuRRx4hODiYihUrcuLECQIDAwtsBFyS+OODZURcvcPSzx+8YaxOK2hWS2JkNw2vDtbQ42GJSl6q3ufsdYVlware55+9ai+vwlhYTJw4kT///BONRsOiRYsYOHDgf84ioRx5o5zw2AHJJbN9RGb0RvJQS8yV+Lspo9wqtdTleQuXhSTZytXz0/FoXDwRWj1YzGTEF5ymEkLYtDzJMfYbEWbB3d0dWZYxm80kJCRQt25dDh06xPPPP8/333/P/PnzycjIQAiBoiiYzXc1NLJ8t2T7yJEjKIqV+nVqAXAzMqHY/XNkSdDQ3wmjTsJkVjgdloLJXDqkR68VtK6jwctFNVMryKBQkiS++eYbPvzwQwDef/99Xn755WIPnQshqOV3f4rr3H8kxSVJ0KQaDH5ETXcZXdwZ/+EmmrbpRWpqKv3792fp0qVlPc1ih4+boPtDarqrfUNwNqg+NXvPwuzNsPGwQmSc/d9f06ZN2b17NzVr1uTSpUsEBARw6tSpEjyCvDFt2asMeqUvr89/oVj362oUPNpIZkJfLc/31xDQWMLNSW1ieuiC2str5koz245Y7HYnHzZsGKtWrcJgMLB27Vp69epFQkLBcoJyOD4civDMmjWLGjVqYDAYaNu2Lfv37893/eXLl9OgQQMMBgNNmza9r6/M008/jRAixyvLPbgwyNLtWDNL0bNIijVb5VR2HU92SB53dTq53WyydDyWfHQ8Qgh0XpUAMMXY595rdPdGSBIWUxqm5ML9WCVJwtNTrUaLjYmx3ZC///57FixYwIoVK4iLi7PNTaPRYDKZuHr1Kjt37uSbb75h4MCBTJs2jenTp1Ormi+yJEgzmYmIKt7UFoBWI2hUzYheK0gzKZwJSy01y3qNLGhZ036DQiEE06ZNs6W0vvvuO8aMGZODNBYX7k1xHf8PpbhAFTV3awb9H4aalZ2Y9MEqWnceitlsZtiwYcz66deynmKJwKgXtK0neCYIerfCVq10+rraoX1pqMKFW4pdDw+1atUiNDSUJk2acOvWLTp06FDgdbUk4ORqZMJXozE63/XOunGheNNsvh6CoFaqq/PT3WVa1BbotRCXBLtOWPl+lZlf15v596yF5AL0Pn369GHz5s24ubkRHBxM586d/5MWCeXICYchPEuXLmXKlCm89957HD58mObNm9OjRw8iI3OPfOzZs4dhw4Yxbtw4jhw5woABAxgwYMB9/VF69uxJeHi47VWUvjNSNg0PgMiM8FjjcyE8cTnnK1y9QJIhIx0lF9HxXT+e/PU2WWktc0I01oyCq54kWcaYOc/ClqgDuLq5odVqsVqtNnIDMGLECNasWYOPjw8nTpxg+PDh9O7dm7p169K5c2fGjh3LvHnzqFGjBitXruS5555DI0tU9VM/w+j4FOIS7fcIshd6rUSjak5oZUFyupWzN1KxFKNQOj9IkqBpNZlqFdSf0/lbFs7dMud7Q5o8eTILFixAlmUWLFhgM9wrbuSV4oqM/2+QHlDNCXu3gP5tdEz9dCEd+05AURRemDSeyW9+TjEXAToMZEnQoKrgqQ6CoYFQv4pa0n8zGtYcUF2c959XSCygGWflypUJDg6mbdu2NouEbdu2ldJR5I5tf4YwruFLrPqh+NOTkhDUrCgxoL2G155QW1rUraKe/zejFNbvV/19/txu5shFa57kJzAwkB07duDj48Phw4cJDAz8z1oklEOFwxCer7/+mmeffZYxY8bQqFEjfv75Z5ycnJg7d26u68+cOZOePXvy+uuv07BhQz766CNatmzJDz/8kGM9vV5PxYoVba+syEVhILlmaXji1P9t0Zxom6bGVpGVHI9iunvjEpJs0/dY427ft2/h7A5aPVjNub6fBVnvhJwpljZF3bJr3s6e6pxMKUl2t5uwzUsIPL1UD6D4uLgc4j29Xg/A2rVrWbx4MZGRkSxdupSDBw9y5MgRjh8/ztdff02/fv1s27g5G/DJ7Kh+43Y8SSmmQs3HHhh1Eg2rGZElSEixcOpaChmllN7KzaDw0CUzaflEU0aOHMnff/+NXq9n9erV9O7dm8TE4vUtyprbvSmu4DMKBy5Z/zPRHoDKXjCgjczvc39k4OipAPzwxVsMGv0mO08pFLPlk0Ohspegz8OCcUHQpi4YdGqX9tAzarpr+W6Fk9fyjt55eXmxdetWunbtSnJyMr1792bVqlWlexDZcPHIFaxWhZvFHOW5FzqN2tJiRFdV79OztURlb7VH3vkbCqv2WPhymZnfNpgJOWEhMi5n2rdly5aEhKgWCefOnaN9+/b/aYuE/9/hEITHZDJx6NAhunXrZlsmSRLdunWzNae8F3v37s2xPpCjmWUWdu7cia+vL/Xr12fixIlER9/fzDML6enpJCQk5HgBLF2/OXOFVKypyaoXj0YHFjNKvCoKFnrj3eqte9JOUmY6yhp9P1ERQqCp4A+AOfJannMD0Ge2rUiPumGXeFnW6jB6qGQr8c6tQus3nJ2dMRqNKIrCncj7fYCmTp3KmjVrOHHiBHPmzMHLywt3d/c8XV79vF1wc9ajANfC44pdxAzgYpBpVM0JjSxISrNy4loKaabSIz01fWWaVZeRJYhNVth7PoOofMTM/fv3Z+PGjbi4uLBjxw66du2a7zn6IMhKcdVSg4pcvQMbjylcvfPf0PaA6gFa3Ufw17xPeOvdLwHYuPQL3n1jPH/vtbD2EFyJhFIK7pU6XI2CgEaCZ7urLs5V1J8316Ng81H4ZROsO6hw+bZynxWEi4sL69at4/HHH8dkMjFo0CB+//33Uj8GgOe+HMl7K15j4jdPl9qYLkZBu4Yy4/toeKG/hk7NVLGzgursvPWIlVn/mJm50syGAxYuh1uxWBXq169PaGgo9evX5/r16wQEBHDkyJFSm3c5ig8OQXiioqKwWCz4+fnlWO7n55dnx+mIiIgC1+/ZsycLFixg27ZtfP755wQHB9OrV688b8iffvop7u7utpe/v0pEJr4wmR/+PQuANfYOQkhIXurY1myl4sJbJTZKTM6nFslL9auxxoTnemPR+FYHwBITjmLOO/Kh9fBBaPUo5gxMMXlHg7LDxbsSCEFGajLpSYXT8gghqODjgxCCtLS0XKMPffr04fLly+zfv586depw6tQpm2dPbvvz93PH2ajDqihcvRVLmqn4tSuuRpmm1Z1smp4TV1NITis9q/2KHjKP1FMruDLMcPiymYvheae4OnXqxI4dO/D29ubAgQN06NCBmzdvlsjctLKgVS3J1nldrTBT2HZS4Xbcf4v4fPrBa8yePRtJkghZP5vZnz7FjTsmtp2AZXvg+DXV4+b/IrSZLs5DAtSoT/sG4OmitrA4dxNW7VMNDXecyPm96vV6li1bxtNPP43VauXpp59m5syZpT5/IQQBj7dFktRbkKIobP59JxZz6fxOfTwEnR9Sxc5TBmno21aibhWBRlI9fvadsfL7FgtfLDWzfJeZOHNVNm0JpkWLFty5c4dOnTrZmiaX478DhyA8JYWhQ4fSv39/mjZtyoABA1i7di0HDhxg586dua4/depU4uPjba/s+dppK7fx0aZ9WKJVQiVlamqyR3PuRnLuITwevqqOJz0l1/JzycUD4eQOihXznbxzxEJI6H1VEpZ+J8yum5Os1doqtpKKEOXRarW21FZMdHSu4toqVapw7NgxOnTowBdffEF8/N1jPHToEGvXrrX9L0mC6pXcMeo1WKwq6TFlFP9FzqiXaFrdydZh/eS1FOJLyZEZMsvW62qo6q3+xC5H5p/ievjhhwkJCaFKlSqcPn2agIAALl68WGLz83YVBDUVNPUXmdEo2HVWIfjMf6eEHeCZZ55h6dKlaLVaDu5cxoJPHwNLCklpsP8iLN4Nu89CPi2d/vNwdxK0rS94ugs81UFtUGrUqRVeRy6rnj6/b4d/z6tuzhqNhjlz5vDKK68A8PLLL/P++++XKdmd+85ivhwzi4+HfVPq83B3FrSuLzOiq4Y3hmgY2kkVPDsbIC0DTl5VWBFqYf5OL8b9bwsPPRxIQkIC3bt3v69QphyODYcgPBUqVECWZW7fzhm1uH37NhUrVsx1m4oVKxZqfVArFipUqJDnjUSv1+Pm5pbjBfDee+8B8PWOQ0ye9iFWqxXJK5PwRGcjPN65R3KErFFJD2CNyV1/Y4vyRIblOX8AvXdlkGSsaSmY7fTYcfbyRUgyZlMaqfGF8+UBtUxdr9djtVq5fft2nhekefPmMWPGDNzd1dTe1atX6dmzJwMGDGDhwoW29WRJokZlT/RamQyzlSu3YjGXgNZGp5VoUt0JNyc5s8ollahS6L2VBVkSNKqqoWm1nCmuvPx6GjZsyO7du6lTpw5Xr14lICCA48ePl9j8JEnQoIqgdwtB3YqqIPZOAuw4pRB61kpc8n+D+AwePJi1a9fi5OTE3l0bmfdBd5pVisPTWY14nLkJf+1T21bciFZ7d/1fhBCCip6Czk0Fz/VQDQ3rV1F9fWKSYPcZ1ddnWajCqeuCTz6bYeuy/sEHH/DSSy+Vmbtww0fqojNoebhHizLp9J4FvVbQsFqm4Hmwhmd6yQQ2kfD1UNOkkUlu9Bq/lrrNe5OWlkb//o/x/S+LitVJvhwlB4cgPDqdjlatWuWoHLBarWzbto127drluk27du3uqzTYsmVLnusD3Lhxg+joaCpVqlSo+U2ZMoXv33oZIWD26g2MGDECS5YQOVtqSXj4ZEZyUu+L5NjSWtG5pyo0Pv6AwJoUgzWfFhJC1qikBzXKYw8kWYOLt5qCS4oKL7T7shACXz8/JEkiPS0tX42Jj89dK/mqVavaUogjR47MISjXyBI1q3ii1UiYMixcCY/FUgIXW40saORvxDuzDcX5m2mExxS/YDo/VPLMmeI6dNnMxQhzrsSxevXqhIaG0rx5c27fvk3Hjh3Zs2dPic7PoBU8VEOi10OCmr5kNkhVq7n2nrcWWAXkCOjevTtbt27Fw8ODPXt289xTnWhf4/+1d97hUVT7H35ntrf0hCR0pF0pImJFrFTxh1KuIBZs4LXXe722a1fsBTsWEBUEVFREqspVROQKihTpnfRsku1l5vz+mOwmgRCSQAo47/PMs5vZKeeczM589ttOHhecqFUxBk3szP8NPv0ZNuyBBjAsNhsMslbQcGgfiX8M1uJ9WlUah0W/wVsLJE4YeB8PPzkJ0MpOXHXVVU1SXfiMYSczZdMkLrju/EY/98GQZYnW6TL9exu4aZiJ24ZrQc9d2tgZc9ssup8+BkWJcusNlzNywut89mOUP3fp01E0a0QzYcaMGcJisYgpU6aI9evXiwkTJoikpCSRm5srhBDiiiuuEP/+97/j2y9btkwYjUbx3HPPiQ0bNoiHHnpImEwm8ccffwghhPB4POLuu+8Wy5cvF9u3bxeLFy8WvXv3Fp06dRLBYLBWbSotLRWAcOfmiPCWteLdSwcKo0EWgLhg8CCR/8o/hffN+4Qa9Mf3CS6aJgKfPCuiO9ZXOZZSWiAC898RgYXvCzUaqfZ8wXXLhO/H2SK0/Y8a26WE/MK96lvhXrVERH1lteqLqigib/MfImfDKuEtzK3VPvvj9XrF1i1bxNYtW4TH46nVPoqiiFtuuUWgxQaKRx99VKiqGv88GIqIddvyxJrNuWLr7iKhKGoNR6s/qqqKrTkBsWx9mVi2vkzsyAtWaUdjEFVUsW53RCz4LSQW/BYSv2wOi2C4+ja43W7Rt29fAQi73S7mz5/faO0s86ti+SZFzFxesfyyRRHeYOOOV334/fffRYsWLQQgOnbsKLZv3y6EEKLEJ8RPfwox5TshJi/Wlg++F2LFZiE8gSZtcqNS6lPFio2qmLJEFc/PqViu+uc0IRsMAhDDhg0TgUDTDkrQHxQvjH9DFOwpbNJ2HIxASBW/b4mIQRf/I35vO2/UY+JfbxQIQJSWljZ1E3WqodkIHiGEmDRpkmjTpo0wm83ilFNOET///HP8s7PPPluMGzeuyvYzZ84UnTt3FmazWXTr1k18/fXX8c/8fr8YOHCgSE9PFyaTSbRt21aMHz8+LqBqQ0zw7PnkHREtzhfuiTeJmddeJGw2mwBE3y7txd4X7xTRfdvj+4RXLRGBT54V4VVLqhxLVVUR+H66CMx/R0TzdlR7vkjBHuH7cbbw/zL3kA9j77Y/hHvVEuHdsa7W/fG5C0XOhlUid+PvQjmI6DoUhYWFYuuWLWLb1q0iFArVah9VVcXDDz8cvzHccccdQlGU+Of+QFis3aKJnu373A0mRFRVFbsLgnHRs2mvXyiNLHqEEGJfcVQsXqOJnu/WhkReiVLtdj6fTwwePFgAwmQyiZkzZzZqO91eVfz4Z4XomfWzIlZtU0Qg1LyFz+bNm0W7du0EIFq2bCnWrav4joQiQvyxU4gZyyqEzztLhFi8RogctxBNcDk0Caqqily3Kr5bo4o3vtFEz7X3fymMZqsARO9TzhG7ckqarH0vjH9D9JdGiQm97qpyr2huqKoq7rv//vi97eyhN+mCpxkjCXGserQPn7KyMhITE9nx1tO0ue4uSp+/E1SFNb0uYNjosZSWlnJC6xZ8Pe1dWp49FABl5wYiK75GSsnC0v+yKseLbFiOsms9hpadMHU/64DzCVUhsHIeRMNYjj8TQ3KLA7aJEfWV4d30P5AkErqdgWyyHLI/QgiKtv9JNBzEkdoCV/qBs53X5hg5+/YRDAYxmUy0bNUqnmlxKF5++WVuv/12QKuCPXnyZIxGIwDeQJgd+9wIAUkuK60yEhrMl59fEmFLjlYrKclhoEsrGwa5ceMGfCHB7zuieMuLnmUkSnRtacRqqtqOcDjMlVdeySeffIIsy7z11ltcd911jdrWIo9g7W5Bfrmn1SBDx0zomi1hNjZdvEVN7N27l4EDB7J+/XpSU1OZP38+ffr0iX+uCi2Ve+1uyKk0mXiaS5u1vUMLrZ9/BVRVsLNAq+S8YNFS3n5sGKGAh9Yd+/Dgi/M4tXsanVtywLXZkOTuyOc/Fz3NzZOupedZxzfaeevLCy+8wF133RX/u7S0NB4DqtN8+It8pQ8P1V2IJBvic2id3qUD33//PenJify+O49zr5jArl1aPE08Nb0kH6FUzQqS44HJuxHiQF+vJBswpmm1dg5Vk8foSNAKEQpBqGBPrfohSRLOcpHjK84nGq57idpYPI/BYCASiVBQUPu5um677TamTp2KwWBgypQpXHLJJYRCWhucNjNtMpMAKPEEySn0Nli2RkaSia6tbMgSlPgat0BhDIdF4tRORtqly0hAfqlg2Z8RdhUoVdLXzWYzH330ERMmTEBVVcaPH8+zzz7bqG1NdUmcfbzM2X+TSHFq0xxs3Adfrxas3yOIRJvfb6aWLVvy3//+l5NPPpmioiLOPfdcvvvuu/jnsgRt02Fobxh+CnTO1gROoQeWrocZy2D1do7ZKs6VkWWJ9i20eJ/n7j6bD2Z9iyspjd1b/seDN5zN7CV7eGs+fLVSsCWncf7fme0yeGPVM1XEzsHKiTQH7rzzTt59913MZnNTN0WnBnTBUwsUtzaFhFye3q0W5dGrVy++nzmN1ikJbN69j759+/Lnn39qxQctNlAVhLtqFpmcnKkVLIwEUd3V19ExZLTTzlm8F3GIKSQsGW0ACBfuRY3WLtDQ4kzAbHeCEJTm7KyXqDAajfEaSD6vlxK3+xB7VHDllVfy6aefYjab+fzzzxk6dCher1YFOsFhoVVGxRQUOYWeBhM9KS4j3dpWFCj8fbsfj79xb6gGWaJztpHTOhtJtGsTkP65T+GnjRFy3RWzzhsMBt58803uueceAP71r39x3333NXr6bkaixHndJPp2kUi0axlQ6/YI5q4SrN7e/IKbU1NTWbJkCeeddx5er5chQ4bw5ZdfHridC876G4zpCyd1ALtZq0b96zaY/iN8sxo250Ck8aoaNBlmk8QlQ/vwy/IfaNmyFXl7NvD6/f3I3bOZzfvgy1/gjfkwZ4VgzQ4Rt1A2BJXreRXluLmh97/438LfG+x8h8s111zD77833/bp6IKnVgi/D9XvxZCuWW+UAi21vOvJZ7Do7ivpnJnKnj176NevH6tXr0ZO12rlKDnbqxxHkmUMLdoBoO7bXO25ZGcSkiMJVJVo7rYa22VKTEO2OhBKlNAh0tnjbZAkEjLbIMkGIgEfnvz6Fbiz2mykpmkWL7fbXaX2zqG46KKL+Oabb3A6nSxZsoT+/ftTXKylyycn2MhOdwFQVBpgV25pg6V8umwGure1YTVLhKNarZ69ReFGFxIum8wpHY38raUBk0GzKqzZpfDzpigFZSpCCCRJYuLEiTz99NOAViTzxhtvbPRfvZIkkZ2s1fA5taNWvDCqwpY8rWrzj3+qzaqAocvl4uuvv+biiy8mFAoxYsQIpk2bVu22NjOc2B5G94VzumlzeAlgb7Fm9fnoR/h+nZbldKxnIXft2pWfflpGp06dKMrbybsPn0Vi9Hft/63AtlxY/Du8vQA++l6wdJ1W3TncQFOVzHjqc7b/sYs37ni/0YoT1ofs7LqHCeg0HnoMTw3EYng23XIJbe96Avyl+L98H0N2O1xX3I0QAv8HT1JQUMDIaUtYtWYtCQkJzJk8idPJR0pIxTL46irHVN25hH/5GgxGLOeMRTKaDjhvNH8X4c0rwWTB1mcIklx95WKASGkhvm1rQJJJOP50ZPOhY3kAgp4SSvZqgiwxux22hLrPMQZQXFwct/BkZGTgdLlqve8vv/zCkCFDKC4uplu3bixcuDB+wyjxBNmTV4oAHDYTbTOTMDRQUIWiCLbmBiks037CJzsNdMi0YjE1/u+BqCLYWaiyM18h5mVLtEt0yjKQ4tTaM3nyZK6//nqEEIwZM4apU6c2mSldCEF+KWzOFeSUVKxPsEGnTIk2aVppgKYmGo1y3XXXxadSeOWVV7jlllsOuV+ZH7bkaktZoGK93QzHZWqxTKm1v+SPOvLz8xk0aBC//fYbiYmJzJ07ly49+rI1VxM9uSVVt5ckaJEIrdO0NPiWqRyROK9IOMIbd0zl73f/H1ntDx7b2NTEnhl6DE/zRBc8NVBZ8LS68mbM7TrhefdxMJlJvOM5JEkm+M0HKLs2Eux5DiPvfYKlS5ficDhY/9LdZCQ4MQ+5FtmliYnYUId/mIUIeDB1PwtDy04HnFeoKsFf5yPCAcwdT8JYbhWqDiEE3s2rUHylmFOzsLf5W63758nfi684H0mWSW3bBaPFWrcBKj9/UWFhfN6xzMxM7A5Hrfdfv349AwYMYN++fbRv355FixZx3HHHAeD1h9mZU4IqBFazkXbZSZiMBxd/h4MQgrySCNvzQgihxXO0SbeQmWxqkkJokahge77CrkI1bk1IcWrCJ9EuM3PmTC6//HIikQhDhgxh9uzZ2O32Rm9nZTwBwZY8wfZ8Lc4HwGyEDhlwXAsJu6VphY+qqtx5553xqRQeeeQRHnzwwVr9f4XQijJuzoFteRCq5N5KdmrCp2MLcNT9K9TsKS0t5cILL+THH3/EZrPx2WefMXjwYAC8QcHuAi0AfHchlPqr7itL0CKpkgBKAdMRCnQv2FNEeqvUI3KsI4UueJo3uuCpgcqCJ3PISJyDRlL6wl2gRHFNeAhDcjrhVd8RWbkYQ8eeiDOGMXr0aIYNG8aVHRNQ83dhPOFsjF1Ojh9TCEHh6v/iKtiCnJKF+eQLqj13ZM9GIjvXItkTsPbqX+NNOeotwbt5FQDOzn0wOmr3RRNC4N69hbDfi9FsJaVdZ+QarEk1HacgPx+v14skSWRlZWG12Wq9//bt2xkwYABbt24lMzOThQsX0qNHDwACoQg79pUQVVRMRpn22clYzMY6t7G2+IIKW3OCeIPaE9tplTkuy4rD2jBC61CEIoJteQp7itV4heD0BImOmQaWLV3IiBEjCAQCnHnmmXz11VckJSU1STsrE4kKthdoVp9Y0K8EtErVrD4pTpqsmq4QgsceeyxePf22227jhRdeqHWmIWhibk+RJn52FVZ1b2UnQ8csaJeuib1jBb/fz6hRo/jmm28wmUxMmzaN0aNHH7BdmV+wpxB2F2kCqKwaAZSZrAmg1mmQlVw/AbTxf1v51/mPMOymwVzzxKVNWp25Mrrgad7ogqcGKguetJP7knzFrXimPI2Stxv78PGYO5+AsmcLwa/fR3ImYb/sn/F4i+jmVURXf4uU1hLLeZeiqipvvPEGs2bNQkaQLgV5/1/XYTtnDJLtQJu4iIYJrPwG1OghU9QBfDvWEXHnIVsduLqcjFTLG7gSjVC040/UaBRrQjKJWW3rdfMQQpCXm4vf70eWZbKys7FYaudeA8jJyWHQoEH88ccfJCcnM2/ePE477TQArRJz+ZxbBlmiXXYSdmvDuXBi1p6d+aG4pSI7xUTrdEujp6/HCIQFW3MV9rkrsskyk2QKtv3MyOEXUlpaSq9evViwYAEZGRlN0sb9EUKwzw2bcwQFleadTXFApyyJVilahlBT8Morr3DbbbcBMG7cON555514iYS6EIpos7Nv2c+9Y5A10dMxE7JTjo0U93A4zLhx45gxYwaSJPHmm28yYcKEGvcpjQmg8sUTqPq5QdYEUKvUcgGUok2Meig+f2Uer9/+Piec042n5t+PyXxgaEBToAue5o0ueGqgsuBJbnccaXc+if/raYTXrsDa9wKsZ16AiITwT3kcVBXbpXchJ2gTbQpfGaGv3wZJwvJ//2D4mMvYvHkzPXr0YPjw4bzx7BOUlZbyzfQptOhzbrXnD2/7nWjOFuSkFli7nVljW9VoGM+GFYhoBGtWe6yZ7Wvdz7DfS/EuLYja1aIVjuT0Q+xxkDaoKrk5OQSDQQwGA9ktW2Iy1f5G5Ha7GTp0KMuXL8fhcDBnzhz69+8PQFRR2bHPTSAURZKgTWYSCY7aC6r6EI6obM8LUeTR/Bdmo0SHTCsprqb76e4LCrbkKuSVasJHAspy/+DGKy8gPz+fzp07s2jRItq0adNkbayOEp9gc66oYhGxmqBjpkSHDG0Oo8Zm2rRpXH311SiKwsUXX8z06dOxWuvvk/IEKuJ9Krt2TAbtgd6m3KphbR7P5nqhKAo333wzb775JgATJ06MZw8eCiEEpX6qCCBvsOo2Blmz+sRcYFnJB48BWzprOX0GnYAjoWlduZXRBU/zRhc8NVBZ8LicDlo8/g6hX5cS/PZTTJ1PwDF8PACBL95Gzd2J+ezhmLpWFDcLLfwAUZLPA4vX8faMT5kxYwYXXKC5sP78YRGjrryat++9idPH/7taq4oa9BH8dT4A1l79kR2JNbY3XJyLf+d6kCRcXU/BYK19LI2vOL88Y0sipW0nzLba71sZRVHI2bePcDiM0Wgku2XLOv1y9vl8jBgxgoULF2I2m5k+fTojRozQjq2q7MotxevX5sLKTHWSlmRvcHO22xtlW26QUHkGSqrLSPsWFsxNENQco8yvsiVXic9svm/XZu6/cSj79uykVatWLFq0iK5duzZZ+w5GMCLYlgdb8wTB8koKsgRt0zSrT6K9cYXPl19+Ga8Hdd555zFnzhxcdQi8rw4htHo+m3M060+g0tRtElpMS5s0bUmq39esSRFCcP/99/PUU08BWpmEiRMn1vl7KISgxKcJnz1FsLsAfPtV4jDImoWssgA6mJX1l29W0/Ps47HaG/aHUE3ogqd5owueGogLntvH4DIaSL/vJdSyInwzJiEnpZFw/cMAhFcuJrLqOwwdT8B6/iXx/aPrfmLxJx9wweNv8/7773PllVfGbwrRUBCbw8kH905g9J3/0Wr0VEPoz59RivZiSG+DpfPJ1W4TQwiBb9saomVFGByJODv1rvVNSAhByb4dhDwlyEYTqe26YKgmg6w2RKNR9u3bRzQSwWQykZWdXSfREwqFuPzyy5k9ezayLDN58mSuueaaeDv35pfh9mg/DZ12M60yEhosmDmGogp2F4TYV6w9pQ0ytErTgpqbys0F4PapbMlRcPsEBXl7uO8fF7Br+0bS0tKYP38+J510UpO1rSZUVbC7SIvzcfsq1qcnQLt0zd3VWNld3333HcOGDcPr9XLyySfzzTffkJp6ZIJhY+JnZ4EW71Psrfp5gk2z/mQlQ1YSNKCn9ojz7LPP8q9//QuACRMm8Prrr1epnVNXKgug2LJ/4UejQYuTyqy0OK0SS2ct54kxL9L9zK48+c39TSZ6dMHTvDkGPMsNj6G84GA0by+G9JYAqCWFqEHNbm1oqWUVKXs2V5mJXMruyJ3vz2H0mSfy94uHVREf77w/hQ6tW9KpZQuiuzYc9NzGlp21YxfsQikrrLGdkiRhb90FZAOKr5RgTs11fPbfNzGzDQazBTUawb17K6pSv0prRqORrKyseDXmfXv31mkGZovFwowZM7j22mtRVZVrr72WF154Id7OlhkJtEx3IUlaJtemnUUUlvgbtPaLQZZo18LKCe3tOK0yigo780Os3uoj1x2uUh25MUl2yPQ5zkjvDkaOa9ea5977lk7H96awsJCzzzmXr+d/12xq4lRGliXapkuc313i3G6awAEtE2rlVsGXvwpWbFHJLRENPrbnnnsu3377LampqaxcuZKzzjqLvXvrV59qfyRJE3F9joMRp8LoM+CMzlq2kixpqe7r98CSP+DDH+CzFbB8I+woIG4Ba67885//ZPLkyUiSxNtvv83YsWMJh8OH3vEgSJJEslOiZzut6vP1g2DceXB+T60Sts2s1QDaVQi/bNaKIL69AN5eINjgTsLitJHSviXU84eazrGPbuGpgZha3/n2M5g3/Y5r6Bgc51xI2duPoLoLcIy8HlPHHghVwf/BUxAKYB02HkNWOwB+/fVXrhp1MY+PGcTQa2/GcNwJSJLEH3/8wSOPPIIaDvL6uEEkuRxY+l2CZHNW247Q5l9R8ncg2VxYe51fY10eqOTaAhwdemJKTKt1n6PhEMU7N6EqUUw2BymtO9Y6AHp/IuEwOTk5RKNRDAYDmVlZdQpkFkJwzz33xKdSuP/++3nsscfiwjEYirK3oAx/+ZPBZjHSMj0BWwMHSWi1Z6LsLggRLi+zbzFJtEm3kJZgbNIMpIIywfrtbm4bP5I1/1uK2WLliZenM/aS/yMzUW6yIOHa4A8JdhTAzkJRJbbDbNR+1WclS2QmNpzlZ/369QwcOJC9e/fSrl07Fi1aRMeOHRvkXADhqFbUMMetLZUtXTFSneXWn2TITAJLM3yWz5o1i8suu4xIJMLgwYP59NNPG6REghCCIg/sLdICxHPdUFQpGN6Xk4s9IwPJIJPirGQFSoK0hMaxGOoWnuaNLnhqIHbx7v3iY+Qfvsbauy9Jl96Af8F0wr8tw9LnXGznjwQg9O0sopt/w9izL5bTtTidVatWcWbfM1j44PWcfMrJWPpfTm5uLs899xzffPMNzz//POelqihF+zC274mpyynVtkNEwwRWLYRICGOrrpjbdjtk2/17NhEu2INkMOLscjIGS+3TxCNBP8W7tiBUBYsjgaRWHer9EI9Go+Tm5BAOh5EkifT09DoVJxRCMHHiRO677z4AbrzxRiZNmhRPIxZCUFwWILfIG6/InJZkJyPFgaGeQq22qKqWzbWnMExE0c5ts8i0STOT4mo64QOQX+xj9OgxfL94LrLBwD8ffZfBF11K6zQDrVLkJgkSri1CCIq9mvDZXaQJgxiyBBkJmvjJTuaI1/bZsWMHAwYMYMuWLbRo0YKFCxfSs2fPI3qOgxEIV4ifHDeU+A/cJtUVE3/ag7y5pL4vWLCAESNG4Pf76du3L3Pnzm2UEgnhqCC/pEIA5bo1q5kQgi0zPyWr7+k4W7XEIGuWtsoiKLkByiPogqd5owueGohdvHkr/os66y2Mma1Ju+spwhtW4f/yPeT0liRccy8A0W3rCC36GCkhBduYO5EkiV27djFy+HDGn9qRsf16Yxo4jpv//SC///47I0eO5J577kEp2EVk1SIUyYD93Etxe3ysXr2azz//HCEEl156KWeeeSbRwj2EN64AScJ6wvmHDGAWqqoVJPSXYbA5cXY+6ZCWocqE/V6Kd28BIQ4rXR207K283FwCAS0n1ZWQQGpqap1qn7z55pvceOONCCEYO3YsU6ZMqZIBFokq5BR6KPVqTn+TUSY7PaHBM7lAi+/JKQ6ztygcT2N3WmXapFtIdBiaTPhEIhGuuuoaPv74QwBu+vdLDBtzg1YLJUmmbbqMy9a8vdqqKij0wD63luK+f1Brkl17+GcnSyQ7jswDLDc3l0GDBrFmzRqSkpKYN28ep59++mEft674Q+Xip0R73b+on4QmgLT+a8HQTSmAfvrpJ4YOHUpJSQknnHACCxYsiM+515j4goLpz3/DJw++jy0lkfPffBnFdKDFyWLUxqyyCHLaDu/60QVP80YXPDUQu3iLd2wj9OqDIBto8cQ7iHCQskma0Em4+SlkhwsRCeOf+gQoUWyjbkFO1YKQ33vvPW664QZObJ/NxtxiMrKyuf7667n99tsBLasptOwz5EAZtOnGpfc9zaZNm3A4HPTo0YOPP/6YZ555hptvvpnwnz+jFO9DdiZj6XnuIW/uajiIZ+NKRDRS5yrMACFvKe49WhyQPSkNV4tW9X6gCCFwu93xaSjMZjMZLVrUaUqEGTNmcMUVVxCNRhk6dCgzZ848wHTu8YXYW1AWn/08wWEhO93V4EHNoE0Lsa8ozL7icDz1OsFuoE26mQR70zyJVFXl9ttvZ9KkSQBMuO1hRlxVkRWY7JBom24gLUFCbibF2w6GEAJPEPa5NQFU2Z0BWrp3zPXVIvHg2Ty1oaSkhKFDh/LTTz9ht9v5/PPPGThw4GH24PDwhapagMr2q2kjSZBWWQAlgqmRL7s1a9YwcOBA8vLy6NixI4sXL6Zt27aN2wigtLCM+y54ksFXn8uF/xhIqb/CApRbAnklFdXAK+O0aiIoq1wEtUiqW8kEXfA0b3TBUwOxi7ekpITgi/9G+Dyk3PII5jbHUfbeU6gFe7EPuwbz33oDEJw/DWXnn5hO7o+5d0VtnT9/WMiCKW9iczoZ+eAL8Uk3QRM8FO0lsnoRlz7+JpuLfdz/wINceumlAEyaNIkvv/ySzz77DIfZSHD1IlAimNr1xFTNtBT7Eykrxrf1NwBsbbpiSa3b5HaB0mJKc3YC4EjNxFU+gWp98fv95OfloapqvVxc8+bNY+TIkQSDQfr168dXX31FYmJVa5eqCvKLvRSU+wRkSaJFqpPURFujWFvCUZW9RWFy3ZF4deQkh4E2GRacTVCxWQjBI488wiOPPALATbfcwfg7nya/VBD78puNmtUnO1nGZZOaTeXamghFtPm79rkFeSXE5x4DLYuuRWK56ysJrOa698fn8zFq1Cjmz5+PyWTi448/ZtSoUUeq+YeNr1z8xaxA+xf1iwVMxzLAMpO0LKeGZsuWLfTv35+dO3fSsmVLFi1axN/+VrcfW0eCSDhSpSBhrCgsaFbZIk9VEVRUBtU9DGPxQDFrUHoN8UC64Gne6IKnBipfvMqstwj9+Tuui67AceYgAks+JfS/7zD36ot9kCZOIn/+j/DSz5HTsrGNvCl+HKFECX31JoSDmM4aybyVa9m5cyc33XQTkiQhhOCFu67ngdemMGXig1x847/iwb1z5szhkksuYevWrbRu3Zpo7nbCW1eBbNBq8xwk0LkywdwdWsaWJOPs3BujvW5fRJ+7AE/eHgAcKRk407OQpPq7QqLRKPl5eQSDWmSqy+UiJTW11imtP/74I0OHDqWsrIwTTzyRBQsWkJ5+YLHEQCjC3nwPgVBFUHNmqhOHzdwoD/RQRGVPYZi8kop0m1SXkcxkEwn2xnd1vfzyy3HL4tVXX80rr75FTqnEniKVSKUJqB0WyEwykOzU6uI0Zdp9bVFUQUFZhesrsF+yUIpDe2ClOCVSHLUXQOFwmCuuuIKZM2ciyzJvvfUW11133ZHvwBHAG9TET0wE7V/UTy4XQNo4QLJDqwPUEFWg9+7dy4ABA9iwYQOpqanMnz+fPn36HHrHBiIcivDwiGfpf1k/zhvbr9ptIlFBXmlVEbT/1BhQPo6JFXFASeVjmWAHn9ejC55mjC54aqCy4JF/+RbvgtlYuvchedztRLauxTf7TSRnEgk3PIoky4iAF/+0iSAEtr/fipxS4b+OrFqCsmU1AUcql7z4EWUeD9OmTaNz585s27aN8845m0vP7MWdfx9MSt//w5DRBiEE//nPf1i6dClffPFFPAgwtPYH1LICJKsTa4+zkcw1V4etXJ8H2YCjQw9MrpQ6jYW3KBdvQQ4ARrOVhMxWmO31L9C2v4tLkiQSExNJTEqqlfBZvXo1gwYNoqCggC5durBw4cJqqwtXF9RsMRlITbKT5LI2eGAzQCCssrsgFJ+NHbSqzWkJRtITTdgtcqOJn6lTp3LttdeiKAojRozg448/xmQ2U+QR7CtWKShTq8wNJUvabO3JTolkh0ySo/kLoFhF35jrq7rsJ5tZE0HJTin+8D/YrN6KonDjjTfy9ttvA1r9mbvvvrshu3BE8AQq3F/VxT+BZgVKsFUIoOTYw9sGh/vVKCws5IILLmDlypU4nU6++uorzjnnnMM7aD354rX5vHrLu9hdNj7Y+iqJabUTI/6QqBIQnVsCwYNk3ksSmEUZN1+cpAueZooueGogJni2T32TlucNpOjlB8FkpsXDb4AsU/ba/YigH8ffb8TU4XgAgoumo2xbi7HziVjOrTB/C18ZoQXvQzRCQXYPNvileEzA9OnTefHFF3nhzvH0SZbBaMJ82kX8tnEr//znP2nVqhVTp06NH2vbxg2oW/9HstWAPSUDa/ezkIw1x8IIJYp32xoUbwlIEvY2XTGn1M09FShz48nbE6/PY01IwZWRXe8ChQCBQICioiLCIe1uLMkySYmJJCYmIh9C+GzatIkBAwawa9cuWrduzaJFi+jSpUu120aiCvluHyVlwXhdF1mWSHbZSE20NeiEpDF8QYUcd4SiskiV+AGLSSLZaSTFaSTBbmjw1PE5c+YwevRowuEw559/PnPmzMHp1CyFEUWQX6JS6BG4fWqVDCnQbuqJdolkh0SKUybRLjVagcD6EggLctyU9+nA2JcYTqv2sE9xagHQyY4K14UQgnvvvZenn34agHvvvZcnnnjiqHD9gVYA0VNuASooA7dXS4Pf//8bwyBrAeHJ+wkhp1W7BmqLx+Phoosu4rvvvsNisTBz5kyGDRt2ZDpVB1RV5a27PuDUob3p3b/+WXcxMZ1Xoi0lvoolqkDQX8b9Y3XB01zRBU8NxATP+rvH0/WZtyh48nbUkiKSxt2OtXsf/ItnEf51KaauvXFcpFUCVvJ3E/z8TZAN2txazor4kuiW34iuWgwGI+ZBVyE7kwB45plneOWVV9i9ayfhld8gSvLY41N4cfFvfD5nDj/88APHHXccM2bMYNGiRcyaNYuTTuxF7q4dPHnTFQwZ0B9LtzORDiE8hKrg37mBSEk+AObUbGytOtUpe0tVongKcgiUaEUQJdmAKz0bW1LqYQU0+/1+3MXF8cJlsiyTmJSkCZ8afmru3r2bgQMH8ueff5Kens78+fPp3bv3QbdXVBV3WZCiUj/hSn4cl91MaqIdp73h3V2qKnB7oxSWRSn2RhH7WVSSnEaSnQaSHcYGm77i22+/ZdiwYfh8Pk499VS+/vrrA6oLC6HNdu72qRR7BW6vSmh/AQQk2KVykaBZgJq7AIooWkXfYi+4fVoKfHXWD6hs/dAsQW+9+jT33aslLPzjH//g1VdfPazqwk2JEOAPV4ifyq/RagJ6QZsXLKlcAKWUvyY5wG4+uBAKBoOMGTOGL774AoPBwPvvv88VV1zRcB2rJWVFHlwpziPyfRdCqx21O7eMbh10wdNc0QVPDcQEz2+XDqTb6zPwL52L/8cFWE86k6Qx/yCatwfvlIlgMJJw0xPI5fNPBb58BzVnO8aufbCcPTx+PCEEke9nohbsRs5ojensS5Akia+++oonnniCr776irQEB3kLP+LFj+fw4ZIVPPXcC1x55ZUsXryYYcOGMWHCBEaMGEGHDh2Y+s7bTHz2Of591SjuuuE6LH/ri3SIm68QgmDONkJ5WiCybHXgaNcdQx3nzgoHfJTl7iYa0n4um6x2EjJbY7LWv+CYEAKfz4e7uDhemVmWZZKSk0lISDio8CkoKGDw4MGsWrWKhIQEvvrqK84666xDnsvrD1NU6sfjr7BRm00GUhNtJDqtjZLZpaiCUp+C26uJn0i06tfRYZVJcRpJdhpxWI+s6+uXX35hyJAhFBcX061bNxYuXEh29sGD2oUQBMJQ7FVx+zQBtH81YAlw2SVSHBLJTk0A1Wb266YmFNGsP24fFHs1EVRdpWNJgmXz3ublx7QSCSNHjeHDD6dgrUNBzeZOzBpUWQAV+6DURxV3Z2WMBk0cJtgg0a7Fs8Te28ygKFGuvfZaPvjgA0Cbrf6WW25pxF5VxZ1fyh39HqR7367c8fb1GI7Qd10PWm7e6IKnBioLnuNuuRdzi0yK33gcyeYg46HXkAxGPO9PRMnfg63/37GcdDYASu5Ogl9oM6XbRt1SJZZH9ZYQXjAFlCjGkwZgPO4E3G43AwcOJBAIcMEFFzBzxnRSrQYuPK0XDz76GHmmFPr168eOHTu47rrrSEhI4MEHHyQxMZEPJr/JphVL+dcVwzGnZ2PpchqS4dDumUhZMf6d6xHRMEgyttadMadk1emBKoTA7y7AW5gTn1LDnpyOMy3rkO6oQx3X6/XidruJlgsfg8FAUlISroMIn7KyMv7v//6P//73v1itVmbPns3QoUNrdb5QOEpRaQC3JxCP8wFtnq4kp5UEp6VRYn2EEPiCKm5vFLc3ijdY9We2yaC5vpJdBpIcxiMSS7Nu3ToGDhzIvn37aN++PYsWLeK4446rdXsDYc0C5PZqLrD9g4UBEmwVMUDJDgnTQWJlmhuBsCh/2Itya1CFC+inxZ8w6ZFxKNEIJ54+mMdfmUV2mj3uDtNcP0dHP2uLqmq1gCqLILdXixWq6SESE0Muq8q7L9zBJx+8AsADDz7CIw8/2CTVv3/8fAWP/f150lunMennJ0lukXREjqsLnuaNLnhqoLLgyTp/COlX30rBozej+spIHv9vLJ27E/r1ewKLZyOnt8R1dUV9k+DCj1G2r8PQpgvWIVdWOW50069Ef/sOjGYsg65CcmhfjHvvvZeSkhJMJhNXX3gefxNFIEn8/YUZLPnvj4waNYpzzz2XuXPnsmHDBr755hsyMjLw5e/Bvm8tqCqyMwXL8WcgmQ79i1ONhPHvXE/UUwyAKSkDe5uutRJMlVEiETz5ewh6SgCQjUZcGa2wupIO66YvhMDj8VDidhONak8ag8FAcnIyroSEA44dCAS45JJLmDt3LkajkQ8++CCe3l+rfqgqJZ4g7rIAgUq+G0nS6vkkuWy4GsHlFSMcVXF7NetPiS9KpWnaymNpDOUCyIj1MFxf27dvZ8CAAWzdupXMzEwWLlxIjx496nUsTSRUWID81Qggl1UqDxY+ugRQzMWnPegF8+fP58HbRhEOBeh6wpnc8+wXOFxJgOb6icW+JNklEu3gstKsp/aoL4qqiZ6ygJbVVOqveO8NVhVDQgjmfvgYX059CIABI2/j+rteIMkpk2CHxEoWIqupbvFCdeWnL1fS5m+taNXp8EptVEYXPM0bXfDUQGXBk5SZTZvn36Ns9jsEflmK/fT+JIy4CjXgo+yNByESxjH6FkzttKBZtaSQwKyXQVWx/t91GLLbx48rVJXwdzMQRfuQM9tj6jci/hCN1YoQQhBZ+1+Wzv+ay596izvuvJu7/30vRqMRt9vNiBEjuO6667jssssAUMoKCW1YDtEwktWJpduZyNZDu6mEEITydxHctw0QyBYb9nbdMdYjAyvkK6Msdw9KRAuIMDsSSGjRCqP58Mz9Qgg8ZWW43W6tbhHaBKWJiYkHWHwikQhXX301H330EZIk8dprr3HDDTfUvS/hKCXeICWeYJVYH4NBIslpJcllw2ZpvOkjVFVQ5q9wfYUiVb+2dku568tlxFkP11dOTg6DBg3ijz/+IDk5mXnz5nHaaacddruDkXIB5BUU+9QDZr8GcNkqXGDJzqPDBRbjxx9/5MILL6S0tJQux/fi8de/QbJmVOv6kSRN9CTatcDvxPIHvN1y7FmDYlQWQ6V+TQSVBeCTqa8w9eXbADh94DjG3fUOhv1+aJmN5eKnkghKLF8aIsdg069byWyfQUJK/bNPdcHTvNEFTw3EBc/lF+AySLR++m2UojxK3n8eOTGF9PtfRpIk/ItmEl71X4wduuH8e8XDNfTDF0TX/4Kc0RrrxddXuampZUWEF34AqoLp1KEY2lYtzCWEAFVh7suPcP2Tr/L1y4/Sa/T18WysTp06cckll/DEE09UHNNfRmj9MkTIDyYr1uP7xgOjD0XUV4pv+1pEJKS5uFp1wpyaXecbsVBVfMV5eIvytGAAScKVloU9JeOwb+qqqsYtPjHhI0kSroQEEhMT41NNqKrKrbfeymuvvQbAE088wb333luv8wshCISilHgClHiDKErF18ViMpDkspHksmI2NV7gquZKKrf+eKKUBZQqn5sMEskuLesr0WGotevL7XYzdOhQli9fjsPhYM6cOfTv3/+Itl2LlakIgq4uWDjmAks5SmKAfv/9dwYOHEh+fj6dO3dmwYKFJKW3iQdFxx72EaX6/Y2GypYNKf6+Oc93diSYOnUa1157NYqicN6gi7nv6emEFCtlgQNrCO2P1bSfCLJVvK9POM7W33dw1zkPkd46lWcW/afeLi5d8DRvmsnUc80ba4fOsHMzgQ1rcPU9D8lsQS0tJrJzC+Z2nbD0OZfwqh+IbluHUpiLIU2bVsJ00nlEN61Gzd+NsmM9xvYVk37KCakYjz+N6NplRH77FjmzLZKlIuBXkiQwGLF1PQWH1UqyHCGyejGm3gOZN38BaWlptGvXrko7ZXsClh7nEFr/I8JfRnDtUixdT8eQlHHIPhodibi6nqK5uMqKCOzeSNTjrrOLS5JlnGlZWF3JlOXtJuz34inYR6CsmITMNpjrGBxdpX+yrFl1XC68Hg+lpaVEIhHKSkspKy3FbreTmJiI1WZj0qRJJCcn8/jjj3P//ffjdrt55pln6ix6JEnCbjVht5rISnPh8Ycp8QQo84UIRRTyir3kFXtxWE0kuawkOq0YGqKS2/5tshiwWwy0TDUTiQrcvihuj+b60lLLI+SXRLQ6Og5DPO29pqyv5ORkFi1axIgRI1i4cCFDhw5l+vTpjBgx4oi13WKSyEwykJmk/R2KCC0IupIFqCwgKAsIdhZoPjyXTSq3iGiLw9K8KkGfcMIJ/Pjjj/Tv359NmzbRr9+ZLFq0iK5du6KFcVfEO5WWu3xKAyLu+okqUOTVlsoOIKtJxC0aMYtQgu3wpsxoTowbdwVJSQmMHj2abxfMgchQ5syZg8vlIqpUtQqVVnKXBcJaQHmwFPJKDzyu3XKgCEq0g8t28CKLskHGYrfgSLRjc9V+omWdowvdwlMDMbW+46PJKAvn4DilHy1uvIeS6W8SXPUjttPOI3Gklo7u+3wykU2/Y+55BvYhY+PHCK9cRGTV90hJadj+fmuVFHChKIQXT0OUFiK3+Rvm0w4Mst23bx+DB/Sne1Yy9156Act2FjFv9WZKSkuZPn06LVu2PGAfEQ0T2rActawQJBlz55MxprWqVZ81F9dugvu2crguLiEEgdJiPPl7Ear289bqSsKZloXRUnOxxFofPxCgtLSUgL+iJKrZbCYhMRGn08lLL73EXXfdBcC1117LW2+9dUTSiBVVpdQbosQTwBeoSOdpqnifGKoQlPkUissDn/d3fcWyvlJcxoMWPAyFQlx++eXMnj0bWZZ55513uPrqqxul/TEXWMwCVF0MkEHWrECJdokEu1YLSIv3aFohsGfPHgYMGMCff/5JWloaCxYsqLFEAmiuSk8w9lAXcUF0sDR50AKi93eLHc1B0t999x3Dhg3D6/VyyimnMG/evANKJFQmHD1QBMWE0f5lEyojUTF2MSEUe3VaIX9nPs4kB86k+v8o0y08zRtd8NRA7OLN/d9yfJMeR3Yl0vblaYS3bcD91lNIVjsZ/3kVyWQmumcr3o9e1FLUb3gM2aEJBBEO4p/+PAT9mM+6GNPfTq5yDrUoh/C3H4MQmE4ZjKFd9wPakZOTw4iL/g/F42bDzr1cPmwIY2+6g379Dp56LVSF8KaVKEV7ATC164kxu2Otb4oHuLhaHoc5rX6Th6rRiGblKS2Or7MmpOBIycBkPTK/psLhMGWlpXg8HmKXtCzLuBIS+PTTT7nhhhtQVZVRo0bx4YcfxqfuOCLnjiiUeoO4PQFC4UrxPrJEostKgsOCw2Zu9Mk5tSBbLeur2HNg1pfZqGV9JToMuGwGzMYKy4miKFx//fW8++67ADz//PPceeedjdp+gGBYUOIXlPlVSv2a5ae6SR/NRs0dlGCTcFplnFYJu4VGH/PCwkIGDx7Mr7/+isvlYu7cuYcskVAdEUVUPMwrCaGaCgXGYl2cVgmXVXuIO60Hn/epObFy5UqGDBlCUVERxx9/PIsWLaqxRMLBCEYOFEExYXQwlyJo9a9ctgpLkNMK677+gXZdMunVrxMWY+0CqHXB07zRBU8NxCcPLSqk+L5/IIIBsu6diLXT8RQ8dQdqSREJl4zHfvLZWir1tOdQcnZiOfk8bOdVuAEia5YRXj4PrHbsl9yOtJ9bJ7ruJ6LrfgJZxnTmcAyZ7fdvCgAbf/4ew5b/0So9GblFO0w9zkYRWgBvdQghiGz7jWiuNuO5IbUl5o4nHbJAYQw1Gom7uAAM9gRsLTtirGVc0P5EggG8hTmEvBV2aLPdiT05A4vzwKyr+qAoCh6Ph7LS0nhmF8CSb7/VZpwPhxk4cKA2Gauj/r/kqkMIQTAcxV0WpNQbJFrpySxLEk67mQSHBZfDgrGB3V7VoWV9RXF7FC3ra79vvsko4bQacNlknFYDDqvMfffew3PPPQfAAw88wKOPPtqklgStwJugzC/KhYDAGxDVpkXLEjgsEk6bVC4CtPfaw6vh+lBWVsawYcNYunQpVquVWbNmceGFFx72cYUQhCIxEQSl/gohdLD6OKDVwXFaKRdBUvyB7rQ0r6yx9evXM3DgQPbu3Uu7du1YvHhxrUskHAohiLsUY66yykHU+4vovFV/sPTexzCYzQx4/WlS2rWsIiKdVnDaiK+zlRde1AVP80YXPDVQ+eINznoP7w+LcZ01kPRrbsX73Vy882ZgzGxN6p1PIkkSkW3r8c16XbPyTPgPcoI2X5VQogQ+ex1RnIehfTcsAy6tcsMVQhD5eS7q7o1aFeazL0FOq/7XjZK7ncia70GorMr1MO7xV5k1aza9evWqdnshBNGcLUR2/AFCIFkdWLqciuxMrtUYCCEIF+4lsG8rlLuljK4UrNkd6jwJaYxwwIevOJ9QeRo7gMFkxp6cji0x9bBq+FRut9/nw+Px4C93dy1btox/3HADfr+/Vqbzwz2/1x+m1BfC4wtVET8AdqspLn4spsafSDRW8LDUF6XMr+ALVV9a12KCj999kRcmamnEN954I5MmTaqx+nVjo6gCT0z8BAWeoPaqVt8ljAYtNd4ZW8oF0ZEMjg4EAowZM4Yvv/wSo9HI1KlTGTt27KF3rAexKr+l8VRwzU3mCdRs1QBtolinNWbVqLAMOZooc2zHjh0MGDCALVu2kJmZyYIFC+jZs/5TQdQGITQXYkwEeQJQXBxg1m3PYUlNpfcdNxxyLGRJGzdZKePvZ+mCp7miC54aiAmeze++Rcu+Z5Dz1L+RrDbavjwNVIWCx29FhEMkj78HS+ceWuG4Ga8Q3bUZc/dTsQ+tKJ+u5O/RihGqCubTL8DUs2+VcwlFIbLsc9TcHWC2Yj53NHLigTOAAyhF+4isXsyF9zzNklXrSUxMZO7cuZx55pkH7YviKSa8cYWWwSXJmNr1wJh1XK1vamo4RDB3O+GiHGKBlaakdKxZHTDUIv292jZFwvjdBfhLiuIxPpIsY0tMxZ6cftjp7PHzKAperxevx8OKFSu45tprtTTiLl2YPXs2x3XogNVma7AbfCzTy+MLUeYLEdzPL2EyyjhsZhw2M06buVEzvmIoqsAXVPAGVLxBBW9AIVgp/ufzGe/y/GN3IoTggotG8/wrk0lJsOK0ydjMjTf5aW2JBQnHBVBA4A1qQdEHu+FZTTELSIUYclikeltBIpEI11xzDR9++CGSJPHqq69y44031r9T9SAU0cSQJ6iNhbdcCHmDB58+AjRrhdNS2Yoh7WfNaLj/d25uLoMGDWLNmjUkJSUxb948Tj/99AY738EIhyIYDDJCMuALVYzb/ouvUq2hgK+MWy/SBU9zRRc8NRATPCsvH0XvKTPY8+8JRAvySJ9wF64zzqXsiw/w/7gQc+cepIy/B4Dovh14pz2npWNfdQ+GjIpg4cja5YSXzQVZxnrhtRiy2lU5n4iGCS+djSjaB1YH5vMuPWhauVpWRMHSzxlx7zP8tG4zNpuNTz/9lCFDhhy0PyIaJrz5fyjF2qznsisFc4detbb2ACghP8Gc7UTcefF1ppRMrC3a1lv4qKpCsLQYn7sAJVwRrWlxJmJPSsPscB2xG2w4HGblypWMGDGC/Px82rRpwwdTp9KuXTucLhcOhwOLxdKgN/RwRNHEjz+Ezx8+4AFcWQDZLUYs5sar91OZSFTFG1TxBhS8QYXZsz7hkXsmoESj9D1nMI+9MBWL1YZBBofVgNNmwGGRsVtkbBa50eNnaoOiCnyhmACqsAaFqplGArRAV3vMHWaVsFskHBawmWs3Z5iqqtx22228+uqrADz++OPcd999TS4QhRAEI1SIoUBlYVSzi8wgV7h1XFbNbWg1a0LIZtKsgofbv5KSEoYOHcpPP/2E3W5nzpw5DBgw4LCOeTgIIZj8r2mkZqcw8o6q7klV1SxE3iDkFpTRu4sueJoruuCpgZjgWda/Lye++T7BP1binvMx1s7dyL7vaaJF+RQ+fRcIQeqtj2BqrfmbfXPeJbJxNXJaFq4r7kYqt1QIIQh9OxNlyxqw2rENG4+cXDVlXISDWlHC0kJN9Jw1EvkgaeWq30Ppsi+49P6nWbByLUajkWnTpjFmzJiD9kkIQTR3G5Eda0Etr16c2hJTm+OR6+CiUgJeAjnbiJYWxtcZXSlYMlpjdKXUu+ZN2OfB584n7PPE18sGI9aEZGyJKRgtR8YSs3XrVvr378+OHTto0aIFU95/n86dOwNaNWer1YrNZsNqs2EymRrsAaWqAn8wjDcQwRcI469mAidZkrBZjNjK0+NtFhMmY+NbVYQQzPliLmMvvYRgMEifU/sx8dXp2BwHXjcSYCsXP9piwG6VsRibV0p5jEi0qgCKCaKarCAWE5oAMmtCSBNDEjZz1dgYIQQPP/wwjz76KAB33XUXzz77bLMcByh3B4epZA0ScSHkC8GhnhgSYDVrFjNbuRCymqRK77VX8yFiqXw+HyNHjmTBggWYTCamT5/OyJEjj2xna8mqJX9wzwDt//fGr8/Q8cTq4yz1GJ7mjS54aqCy4Gn39zFk/H0Mu+6+FhSFlg+9iKV9J0pmvEnw1x8xdehKyj/uR5IkVG8ZnikTEb4yTMefjP3CKysqKUdCBL96F7VgL5IjEetFE5DLy9HHEAEf4f/O0kSPyYyp78UYMtpU20YRDuD730Kuue9JZi39Rasu/PIL3HDL7TX2TQ35iexYi1K4O77OkNEWU+u/1apCc4yor5Rg7k6iZRXCR7bYsWS0xpycecjJTA963FAQv7uAgMeNUCoCEYxmK9bEZGwJKRhM5nodO8a+ffsYNGgQa9euJTk5mY8++oiuXbqw/1fCYDBgtdmw2WzYrFaMDSiAFFXFH4zg84fxhyIEglHUar6iRoNcIYIsJmxWU6MFQv/www9ceOGFlJWV0bt3bz6d8zU2VwregIo/pOAPqdVmUoFmHYgLoJgYshqaZXHBCitIuQAKCfwhzUIUPURsjM2siSG7WcJmlrCaJd5/+2Xuu0fLdLvmmmt46623Dppw0FxRVRG3ZsTcZP6Qlh0Vq49TW2RJE0a2cgGkvZcq3pvBIMJcfdUVzJo1C1mWmTx5Mtdcc03DdfAgCCGY/tTnWGzmAyw8ldEFT/NGFzw1UFnwJKalcfy02RS8+zLe5d/hOO1sWvzjnyjuQgqe/RdEwiT8/Trsp5wDQHT3FrzTXwGhYhs4GsuJ/eLHFQEfgS8nI0oKkBLTsF00HsnmrHJuEQ4SXjYHUbAHZAOmkwcfUI05vq1QCW9ZzS13/pPJc78D4Il/3sq9E19EOkRwqeorJbJrXdzNhSRhzOyAqVVXJHPta+UoIT+hgj1ajE8sHsdgxJyajSW9FXIdjrV/30JeD4GyYi27q9LlarTasDoTsTgT6235KS4u5oILLmDFihXx6sJ9+/YlGAgQCAQIhULVCqCY9cdms2E0NpzLSQhBKKyUi58IgVCkyjxflTGbDNgsJuxWIzaLZglqqCyc1atXM2jQIAoKCujatSsLFy6kdevWFW2OCvzBCgHkD6kEQupB42fMRukAa5DdLDerLKLKhKOa+PHHRRD4w9r7g4k9gIVffMCLj1yPqqqcO+hiXnj1QxJc1vKHvITNJB0Rl1BToZZnkgXC5QIoDIGIqPReW3+w9PpqEQrvPXcj8z97B4A773+WCTfedYDFqLHT70OBEAajAaOpQrTqgqd5owueGohdvCv+PhSr10vbBx7D2jKbvQ/dBrJMqyffwJzZEt/3X+P5ejqSzU7aHU9iSE4DILhiMcHv54BswDn6ZoxtOsWPrXpLCX7xNsJbgpyaieWCq5D3K+4nlCiRn79G3bsZALnt8ZhOOAfJaqc6FG8J999yPU9PmQnAXWMvYuJzL2DIbH/IG6jiKSaycx1qab62QjZgTG+DMbtjnVxdQokSLsohVLAHNRyIrze6UjCnZGJMSEWuZVr8/qhKlKCnhECpm0jAW+Uz2WjEbHdhtjsx250YTLWPw/F6vQwfPpzFixdjNpuZMWMGw4cP186pqoRCIQKBAMFAgGDwwJr3BoMBi8WCxWrVXi2WI1Lc8GCoqiAYjuAPRgkEI/hDkSrzfVXGYjJgLY8DspYv5iOUFbZp0yb69+/P7t27adOmDYsWLYq7BatttxAEysWPtmhiaP/iiFXbL2E1y1iMMhaThMUkYzGXvzZD95gQgnAUfJXEkGb9EATDglAUfvr2C56853IikTAnnnoeD704C5u94gePBJhN5X03Ut5vTQhZjOWvJgmT4egVRoqqjUuwXBgFIlrNpYr32vrYZS2E4KPX7+XLD58FYPi4exlz/WNV+m+QNTejNmYVi9WklSKo+BssxsNLyY+EIzw0/FkMBpkHPrkDi00LW9AFT/NGFzw1ELt4N056geC8L3D0PJHjJr5IzgsPE1jzP6xdupN1z5MgBMWvPUpk91ZMrY8j5cYHkIwmzRf+xXtENq4GsxXn2NswtmgdP75aUkjwy8mIgBfJmYRlwKVVgpxBm5squu4nlA0/ayvMVownnI2hXfdqb3ZCCJ596F7ueexpAK4ZchaT7rsVS5dTkGtROFApySeyax2qp6JIoJyUgSmrI3JyZq1vsEIIomWFhPL3EPW6K30iYXQmYkxMw5SYhsFSvXg7FEo0QshbRshbStjnQYiqP6tlo6lc/LjKBVDNVY9DoRBjx47ls88+Q5Zl3nvvPcaNG3fAdqqqEgoGCQSDBxVAACaTKS5+LFYrZrO5QVO5FUWNW4H8IU0I7Z8KH0OSiIsfqyUmgoz1igvavXs3AwYMYOPGjaSnp7NgwQJOPPHEOh0jqogqAij2/lBuI9AsQ9aYCKosikza++ZmIVLLH/QLFi7m8kuH4/f56HniKTz/9peYHcmEwgfPItsfSYo94CtEkCX+cK8QSUb56BVGUUVUuMvC8PILE3n+qfsAGDb6H0z41yRCUblGq9rBMBkqCSNjuRCqJCqtlT4zm6oWsdz4v63cedaDSJLEiz88RqfeHQBd8DR3dMFTA7GLt2DrFvbdNgEUheOeeQVziwz23H8TIhwi7epbSDh7ENHiAopeegAR8MVnUgcQkTDeWa+j7N6CZHfiHHsHhtQW8XOopUUE501BlBWDbMB8+hCM3U474AalFuUQ+d9CRGkBAFJ6K0wnDUBOqL6OzOS33uQfN96EqqqM6NeH9/55LdaMVpg6nYScklVjv4UQqGWFRPdtQSneF18vWR0YM9piSG9TpzgfJegn7M4lUlKAGvRV+Uy22DHFxI8jAUmquygQqko44CPs9xL2e4kEfQdEVtZGAEWjUSZMmMD7778PwIsvvsjtt99e47lVVSUcDhMKBgmGQoSCwSoFDytjtliwVhJBDRkMLYQgqqgEQ1GC4YolFI4eNOhUe4BqFiCLyYDZbNReTQaMhoOLofz8fAYPHszq1atJSEhg7ty59OvXr9pt69L+iKJNkhoKC0IRtXwR8dfa3LhMBqmSCKoqhiwmGUMTioEVK1YwZMgQ3G433bt3Z8GCBWRlZRGKQCiquYa0/mqWoWCkYt2h6utURpap0VJkMUmYjUePMHrrrbe44YYbEEIwZswYpk6dimQwlY+NFkcUilJp/CAY/1tb6vPQMxurWpD2rl4PkQgn9u8VF06hQBmtM5N0wdNM0QVPDcQEz66lS5F+X0nxN19h69yVjs+/Runiryie/g6yzUHLR1/GlJ5J6M/fcb/3HAhBwoirsZ9+PgAiFMA7/RWUvN1INgf2C8dh6nB8/DwiFCD0/WcoO9YDYGjVEfOZw5ATq4oZoSoom1YRXbcMlCjIMob2PTEef9oBMUAAs2fPZuzYsUQiEfqf1I0ZD96Iw2pBSsrA2OZ45Iy2h5wYVA36iOZsJZq3XTtnOZIjEUNyFoaULGRncq1vlEooQLS0kEhpIVFvCVVuPbIBozMJozMZoysJg61+6ehVBZCHSMDP/rc42WDEZLVjstkxWR2YbHZkgxFVVbn77rt58cUXAXjwwQd55JFH6tQORVEIlYufUChEKBSKz+5eGUmSMJlMmMxm7bXS0lAuMa1ar0IoHK0ihg7mEqtoK5iMBsxGAyaTAbNR1v42GTAZDfh9Hi666CL++9//YrVamT17NkOHHjg33JHsRzgqKgmgqmIoFFFrTK2OIUualchslDGZpPh77bX8vUlqsAk7165dy8CBA8nJyaF9+/YsXryYDh06HHI/VRXlD/VKwiiqWUPi66KHDq6ujCRpD3Wt34d+bcpJTD/55BMuv/xyotEoQ4YMYfbs2djttbMWa2K6kjiKLZVFZbTi89rGG/kKivF7S7lpbEdd8DRTdMFTAzHB8+35/Tnjww/YfPN1qH4f2RNuJnXYcPY9eQ+hLX9ibnsc2fc/i2w24130Od6Fn4IkaaLntPMALYXcN+t1lFwtK8py+kCsZw6NTyYqhCC6djnhFQs0YWEwYjrpPEw9zzwg00n1lRJdtQQ1R5syAoMRQ8cTMXY9BclSdW6qhQsXMnz4cPx+P6f36sGnD0wg2Vm+jcGEIbMdclZH5JSsGh/qIhpBKdpLtGAXarmVKYZksiKnZGJMyUZOzKh1ZpYajRD1FGvip6wIoex3ZzEYMTqSMLo0EWSwOQ9DAHkrLEDVCCDQqj2brHaMFhvPvfIajzz2OAC33HILL730Ur1dUkIIotFoXPzEhFBNXz1ZlisE0H6CqCFcY0IIwhGFUEQhHIlqr2GFUCRKpKbc7Eoo0RB33jye75YsxGg08vpb73DpmDGYykVSY9bl0Sxc4gARFIoIguUCqS5uEINMFSFkMlUWRRUiqT4utO3bt9O/f3+2bdtGVlYWCxcupHv3A+fUqw+KWrOlKGZJqo9LyCCDyQhmg4TJqFnTTAb2e1/+apAwln92pK6Db775hpEjRxIIBOjXrx9fffUViYmJR+TYldGC8KtaiIJVxCYUF5QxbdzD+Hwevs57Txc8zRRd8NRAaWkpSUlJfNq1G93v+Sdmp4V9b72KbLPR+Y0pEAmx96l7UD1lJF10KclDRiCEwPPVxwR++R6AxCtuxtrlBEATDYGlXxJesxwAc4/TsPcfVeWcakkRoeVfo+bsAMDQuhPWAZdW2z6lYDfRdcsRxbnaCoMRc7+RyCktqmz3yy+/MGrUKEpLS+l//nl88uxDKLlbEYEK95Jkc2A+7aJDWnxAS61XSvJQ3HkoJfnxej6A5pbr0KvWs7PHjykESsBL1FuiLb7SeLZXvI0GI/bWXTAlptXp2AecS1WJhAJEAgEiIR/RYAAlcuD01B/O/IxHn30JgEcffZTbbrvtsM5bpQ3lIigcDhOJRIhGo0TK31dnDaqMwWDA4XCQmnZ441CXtkaiKuGIQiRasYSjKtGoSiSixOVjJBrh8f/cw6L5XwHw9tTZdO+hXf9Gg4zJJNM+u/YWwYZEUQWRqCAcVctfq39fG0tRDIMBzAaZzCQTGcm1L5uQm5vL8OHDWb9+Penp6fz22284nQdabRsKRdWCrcNRQaT8NaxQPhbaa0QpXx+tuTDhoTDKYCwXRekJMu0z6m/NXL58OZdccgllZWUMHjyYTz75pP4NOwwK9hTxn4uexuv18GXux5SUlDSI+NI5PHTBUwN79uyJp9rq6Ojo6OjUht27d9OqVd1+9Ok0PLrgqQFVVdm3bx8u15Gb2qApKSsro3Xr1uzevfsva27Vx0AfA9DH4K/ef2iYMRBC4PF4yM7OblYT7OpoHF1lPhsZWZaPSZWekJDwl73JxdDHQB8D0Mfgr95/OPJjoLuymi+6BNXR0dHR0dE55tEFj46Ojo6Ojs4xjy54/kJYLBYeeughLBZLUzelydDHQB8D0Mfgr95/0Mfgr4getKyjo6Ojo6NzzKNbeHR0dHR0dHSOeXTBo6Ojo6Ojo3PMowseHR0dHR0dnWMeXfDo6Ojo6OjoHPPogkdHR0dHR0fnmEcXPEc5r732Gu3atcNqtXLqqafyyy+/1Lj9rFmz6Nq1K1arlR49ejBv3rwqn1911VVIklRlGTx4cEN24bCpyxisW7eOkSNH0q5dOyRJ4qWXXjrsYzY1R7r/Dz/88AHXQNeuXRuwB4dPXcZg8uTJ9OvXj+TkZJKTk+nfv/8B2wsh+M9//kNWVhY2m43+/fuzefPmhu7GYXGkx+BYvxd89tln9OnTh6SkJBwOB7169WLatGlVtjkarwOdGhA6Ry0zZswQZrNZvPfee2LdunVi/PjxIikpSeTl5VW7/bJly4TBYBDPPPOMWL9+vXjggQeEyWQSf/zxR3ybcePGicGDB4ucnJz4Ulxc3FhdqjN1HYNffvlF3H333WL69OkiMzNTvPjii4d9zKakIfr/0EMPiW7dulW5BgoKChq4J/WnrmMwduxY8dprr4nVq1eLDRs2iKuuukokJiaKPXv2xLeZOHGiSExMFHPmzBG///67GDZsmGjfvr0IBAKN1a060RBjcKzfC7777jvx2WefifXr14stW7aIl156SRgMBjF//vz4NkfbdaBTM7rgOYo55ZRTxE033RT/W1EUkZ2dLZ566qlqt7/kkkvE0KFDq6w79dRTxfXXXx//e9y4ceKiiy5qkPY2BHUdg8q0bdu22gf+4RyzsWmI/j/00EPihBNOOIKtbFgO9/8VjUaFy+USU6dOFUIIoaqqyMzMFM8++2x8m5KSEmGxWMT06dOPbOOPEEd6DIT4a90LYpx44onigQceEEIcndeBTs3oLq2jlHA4zK+//kr//v3j62RZpn///ixfvrzafZYvX15le4BBgwYdsP33339PRkYGXbp04YYbbqCoqOjId+AIUJ8xaIpjNhQN2dbNmzeTnZ1Nhw4duOyyy9i1a9fhNrdBOBJj4Pf7iUQipKSkALB9+3Zyc3OrHDMxMZFTTz212V0D0DBjEOOvci8QQrBkyRI2btzIWWedBRx914HOodEFz1FKYWEhiqLQokWLKutbtGhBbm5utfvk5uYecvvBgwfzwQcfsGTJEp5++mmWLl3KkCFDUBTlyHfiMKnPGDTFMRuKhmrrqaeeypQpU5g/fz5vvPEG27dvp1+/fng8nsNt8hHnSIzBPffcQ3Z2dvzBFtvvaLgGoGHGAP4a94LS0lKcTidms5mhQ4cyadIkBgwYABx914HOoTE2dQN0mhdjxoyJv+/Rowc9e/bkuOOO4/vvv+f8889vwpbpNBZDhgyJv+/Zsyennnoqbdu2ZebMmVx77bVN2LIjz8SJE5kxYwbff/89Vqu1qZvTJBxsDP4K9wKXy8Vvv/2G1+tlyZIl3HnnnXTo0IFzzjmnqZum0wDoFp6jlLS0NAwGA3l5eVXW5+XlkZmZWe0+mZmZddoeoEOHDqSlpbFly5bDb/QRpj5j0BTHbCgaq61JSUl07tz5mLsGnnvuOSZOnMjChQvp2bNnfH1sv6PhGoCGGYPqOBbvBbIs07FjR3r16sVdd93FqFGjeOqpp4Cj7zrQOTS64DlKMZvNnHTSSSxZsiS+TlVVlixZwumnn17tPqeffnqV7QEWLVp00O0B9uzZQ1FREVlZWUem4UeQ+oxBUxyzoWistnq9XrZu3XpMXQPPPPMMjz32GPPnz6dPnz5VPmvfvj2ZmZlVjllWVsaKFSua3TUADTMG1fFXuBeoqkooFAKOvutApxY0ddS0Tv2ZMWOGsFgsYsqUKWL9+vViwoQJIikpSeTm5gohhLjiiivEv//97/j2y5YtE0ajUTz33HNiw4YN4qGHHqqSlu7xeMTdd98tli9fLrZv3y4WL14sevfuLTp16iSCwWCT9PFQ1HUMQqGQWL16tVi9erXIysoSd999t1i9erXYvHlzrY/ZnGiI/t91113i+++/F9u3bxfLli0T/fv3F2lpaSI/P7/R+1cb6joGEydOFGazWcyePbtKyrXH46myTVJSkvjiiy/EmjVrxEUXXdSs05GP9Bj8Fe4FTz75pFi4cKHYunWrWL9+vXjuueeE0WgUkydPjm9ztF0HOjWjC56jnEmTJok2bdoIs9ksTjnlFPHzzz/HPzv77LPFuHHjqmw/c+ZM0blzZ2E2m0W3bt3E119/Hf/M7/eLgQMHivT0dGEymUTbtm3F+PHjm+WDvjJ1GYPt27cL4IDl7LPPrvUxmxtHuv+jR48WWVlZwmw2i5YtW4rRo0eLLVu2NGKP6k5dxqBt27bVjsFDDz0U30ZVVfHggw+KFi1aCIvFIs4//3yxcePGRuxR3TmSY/BXuBfcf//9omPHjsJqtYrk5GRx+umnixkzZlQ53tF4HegcHEkIIRrXpqSjo6Ojo6Oj07joMTw6Ojo6Ojo6xzy64NHR0dHR0dE55tEFj46Ojo6Ojs4xjy54dHR0dHR0dI55dMGjo6Ojo6Ojc8yjCx4dHR0dHR2dYx5d8Ojo6Ojo6Ogc8+iCR0dHR0dHR+eYRxc8Ojo6Ojo6Osc8uuDR0dGpluXLlyNJEkOHDm3qpujo6OgcNvrUEjo6OtVy3XXX4ff7+eyzz9i2bRvZ2dlN3SQdHR2deqNbeHR0dA7A6/XyySefcPvtt3PuuecyZcqUpm6Sjo6OzmGhCx4dHZ0DmDlzJpmZmZxyyilcdtllvPfee+jGYB0dnaMZXfDo6OgcwLvvvstll10GwMUXX0xOTg5Lly4F4M0336RXr1706NEDs9lMr1696NWrF6+99hq7d+9m+PDh9OnTh44dO3LNNdc0ZTd0dHR04ugxPDo6OlXYuHEjXbt2ZdOmTXTq1AmAsWPHYjAYmDZtWny7NWvWMH78eFasWBFf17dvX/7zn/8waNAghBBs2LCB448/vtH7oKOjo7M/uoVHR0enCu+++y4nn3xyXOwAXHbZZXz66aeUlpbG161bt45u3brF/w4Gg6xcuZK+ffsCIEmSLnZ0dHSaDbrg0dHRiRONRvnggw8YO3ZslfUDBw7Ebrczffr0+Lq1a9dWETxWq5UzzjiDrl27cuutt/Lbb781VrN1dHR0DokueHR0dOLMnTuXvLw8unfvztq1a+PLxo0bOeuss3j33Xfj265bt47u3btX2f/bb79l2rRpqKrKGWecwerVqxu7Czo6OjrVYmzqBujo6DQfYoJmwIABB91mzZo19OzZ8wALD4Asy5x77rmce+65bN26lfXr13PiiSc2aJt1dHR0aoNu4dHR0Ynz1VdfIYSocenZsyeBQAC3202rVq3i+y5YsIBIJALA9u3b+fPPP+PxPDo6OjpNjW7h0dHRqTMbNmyga9euVdbNmjWLG2+8EZfLhcPhYPLkybRr165pGqijo6OzH3pauo6Ojo6Ojs4xj+7S0tHR0dHR0Tnm0QWPjo6Ojo6OzjGPLnh0dHR0dHR0jnl0waOjo6Ojo6NzzKMLHh0dHR0dHZ1jHl3w6Ojo6Ojo6Bzz6IJHR0dHR0dH55hHFzw6Ojo6Ojo6xzy64NHR0dHR0dE55tEFj46Ojo6Ojs4xjy54dHR0dHR0dI55/h91oFYLhzU9FQAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "surf = ax.contour(\n", @@ -675,30 +387,9 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0, '$\\\\Delta_{TS}/\\\\epsilon$')" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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OnTvz9NNPS5IjhHA6eWe7rgK8JNERtWz16tVkZGTQrVs3kpKSKpVdc801xMfHM2rUKDp27EhiYqKt7IYbbrigReehhx6qdH1mZiaJiYmsWrUKULtkTp8+zeeff87VV19NcXExGzduZNq0abi4XPzX60q7roqLizly5IjtfUpKCrt27SIwMNDWCrRw4UJWrFhBQkKC7bzZs2czZcoU+vbtS//+/Xn77bcxGAxMnTrVrnJ7zvHx8eHJJ59k1qxZWK1WhgwZQkFBARs3bsTX15cpU6Zc8rmqqtvHx4du3bpVOt/Ly4ugoKALjgshhDOoGIwcIF1XorbFx8cDXDBO5HyrV6+mS5culY4lJydX+tC8WIvODz/8QP/+/WnWrBkAEydO5K+//uLZZ58lIyODwMBAhg8fXmkMSm1LTExk2LBhtvezZ88GYMqUKbYxSdnZ2Rw9erTSdXfeeSdZWVm88MILnDlzhp49e/Lzzz/bBgBXV27vOa+++irBwcHMnz+fY8eO4e/vT+/evXn22WerfC576hZCiIbC9/Rm5rt8h0/JYCDaYXFolNqab9xAFRYW4ufnR0FBAb6+vpXKysrKSElJoU2bNo1uAGhsbCwajcbWVWUwGAgLCyM/Px+A0tJSwsPDycnJqXTduHHjGDJkCHPmzKnvkMVlasy/x0II57Xq/WcYl/kBh0JuoONDX9V6/VV9fp9P1tFpopKSkiq16Bw4cMA2jRzUmVWdO3e+4LohQ4YwadKkeolRCCFEw6Ury1P/wyPIoXFI11UT9cUXX1R636dPHzZt2mR737t3bzZu3HjBddKSI4QQwh6uRjXR0Xo5NtFpsi06cXFxREVF0a9fP0eHIoQQQlyU1WjEajQ6OozLojcVAODq08yhcTTZRCcmJobk5GRZZVYIIYTTKli+nIM9ojk5a5ajQ6kxL4ua6Lj7SqIjhBBCiIuw5KvJgtbT08GR1IzJYsXHWgiAp79jZ45KoiOEEEI4KUuhmizofP0cHEn1zp/EnV9iIkBTBIBXQHNHhQRIoiOEEEI4LUtBPgA6P+dPdJ5Y/wSDvhjEqqOryDeUE4C6JY5OBiMLIYQQ4mKsFS06fpdeJ8ZZ5JXlUWQqwk3rRkF+Di4aq1rg4dhNnCXREUIIIZxUxRidhtCik1+eD4Cfux8l+eoGxaUaPbg6dqFSSXSEEEIIJ1UxRkfbAMboVCQ6/u7+lBVmA2DQOT5uSXSEEEIIJ2UpaBgtOoqikF+WD0CAPgBjYRYApS7+jgvqLEl0hBBCCCdlaSBjdAwmA2bFDKhdV1aDuk+iyc3fgVGpJNERQgghnJBiNKKUlADO36JT0W2l1+nxcPFAKckFwKx37EBkkESn0du8eTMajYYxY8bUet1Tp07lueeeq/V6q7NhwwbGjh1LaGgoGo2G77//3u5r4+LiiIyMRK/XM2DAALZt21ajcnvPuVxV1f3BBx/Qo0cPfH198fX1ZeDAgfz000+1dm8hhHOpaM1Bo0Hr4+PYYKpx/kBkAF2ZmujgGeCgiM6RRKeRi4+PZ9KkSSQkJJCenl5r9VosFlavXs24ceNqrU57GQwGoqOjiYuLq9F1y5YtY/bs2bz44ovs3LmT6OhoRo8eTWZmpl3l9p5zuaqrOzw8nNdff50dO3aQmJjIddddx/jx49m3b98V31sI4XwqxudofX3RaJ374/r8gcgArmffO3pDTwCUJq6goEABlIKCggvKSktLleTkZKW0tNQBkV25oqIixdvbW9m6daty/fXXK/Pmzau1ujds2KC0bNlSsVqtiqIoyscff6x0795d0ev1iq+vrzJs2LBau1dVAGXFihV2ndu/f38lJibG9t5isSihoaHK/Pnz7Sq39xyLxaLExsYqkZGRil6vV3r06KF88803VxzfxQQEBCiLFi2qst6G/nssRFNl2LFTSe7UWTk8YqSjQ6nWqiOrlG6fdlOm/TJNURRFWffKKEV50Vc5/r8FdXbPqj6/z+fcKaKzURQwGhzzOm9pbXt9/fXXtGjRgv79+zN58mQWL15caYnuK7Fq1SrGjh2LRqNh+fLlzJkzh+eff56DBw+yadMmnnjiiUteGxsbi7e3d5Wv1NTUWomzgtFoZMeOHYwYMcJ2TKvVMmLECDZv3lxtuT11VJg/fz5Llizhww8/ZN++fcyaNYu7776b9evXX3Z8f2exWPjqq68wGAwMHDjw8n4oQgin1pBWRS4oV1ufKlp0PC1qt5u7b7CjQrJxcXQADYqpBGJDHXPvZ9PBzatGl8THxzN58mQAJkyYwIwZM1i/fj1Dhw4F4K233iI3N5fXXnsNALPZTFBQEPPmzWPRokVYLBYOHjxIVFQUANOnTycmJgaAlStXsmDBAgAOHjxI69atGTlyJP7+/gB07dr1knE9+OCD3HHHHVXGHhpauz/n7OxsLBYLISGVN5cLCQnhwIED1ZbbUwdAeXk5sbGxrF271paAtG3blj///JOPPvqIa6+99rLiq7B3714GDhxIWVkZ3t7erFixwvbvI4RoXGyrIvs694wrqNx1ZbZY8bUWghY8/B27zxVIotNoVbSsfPrppwB4e3szfvx44uPjbYlOUlISN954o+2a/fv3ExkZycyZM5k5cyZ79uxh+vTpbN26tVLd+/fvJz09neHDhwNqArRs2TICAwPx9PRk7969tGnT5pKxBQYGEhjo+JH4deHIkSOUlJQwcuTISseNRiO9evUCYOnSpcyYMcNW9tNPP9GuXTu76u/UqRO7du2ioKCAb7/9lilTprB+/XpJdoRohGxr6Pg7f4vO+YlOfqmJwIoNPSXRaWBcPdWWFUfduwbi4+Pp168fHTp0sB2bPHkyt99+OwsXLsTPz4+kpCTmzp1rK9+1axfdu3e3vd+3b99FW2ZWrVrFyJEj0ev1mEwmJk6cyKBBg4iPj8fPz4/IyMgqY4uNjSU2NrbKc5KTk4mIiLDzaavXrFkzdDodGRkZlY5nZGTQokWLasvtqQOguFjdxO7HH38kLCys0nnu7u4AjBs3jgEDBtiOh4WFodPpqq0bwM3Njfbt2wPQp08ftm/fzjvvvMNHH31Usx+IEMLpWQoqVkVuWC06+YZyWp/d0NPFu5kDo1JJolMTGk2Nu48cwWw2s2TJEp555plKx0eNGoWnpydffvklM2bM4MCBA5VaHvLy8iolPklJSRdNdFauXMkDDzwAwIoVKzhy5Ahr1661Oz5HdF25ubnRp08fEhISmDBhAgBWq5WEhARmzpxZbbk9dQBERUXh7u5OamrqJbupfHx88LnIVNHq6r4Yq9VKeXn5ZfxEhBDO7tyqyP6ODcQOtkRH709Bfi6uGota4On41ntJdBqh1atXk5GRQbdu3UhKSqpUds011xAfH8+oUaPo2LEjiYmJtrIbbrjhghadhx56qNL1mZmZJCYmsmrVKkDtkjl9+jSff/45V199NcXFxWzcuJFp06bh4nLxX68r7boqLi7myJEjtvcpKSns2rWLwMBAWyvQwoULWbFiBQkJCbbzZs+ezZQpU+jbty/9+/fn7bffxmAwMHXqVLvK7TnHx8eHJ598klmzZmG1WhkyZAgFBQVs3LgRX19fpkyZcsnnqq7uuXPncsMNNxAREUFRURFffPEF69at45dffrnsn6UQwnk1lO0fANv2D/7u/hiy1O0fynBH7+rhwKhUkug0QvHx8QAXjBM53+rVq+nSpUulY8nJyXTr1s32/mItOj/88AP9+/enWTO1OXLixIn89ddfPPvss2RkZBAYGMjw4cMrjUGpbYmJiQwbNsz2fvbs2QBMmTLFNiYpOzubo0ePVrruzjvvJCsrixdeeIEzZ87Qs2dPfv75Z9sA4OrK7T3n1VdfJTg4mPnz53Ps2DH8/f3p3bs3zz77bJXPVV3dmZmZ3HvvvZw+fRo/Pz969OjBL7/8UuW/sxCi4bIUViQ6DafrKsA9gFMFxwF1Q0/H7luu0ii1Nd+4gSosLMTPz4+CggJ8/9YPWlZWRkpKCm3atEGvd4Z/rtoTGxuLRqOxdVUZDAbCwsLIz88HoLS0lPDwcHJycipdN27cOIYMGcKcOXPqO2RxmRrz77EQjdnxOydSuns34Qvfw+e8pSecUd//9qXcUs5Pt/zErl8TuGnvo5zUdyD8mcTqL75MVX1+n0/W0WmikpKSKrXoHDhwoNLMnf3799O5c+cLrhsyZAiTJk2qlxiFEKIpq9gCwtkHI5eaSym3qGMF/d39bRt6Gt0cv/0DSNdVk/XFF19Uet+nTx82bdpke9+7d282btx4wXXSkiOEEPWjoQxGrlgs0EXrgperF8rZRMeid45ER1p0hBBCCCejKIqtRcfZx+jkleUBamuORqOxbeipeDh+xhU04UQnLi6OqKgo+vXr5+hQhBBCiEqshhIwmwHnn3V14YaeauKj8XL8GjrQhBOdmJgYkpOT2b59u6NDEUIIISqxnp1xpXF1RePkkwj+nui4m9TYXb2dYOdymnCiI4QQQjirivE5Wn8/NBqNg6Op2t8THS+LGrszbOgJkugIIYQQTqdi+wedr3N3W0HlVZHNFis+VjV2T39JdIQQQghxEQ11VeSCUhMBtg09Q6q4qv5IoiOEEEI4GduqyE6+hg5U7rrKMxgJQE10XHxkMLIQQgghLsLakFp0zkt0CgrzcKvY0FOmlwshhBDiYirG6GidfA0dqJzolORlAFCOO7h5OjCqcyTREUIIIZxMQxqjU7Eysr/en9KCbAAMOudJ0CTRaeQ2b96MRqNhzJgxtV731KlTee6552q93ups2LCBsWPHEhoaikaj4fvvv7f72ri4OCIjI9Hr9QwYMIBt27bVqNzecy5XVXXPnz+ffv364ePjQ/PmzZkwYQIHDx6stXsLIZyHbVXkBjDr6vyVkY2FWQCUuDpP3JLoNHLx8fFMmjSJhIQE0tPTa61ei8XC6tWrGTduXK3VaS+DwUB0dDRxcXE1um7ZsmXMnj2bF198kZ07dxIdHc3o0aPJzMy0q9zecy5XdXWvX7+emJgYtmzZwpo1azCZTIwaNQqDwXDF9xZCOBdLQT4AOn/nSRguxmgxUmIuASpv6Gly9XdgVH+jNHEFBQUKoBQUFFxQVlpaqiQnJyulpaUOiOzKFRUVKd7e3srWrVuV66+/Xpk3b16t1b1hwwalZcuWitVqVRRFUT7++GOle/fuil6vV3x9fZVhw4bV2r2qAigrVqyw69z+/fsrMTExtvcWi0UJDQ1V5s+fb1e5vedYLBYlNjZWiYyMVPR6vdKjRw/lm2++ueL4/i4zM1MBlPXr11dZb0P/PRaiKTp28y1KcqfOSlE1//92tAxDhtLt025Kj896KBarRfn+g38qyou+yqGFt9X5vav6/D6ftOjUgKIolJhKHPJSFKXG8X799de0aNGC/v37M3nyZBYvXnxZ9VzMqlWrGDt2LBqNhuXLlzNnzhyef/55Dh48yKZNm3jiiScueW1sbCze3t5VvlJTU2slzgpGo5EdO3YwYsQI2zGtVsuIESPYvHlzteX21FFh/vz5LFmyhA8//JB9+/Yxa9Ys7r77btavX3/Z8V1Mwdk+/MBA55jZIISoPbYxOk4+vbxiILKfmx9ajRbt2W4sZ9nQE8DF0QE0JKXmUgZ8McAh995611Y8XWs2gj0+Pp7JkycDMGHCBGbMmMH69esZOnQoAG+99Ra5ubm89tprAJjNZoKCgpg3bx6LFi3CYrFw8OBBoqKiAJg+fToxMTEArFy5kgULFgBw8OBBWrduzciRI/H39wega9eul4zrwQcf5I477qgy9tDQ0Bo9a3Wys7OxWCyEhFRewCokJIQDBw5UW25PHQDl5eXExsaydu1aBg4cCEDbtm35888/+eijj7j22msvK76/s1qtPP744wwePJhu3brV4CchhGgIbFtAOPlg5PMHIsO5DT21XpLoiDpW0bLy6aefAuDt7c348eOJj4+3JTpJSUnceOONtmv2799PZGQkM2fOZObMmezZs4fp06ezdevWSnXv37+f9PR0hg8fDqgJ0LJlywgMDMTT05O9e/fSpk2bS8YWGBjYaFshjhw5QklJCSNHjqx03Gg00qtXLwCWLl3KjBkzbGU//fQT7dq1q9F9YmJiSEpK4s8//7zyoIUQTkUxm7EWFwPOP+vq/IHIAHpTPgA6b+fY/gEk0akRDxcPtt61tfoT6+jeNREfH0+/fv3o0KGD7djkyZO5/fbbWbhwIX5+fiQlJTF37lxb+a5du+jevbvt/b59+y7aMrNq1SpGjhyJXq/HZDIxceJEBg0aRHx8PH5+fkRGRlYZW2xsLLGxsVWek5ycTEREhJ1PW71mzZqh0+nIyMiodDwjI4MWLVpUW25PHQDFZ/84/fjjj4SFhVU6z93dHYBx48YxYMC5lsGwsDB0Ol21dVeYOXMmq1evZsOGDYSHh9fo5yCEcH6WoiLbfzeYrit3NSHzMKstPHpf51gVGSTRqRGNRlPj7iNHMJvNLFmyhGeeeabS8VGjRuHp6cmXX37JjBkzOHDgQKWWh7y8vEqJT1JS0kUTnZUrV/LAAw8AsGLFCo4cOcLatWvtjs8RXVdubm706dOHhIQEJkyYAKjdPwkJCcycObPacnvqAIiKisLd3Z3U1NRLdlP5+Pjg4+NzwfHq6lYUhUceeYQVK1awbt26KlvNhBANV8WqyFovLzQuzv0xXZHoBLgHYLEq+FoLQQse/s0dG9h5nPsnKC7L6tWrycjIoFu3biQlJVUqu+aaa4iPj2fUqFF07NiRxMREW9kNN9xwQYvOQw89VOn6zMxMEhMTWbVqFaB2yZw+fZrPP/+cq6++muLiYjZu3Mi0adNwucT/Qa+066q4uJgjR47Y3qekpLBr1y4CAwNtrUALFy5kxYoVJCQk2M6bPXs2U6ZMoW/fvvTv35+3334bg8HA1KlT7Sq35xwfHx+efPJJZs2ahdVqZciQIRQUFLBx40Z8fX2ZMmXKJZ+rurpjYmL44osvWLlyJT4+Ppw5cwYAPz8/PDxq1uInhHBeDWmxwPNXRS4sNeGvUVu1vQKcY0NPQKaXN8bp5TfddJMCVPl65513lLvvvrvSdREREcrx48dt79u1a6ekpaVVOmfRokXK4MGDbe9NJpMye/ZsJTw8XHF1dVVCQkKUu+66q06f7/fff7/oM02ZMsV2zosvvqi0bt36gmvfe+89JSIiQnFzc1P69++vbNmypUbl9pxjtVqVt99+W+nUqZPi6uqqBAcHK6NHj652Gnh1dV/q3/KTTz6pss6G+nssRFNVtGGDktyps3J0ws2ODqVaz/7xrNLt025K/N545UhGoVL2QpCivOirKHkn6vze9k4v1yhKLc03bqAKCwvx8/OjoKAA37/1hZaVlZGSkkKbNm3Q6/UOirBuxMbGotFobF1VBoOBsLAw8vPzASgtLSU8PJycnJxK140bN44hQ4YwZ86c+g5ZXKbG/HssRGNU8MNq0p96Cs+rrqL1p584OpwqPbz2Yf449QcvD3qZSPrRe+nZWaDPpoObV53eu6rP7/PJOjpNVFJSEl26dLG9P3DggG0aOagzqzp37nzBdUOGDGHSpEn1EqMQQjRFlsKGsYYOnJte7ufuhyFf3f7BiCs40XhWGaPTRH3xxReV3vfp04dNmzbZ3vfu3ZuNGzdecJ205AghRN1qSGN08s6umxPgHkBOgbpdTbHOj0CNxpFhVSItOkIIIYQTsRac3dDTz/lbdM4fjGwsUncuL3Xxd1xAFyGJjhBCCOFEGsqqyGarmSKjuuaPv94fc7Ga6BjdnCtuSXSEEEIIJ2IpPNui4+tcCcPfVYzPAfB180Ux5AJg1jvXyveS6AghhBBOpKGM0alIdHzcfHDRumA526Kjc6J9rkASHSGEEMKpWCtmXfk7d6Jz/kBkAErV5Uj0fs6zKjJIoiOEEEI4FUt+w5hefv5AZJPFiptRfe/jTKsiI4mOEEII4VRsY3QaSNeVv96f9PxSAlAHJnsHSIuOEEIIIS7CWlaGUl4OOP+sq7wytevK392f1NwSAs7uc6XxCnJkWBeQREcIIYRwEhUDkdHp0HrV7RYKV+r8VZHVREdt0cFDBiMLIYQQ4iJsM658fdE40erCF3P+YOS03FICz3Zd4SktOqIebd68GY1Gw5gxY2q97qlTp/Lcc8/Ver3V2bBhA2PHjiU0NBSNRsP3339v97VxcXFERkai1+sZMGAA27Ztq1G5vedcrqrqvpLnFkI0DNYGMrUczg1G9nP3IzM7B73GpBZIoiPqU3x8PJMmTSIhIYH09PRaq9disbB69WrGjRtXa3Xay2AwEB0dTVxcXI2uW7ZsGbNnz+bFF19k586dREdHM3r0aDIzM+0qt/ecy1Vd3Zf73EKIhqNiILK2AWz/UNF1FaAPwJidAoDRzR/cvR0Y1UUoTVxBQYECKAUFBReUlZaWKsnJyUppaakDIrtyRUVFire3t7J161bl+uuvV+bNm1drdW/YsEFp2bKlYrVaFUVRlI8//ljp3r27otfrFV9fX2XYsGG1dq+qAMqKFSvsOrd///5KTEyM7b3FYlFCQ0OV+fPn21Vu7zkWi0WJjY1VIiMjFb1er/To0UP55ptvrji+y33uhv57LERTkvftd0pyp87KienTHR1KtW5afpPS7dNuyrbT25RHXnxNUV70VUoWDqm3+1f1+X2+JtuiExcXR1RUFP369bP7GkVRsJaUOOSlKEqNn/Hrr7+mRYsW9O/fn8mTJ7N48eLLqudiVq1axdixY9FoNCxfvpw5c+bw/PPPc/DgQTZt2sQTTzxxyWtjY2Px9vau8pWamlorcVYwGo3s2LGDESNG2I5ptVpGjBjB5s2bqy23p44K8+fPZ8mSJXz44Yfs27ePWbNmcffdd7N+/frLjk8I0TQ0lO0f4FyLjk7xopnpNACuQW0cGdJFuTg6AEeJiYkhJiaGwsJC/OzsC1VKSznYu08dR3ZxnXbuQOPpWaNr4uPjmTx5MgATJkxgxowZrF+/nqFDhwLw1ltvkZuby2uvvQaA2WwmKCiIefPmsWjRIiwWCwcPHiQqKgqA6dOnExMTA8DKlStZsGABAAcPHqR169aMHDkSf39/ALp27XrJuB588EHuuOOOKmMPDQ2t0bNWJzs7G4vFQkhI5YWsQkJCOHDgQLXl9tQBUF5eTmxsLGvXrmXgwIEAtG3blj///JOPPvqIa6+99rLiE0I0DZaCfMD5x+hYFSsFRjXRMZS600qjdrG7SKIj6ktFy8qnn34KgLe3N+PHjyc+Pt6W6CQlJXHjjTfartm/fz+RkZHMnDmTmTNnsmfPHqZPn87WrVsr1b1//37S09MZPnw4oCZAy5YtIzAwEE9PT/bu3UubNpf+ZQ8MDCQw0LmmH9aWI0eOUFJSwsiRIysdNxqN9OrVC4ClS5cyY8YMW9lPP/1Eu3bt6jVOIYRzsuSo2yjoAgMcHEnVioxFWBUrAPlFrrZEh4BIxwV1CZLo1IDGw4NOO3c47N41ER8fT79+/ejQoYPt2OTJk7n99ttZuHAhfn5+JCUlMXfuXFv5rl276N69u+39vn37Ltoys2rVKkaOHIler8dkMjFx4kQGDRpEfHw8fn5+REZGVhlbbGwssbGxVZ6TnJxMRESEnU9bvWbNmqHT6cjIyKh0PCMjgxYtWlRbbk8dAMXF6oJZP/74I2FhYZXOc3d3B2DcuHEMGDDAdjwsLAydTldt3UKIxs+UfrYLqGXttmrXtooZV16uXpwuMDHUlui0dlxQlyCJTg1oNJoadx85gtlsZsmSJTzzzDOVjo8aNQpPT0++/PJLZsyYwYEDByq1POTl5VVKfJKSki6a6KxcuZIHHngAgBUrVnDkyBHWrl1rd3yO6Lpyc3OjT58+JCQkMGHCBACsVisJCQnMnDmz2nJ76gCIiorC3d2d1NTUS3ZT+fj44OPjc8Hx6uoWQjR+ptNnE53Qlg6OpGqVVkXOMdBKk6UWSIuOqA+rV68mIyODbt26kZSUVKnsmmuuIT4+nlGjRtGxY0cSExNtZTfccMMFLToPPfRQpeszMzNJTExk1apVgNolc/r0aT7//HOuvvpqiouL2bhxI9OmTcPF5eK/XlfadVVcXMyRI0ds71NSUti1axeBgYG2VqCFCxeyYsUKEhISbOfNnj2bKVOm0LdvX/r378/bb7+NwWBg6tSpdpXbc46Pjw9PPvkks2bNwmq1MmTIEAoKCti4cSO+vr5MmTLlks9VXd32PLcQouFSFAXT2WVAXGv5y15tyy7NBiBQH0hBejoeGiNWtGj9Wjk4souohxlgTq0xTi+/6aabFKDK1zvvvKPcfffdla6LiIhQjh8/bnvfrl07JS0trdI5ixYtUgYPHmx7bzKZlNmzZyvh4eGKq6urEhISotx11111+ny///77RZ9pypQptnNefPFFpXXr1hdc+9577ykRERGKm5ub0r9/f2XLli01KrfnHKvVqrz99ttKp06dFFdXVyU4OFgZPXq0sn79+mqfraq67Xnui2mov8dCNDWm3FwluVNnJblTZ8VSXu7ocKr0adKnSrdPuylPrXtKmfn6+4ryoq9S+q8u9RqDvdPLNYpSS/ONG6iKWVcFBQX4+lZeoKmsrIyUlBTatGmDXq93UIR1IzY2Fo1GY+uqMhgMhIWFkZ+fD0BpaSnh4eHknB0YV2HcuHEMGTKEOXPm1HfI4jI15t9jIRqT0n37OH7rbeiCm9Hxjz8cHU6VXtvyGssOLuMf3aZxakUm/3aJoyx8MPr7/1dvMVT1+X2+JruOTlOXlJREly5dbO8PHDhgm0YO6syqzp07X3DdkCFDmDRpUr3EKIQQTYn5dMMYiAxwqvgUAD66EFoq6iQKt2bON7UcZIxOk/XFF19Uet+nTx82bdpke9+7d282btx4wXXSkiOEEHXj3Iwr5x6IDOcSHRdLEBFnZ1xpAyMdGNGlSYuOEEII4QRsM66cPNGxKlZOFamJjrHcnwhtxdRy52zRkURHCCGEcAINacaV0WpEq9FSWORFeMXUcn/nW0MHJNERQgghnEJDWUOnotuqpVdLzuQaaEmuWuCEa+iAJDpCCCGEUzCdVlt0XJy86+pk0UkAwrzDKMs+jlajYNZ5glczB0d2cZLo2KGJz8AXDZz8/grh/KxGI5YsdRE+Z++6Oll8LtHR5J8AwOTbCjQaR4Z1SZLoVEGn0wHq6r9CNFQlJSUAuLq6OjgSIcSlmM+cAUCj16Pz93dsMNWoGIgc4hmKX9nZ2VdOuGt5BZleXgUXFxc8PT3JysrC1dUVrVbyQtFwKIpCSUkJmZmZ+Pv72xJ3IYTzOX9qucZJW0YqVLTo6AmmlWYDIIlOg6XRaGjZsiUpKSmcOHHC0eEIcVn8/f1lB3QhnFxDmVoO5wYjYw60beapcdKByCCJTrXc3Nzo0KGDdF+JBsnV1VVacoRoAEzpavLgGubc43NMFhMZBnUl5NISP9tigc464wok0bGLVquVPYKEEELUmYoWHWefcZVuSEdBwcPFg5wC9/MSHedcQwdkMLIQQgjhcOb0hrHPVcVA5DDvMHKyM/HVqJMdnHWxQJBERwghhHC4hjJG5/yp5ZbcFADK9cHg5unIsKokiY4QQgjhQIqiNJhVkc9PdFwLUwFQ/CMcGVK1JNERQgghHMiSn49SVgaAi5PPkKzougpwa0GIRV37x7VZW0eGVC1JdIQQQggHMp06u/VDcDBaNzcHR1O1ihYdnTXINhBZF+i8a+iAJDpCCCGEQ9n2uHLybis4t4ZOYZEvrRrAjCuQREcIIYRwKPPphjHjqshYREF5AQAZuZ7nJTqRjgvKDpLoCCGEEA50/vYPzqyiNSfAPYAj6WWEadRNSJ15ajlIoiOEEEI4VEOZWl4xEDnUO4y8jBO4aSwoWlfwde6WKFkZWQghhHCghja1PMCtBVjOgA7wjwCtc28zIy06QgghhAPZBiM7eYvOyaKzM64sQbbxORonH4gMkugIIYQQDmMtL8eSpY51cQ117i6gijE6ZaV+DWYgMkiiI4QQQjiM+Yy66J7GwwOdv79jg6lGRaKTk+9DG40atyQ6QgghhLik8wciazQaB0dzaYqi2BKdtEw9XTTq9g807+rAqOwjiY4QQgjhIA1lanl2aTbllnK0aCnO19FGo8ZNi+6ODcwOMutKCCGEcJCKgcjOPuOqojXH3z0Yf006Oo0CXsHgE+LgyKonLTpCCCGEg1R0XTn7jKu0ojQAPDTBRGlPqAcbQGsOSKIjhBBCOIzx+HEA3Fq1cmwg1aho0bEaA4nSSKIjhBBCiGooioLx8BEA3Nu1c3A0VatIdIqKfemiPTsQuUUPB0ZkP0l0hBBCCAew5ORgKSgArRa3tm0dHU6VKhYLzMn3pEtFi05INwdGZD9JdIQQQggHKD+itua4tgpHq9c7OJqqpRSkABBs1OClKUdx0UNQewdHZR9JdIQQQggHKLd1Wzl3wpBdmk1OWQ6goauxBABN8yjQNYyJ25LoCCGEEA5QfvRsotPeuROdg7kHAfDWtqS7Rh2rQ4uG0W0FkugIIYQQDlHRdeXewckTnTw10dEYW54346phDEQGSXSEEEKIeldpxlUDadEpLGxOlwa2hg5IoiOEEELUO0t29rkZV23aODqcKh3KOwSAzuBDqCZXPRji/HtcVZBERwghhKhn5UePAs4/46rcUm6bcdXRaFIPBrQBdx8HRlUzkugIIYQQ9cw246p9BwdHUrUj+UewKBbcNT50t2SpBxtQtxVIoiOEEELUO9tAZCdfEflQrtpt5a6E07WBrYhcQRIdIYQQop7ZppY3kBlXhqLm5824ajhTy0ESHSGEEKJeNcQZV2VFQbTXpKsHpetKCCGEEJfSUGZcKYpiS3RalWtx1VjAIwB8wxwcWc1IoiOEEELUo4Yy4+q04TRFpiI06OhhKlQPhnQDjcaxgdWQJDpCCCFEPWooM64qWnM0phC6atTdyxvaQGSQREcIIYSoV7YZV84+PufsQORyQwhRDXBF5AqNItFZsGABXbt2JSoqikcffRRFURwdkhBCCHFR5xId555aXtGiYylvSTddxdRySXTqXVZWFgsXLmTHjh3s3buXHTt2sGXLFkeHJYQQQlxAUZQG16ITVO6Jt2IArSs06+jgqGrOxdEB1Aaz2UxZWRkAJpOJ5s2bOzgiIYQQ4kKW7GysDWDGlcFkIK0oDYBoYzHogJAocHFzbGCXweEtOhs2bGDs2LGEhoai0Wj4/vvvLzgnLi6OyMhI9Ho9AwYMYNu2bbay4OBgnnzySSIiIggNDWXEiBG0c/KVJoUQQjRNFa05zj7j6nDeYQCsJl8Go+51RcQgB0Z0+Rye6BgMBqKjo4mLi7to+bJly5g9ezYvvvgiO3fuJDo6mtGjR5OZmQlAXl4eq1ev5vjx45w6dYpNmzaxYcOG+nwEIYQQwi7lR9Sp5Q1lxpW1vCVXu6pJD60l0bksN9xwA6+99ho333zzRcvfeustpk+fztSpU4mKiuLDDz/E09OTxYsXA7B27Vrat29PYGAgHh4ejBkzpsoxOuXl5RQWFlZ6CSGEEPWhoYzPOZB3AACXsiDaWI+rByXRqX1Go5EdO3YwYsQI2zGtVsuIESPYvHkzAK1atWLTpk2UlZVhsVhYt24dnTp1umSd8+fPx8/Pz/Zq1apVnT+HEEIIAVB+WG0dcfYZVxWbebY3WtUDzTqBVzMHRnT5nDrRyc7OxmKxEBISUul4SEgIZ86cAeCqq67ixhtvpFevXvTo0YN27doxbty4S9Y5d+5cCgoKbK+0tLQ6fQYhhBACQDGbKUtOBkAfFeXgaC7NYrVwKE9NdAYYc9SDrQc6MKIr0yhmXc2bN4958+bZda67uzvu7u51HJEQQghRWfmhQyhlZWh9fJx6xlVaURplljIUqytjNSdAAVoPdnRYl82pW3SaNWuGTqcjIyOj0vGMjAxatGjhoKiEEEKImivdswcAj+7d0Wid9+P3QK46PofyYDpbz864aqDjc8DJEx03Nzf69OlDQkKC7ZjVaiUhIYGBAxtuM5oQQoimp3S3mujoo517v6hdWbsAaFbmjQ4L+EeAX7hjg7oCDu+6Ki4u5sjZUegAKSkp7Nq1i8DAQCIiIpg9ezZTpkyhb9++9O/fn7fffhuDwcDUqVMdGLUQQghRM6W7dwPg0cO5E53EMzsAiC4zqgcacLcVOEGik5iYyLBhw2zvZ8+eDcCUKVP49NNPufPOO8nKyuKFF17gzJkz9OzZk59//vmCAcpCCCGEs7IUFmI8dgwAj+hoB0dzaUXGIg6d3fphvFldr46Iht2D4vBEZ+jQodVuwjlz5kxmzpxZTxEJIYQQtat0714AXFu1wiUw0MHRXNquzF0oKCjGQK627FMPNvAWHaceoyOEEEI0BrZuKyduzQHYmbkTAL+SQFwVE3g1hyDnXvOnOk020YmLiyMqKop+/fo5OhQhhBCNXNnZgcjOPj5n86ntAHQrP9vT0noQaDQOjOjKNdlEJyYmhuTkZLZv3+7oUIQQQjRiiqKcm1ruxDOuyi3lHMhTu6tuJlc92MC7raAJJzpCCCFEfTClpWHJy0Pj6op7ly6ODueS9mbtxaKYUczeDCuv2MizYQ9EhstIdFJTUy86eFhRFFJTU2slKCGEEKKxqFg/xz2qC1o3NwdHc2lb0hMB8CppjrulBPR+0Nx5t6qwV40TnTZt2pCVlXXB8dzcXNo48ZLWQgghhCPYuq16OPdA5HUntgLQy3J2QnbEQNDqHBhR7ahxoqMoCpqLDEwqLi5Gr9fXSlBCCCFEY9EQZlxZrBaOFiYBMF5bpB5swNs+nM/udXQqFvLTaDQ8//zzeHp62sosFgtbt26lZ8+etR6gEEII0VBZjUbK9+8HnHsg8oGcA5gpQ7Houa6oYv2cIY4NqpbYnej89ddfgNqis3fvXtzO62d0c3MjOjqaJ598svYjFEIIIRqo8v37UUwmdAEBuIY7735Rqw9vBMC/LAh30yHwbAahvRwcVe2wO9H5/fffAZg6dSrvvPMOvr6+dRaUEEII0Ric3211sWEfzuKPtG0ADNGcHdHScTQ48Q7rNVHjLSA++eSTuohDCCGEaHQqZlw5c7eVoiiklewDLYwtP6ke7DjasUHVosva6yohIYGEhAQyMzOxWq2VyhYvXlwrgdW1uLg44uLisFgsjg5FCCFEI1XRoqN34hWRd6QfxKotBquOfvkpoHWFtsOqv7CBqHG71Msvv8yoUaNISEggOzubvLy8Sq+GQlZGFkIIUZeMJ09iOnkSXFzwiO7p6HAu6ZukPwAIM/viBhA5GPSNZ3hKjVt0PvzwQz799FPuueeeuohHCCGEaBQMmzcD6vgcnbeXg6O5tG1n1IUCr7YY1QMdr3dgNLWvxi06RqORQYMax9x6IYQQoq6UnE10vAY67zYKJeVmskzq9PehBWd3N2hE43PgMhKd+++/ny+++KIuYhFCCCEaBcVqxbB5CwBeg5w30fliVyIa1zy0ipZeZSXQrCMEtnV0WLWqxl1XZWVl/Oc//2Ht2rX06NEDV1fXSuVvvfVWrQUnhBBCNETlBw5gyctD6+mJR/fujg7nklYcWANAd4snnorS6Fpz4DISnT179thWQE5KSqrteIQQQogGr2J8jmf//mj+1iDgLPIMRlJKEtF5wvWGHPVgIxufA5eR6FQsHCiEEEKIizNsOjs+x4m7rZbvPozW4zgA1xZmq7uVtxrg2KDqQI0TnVdeeeWSZRX7YAkhhBBNlbW8nJIdOwDnHoj8zb7f0LhZCVU8aWW2QOcRoHPO1qcrUeNEZ8WKFZXem0wmUlJScHFxoV27dpLoCCGEaNJK/9qFUlaGS3Awbu3bOzqcizqVX8rxkkRc3WC4yaQebITdVnAZiU7F5p7nKyws5L777uPmm2+ulaCEEEKIhqpifI7XoIFOu7/V93+lofM+CMCwnJOg0UL7EQ6Oqm7Uyo5dvr6+vPzyy9KaI4QQosmzDUR24m6r75I2o3Ux4IErPcvK1bE5noGODqtO1NrWpAUFBRQUFNRWdXUuLi6OqKgo+vXr5+hQhBBCNBKWggLKzs5IdtbxOQfPFHHSuBOAqy1aXAG6jHVoTHWpxl1X7777bqX3iqJw+vRpPv/8c2644YZaC6yuxcTEEBMTQ2FhIX5+fo4ORwghRCNg2LoVrFbc2rXDNSTE0eFc1Mpdp3DxVldDvjY3HdBAt1sdG1QdqnGis2DBgkrvtVotwcHBTJkyhblz59ZaYEIIIURDY3DybR+sVoUVe5LRhZxGAwwpKYU214BPC0eHVmdqnOikpKTURRxCCCFEg1fi5Ovn7EzNI9u6Gz3Q3aIh0GqFHnc4Oqw6VWtjdIQQQoimzHj8OMYTJ8DFBU8nHf+5/K9T6M52W11TmAc690Y9Pgcuo0UHID8/n/j4ePbvV39YUVFRTJs2Tca6CCGEaLKK1q4FwKt/f3Q+Pg6O5kJFZSa+33Ucl8gjAFxTUgodb1BXRG7Eatyik5iYSLt27ViwYAG5ubnk5uayYMEC2rVrx86dO+siRiGEEMLpFa1REx2fkc65Hs33f52i3OUIGq2J5laFzkYTdL/d0WHVuRq36MyaNYtx48bx8ccf4+KiXm42m7n//vt5/PHH2bBhQ60HKYQQQjgzU0Ympbt3A+B93XAHR3MhRVH475ZUXHzUqe9XFxvQuPtBh1EOjqzu1TjRSUxMrJTkALi4uDBnzhz69u1bq8EJIYQQDUHxbwkA6KN74BrS3MHRXCjxRB4HM/Lw7rgXgOsNBoi6HVz1Do6s7tW468rX15fU1NQLjqelpeHjhH2SQgghRF2zdVuNcM5uq/9uOYHO+xAaXSnNLFb6lZVD98Y926pCjROdO++8k2nTprFs2TLS0tJIS0vjq6++4v7772fSpEl1EaMQQgjhtCwFBRi2bQOcM9HJKS7np71ncPVVu9auLy5G59MSIoc4OLL6UeOuq3//+99oNBruvfdezGYzAK6urjz00EO8/vrrtR6gEEII4cyK168Hsxm39u1wb9PG0eFc4OvEkxitZfj67kcBbiwugT53g1bn6NDqRY0THTc3N9555x3mz5/P0aNHAWjXrh2enp61HpwQQgjh7IrWquNznLE1x2JVWLr1BC4+ySgaI61MZroZjU1itlWFy1pHB8DT05Pu3bvXZiz1Ki4ujri4OCwWi6NDEUII0UBZy8oo/uMPAHxGjHRwNBfacCiLk3ml+LTeA8ANxQY0zbtCy2gHR1Z/ajxGZ/78+SxevPiC44sXL+aNN96olaDqQ0xMDMnJyWzfvt3RoQghhGigDJs2oZSW4hLaEn3XKEeHc4H/bjkBOgMaz4MAjDEYoN8/QKNxcGT1p8aJzkcffUTnzp0vON61a1c+/PDDWglKCCGEaAhss62Gj0DjZMlDWm4Jvx3MxNUnCQULncqNtNXoocedjg6tXtU40Tlz5gwtW7a84HhwcDCnT5+ulaCEEEIIZ6eYzRT//jvgnONz4v9MQVGgWUgyADcaDOoGnu5NaymYGic6rVq1YuPGjRcc37hxI6GhobUSlBBCCOHsDJs3Y8nPRxcQgGef3o4Op5Kc4nK+2p6KxqUAg/YQADcUl0DfaQ6OrP7VeDDy9OnTefzxxzGZTFx33XUAJCQkMGfOHJ544olaD1AIIYRwRgXfrwTAd8wYNC6XPbenTny26ThlJiut2xwiF4XeZWW0DO0LLbo5OrR6V+N/maeeeoqcnBwefvhhjEYjAHq9nqeffpq5c+fWeoBCCCGEs7EUF9t2K/cbP97B0VRWXG7ms80nAPAI2AOlZ9fOGdH0WnPgMhIdjUbDG2+8wfPPP8/+/fvx8PCgQ4cOuLu710V8QgghhNMp+uUXlPJy3Nq1Q9+tq6PDqeSrbakUlJqICMnnVOlhXBSFkRY3iHKuhKy+XHZbm7e3N/369avNWIQQQogGoaLbym/8eKeabVVutvDxH8cAaNtuLzvyYFhJKYE9JzeJDTwvpsaDkYUQQoimzHjyFCXbt4NGg9/YmxwdTiUr/0ono7Cc5n5woOg3AO4oLII+Ux0cmePYneisXbsWRVHqMhYhhBDC6RX+sAoAz6sG4HqR5VYcxWJV+HCDujXTwO5pGMyltDaZ6B86CILaOTg6x7E70Rk9ejRZWVl1GYsQQgjh1BRFqdRt5UzWJJ/hWJYBH72Ok+ZfAbi9sBjtgBkOjsyx7E50pDVHCCFEU1e2ezfGEyfQeHjgO9J59rZSFIW439XWnDF9LRwqOIybVWG8exh0GO3g6BxLxugIIYQQdspfeXbtnFEj0Xp5OTiac35OOsPeUwV4uulQvNRNRkcbSvAfMgu0TfujvkZP/8EHH5CQkEBeXl5dxSOEEEI4JavRSOH/fgLAb8IExwZzHrPFyr9/VTftvGdQc35L+wWAOxRP6HarI0NzCjWaXr5w4UJefvllNBoNrVq1onfv3pVeLVq0qKs4hRBCCIcqWrMGa0EBLiEhePbv7+hwbJb/dYqjWQb8PV1p3nIPZRlmOhiNRPd9HHSujg7P4WqU6Ozbtw+z2cxff/3Fzp072blzJx9//DFpaWloNBpatGjBqVOn6irWWhUXF0dcXBwWi8XRoQghhGgA8r74EgD/225Do9M5OBpVudnCO2sPA/DQtW354cDrANxRakXT515HhuY07E50KhZECg0NJTQ0lDFjxtjKcnJy2LFjB7t27ar1AOtKTEwMMTExFBYW4ufn5+hwhBBCOLGyAwco3bEDXFzwv+MOR4dj88XWVE7ll9LCV0+PdrksPJ6Fh9XKTd3uBTfnGUPkSHYnOlXNugoKCmLUqFGMGjWqVoISQgghnEne0i8A8Bk5AteQ5g6ORmUoN7PwtyMAPDq8A9/sehaAG0uMeF8V48jQnIrdg5F//vlnafkQQgjR5FgKCylYvRqAwLvucnA05yz+M4Ucg5HIIE8GdLSwNns3AJNbjwbPQAdH5zzsTnRGjRolG3cKIYRocgpWrEApLcW9Qwc8+vZ1dDgA5BmM/GeDuqfVrJEdWZr4bxQNXFNSRoer5zo4OufStCfXCyGEEFVQrFbbIOSAyXc5zQae7/52mKJyM11a+jKwvSsrT20AYGrzq8AvzMHRORdJdIQQQohLMGzchPHECbTe3viNHevocAA4lFHEks0nAHj2xs58tXU+Rg30KDfSZ/h8B0fnfCTREUIIIS4h7wt1ELLfzTc7xUrIiqLw8g/7sFgVRncNoXeEnq/SEgD4R8hgNP7hDo7Q+dRoHR0hhBCiqTCePEXxunUABEya5Nhgzvpl3xk2HsnBzUXLc2Oi+PbPFyjSKESaLAwb/oajw3NK0qIjhBBCXETuks9AUfAaNBD3tm0cHQ5lJguvrt4PwIPXtKWFt4Ylqep2D/eFDETrHezI8JyWtOgIIYQQf2POyyP/m28BCPzHNAdHo/po/TFO5ZcS6qfnoaHt+fGPuWRqIdhiZex10ppzKdKiI4QQQvxN3uefo5SWoo+KwmvwIEeHw8m8Et5fpy4O+OyYLrhTzqfH/wfA3c0H4ubVzJHhOTVp0RFCCCHOYyk2kHt2JeSgB6Y7xZTy+f87QLnZyoA2gYzp3pJf1z7JURcNXlaF24fJTKuqSIuOEEIIcZ78r7/GWlCAW2QkPiNHOjocfjuQwY97T6PVwEvjumIxZLEw9ScA7g0ZhI+XjM2piiQ6QgghxFlWo5HcTz8FIOj+aQ7fpbyozMQ/VyQBMG1IG7q09GX1r49x3EWHnwL3DvuXQ+NrCCTREUIIIc4qWLkSc2YmLs2b4ztunKPD4V8/H+R0QRkRgZ7MHtkJ4+ldfJC3C4BpbSfg7eHv0PgaAkl0hBBCCECxWMhdFA9A4NSpaN3cHBrPtpRcPt+iroD8+i3d8XDV8t0vj5Hu4kIwLkwc9KxD42soJNERQgghgKJff1W3e/Dzw//22x0aS5nJwjPf7QHgzr6tGNS+GaV7v+Y/liwAHugxHQ8XD0eG2GBIoiOEEKLJU8xmst5bCEDg3Xej83bsdg/v/XaYY9kGgn3cefbGLmAs4cs/XyHbRUeYzotbe0x3aHwNiSQ6QgghmryCVT9gPHYMnZ8fgfdNcWgsyemFfLT+GACvju+Gn6crRX/8H4v1CgAP93sSV52rI0NsUJpsohMXF0dUVBT9+vVzdChCCCEcyGo0kr1Qbc0JemA6Oh8fh8VSbrYw++tdmK0KN3RrwfXdWkDeCT7b9xkFOh1t9cGM6XCzw+JriJpsohMTE0NycjLbt293dChCCCEcKP/rbzClp+MSHEzAXXc5NJb/+/kgB84UEeTlxivju4GicHr1I3zmo47HmTngGXRax055b2iabKIjhBBCWEtKyP7wQwCaPfwQWg/HDfD983A2i/5MAeBft/Ug2Mcd9izjrcIkyrRa+gRGMaK14xcwbGgk0RFCCNFk5f53KZbsbFzDw/G/9VaHxZFnMPLEN7sAmDwgguFdQqA4ix0J/+Rnby+0aHhm8MtOsR1FQyOJjhBCiCbJUlhIzqJFAAQ/+ggaB62boygKz67YS0ZhOW2DvXhuTJQa309zeMNb3ZLy1g630Dmws0Pia+gk0RFCCNEk5Xy8CGthIe4d2uM7ZozD4vh2x0l+SjqDi1bDO3f2wsNNBwd/YkXqL+x3d8PHxZOZvR91WHwNnSQ6QgghmhxjWhq5n30GQPDjjztsT6uUbAMvrdoHwOxRHeke7gdlBRT+OJv3AvwBeKjXTAL1gQ6JrzGQREcIIUSTk/mv/0MxGvEaNBDv665zSAylRgsP/XcHBqOFAW0CmXFNO7Vg7Ut86FJCrk5HG99IJnae6JD4GgtJdIQQQjQphi1bKFqzBnQ6mj/zjEMG+CqKwnPfJ3HgTBHNvN15b1IvdFoNHFnL0d1L+NJXXcvn6f7P4KqVxQGvhCQ6QgghmgzFbCYjdj4AARMnou/Y0SFxLNuexnc7T6LVwHuTetHcVw+GHKzfP8xLzYIwazQMDR/K4LDBDomvMZFERwghRJOR/+23lB86hM7Pj+BHZjokhqRTBbxwdlzOk6M7MbBdECgK/PAoX2sM7NK74+niyT+v+qdD4mtsJNERQgjRJFgKCsh6+x0Amj36CDp//3qPoaDExENLd2A0WxnRpTkPVozL2bmEM4d/4u1ANabHej9GC68W9R5fYySJjhBCiCYh672FWPLzce/QnoA776z3+1usCrO+3kVabimtAj148/aeaLUayDmK8vMzzAsKwKDVEh0czZ2d6j++xkoSHSGEEI1e6Z495C1dCkDI3LloXFzqPYZ//XKA3w5k4uai5f27+uDn6QoWEyyfzq+uCuu8PHHRuvDSwJdkP6taJImOEEKIRk0xmTj9wougKPiOG4vXoEH1HsO3O07y0fpjAPzfbT3U9XIA1s2n4PRO5jcLAuD+7vfTPqB9vcfXmEmiI4QQolHLXbKE8gMH0Pn5EfLMM/V+/x0ncnl2+V4AZg5rz/ieYWrBoV/gjzf5d2AAOTotbfzaML379HqPr7GTREcIIUSjZUxLI+u9hQA0f/ppXALrd4Xhk3klPLBkB0aLldFdQ5g98ux09rwTsPwBEjw9+N7HGw0aXhr4Em46x+y31ZhJoiOEEKJRUhSFMy+9jFJWhueAAfjdPKFe728oN3P/Z4nkGIx0aenLgjvPDj42l8M3U8g2FfJy8+YA3Nf1PnqH9K7X+JoKSXSEEEI0SoWrV2PYuBGNmxstX36pXldANlmsPLx0p23l40VT+uLpdnYA9M9zUdL/4oXmLcjTKHQK6MTMXo5Z06cpkERHCCFEo2PKzCRjXiwAzR5+CLfIyHq7t6IoPPPdXtYfykLvquXje/sQ5u+hFu75GhLj+cbHmz/0Lrhp3Zh/9XzpsqpDkugIIYRoVBRF4czzL6hr5nTpQtA//lGv9//3rwf5budJdFoNcXf1pldEgFpwZi/88BjHXVz4d3AwoC4M2CGgQ73G19RIoiOEEKJRKfjuO4rXr0fj6kroG6+jcau/1pLPNx8n7vejAMy/uTvDu4SoBcWZ8OUkTKYS5rZqQ6liYUDLAdwddXe9xdZUSaIjhBCi0TCePGnbtDP48cfrddPOn5NO2/awmj2yI3f0a6UWmMth2d1QkMbClpEkUY6Pmw+vDX4NrUY+huua/ISFEEI0CorFQvozz2AtKcGjbx8C75tSb/f+43AWj365C0WByQMieOS6s4v+KQr88BikbWW9XxCL9VYAXhr4kuxlVU8k0RFCCNEo5H62hNLEHWg9PQmdPx+Nrn62Udh6LIfpSxIxWqxc37UFr4zvdm6G16Z3YfeXnHJ149nm6ricyV0mMypyVL3EJppwohMXF0dUVBT9+vVzdChCCCGuUOm+fWQtWABA87nP4NaqVb3cd1daPv/4dDtlJivDOgXz7qRe6LRnk5yDP8OaFzECT7bvQaG5hB7NevBEnyfqJTah0iiKojg6CEcqLCzEz8+PgoICfH19HR2OEEKIGrIUF5Nyy62YUlPxHj6c8IXv1cuaOfvSC5j0ny0UlpkZ1C6Ixff1Q+96thXp1A749CYwlRDbeRBflp/Ez92Pb276hpbeLes8tqbA3s/vJtuiI4QQouFTFIUzL7yAKTUVl9CWhM57rV6SnCOZRdwTv43CMjN9Wgfw8b19zyU5OUdh6R1gKuHnNv34svwkALFDYiXJcQBJdIQQQjRY+d98Q+H/fgIXF8Lfegudv3+d3/PgmSIm/mcLuQYj3cP8+GRqP7zcz656XJwJ/70FSrI5GNqVF1wKAXVX8mvCr6nz2MSFJNERQgjRIJUdPGhb/bj5rMfx6Nmzzu+5L72Aif/ZTHaxka6hviz5R3989a5qYXkxLL0d8o6TGxDBowGelJpLGdByADE9Y+o8NnFxkugIIYRocCzFBk7Nmo1SXo7XNVcTOHVqnd9zz8l87vp4K3klJqJb+fPF/VcR4HV2MUKLCb6+F07vwuQZxOy2XUkvySDCJ4I3r30TF61LnccnLk4SHSGEEA2KYrVyeu4zGI8dwyUkhNA33kCjrduPsx0n8pj88VYKSk30aR3A59P64+d5tiXHaoHlD8DRBBRXT2J7jWFH7j68Xb1577r38HP3q9PYRNUk0RFCCNGg5Hz0EUVr1qJxdSX83XdwCQio0/ttOpLNvfFbKSo3079NIJ+d311ltcKqR2DfctC68tXVD/Bt+jo0aHjjmjdo69+2TmMT1ZNERwghRINRtG4dWe++B0CLF1/AIzq6Tu/3v72nue+T7RiMFga3D+LTqf3wrhh4rCjw01OwaylodGwe+SxvHF8JwKw+s2TwsZOQTkMhhBANQnlKCulPPgWKgv+kifjfdlud3u+/W07w/MokFAVu6NaCtyf2xN3l7BRyRYE1z8P2RYCGA6NfYtaxL7AoFsa2Hct9Xe+r09iE/STREUII4fQsRUWcnPkI1uJiPPr0ocXcuXV2L0VReO+3I7y15hAAdw2I4NXx3c6teAywbj5sUluW0ke/wsOp32MwGejfoj8vDXqpXtbyEfaRREcIIYRTU0wmTj0+C+PRo7iEhBD+9gI0bm51ci+LVeHlH/axZPMJAB69rj2zRnY8l7goCvw+Dzb8HwAFI1/moYwEskqzaO/fngXDFuCmq5vYxOWRREcIIYTTUhSFM6/Nw7BxIxoPD8Lfj8MlOLhO7mUoN/Pol3+RcCATgBfHRjF1cJvzg4G1L8LGdwAoH/ESjxb+xbGCYzT3bM4HIz7A1022EnI2kugIIYRwWrmLPyF/2TLQaAh78008unatk/tkFJbxj0+3sy+9EHcXLQvu7MmN3c/brkFR4Oe5sPUDACzXv87cssPszNyJt6s3H4z4gBZeLeokNnFlJNERQgjhlAp/+ZXM/1O7iELmPoPPdcPq5D7J6YVM+2w7pwvKCPJy4+Mpfekdcd6UdasV/vckJMarb298k5dNqaw5sQYXrQvvDHuHjgEd6yQ2ceUk0RFCCOF0Sv76i/Q5cwAImDyZgHvuqZP7/HYgg0e/3EVxuZl2wV58OrU/rQI9z51gMcMPj6pTyNGgjH2Xf1nSWXFkBVqNln9d8y/6t+xfJ7GJ2iGJjhBCCKdSfvgwaQ8+pG7vcO01hMx9ptZnMSmKwvvrjvLvXw+iKDCwbRAf3t3n3GrHAKZS+GYqHPoJNFqY8AHvWTJZun8pAK8MeoWRrUfWalyi9kmiI4QQwmmYTp0iddr9WAsK8OjZk/AFC9C41O5HVYnRzJxv97B6z2kA7r4qghdu6oqby3lr6Jbmw5cTIXUzuOjhtk+IN53m410fA/DPAf9kfPvxtRqXqBuS6AghhHAK5txcUqfdjzkzE7f27Wj14QdoPT2rv7AGTuaV8MCSHSSfLsRFq+Hl8V2ZPKB15ZOKzsB/b4WMJHD3hUlfsbT0OG/vfBuAx3s/zsTOE2s1LlF3JNERQgjhcJbiYtKmP4Dx+HFcQlsSsWgROn//Wr3HhkNZPL5sF7kGI0Febnxwdx/6twmsfFL2YTXJyT8BXs3hnuUsyfmL/0tUB0VP7z6dad2n1Wpcom5JoiOEEMKhrAYDaTMepGzfPnQBAUTEx+PaovamalutCu/+dph3Eg6jKNAtzJeP7ulLmL9H5ROP/wlfTYayfAiIhHu+59P09by5401ATXIe6fVIrcUl6ockOkIIIRzGWlpK2kMPU7pjB1ofH1p9/DHubdpUf6Gdcg1GHl+2iw2HsgCY1D+CF8dGoXfVVT5x9zJYGQNWE4T3g4lfsihlJe/sVBcHfDD6QR6Ofli2dmiAJNERQgjhENbyck7GxFCybRtaLy8iFn2MR7faWxBwZ2oeM5fuJL2gDL2rlnkTunNrn/DKJykKrH9D3bsKIGo83PwRHyUvYeGuhQA83PNhHop+qNbiEvVLEh0hhBD1zmo0cnLmIxg2bUbj6Umrj/+DR3R0rdRtsSp8uP4ob605hMWq0KaZFx/c3ZvOLf62PYOpFH54DPYsU98PfgzluhdZ8Nc7fLLvEwAe7fUo03tMr5W4hGNIoiOEEKJeWcvKOPnooxj++AONXk+rDz/As3fvWqk7o7CMWct2seloDgDjokOZd3M3fPSulU8sTFfH46TvBI0OxryJufc9vLrlFZYfXg7Ak32fZErXKbUSl3AcSXSEEELUG6vBQNrDMZRs3aomOR+8j1f/2llZOGF/Bk9+s5u8EhOebjpeHteV2/qEXziuJm0bLLsbijPAIxBu/5Ty1lfxzPqnWJu6Fq1Gy0sDX+LmDjfXSlzCsSTREUIIUS8sRUWkzXiQ0p070Xp60uqjD/Hs1++K6zWUm5n3v/18sTUVgK6hvrw7qRftgr0vPHnn5/DjbLAYoXlXmPQFBu9gHlsbw9YzW3HVuvJ/1/wfw1sPv+K4hHOQREcIIUSds+Tnkzr9Acr27kXr60tELY3J2XEij9lf7+JETgkA/xjchqdv6IS7y99mVZnK4OdnYIc69oYu42DCB2RaSpj581T25+7H08WT9657T/auamQk0RFCCFGnTBkZpN1/P+WHj6Dz9ydicTz6qKgrqtNotvJOwiE+WHcUqwIt/fT8+/ZoBrdvduHJecfh6ylweheggaFz4ZqnOFxwlIcTHuaM4QyB+kDeH/4+XZvV3qwv4Rwk0akj6w5msv5QFi+Olf/TCCGarvJjx0i9/37M6adxCQ6mVfwi9B07XlGdSacKeOrbPew/XQjALb3DeHFsV/w8XC88+dAvsPwBdRFAj0C49WNoP4Itp7cw6/dZFJuKifSN5P0R79PKp9UVxSWckyQ6deBUfin3f5aI2arQp3UAN/UIdXRIQghR70r37CHtgRlY8vNxi4yk1aJFuIWHXXZ9ZSYL7yYc5qMNx7BYFQI8XYm9uTs3dG954ckWE/weC3++pb4P6wu3fwr+rVh5ZCUvbXoJs2Kmd/PevHvdu/i5+112XMK5SaJTB8L8PXh4aDve/e0Iz3+fxIA2QQT7uDs6LCGEqDfFf/zByUcfQyktRd+tG63+8xEugYHVX3gJO07kMufbPRzNMgBwU4+WvDSuK828L/K3Ne8EfHc/nNymvu//AIyah0Wr490dC1ictBiAGyJv4NUhr+Kuk7/PjZkkOnVk5nUdWLs/k+TThcxdvpeP7+0jS4cLIZqEvK+WcebVV8FiwWvQIMLfexetl9dl1VVYZuLfvxzk8y0nUBQI9nHn1fHduL7bJfbC2rcCVj0G5QXg7gdj34Zut1BsLOaZ9c+w/uR6AO7vfj+P9HoErUZ7mU8pGgpJdOqIm4uWt+6MZux7f7J2fwbLd566cOlxIYRoRBSrlcw33yQ3Xm0x8Rs/jpavvorGza3mdSkKPyWd4aVV+8gsKgfUsTgv3BSFv+dF6jOWqLOqdn6mvg/vB7fGQ0BrUgtTeeS3RzhWcAx3nTsvD3qZMW3HXPZzioZFEp061LmFL7NGduRfPx/kpR/2MbBdEKF/3y1XCCEaAWtpKelPP0PRr78C0OyRmTR7+PI2wUzLLeHFVfv47UAmAJFBnrw2oTtDOlxkRhXAyUR1wHHuUUADV89WZ1bpXNmcvpkn1z9JobGQ5h7Neee6d+jWrNvlPqZogJpsohMXF0dcXBwWi6VO7/PA1W1Zk5zBX6n5PP3dHpb8o790YQkhGhVTRgYnZz5C2d69aFxdaRk7D7+xY2tcT5nJwscbjhG37ghlJiuuOg0PDW3Pw0PbXbjbOKgDjtf/C/54ExQL+ITCzR9C22uxKlbi93zMwl0LsSpWejTrwdvD3ibYM7gWnlg0JBpFURRHB+FIhYWF+Pn5UVBQgK+vb/UX2EtRoCQHvJpxLKuYG9/9gzKTlVfHd+WegZG1dx8hhHCgkr/+4uSjj2LJykbn50d43EI8+/atcT0J+zN4+YdkUnPVhf+uahvIaxO60775RVY3Bsg8ACsegNO71ffdb4cb/w88Aig0FvLPP/7JupPrALi5/c3886p/yqDjRsbez+8m26JTp0rz4IfHIf0veGgjbYN9mDO6M6+sTubV1fuJCvWjT+sAR0cphBBXJP+75Zx56SUUkwn3jh0Jj1uIW6uarUWTkm3g1dXJtm6qEF93/jkmirE9Wl689dtihs3vwe/zwVIOHgEw5i3odgsAB3MP8vjvj3Oy+CRuWjeeHfAst3a89YqfVTRckujUBY0OTu2EglT49XkY+zb3DYpk+/Fcfko6w4zPd/DDI4Np6SfjdYQQDY9iNJLxr/8j77//BcBn5AhCX3+9RjOrCkpMvPvbYZZsPo7JouCq0zBtSFseua49Xu6X+GjK2AffP3x2hWOg/UgY9x74tkRRFJYfXs78bfMpt5QT5h3Gm0PfpGuQLNra1EnXVV11XaVsgM/O9lHf/R20H4Gh3MytH2ziwJkiuof58fWMgXi4XaTfWQghnJQpI4NTj8+i9K+/gLODjh96CI3WvmnaJouVL7am8vbaQ+SVmAAY2imY52+KuvgmnABmI/y5ADb8H1hNoPeD61+H6Emg0VBkLOLlzS/zy/FfABgSNoTXr35dFgFs5Oz9/JZEp64SHYCfnoatH6oD5B7eBB4BpOWWMD5uI7kGI2OjQ3l3Yk8ZnCyEaBAMW7ZwavYTWHJz0fr4EPr6fHyG27fLt6IorEnO4I2fD9gW/evQ3Jvnbori2o5VDBBO3QqrH4fMZPV9pzFw01vgo66jszdrL09teIpTxadw0bjwaO9HmdJ1iqyP0wRIomOnOk10jCXw4RB1ymOPiXDLRwBsOZbD3Yu2YrYqzLm+Ew8PbV+79xVCiFqkWK3kfLyIrHfeAasV986dCX/3HdwiIuy6PvF4Lq//dIDEE3kABHq5MWtkRyb1a4WL7hIJSWkerH0JdnyqvvcMghv+Bd1uBY0Gi9XCZ8mf8d7O9zArZsK8w/jXNf+iR3CPK39g0SBIomOnukp0rKWllO7eg1eYBhaPBsUKdy6FLjcB8N8tJ3ju+yQ0Gnh3Yi/GRst+WEII52POziZ9ztMYNm0CwO+WW2jxwvNo9fpqrz2cUcS/fjnImuQMAPSuWv4xuA0PDm2Hr/4iG3CCOmM16Tt18T9Dlnqs1z0w8hXwVLeQSC9O59k/n2VHxg4ARrUexYuDXsTXrZa/rAqnJrOuHMiUmUnKzbdgLSyk3dq1uA5+TO1fXv04RFwFXs24+6rWHMooYsnmEzy+bBfuLlpGdb3EkuZCCOEAxRs3kv70M1iys9Ho9bR47p/433ZbtdedyDHw9trDfL/rFIoCWg3c2a8Vjw3vSAu/KhKkzP3wv6fg+B/q+2ad4KYFEDkYULu/fjj2A/O3zqfYVIyHiwfP9H+Gm9vfLEMAxCVJolMHXJs3h4hQlL9yyF0cT8hTc+HQL2of88qZMPEL0Gp5cWxXisrMrPjrFDFf7OQ/9/ZlWKfmjg5fCNHEKUYjWe+9R87HiwBw79iRsLfexL191d3sp/JLWfjbYb5OPInFqnYWjO4awlOjO9G+uc+lLywrgHVvqGMaFQu46OHqJ2HwY+CibveQV5bHa1te49cT6srLPYN7Ejsklla+NZvOLpoe6bqqg66r9OJ0nl8wjieXGlDc3ej422+4GE/CopHqug/XPAXXPQeA2WLlsa928ePe07i7aPnkvn4Man+JZc6FEKKOlR89SvpTcyhLVgf/+k+aSMjTT1fZVXUqv5QP1h3h6+0nMVqsgDqT6omRnegeXsXMJ6sV9iyDNS+AQV1Hh843wehYCGhtO23NiTW8tuU1cstycdG48FDPh/hHt3/gopXv6k2ZdF05UKh3KAFXX8vh3/5Hh9NGMhfHE/rUHBj7Dnz/oDpFsnkUdLsFF52Wtyf2pNxsYe3+TKZ9lsiSaf3pFxno6McQQjQhitVK3tIvyPz3v1HKy9H5+dHilVfwHT3qktek5Zbw/rqjfLsjDZNF/c58VdtAnhzVib7V/Q07sRl+masurAoQ2A5u/Be0H2E7Jac0h9itsbZWnPb+7XltyGuyNo6oEWnRqaPByHlleTz/rxuJ+SIfs96VLr+vxyUgAH75J2xeCC4eMO0XaBkNQLnZwvQlO9hwKAtPNx1xk3tLN5YQol6Yzpzh9D+fw7BxIwBeQ4bQct48XEMu/jfoaFYxH60/yvKdpzCf7aIa1C6IR4d34Kq2QVXfLO+42oKTvFJ97+YD1zwBVz0MLuoWDYqi8MvxX4jdGkteeR46jY5p3acxo8cM3HQ13wldNE4y68pOdTm9/PcTv1F6bwxtMsA85Wa6z40FqwW+uAOOrAXfcHjgd/BW/5iUGi088HkifxzORqfV8Pot3bm9r/Q/CyHqhqIoFCxfTsb817EWF6Nxd6f5nKcIuOuuiw7u3XuygA/WH+GnpDNUfHJc3aEZjw7vUH0rdEmuuvnmtv+AxQgaLfS+F4b90/Y3ENSu/9e2vMYfp9QByR0DOvLq4FeJCoqqtecWjYMkOnaq03V0gI/emco1H2yhVK+l42+/4R0YAqX5sGg45ByBVlfBlFW2bzJGs5Wnv9vDir9OAfDkqI7EDGsvMwqEELXKdPo0p194EcMfakKhj+5B6Pz5uLdtW+k8RVHYdDSHD9cf5Y/D2bbjI7qE8PCwdvSOqGbfPlOpOsj4jwVQXqAeaztUHYcTcq4Lymw1s3T/UuJ2xVFqLsVV68r07tO5v/v9uOouMRVdNGmS6NiprhOdwrICEkcOoWWWmX23RHNb7FdqQfZh+Hi4+n/8zjfB7Z/C2f8zK4rCGz8f5MP1RwG4+6oIXh7XDZ1Wkh0hxJVRrFbyv/6GzH//W23FcXMj+LHHCLxvChrduS1pTBYrq/ek8/GGFJJPFwKg02oYHx3KjGvb0alFFbOoQN18c/eX8HssFKWrx5p3hZEvq+NwzvvylpSdxCubX2F/7n4A+oT04YWBL9DWr+3FahYCkETHbnWd6ABsX/IW3rEfU+gB1q/jGNjhOrXg2DpYeoc6E6vbrXDLx6A994fm040pvLw6GUWBif1aMf+W7tKyI4S4bOVHj3L6hRcp3aEutOfRsyctY+dVasUpKDHx1fZUPt10nNMFZep5rjru6BvO/Ve3pVWgZ9U3sVph33I1wclVv6zh10rtoupxR6W/cXllebyz8x2WH16OgoKvmy9P9H2CCe0nyBYOolqS6NipPhIdxWxm+/BB+GQUsTPKnWGf/Y9Qn7MrIR/8GZZNBqsZou+C8XFw3uZ4q/ek8+iXf2FV4NkbO/PANe3qJEYhRONlNRrJ+eg/ZP/nP2AyofH0pPnjjxMw+S5bK86RzCI+2Xic5TtPUWqyANDM252pgyOZPCACf89qBgErChz4EX6fd25fKs8gGDIL+k0H13PT0y1WC8uPLOedne9QcLY7a2zbsczuO5tmHrK8hrCPJDp2qo9EByB3+2ZO3TcNF4tCwugQpr35Ex4uHmph8kr4Zqq6UFafqepKoOe13Cz+M4VXViej0cCHd/dhtKygLISwU/GfG8l49VWMJ04A4D10KC1eeB7X0FAsVoXfD2Ty2ebjlcbfdG7hwz8Gt2F8r1DcXXSXqlqlKHDwf7D+DTi9Wz3m7geDHoGrHgT3yl1cuzJ38fq219mXsw+ADgEd+OeAf9InpE/tPbRoEiTRsVN9JToAx/+7iNLX3gRg7UN9mfnoknNdUXu+geXTAQX6PwDXv2Fr2VEUhedXJvHfLal4uOr45sGBdAurYhEuIUSTZzp9moz5r1P0q7oGjUtwMCH/fBaf0aPJMRhZtj2NL7amciq/FFC/W43sEsLUwW24qm1g9d3kVisc+AHW/x9k7FWPuXrBVQ/BoJngUXmQ8uni0yzYsYCfjv8EgLerNzE9Y5jYeaIs/CcuiyQ6dqrPRAdgz9xHcV2xhlI3SH59Cnff+My5wr/+Cytj1P/udhtMeN82G8tssTL10+38cTibEF93VsYMqXrPGCFEk2Q1Gsn99DOyP/wQpaQEdDoC776boJkx7Mgy8sW2VP6397RtgT8/D1fu6BvOvQMjqx9/A+og430r4M+3znVRufnAgAfgqhjwqryOTomphMVJi/l036eUW8rRoOGWDrcws9dM6aYSV0QSHTvVd6KjmEwk3jUO773HOR0A2o//j6Hdbjp3wu6v1GTHaoY218Cd/wW92npTWGbi1vc3cTizmKiWvnw8pS9h/h51HrMQwvkpikLxb7+R8ca/MKWmAuDRuzcec+ayqtCTL7encizLYDs/upU/dw+IYGx0KHrXarqnAExlsGspbHpXXfQPwN0XBjyotuJ4Vl5Hx2w1s+LICt7f9T7ZpWq3WJ+QPjzd72m6BHWplWcWTZskOnaq70QHwJyby66xo/DKMXA0VEvbT5cQFXFe//SRBPj6XjAWQ0g3mPwt+LYEIDWnhJvf30iOwYiPuwsvjI3itj7hMhtLiCas7NAhMl9/HcOmzQDogoPJmzydz3268Gtylm3/KU83HWN7hDL5qgh6hPvbV3lpPuz4BLZ8AMUZ6jHPIBjwEPS//4IuKkVRWJe2jgU7F5BSkAJAmHcYT/R9ghERI+Rvlag1kujYyRGJDkDxwf0cmnQHHiVmDkS60PWTL+nYstu5E9J3wdLb1Y3u/FrBXV9DiLoy6PFsA7O/3sXO1HxAXbhr/i3dCfZxr7f4hRCOZ8rMJPu998j/brk6ZsbNjcNDx/FWs4EcLzn3p71HuB8T+0Uwrmco3u52jofJO6EmN399rn7pAnU190GPqCsau13YzbUzYyfv7HyHnZk7AfB39+fB6Ae5o+MdsuifqHWS6NjJUYkOQO5f20i9byru5Vb2dnCj7yff0LZZx/NOSIH/3qquReHqCePeg+63AWCxKny04SgL1hzCZFEI8HTlpXFdGRcdKt+YhGjkrAYDOYs/IWfxYpRSdTDxvvZ9+HebUZw5O0bGz8OVCT1Dub1vq5pNXjiZCJvjIPl7UNSWIJpHqQlOt9vA5cJp5vty9vHeX++x8ZS6V5a7zp17ou7hH93+gY9bNQsLCnGZJNGxkyMTHYDMzes5/cBDuJkUdnbVc3X8CiL8I8+dYMiB7/6hLi4I0H8GjHrN9sdm/+lCZn+9m/1nVy4d1C6IV8Z3o31z7/p9ECFEnbMajeR//Q3ZH3yAJScHgAOBrflP17HsD4pEq4GrOwRzR99WjIhqXv3U8Apmo7rMxdYP4NSOc8fbDlNnULUbXmnJiwpH848StyuONSfWAKDT6Li5w83M6DGDFl6yDIaoW5Lo2MnRiQ7A6d9+IvuR2bhY4K9ungz9z3JCA1ufO8FqUVcZ/ePf6vtWA9QtI3zVRQeNZisfrDvK++uOUG624qrTcP/VbXnkuvZ4usm0TSEaOsViIX/VD5xa8C66zNMApHsF8UnUjfwZ2oPOLX25pXcY43uGEeJbg9mYRRmwcwlsXwTFZ9RjOje15Wbgw9Ci+0UvO5J3hI/2fMQvx39BQUGDhhvb3sjD0Q8T4RtxpY8rhF0k0bGTMyQ6AKd+XE7unH/iYoGD7fT0+Pi/tA3tWvmkgz/B8hnq/liezWDsO9Dl3Iyt1JwSXvphH78dyAQg1E/PU9d3Ynx0GFrZJ0uIBsdstrB76XJM8f/BL/MkADl6X77oNILd3a9lTO9W3NwrjC4ta/C3S1EgdbOa3CSvAqtJPe4dAv3uhz73VdpN/Hx/T3AArmt1HTG9YugY0PGi1whRVyTRsZOzJDoAaQmryZk1B3ejQkqojtAP36dnx2sqn5R7DJbde26BruhJcP3r4OEPqDMe1iRn8PIPybaFwLq09OWZGzpzTYdmMn5HCCdnsSrsSMlh91craf3DF7TKVzfELHL14IeuI+DmOxjTrw39IwNr9gWmNB/2fgOJi8+tfwMQ3h/6T4eoCRcdfwOwL3sfi/YuIiE1wZbgjIgYwYPRD9IpsNNlPqkQV0YSHTs5U6IDkJG4kZMPzMCzxEJ6kAaXt1/h2n63VT7JXK52ZW16Vx0s6Bum7pHVbpjtlFKjhcUbU/hw3VGKys2AOn5nzvWd6dnKvx6fSAhRHaPZypZjOfy8N528n39lzO6faVeoJjglrnoODB5D6P33MbhnW9xcarDZpaJA2lbY8Zm6yJ9Z/fKDqyd0vx36TYOW0Ze4VCExI5FFexexKX2T7bgkOMJZSKJjJ2dLdAAKDu7jwH2T8c0rp8ATcp/7Bzfe8tSFJ6ZugRUPQp66VgXXPg1D51YaNJhnMBL3+xGWbD5hW0vj6g7NmDmsPQPaBl1YpxCiXhSVmdhwKJu1+zP4fV86vY4mMvFQAhFFatez0d2D8vG30+2xGXgGBVZT298UZ6qLj+5aClkHzh1vHgW9p0D0RFsr8N9ZrBbWpa3jk32fsDtL3btKp9FxY5sbmdZ9Gu38ZWNh4Rwk0bGTMyY6AKXpJ9k59XYCT+Rj0UDy5AFMmPsf3HR/a1ouL4a1L6r97QDXPQfXXJgUncwrYcGaw3y/6xQWq/pP3j8ykJjr2kuXlhD15FR+Kb/tz+DX5Ay2HMtBYzQyInU7tx5ZT6hBnUVl9fIm6J57aDblHlwCAqqp8TwWExz+Vd1K5tAv6ibBoLbedLsFet8H4X0vOnsKoMxcxqqjq1iSvIQTheoGoG5aN27ucDP3db2PcJ/wK3l0IWqdJDp2ctZEB8BSWsqGRybR4s+DAOzt14yh735J84CL/MHZtBB+/af636PnqzMmLiItt4QP1h/l28STthaezi18mDo4kvE9w+xbCl4IYRezxcrO1Hx+O5DJbwcyOJShLrznbSxhTMpmbkn5E9+yIgB0AQEE3ncfAXdNQudj59ozigLpO2H3Mkj6DkrO7UBOWF/odbea5OgvvY5Odmk2yw4u4+uDX5NblguAr5svd3a6k7u63CX7UQmnJYmOnZw50QG1n3zb28/j/Z/v0CqQ1sKFZm/Mo/eAcReevO4NWBer/vfYd9TZE5dwpqCM/2w4xpfbUik1qd/8AjxdmdQ/gnsGtqaln+yhJcTlOF1QyoZDWWw4lM0fh7MoLDPbykJLcrg/czv99m/EpVwdL+MS2pKg+6bif9utaD3t2FQT1L2m9n6jJjg5h88d92qudkv1nAzNO1dZxb6cfSxNXspPx3/CbFVjDPUK5d6u93Jz+5vxdLUzFiEcRBIdOzl7olPh2NrvyX36ObwMFowucOq+kYyevQCd9rwWGEVRu7E2vgNo4Jb/QI87qqy3oMTEssRUPtt0wjZLS6uB6zo3546+rRjWuTmuuhoMfhSiiSkxmtmaksvGg5lkrP+THjt/Iyr3OKe8gznsH05689a0aRfKNYc24pO4Sd2qAXDv2JGg6ffje/31aFzt2B6hOFMdULz3Wzi57dxxFz10vklNcNoOA92l184yWUysTV3LVwe+sm3TANAzuCd3R93N8IjhuGhl7S3RMEiiY6eGkugAFJ1OZevMuwnblwXA0Sh/ohd8TKvW5+2RpSjwvyfVMTsaHfT9hzpI2Tu4yrotVnVa+icbU9iakms7Huzjzq29w7mtT7istiwEYLJY2XOygM1Hs/njcDaHD6cxLGU7NxzfQpghu9rrvYYMIfC++/AaPKj6sXGGHDjwg5rgpGw4tyUDGmhzNfS4E7qMA33Vf7syDBl8c+gbvj30LTll6lggF40LoyJHcXeXu+kefPGFAYVwZpLo2KkhJToAVouF39+eQ7PF/8PNAkUeGopm3MqwB15Gqz3b8mK1wurHYedn6ns3b3WfmoEx4F593/+RzGK+SUzj2x0nyTEYbce7hvoyvmcoY6NDpWtLNBlmi5V96YVsOZbDpqM5bD+eS2m5iZ5ZRxh9YiuD0pNwPTvw1+Lhhc+4sTQbcwOmk6co27ePsn37MJ06hffQawm8917cO3So+oaGHDiw+rzkxnKuLKyvut9d15vBp+otFixWC5tPb+abg9+w/uR6LGfrCfYI5raOt3Fbx9to7nnxhQGFaAgk0bFTQ0t0KhzfuYHjTz5OSLra3XSyYwDd/rWQlp17nzspZQOseVEdrAjgFQxXPwl9poBr9YmK0WzltwMZfJ14kg2HsjCfna2l0UC/yEBu6NaCUV1bEOYvSY9oPMrNFvaeLGBrSi5bU3LZcTwXg1FNEpobchmRlsjotESaG861fOq7dsV/4p34jRlj/zib8xWchAM/wv4f4MTG81pugBY91MSm6wQIbFttVVklWaw4soLvDn1HuiHddrxPSB8mdp7I8IjhuGplJ3HR8EmiY6eGmugAmMvLWPt/j9Hiqw24m8Gkg4zbr+baOW/h5nm2m0lR1F2IE15RV1UGdcDiwBh1sTA7WngAcg1G/rf3NKt2pbPteG6lsu5hfozuGsLIqBZ0DPGWqeqiQckvMbLjRB7bj+ex40Quu08WYDSfSzT05nJGZO1j7Jm/iDix33Zc6+OD39ix+N9+G/ouXWp2U0WBzP1w8Ec48L9zX0YqtOiuJjdREyCo+nVrTFYTG05u4PvD3/PHqT9srTc+bj6MazeO2zrcRvuA9jWLUQgnJ4mOnRpyolPhYNIGjjz7JG0PqdNU8/1d8HxkBj3uijmXdFhM8Nfn8McCKEhVj+n94aqHoN908LJ/8cBT+aX8tPc0v+7LYPuJXM7/DWrpp2dop2Cu7dicwe2D8NHLN0fhPCxWhYNnivgrLY+dJ/L5KzWPY9mGC84L9tBxK+kMTkkkaNdmNGVlaoFGg+dVA/C/+WZ8Ro1Cq6/BBppmI6RugoM/w8H/Qf6J8wo1EHEVdBkLncdAQKRdVR7JO8LKoytZdXSVbWo4qIOLb+90O6Naj0LvUoMYhWhAJNGxU2NIdEDtj0+IfxnPj78jqFD9NprVNoC2/3yZ8MEjzzvRpE5L/eNNyDmiHnPRq8vBX/UQhHS9SO2Xll1cztrkDH7Zd4ZNR3MoP++bsItWQ89W/gxqF8TAds3oFeEv6/SIeqMoCifzStl9Mp/dafnsPllA0qkCSoyWC85t28yLfq0DuNp8ho77tsDva7Dk5NjKXVtH4H/zzfiNG4draKj9QRRlqIv4Hf4Fjq4DY9G5Mp27um1Lpxug4w3gE2JXlTmlOfyU8hOrjq5if+65FqYgfRDj2o1jQocJtPWrvotLiIZOEh07NZZEp0Ju/hnWvPEIHVcnoT+7KXFWj1ZEPf0yzfoMPHei1aJ2aW18B07vPnc88moY8CB0HA26mrXGlJksbDmWw7qDWaw7mMnxnJJK5e4uWnpHBNA3MoC+kYH0ivDHV1p8RC1QFIW03FKS0gvYe0pNaJJOFZBXYrrgXG93F3q28qdXhD+9W/nTteQMrFtL4Y//w5R+bkyLLiAA3xtvxG/sTeijo+3rkrWY1L2ljiTAkbVwZk/lcq9g6DAKOt2oJjluXnY9X4mphHVp6/gx5Uc2ntpo65py0bpwddjV3Nz+ZoaED5GxN6JJkUTHTo0t0amw58AG9r3+HN23ZqE7+y+c16cd3Z5+Fd8evc6dWLHp35YP1IGQFTM8vEOg513Q6x67xghcTGpOCZuPZbPpqDpbJauovFK5RgOdQnzoFRFAj3A/eoT70THER9btEVUqM1k4nFHM/jOFJKcXkny6kP3phbbNa8/nqtPQpaUv0eH+RLfyJzrcjzbNvDAlJ1P06y8U/vIrptRU2/laT0+8hw/Hd8yNeA8eXP36NooCOUfh2O9w9Hd1AsD5rTYAob2gw2joOApa9gKtfb/fJquJzemb+V/K//gt9TdKKzbkBLoFdWNc+3FcH3k9AfoabBMhRCMiiY6dGmuiA+q33I3bvuP4u/9Hr52FaM/+Sxd0b02HmCcJvHZ45W+p+Wnq+ju7loIh69zx1kPUpKfLTVUuJV9dLEezitmWkkfiiVwSj+eRmltywXnuLlq6hvoSFepLl5a+RLX0pVMLHzzdZBGzpsZssXIit4TDGUUcyijm4Jki9p8p5Hi2AetF/mq56bR0auFDtzA/up99dWzhjbuLDsVioWTHDooTEihas7ZSy41Gr8f7mmvwHTMG72uvqX7cTdEZSPkDUtap3VGFJyuXewZBu+HQfoTaauNt/xRus9XMtjPb+PX4r6xNXUtBeYGtLNw7nBvb3siYNmNo6y9dU0JIomOnxpzoVLBYLfy0YTG5H3xI7z0lthYeQ0Qzwqc/TPNxt6B1dz/vAhMc+hl2LlGb3yumuurc1W+l3W9Xv6G6Xtkgx8zCMnacyGP3yQL2nMxn78mCi34r12igdaAnHUJ86NDcmw4h3nRo7kObZl54uUsC1NCVmSwcyzJwNKv47MvA4YwijmUZbPux/V2ApyudWvjQNdSPqJa+dA3zpV2wd6XWQGtJCYZNmyj6/XeKf/sdS16erUzj4YH30GvxHT0a76uvRutVRRdScaY65TvlDzj+B2Qfqlyuc4NWA6DtUDWxqUGrDagtN9vPbGftibWsPbGWvPJzcQbqA7k+8nrGtB1D92bdZUajEOeRRMdOTSHRqWCymPhl8+ec/OQjBmwrtI3hUfx8CLrtdgLuvBO3iIjKFxWchN1fwp5vIPvgueNuPtBhpNrK02GU3dPUq2K1KqTkGEg6VUDyabVbYv/pQrKLjZe8JtjHnTbNvGgT5EXrZp60CvAkPMCDVoGeBHm5yQeDkyg3W0jLLSU118Dx7BJSsg22V3pBKZf6K+ThqqP92eS2cwsfOrXwpUsLH4J93C/6b2tKT6d4wwaKfvuNki1bUYznfne0fn74DB2Kz8gReA0ejNbjEus/FZyEE5vU5ObEpgsTGzTQsoc6nq3dMIgYaPdYG9vPw1LO5vTNrDmxhnVp6yg0FtrKAtwDGNF6BKMjR9M3pG/lbV6EEDaS6NipKSU6FcxWM2v2riB58Tv035xDs3N/Y/EaMgT/W2/B+7rrKrfyKApkJKkztvZ+V7m5XuemfpvtdKOa/PhdZHf1K5BZVMbhjGIOZxRxOLOYw5nFHMksJtdw6QQI1A/Jlv56WvrpaennQUs/PSG+epr7uBN83svdRT5IrpTFqpBZVMbJvFJO5pWQlnvuf0/kGDhdWHbJZAbAz8OV9s29aRfsRbtgb9oFe9MxxIfwAA+02ksnq4rRSMnOvyj+YwOGDRsoP3ykUrlreDjeQ4fiM2I4nn36XDjmxmqBzGRI3XLu9feuKDTqbMTIIWpyEzkYPGo+Lia/LJ8Npzbwe+rvbEzfWGnMTaA+kOsirmNk65H0b9Ff9psSwg6S6NipKSY6FQwmA4+veRTTpi1cvxN6pChozv46aH188L3hBvwmjMejV6/K356tVji1Q92DZ/9qyD1aueLgLtBhBLQfqTbpX2EX16UUlJo4nm3geI7aMpCaU0La2Q/XjKKqP1jP56N3IdDLjQBPN4K83AjwcsPPwxVfvSt+Hi74nv1vL3cXvN1d8HLX4a13Idj74q0KjY3ZYiXHYORMQRmnC8rIKFT/90xBKen5ZZzKLyWjsMy2cvaleLrpaB3kRetAT9oGexHZzIu2zdT/tbf1TVEUjCnHMWzcqL62bUMpOW+sl1aLR3Q03sOG4TNsKG7t21eu15ANJ7err7RtcGonmP62jo5Gp7bYtB6sviKuAs/AGvzEzsV6vPA4G05uYP3J9ezM2GmbLQUQ4hnCiNYjGB4xnN7Ne0vLjRA1JImOnZpyogNqE/rTG54mITWBlvlaXsgZTMifBzCnn7ad4xLaEt/R1+N7/Wj0PXpU/uBQFMg6oO7Nc3iN+gFy/vL1Lno12WlzDbS5Vp2BUsXuyrX2XGYL6fllnC4o5XR+GWcK1f8+U1BGVrGR7KJysorKLzkGxB7tgr24/+q23NwrrEGtD6QoCiVGC3klRvIMJnJLjOQUl5NTbCTbUE52kZHs4nIyi8rJKiojx2C0K2nUaTW09NNX6j4MD/CgdZAnEYFeNPO+vK5EU0YmJVu3YNi8BcOWLZhPn65UrgsKwnvIELyvvQavQYPQ+furBeXF6vTuUzvOvfJTL7yBmw+06getrlKTmrA+4H55G9gaLUYSMxL54+QfbDi5gdSiyvfrGNCRYa2GMSxiGFGBUU0iURairkiiY6emnuiA2pX10qaXWHl0JQC3truF20ui8P9tF8W//or1vG/MLqEt8Rk+Ap9hQ/Hs2xeNm1vlykpy4ehv6iDmIwlgyKxc7uoF4X3OfqgMgPB+lz2T60opikJ+iYkcg5G8EiO5BiN5BiO5JUYKSk0UlpooLDVTUGqiqMxEcbkZQ7kFQ7mZYqPZ9uHfzNuNe66K5J6BrQn0cqv6plcQa7nZSrnJSpnZQrnJSqnJgsFopqTcQonRTInRQlG5meIyM8XlJorLzBSWmSksNVFw3iu/xFTjBE+n1dDcx50QX72tC7CFn54wfw9C/fWE+nvQ3EePropuJnuZMjMp2b6dkm3bKdm+HeOxY5XKNa6uePTtg/fgwXgNHox7p05ojEVwJglO74L0Xer/Zh8GLvLnrVlHCO+vJjfh/SC4M1xBa0paYRp/pv/Jn6f+ZPuZ7ZW6pFy1rvRr0Y9rwq/h2vBrCfep3W5dIZoySXTsJImOyqpYeTPxTZYkL7Ed6xzYmVsjxjL0lB+WhD8o+v33St0EWm9vvIYMIXDKvXj26nVhpYqiDuRM2QAp6+H4n1Ca97eTNBDcSf0WHdoLwnpDSDdwcb+wPidSVGZi2fY0Ptl4nFP55z7YXLQa3Fy0uOoqXhq0Gg06rfrSarjgW7xVUbBaFSyKgtWqvjdZFEwW63mv2v+/qZuLlkBPN/w9XQn2cSfIy40gb3eCvN1o5u1Ocx93mvvoCfZxJ9DLrVaSmL9TFAVTaiolO3ZSsiOR0sQdGE+cqHySRoM+KgqvgVfhedVVeLYLRlt4TE1szuxRX3nHL34Dn5bq71ZY73O/Y1eYWBcaC9l2ehub0zezKX0TJ4srj+kJ9ghmcNhghoYP5arQq/ByrdlAZSGEfSTRsZMkOpVtP7Odbw99y9oTazFa1cG+blo3rm11LTeGjqD3cQ1l/9/evQdFdd7/A3+fc/YOy8KywAJyFQnxikFgNCapion52qhNO9XqNCZtTKbX6Vcdm7ZG2+n8qo3O1BlLm441Y2On3hpT21w0CY32WzVqDIiAWUBBUWFhue6y9z3P74+Dqyt3XVhYPq+ZM4c9++zhOR8PZz8+z3POc+q/sJ08dfcR+XI5kn7z/6B77rmBdy6KUjdXw2fAjXPSuq8vKF4uJT/GGVLSY5whDQaNMAT3YIPA6xPxQUUT/vx/11B+s3PwDwQBzwEquQC1XIBGKUAjl0GtEKSxQ0oZIpVyaFXSeCKtSgadWu5fotRyRGvk0EcooJYLo951IrpccFZWwlFaBkdZKexlZfC1WAILcRyUj+YgYtZ0aKYYoEkABEcdYK6SBg67uvreeVQykDgLSMwFknKl9RCnVRiIw+tAWXMZzjedx/nG86horYB4T/esjJNhdsJsPJ70OOYnz0d2TDZ1SREyCijRGSJKdPrW6erEe9fewzs176Cmvca/XSvXoiitCEtSn8YMixqdb/0F1o8/BgDE/e//IvaVdcO7yFvN0szNt764u3a09V1WEyt1M8Q9AhgeAWKzpKc2R6c+VNdDsLR3u+HySi0wLq8It1eEr6elxicyiD3rvkitPT0tPxwHmcBBLvBQCLz/Z5Wch0ouQMZz4+KLlIki3PXX4Si/BGf5ZTjKy+E0mQDPfdMyyGRQT0mBJiMG6gQGTVQrBGsNYG/te8d3EuGEaYBxppQIG2c80IDhvji9TpS3lONz8+e40HQBl1ouwSMG1jlDl4F5SfMwN3Eu5hjnUKsNISFAic4QUaIzMMYYqtur8f619/FB3Qcw283+97RyLZ5KfhJf+9gG7d9LAADRK1fC+PpmcLIHHHDMGNDZIHVLmO90TVT0tPz0c6rycmm2Z30mEJMmJT7RPWtdivQFOA4Sg/GM+XxwX78BZ1UVnJWVcFZUwFlVBbG798zgglYFdbIKmlgn1BHNUEXZ0Pfd1Jz07xn3KBCfI7XuxU8FDFOGPQ/bQKxuKy61XMIX5i9w0XwRly2XeyU2CZoEFCYWojCxEAXGAhgjjEH7/YSQB0OJzhBRojN0IhPxhfkLfFj3IUpulKDVefd/3Eu/4PHCR25wDOAy0xDzxFegyZ8DdV4eZDFBmIvHbQdaa4AWk9T91WKS5hhquwb4XAN/VlACUUlS10ZUkvRI/sgEQGuUfo6Ik1qL1HpANjKDicOJz2aDq7oGrupqOE1fwlV1BU6TCczp7FWWEwCV3gO13gV1rBsqvQfyCF9g3snLpZY5Q/bdJS5barVTaIJad8YYbtlu4VLLJZQ1l6G0uRTV7dVg9yXR8ep45BnzMCdhDgoTC5GqTR0XrWiETCQTJtExmUxYuXJlwOsDBw5gxYoVQ/o8JToPxif6UG4pxyfXP0HJjRLcst1CfrWIHx8TobxvFgdZZgYicmdDPWsW1LmzoMzKAicEqatJFIGuW0BrrdTq03EdaL8u3UbccT1wzq6hUOqkFiB1tDRoVXVnHSXdhqyMBBSR0lquAeRqQKaW1nK11NIgKKTkSpADvKxnEQCO779liTHp4XXMJ61FjzQVh88tLV434HVKi8fRs7ZLCaC7u+dZMByQs1Rq8QgC0W6H69pVuCvL4PqyAq7aq3DV34Snpe+xSJwgQhXthUrvgSpGSmqUUV5wPKQY6FIAfQagn9zT7djT9ahLGbFHDtg9dlS1VuGy5TIutVzCpZZLsDgsvcqlaFMwO3428hKk5CZFm0KJDSFj3IRJdO5ls9mQnp6O69evI2KguWvuQYnOw2OMobajFv+99V9c/PLfEC9cwiM3vHi0gSGl93cKOJUKqpwcqKZOhWraVChzcqCcPHnwyRQfhNcFWBuBrttA5y3Aeluau8jWDNjM0tJtkcYF3fv8n5HC8QDu+wJlIvrtlnsQafOBvLXAo8t6P6yRMcBtkx4DYG8Fs1rgabgGd901uBtuwn2rBe6mDrha7PBa+4+HTO2DMtoDpc4LVYwHqhgPFAl6cLH3dB3e6UaMyRjRZOYOj8+D6o5qVLVWodJSiXJLOa52XA0YOAxIg4dz9DmYFT8Lj8U/htnxsxGniRvRuhFCgm9CJjp/+9vfcOzYMRw6dGjIn6FEJ/hsbhvONZ7DZ42fobL2DBRf1mPKbYas20DWbQZNXzM38DwUqalQZmdDmZUFRUaGtKSnQ4gchYGeogg4O6QBsPZWwNkJODqktbNn7bZJD6G7s/Y6pNYVjx3wOKXXPo+UXN3zBNyHwst6WojkPS1HKukhjDKVNL+SXCN17ygipeTtasndhE0dAyQ9Bl9nBzwt7fC02uBpd8LdBbhtMni6BXhsMjCx/5YLQeGDUueFIlYGZaIOypQEKCenQZaYDmiTgOgUacqPqORRfSSAw+tATXsNvmz7ElfarqCqtQo17TW9xtYA0viaGYYZmBk3E7PiZmFq7FSoZCPztG5CyOgZN4nOf/7zH+zYsQMXL15EY2Mj3n333V7dTsXFxdixYweampowa9Ys7N69GwUFBb32tWLFCrzwwgt4/vnnh/z7KdEZeS32FpxvOo8LTRdQ1vQFHPXXkNHEkNnEkGEG0s0Mkb2Hd/jJ4uMhT02BYlIK5CmToEhJgTwxEbLEJMgT4nvPXzQWiD6py0n09nRJiXe7pnrh7nZt8YI0sEWQS2NXBpgFmzEGsbMTnuZmeJtb4G1uhvd6NTxVZ6R1lweebgGiZ+CZtDmBg1yvgiJeB0VyHJRpk6CYnAXFI9MgS86WxjA9ZGuMR/TglvUWrnddR31XPa53XUebsw35xnw8k/4MDOq+Hx3AGEOzvRnV7dX+xdRmQl1XXa+WGgDQKXWYqp+KaYZpmG6YjhmGGYjXxD9U3QkhY9O4SXQ+/PBDnD59Gnl5eXj++ed7JTqHDh3CCy+8gDfffBOFhYXYtWsXjhw5ApPJhPj4uxewrq4uZGVl4caNG1ANowuEEp3R1+HsQFmLNBD0suUyqiyVkHd0I6WFIbUFmGRhSGplSGoDdPZBdsZxkBkMkMXHQxYX51+EWD1kej2EGD0EfQwEXTQEXdTIdI8FAWMMzOmEr8sKsasTvq4u+Do74WvvgK+jHb72dnjb2+GztMLb2gpvWyt8ltaA2bkHIkRFQm6MlxLFtAzI0zKkhDE1FfLExKCMmepyd+GW9RZu227jpu0mGqwNuNF1Aw3WBjR2NwbM83QvnuORb8zH/2T8D1K0KajtqMXVjquo7ahFbUctOl19jwnSq/R4VP8ocvQ5mBo7FVNjpyI5MpnG1hAyQYybROdeHMf1SnQKCwuRn5+P3//+9wAAURSRkpKCH/3oR3jttdf85fbv348TJ07gr3/964C/w+VyweW6e5dOV1cXUlJSKNEJIZGJqO+sR0VrBSosFTC1mVDdXg2bx4YIB0NiG5DQwRDfCcR3MCR28kiwCtB1eiHzDm9cDadQgI+KghAZCT4iArxG419zKhV4lRKcSg1OqQAnl4OTycHJZNLt8gIPjud7Wl44aVgNYwATwRgDvD4wrxfM5wW8XohuN5jbDeaS1qLTAeZwQLQ7IDocEO12iDYbRJsNvu7u3s+XGSJBp7ub6CUaIU8wSmujEfKkJMgTE8FrHu7uJa/ohcVhgdluRlN3E5q6m3DbdhuN3Y1o7G7ELdstWN3WAfehlqmRFpXmX9QyNT698SnKLeUDHx8nID0qHdkx2cjWZyM7Jhs5+hzEqeMoqSFkAhtqojPysys+BLfbjYsXL+JnP/uZfxvP8ygqKsLZs2cDyh4+fBivvPLKoPvctm0bfvWrXwW9ruTB8RyPzOhMZEZnYtnkZQDu3gZsajOhpqMG1zquoaKzFvWd9T3jMESAcYiyC4jtAvQ2hmgbYOgWkOxSI9algM7BIaLbC6XVBcHmBCeKYG43fBYLfJY+RkmPBTwPISoKvC4KQpQOQkw0ZDExEKJjIMTESC1VsQbIYvUQYg2QxRnAKx98bIzdY0ebsw1tzja0OlrR6mxFi70FLQ5psdgtaLY3w+K09NlVdD+9So/kyGQkRSYhVZuKFG0KUrQpSI1KhUFtAM8FdqO9PONlNFgbcKL+BE7Un4DNbcPk6MnIis7yrzOjM6EUxvaUIISQsWtMt+jcvn0bycnJOHPmDObOnesvt2nTJpw6dQrnzp0DAHR2diI7OxsNDQ1Q3D/J5H2oRWd884pe3LTexA3rDdR31kvrrnrctN5EU3dTv90jYAwqNxDhBGLcchgRhThoESuqofMpEeWTI0JUQOMToPLxUHg5KBgPuchD5mMQRIBjAEQRjImAyACOA8dzADjpZ5kAyGTgBKkFiFMopEUprXmVGrxaBU6tBq/WgNdoIGh7WpYiI8FrteAjIobVSsEYg0f0wOaxodvdDZvHBpvHBqvbii53F7pcXdLa3YVOVyc6XB1od7ajw9WBDldHwASUg5FxMsRp4pCgSUBiZCISI+4uSZFJSI5MhkYe3OfeEEJIf8KiRWeodDodzGbz4AUBKJVKKB/if8AktGS8DOm6dKTr0vHkpCcD3vOKXn+Xyi3bLTR1N/m7Wsx2M8zdZrQqrWiFF7VoA9DPVBP9UApKRMgjoJFpoJaroRJUUMlUUAkqKAUl5LwMckEOOS+HjJdBxsvAc4DA+cBzLvCcBxzudu8wMIg+EWKnCG+7FyIT4WM+eEUvPKIHHp8HHtEDl88Fl88Fp88Jl1f62e61w+FxwO6195/cDeO4YlWx0Kv0iFXHwqA2IE4Thzh1HAxqAxIiEpCgSYBepe/VIkMIIWPdmE50DAYDBEHolcSYzWYYjfQIdhJIxsswSTsJk7ST+i3j8rnQ6miFxWFBi6PF37rR5mxDh7MD7a72Xq0hd25ZvpNwtA0zQRotapkaWrkWEYoIRMojEaWIQpQySloroqBT6hCjikG0Mtq/xKpjoZFpaKwLISRsjelER6FQIC8vDyUlJf7uLFEUUVJSgh/+8IehrRwZl5SCEkmRSUiKTBryZ9w+N7o93f7F7rXD6XVKrSxeJxxeB9w+t9QK07O4fW5/C82ddV+9xBzHQeAECJwAnuMh8AIUvMLfKiTn5VAICqhkKmnd03qkkWukliWZGmq5GhqZBrK+J4wihJAJLeRXRpvNhtraWv/ruro6lJWVQa/XIzU1FevXr8fatWsxZ84cFBQUYNeuXeju7sZLL70UwlqTiUQhKKAQFIhRBWHOLkIIIaMq5InO559/jgULFvhfr1+/HgCwdu1a7Nu3DytXrkRLSwu2bNmCpqYm5Obm4vjx40hISAhVlQkhhBAyToypu65CgR4YSAghhIw/Q/3+plsoCCGEEBK2JmyiU1xcjKlTpyI/Pz/UVSGEEELICKGuK+q6IoQQQsYd6roihBBCyIRHiQ4hhBBCwhYlOoQQQggJW5ToEEIIISRsUaJDCCGEkLBFiQ4hhBBCwhYlOoQQQggJW5ToEEIIISRshXxSz1ApLi5GcXExvF4vAOnBQ4QQQggZH+58bw/23OMJ/2TkmzdvIiUlJdTVIIQQQsgDaGhowKRJk/p9f8InOqIo4vbt29BqteA4LtTVGXO6urqQkpKChoYGmiKjHxSjoaE4DY5iNDiK0dBMhDgxxmC1WpGUlASe738kzoTturqD5/kBM0EiiYqKCts/lmChGA0NxWlwFKPBUYyGJtzjpNPpBi1Dg5EJIYQQErYo0SGEEEJI2KJEhwxIqVRi69atUCqVoa7KmEUxGhqK0+AoRoOjGA0NxemuCT8YmRBCCCHhi1p0CCGEEBK2KNEhhBBCSNiiRIcQQgghYYsSHUIIIYSELUp0CCGEEBK2KNGZYIqLi5Geng6VSoXCwkKcP39+wPJHjhxBTk4OVCoVZsyYgQ8++CDg/RdffBEcxwUsS5YsGclDGBXDiVNlZSW+/vWvIz09HRzHYdeuXQ+9z/Eg2DH65S9/2etcysnJGcEjGB3DidOePXvwxBNPICYmBjExMSgqKupVnjGGLVu2IDExEWq1GkVFRaipqRnpwxhRwY5ROF6XhhOjo0ePYs6cOYiOjkZERARyc3Oxf//+gDLheB71i5EJ4+DBg0yhULC33nqLVVZWsnXr1rHo6GhmNpv7LH/69GkmCAJ74403WFVVFdu8eTOTy+Xs8uXL/jJr165lS5YsYY2Njf6lra1ttA5pRAw3TufPn2cbN25kBw4cYEajkf3ud7976H2OdSMRo61bt7Jp06YFnEstLS0jfCQja7hxWr16NSsuLmalpaXsypUr7MUXX2Q6nY7dvHnTX2b79u1Mp9Oxf/zjH+zSpUts2bJlLCMjgzkcjtE6rKAaiRiF23VpuDH69NNP2dGjR1lVVRWrra1lu3btYoIgsOPHj/vLhNt5NBBKdCaQgoIC9oMf/MD/2ufzsaSkJLZt27Y+y3/zm99kS5cuDdhWWFjIXn31Vf/rtWvXsuXLl49IfUNluHG6V1paWp9f4g+zz7FoJGK0detWNmvWrCDWMvQe9t/d6/UyrVbL/vKXvzDGGBNFkRmNRrZjxw5/mY6ODqZUKtmBAweCW/lREuwYMRZ+16VgXD9mz57NNm/ezBgLz/NoINR1NUG43W5cvHgRRUVF/m08z6OoqAhnz57t8zNnz54NKA8AzzzzTK/yJ0+eRHx8PB555BF873vfQ2tra/APYJQ8SJxCsc9QGsnjqampQVJSEjIzM7FmzRrcuHHjYasbMsGIk91uh8fjgV6vBwDU1dWhqakpYJ86nQ6FhYUT9ly6P0Z3hMt16WFjxBhDSUkJTCYTnnzySQDhdx4NhhKdCcJiscDn8yEhISFge0JCApqamvr8TFNT06DllyxZgrfffhslJSX47W9/i1OnTuHZZ5+Fz+cL/kGMggeJUyj2GUojdTyFhYXYt28fjh8/jj/+8Y+oq6vDE088AavV+rBVDolgxOmnP/0pkpKS/F9Idz5H59Jd98cICK/r0oPGqLOzE5GRkVAoFFi6dCl2796NxYsXAwi/82gwslBXgIxvq1at8v88Y8YMzJw5E5MnT8bJkyexaNGiENaMjDfPPvus/+eZM2eisLAQaWlpOHz4ML773e+GsGahsX37dhw8eBAnT56ESqUKdXXGpP5iRNclQKvVoqysDDabDSUlJVi/fj0yMzPxla98JdRVG3XUojNBGAwGCIIAs9kcsN1sNsNoNPb5GaPROKzyAJCZmQmDwYDa2tqHr3QIPEicQrHPUBqt44mOjkZ2dvaEPJd27tyJ7du346OPPsLMmTP92+98js6l/mPUl/F8XXrQGPE8j6ysLOTm5mLDhg34xje+gW3btgEIv/NoMJToTBAKhQJ5eXkoKSnxbxNFESUlJZg7d26fn5k7d25AeQD4+OOP+y0PADdv3kRraysSExODU/FR9iBxCsU+Q2m0jsdms+Hq1asT7lx644038Otf/xrHjx/HnDlzAt7LyMiA0WgM2GdXVxfOnTs3oc6lgWLUl/F8XQrW35soinC5XADC7zwaVKhHQ5PRc/DgQaZUKtm+fftYVVUVe+WVV1h0dDRrampijDH27W9/m7322mv+8qdPn2YymYzt3LmTXblyhW3dujXg9nKr1co2btzIzp49y+rq6tgnn3zCHnvsMTZlyhTmdDpDcozBMNw4uVwuVlpaykpLS1liYiLbuHEjKy0tZTU1NUPe53gzEjHasGEDO3nyJKurq2OnT59mRUVFzGAwsObm5lE/vmAZbpy2b9/OFAoF+/vf/x5wa7TVag0oEx0dzY4dO8bKy8vZ8uXLx/VtwcGOUThel4Ybo9/85jfso48+YlevXmVVVVVs586dTCaTsT179vjLhNt5NBBKdCaY3bt3s9TUVKZQKFhBQQH77LPP/O899dRTbO3atQHlDx8+zLKzs5lCoWDTpk1j77//vv89u93Onn76aRYXF8fkcjlLS0tj69atG7df3vcaTpzq6uoYgF7LU089NeR9jkfBjtHKlStZYmIiUygULDk5ma1cuZLV1taO4hGNjOHEKS0trc84bd261V9GFEX2+uuvs4SEBKZUKtmiRYuYyWQaxSMKvmDGKFyvS8OJ0S9+8QuWlZXFVCoVi4mJYXPnzmUHDx4M2F84nkf94RhjbHTbkAghhBBCRgeN0SGEEEJI2KJEhxBCCCFhixIdQgghhIQtSnQIIYQQErYo0SGEEEJI2KJEhxBCCCFhixIdQgghhIQtSnQIIYQQErYo0SGEEEJI2KJEhxAyLpw9exYcx2Hp0qVB3e9LL72EzZs3B3WfhJCxg6aAIISMCy+//DLsdjuOHj2Ka9euISkp6aH36fP5YDQa8f7776OgoCAItSSEjDXUokMIGfNsNhsOHTqEn/zkJ1iwYAH27dsXlP2eOXMGcrkc+fn5AIA///nPmDlzJtRqNXQ6HRYuXBiU30MICR1KdAghY97hw4dhNBpRUFCANWvW4K233kIwGqP/+c9/4rnnngPHcTh69Cg2bdqE119/HSaTCWfOnMGGDRuCUHtCSChRokMIGfP27t2LNWvWAABWrFiBxsZGnDp1CgDw5ptvIjc3FzNmzIBCoUBubi5yc3NRXFyMhoYGfO1rX8OcOXOQlZWF73znOwH7PXbsGJYtWwYAMJlMSEtLw+LFi5Gamopp06YFfTwQIWT00RgdQsiYZjKZkJOTg+rqakyZMgUAsHr1agiCgP379/vLlZeXY926dTh37px/2+OPP44tW7bgmWeeAWMMV65cwdSpUwEAV65cQX5+PiwWC1QqFSwWC4qKilBeXg6NRoPLly8jIyNjdA+WEBJ01KJDCBnT9u7di/z8fH+SAwBr1qzBO++8g87OTv+2yspKTJs2zf/a6XTiwoULePzxxwEAHMf5kxxA6rZavHgxVCoVPB4PVq1ahXnz5uHChQsoKytDenr6yB8cIWTEUaJDCBmzvF4v3n77baxevTpg+9NPPw2NRoMDBw74t1VUVAQkOiqVCvPmzUNOTg5+/OMfo6ysLGAfx44dw/LlywEA7777Lmpra/GHP/wBeXl5yMrKAsdxI3dghJBRQ4kOIWTMeu+992A2mzF9+nRUVFT4F5PJhCeffBJ79+71l62srMT06dMDPv/vf/8b+/fvhyiKmDdvHkpLSwEAzc3N+Pzzz/HVr34VAOB2u9HY2Ij9+/ejvr4eFRUV+NOf/gSv1zt6B0sIGRGyUFeAEEL6cyeRWbx4cb9lysvLMXPmzF4tOgDA8zwWLFiABQsW4OrVq6iqqsLs2bPxr3/9CwUFBTAYDACAVatWobS0FD//+c9hNpuh1+uxaNEivPrqqyN3cISQUUGDkQkh457D4cCkSZPQ2trq33bixAksXLgQcrkcdXV1WLhwIT799FOkp6dj2bJlmD9/PjZt2hTCWhNCRgO16BBCxr0rV64gJycnYNuRI0fw/e9/H1qtFhEREdizZ49/gPH8+fPxrW99KwQ1JYSMNmrRIYQQQkjYosHIhBBCCAlblOgQQgghJGxRokMIIYSQsEWJDiGEEELCFiU6hBBCCAlblOgQQgghJGxRokMIIYSQsEWJDiGEEELCFiU6hBBCCAlblOgQQgghJGxRokMIIYSQsPX/AWQAra9Thw2JAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "delta_ht_vals = np.array([1e-4, 1e-3, 1e-2, 1e-1]) * epsilon\n", "fig, ax = plt.subplots()\n", @@ -724,20 +415,9 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\\Delta_{HT} = 0.012907636363636364\n", - "\\Delta_{TS} = 0.11015760326314578\n", - "\\Delta_{PE} = 0.20333476037321788\n", - "T_{opt} = 9.226e+06\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from scipy.optimize import minimize, bisect, newton\n", "def objective(delta_ts, delta_ht, n_rot, n_t, xi_bound, prod_ord):\n", @@ -782,28 +462,16 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "ename": "ImportError", - "evalue": "cannot import name 'build_plaq_hwp_unitary_second_order_suzuki' from 'qualtran.bloqs.chemistry.trotter.hubbard.trotter_step' (/usr/local/google/home/fmalone/projects/qualtran/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step.py)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[26], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mqualtran\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbloqs\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mchemistry\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtrotter\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhubbard\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtrotter_step\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m build_plaq_hwp_unitary_second_order_suzuki\n\u001b[1;32m 2\u001b[0m trotter_step \u001b[38;5;241m=\u001b[39m build_plaq_hwp_unitary_second_order_suzuki(length, hubb_u, timestep, eps\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-10\u001b[39m)\n\u001b[1;32m 3\u001b[0m n_t, n_rot \u001b[38;5;241m=\u001b[39m t_and_rot_counts_from_sigma(trotter_step\u001b[38;5;241m.\u001b[39mcall_graph(generalizer\u001b[38;5;241m=\u001b[39mcatch_rotations)[\u001b[38;5;241m1\u001b[39m])\n", - "\u001b[0;31mImportError\u001b[0m: cannot import name 'build_plaq_hwp_unitary_second_order_suzuki' from 'qualtran.bloqs.chemistry.trotter.hubbard.trotter_step' (/usr/local/google/home/fmalone/projects/qualtran/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step.py)" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from qualtran.bloqs.chemistry.trotter.hubbard.trotter_step import build_plaq_hwp_unitary_second_order_suzuki\n", - "trotter_step = build_plaq_hwp_unitary_second_order_suzuki(length, hubb_u, timestep, eps=1e-10)\n", - "n_t, n_rot = t_and_rot_counts_from_sigma(trotter_step.call_graph(generalizer=catch_rotations)[1])\n", - "print(f\"N_T(HWP) = {n_t} vs {(3*length**2 // 2)*8}\")\n", - "print(f\"N_rot(HWP) = {n_rot} vs {(3 * length**2 + 2*length**2)}\")\n", - "delta_ht_opt, delta_ts_opt, delta_pe_opt, t_opt = minimize_linesearch(n_rot, n_t, xi_bound, prod_ord)\n", + "trotter_step_hwp = build_plaq_hwp_unitary_second_order_suzuki(length, hubb_u, timestep, eps=1e-10)\n", + "n_t_hwp, n_rot_hwp = t_and_rot_counts_from_sigma(trotter_step_hwp.call_graph(generalizer=catch_rotations)[1])\n", + "print(f\"N_T(HWP) = {n_t_hwp} vs {(3*length**2 // 2)*8}\")\n", + "print(f\"N_rot(HWP) = {n_rot_hwp} vs {(3 * length**2 + 2*length**2)}\")\n", + "delta_ht_opt, delta_ts_opt, delta_pe_opt, t_opt = minimize_linesearch(n_rot_hwp, n_t_hwp, xi_bound, prod_ord)\n", "print(rf\"T_{{OPT}}(HWP) = {t_opt:4.3e}\")\n", "# The reference counts Toffolis as 2 T gates, we count them as 4.\n", "print(rf\"Reference value = {1.7e6 + 4 * 1.8e5:4.3e}\")" @@ -822,11 +490,11 @@ "metadata": {}, "outputs": [], "source": [ - "trotter_step = build_plaq_hwp_unitary_second_order_suzuki(length, hubb_u, timestep, eps=1e-10, strip_layer=True)\n", - "n_t, n_rot = t_and_rot_counts_from_sigma(trotter_step.call_graph(generalizer=catch_rotations)[1])\n", - "print(f\"N_T(HWP) = {n_t}\")\n", - "print(f\"N_rot(HWP) = {n_rot}\")\n", - "delta_ht_opt, delta_ts_opt, delta_pe_opt, t_opt = minimize_linesearch(n_rot, n_t, xi_bound, prod_ord)\n", + "trotter_step_hwp = build_plaq_hwp_unitary_second_order_suzuki(length, hubb_u, timestep, eps=1e-10, strip_layer=True)\n", + "n_t_hwp, n_rot_hwp = t_and_rot_counts_from_sigma(trotter_step_hwp.call_graph(generalizer=catch_rotations)[1])\n", + "print(f\"N_T(HWP) = {n_t_hwp}\")\n", + "print(f\"N_rot(HWP) = {n_rot_hwp}\")\n", + "delta_ht_opt, delta_ts_opt, delta_pe_opt, t_opt = minimize_linesearch(n_rot_hwp, n_t_hwp, xi_bound, prod_ord)\n", "print(rf\"T_{{OPT}}(HWP) = {t_opt:4.3e}\")\n", "print(rf\"Reference value = {1.7e6 + 4 * 1.8e5:4.3e}\")" ] @@ -931,6 +599,7 @@ ")\n", "symb_t_count = symb_t_count.evalf(subs={s_p: prod_ord})\n", "tot_t_count = qpe_t_count(delta_pe, delta_ts, delta_ht, n_rot, n_t, xi_bound, prod_ord)\n", + "print(symb_t_count, tot_t_count)\n", "assert int(symb_t_count) == int(tot_t_count)" ] } diff --git a/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step.py b/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step.py index 922edb387..8cc46e96a 100644 --- a/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step.py +++ b/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step.py @@ -21,13 +21,18 @@ $$ """ -from qualtran.bloqs.chemistry.trotter.hubbard.hopping import HoppingTile -from qualtran.bloqs.chemistry.trotter.hubbard.interaction import Interaction +from qualtran.bloqs.chemistry.trotter.hubbard.hopping import HoppingTile, HoppingTileHWP +from qualtran.bloqs.chemistry.trotter.hubbard.interaction import Interaction, InteractionHWP from qualtran.bloqs.chemistry.trotter.trotterized_unitary import TrotterizedUnitary def build_plaq_unitary_second_order_suzuki( - length: int, hubb_u: float, timestep: float, hubb_t: float = 1.0, eps: float = 1e-9 + length: int, + hubb_u: float, + timestep: float, + hubb_t: float = 1.0, + eps: float = 1e-9, + strip_layer: bool = False, ) -> TrotterizedUnitary: """Build second order Suzuki-Trotter unitary for the square lattice Hubbard model. @@ -37,18 +42,25 @@ def build_plaq_unitary_second_order_suzuki( timestep: The time step for the unitary. hubb_t: Hubbard t. Default = 1. eps: The precision for single-qubit rotations. + strip_layer: Whether to strip one application of the interaction term + which is a common optimization if multiple trotter step are merged. Returns: unitary: The trotterized approximation to the unitary e^{-i t H}. """ - # Trotter splitting parameters when H = H_I + H_h^p + H_h^g - indices = (0, 1, 2, 1, 0) - coeffs = (0.5, 0.5, 1.0, 0.5, 0.5) # Build the basic bloqs which make up the 2nd order PlAQ unitary. # The pink and gold "tiles". pink = HoppingTile(length=length, angle=0, eps=eps, pink=True, tau=hubb_t) gold = HoppingTile(length=length, angle=0, eps=eps, pink=False, tau=hubb_t) interaction = Interaction(length=length, angle=0, eps=eps, hubb_u=hubb_u) + if strip_layer: + # H_p H_g H_p H_I + indices = (1, 2, 1, 0) + coeffs = (0.5, 1, 0.5, 1) + else: + # Trotter splitting parameters when H = H_I + H_h^p + H_h^g + indices = (0, 1, 2, 1, 0) + coeffs = (0.5, 0.5, 1.0, 0.5, 0.5) unitary = TrotterizedUnitary( (interaction, pink, gold), indices=indices, coeffs=coeffs, timestep=timestep ) From a90d3864f55f81d9a5cc2be71d13ad17163a2b3c Mon Sep 17 00:00:00 2001 From: Fionn Malone Date: Sat, 11 May 2024 14:52:22 +0000 Subject: [PATCH 09/16] Fix formatting. --- qualtran/bloqs/chemistry/trotter/hubbard/interaction.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/qualtran/bloqs/chemistry/trotter/hubbard/interaction.py b/qualtran/bloqs/chemistry/trotter/hubbard/interaction.py index 3c11a960f..95cb114c7 100644 --- a/qualtran/bloqs/chemistry/trotter/hubbard/interaction.py +++ b/qualtran/bloqs/chemistry/trotter/hubbard/interaction.py @@ -66,6 +66,7 @@ def build_call_graph(self, ssa: 'SympySymbolAllocator') -> Set['BloqCountT']: # Page 13 paragraph 1. return {(Rz(angle=self.angle * self.hubb_u, eps=self.eps), self.length**2)} + @frozen class InteractionHWP(Bloq): r"""Bloq implementing the hubbard U part of the hamiltonian using Hamming weight phasing. @@ -125,6 +126,7 @@ def _interaction() -> Interaction: examples=(_interaction,), ) + @bloq_example def _interaction_hwp() -> InteractionHWP: length = 8 @@ -138,4 +140,4 @@ def _interaction_hwp() -> InteractionHWP: bloq_cls=InteractionHWP, import_line='from qualtran.bloqs.chemistry.trotter.hubbard.interaction import InteractionHWP', examples=(_interaction_hwp,), -) \ No newline at end of file +) From a0535e4b319980346215738d908794d50c0f26b2 Mon Sep 17 00:00:00 2001 From: Fionn Malone Date: Sat, 11 May 2024 15:03:18 +0000 Subject: [PATCH 10/16] Fix mypy errors. --- qualtran/bloqs/chemistry/trotter/hubbard/hopping_test.py | 2 +- qualtran/bloqs/chemistry/trotter/hubbard/trotter_step.py | 6 ++++++ qualtran/bloqs/rotations/hamming_weight_phasing.py | 5 +++-- 3 files changed, 10 insertions(+), 3 deletions(-) diff --git a/qualtran/bloqs/chemistry/trotter/hubbard/hopping_test.py b/qualtran/bloqs/chemistry/trotter/hubbard/hopping_test.py index 6ca949b97..84a586bae 100644 --- a/qualtran/bloqs/chemistry/trotter/hubbard/hopping_test.py +++ b/qualtran/bloqs/chemistry/trotter/hubbard/hopping_test.py @@ -34,7 +34,7 @@ def catch_rotations(bloq) -> Bloq: if isinstance(bloq, (Rz, ZPowGate)): if isinstance(bloq, ZPowGate): return Rz(angle=PHI) - elif abs(bloq.angle) < 1e-12: + elif abs(float(bloq.angle)) < 1e-12: return ArbitraryClifford(1) else: return Rz(angle=PHI) diff --git a/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step.py b/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step.py index 8cc46e96a..807d1bbe6 100644 --- a/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step.py +++ b/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step.py @@ -21,6 +21,8 @@ $$ """ +from typing import Sequence + from qualtran.bloqs.chemistry.trotter.hubbard.hopping import HoppingTile, HoppingTileHWP from qualtran.bloqs.chemistry.trotter.hubbard.interaction import Interaction, InteractionHWP from qualtran.bloqs.chemistry.trotter.trotterized_unitary import TrotterizedUnitary @@ -53,6 +55,8 @@ def build_plaq_unitary_second_order_suzuki( pink = HoppingTile(length=length, angle=0, eps=eps, pink=True, tau=hubb_t) gold = HoppingTile(length=length, angle=0, eps=eps, pink=False, tau=hubb_t) interaction = Interaction(length=length, angle=0, eps=eps, hubb_u=hubb_u) + indices: Sequence[int] = () + coeffs: Sequence[float] = () if strip_layer: # H_p H_g H_p H_I indices = (1, 2, 1, 0) @@ -96,6 +100,8 @@ def build_plaq_hwp_unitary_second_order_suzuki( pink = HoppingTileHWP(length=length, angle=0, eps=eps, pink=True, tau=hubb_t) gold = HoppingTileHWP(length=length, angle=0, eps=eps, pink=False, tau=hubb_t) interaction = InteractionHWP(length=length, angle=0, eps=eps, hubb_u=hubb_u) + indices: Sequence[int] = () + coeffs: Sequence[float] = () if strip_layer: # H_p H_g H_p H_I indices = (1, 2, 1, 0) diff --git a/qualtran/bloqs/rotations/hamming_weight_phasing.py b/qualtran/bloqs/rotations/hamming_weight_phasing.py index 457dd21f3..779bc7fe4 100644 --- a/qualtran/bloqs/rotations/hamming_weight_phasing.py +++ b/qualtran/bloqs/rotations/hamming_weight_phasing.py @@ -13,9 +13,10 @@ # limitations under the License. from functools import cached_property -from typing import Dict, Set, TYPE_CHECKING +from typing import Dict, Set, TYPE_CHECKING, Union import attrs +import sympy from qualtran import GateWithRegisters, QFxp, QUInt, Register, Signature from qualtran.bloqs.arithmetic import HammingWeightCompute @@ -60,7 +61,7 @@ class HammingWeightPhasing(GateWithRegisters): bitsize: int exponent: float - eps: float = 1e-10 + eps: Union[float, sympy.Expr] = 1e-10 @cached_property def signature(self) -> 'Signature': From 93414cff8259d97f488b027ff50e50a39ff243ca Mon Sep 17 00:00:00 2001 From: Fionn Malone Date: Sat, 11 May 2024 15:05:47 +0000 Subject: [PATCH 11/16] Fix serialization. --- qualtran/serialization/resolver_dict.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/qualtran/serialization/resolver_dict.py b/qualtran/serialization/resolver_dict.py index eb3bc5fde..ade1ce2ce 100644 --- a/qualtran/serialization/resolver_dict.py +++ b/qualtran/serialization/resolver_dict.py @@ -240,8 +240,10 @@ "qualtran.bloqs.chemistry.trotter.ising.unitaries.IsingXUnitary": qualtran.bloqs.chemistry.trotter.ising.unitaries.IsingXUnitary, "qualtran.bloqs.chemistry.trotter.ising.unitaries.IsingZZUnitary": qualtran.bloqs.chemistry.trotter.ising.unitaries.IsingZZUnitary, "qualtran.bloqs.chemistry.trotter.hubbard.interaction.Interaction": qualtran.bloqs.chemistry.trotter.hubbard.interaction.Interaction, + "qualtran.bloqs.chemistry.trotter.hubbard.interaction.InteractionHWP": qualtran.bloqs.chemistry.trotter.hubbard.interaction.InteractionHWP, "qualtran.bloqs.chemistry.trotter.hubbard.hopping.HoppingPlaquette": qualtran.bloqs.chemistry.trotter.hubbard.hopping.HoppingPlaquette, "qualtran.bloqs.chemistry.trotter.hubbard.hopping.HoppingTile": qualtran.bloqs.chemistry.trotter.hubbard.hopping.HoppingTile, + "qualtran.bloqs.chemistry.trotter.hubbard.hopping.HoppingTileHWP": qualtran.bloqs.chemistry.trotter.hubbard.hopping.HoppingTileHWP, "qualtran.bloqs.chemistry.trotter.trotterized_unitary": qualtran.bloqs.chemistry.trotter.trotterized_unitary, "qualtran.bloqs.data_loading.qrom.QROM": qualtran.bloqs.data_loading.qrom.QROM, "qualtran.bloqs.data_loading.select_swap_qrom.SelectSwapQROM": qualtran.bloqs.data_loading.select_swap_qrom.SelectSwapQROM, From 804fbf1e55a4ef4961f30d42e91166c87108bc1e Mon Sep 17 00:00:00 2001 From: Fionn Malone Date: Wed, 15 May 2024 18:30:05 +0000 Subject: [PATCH 12/16] Use symbolicfloat. --- .../chemistry/trotter/hubbard/hopping.py | 12 ++++++------ .../chemistry/trotter/hubbard/interaction.py | 18 +++++++++--------- .../chemistry/trotter/trotterized_unitary.py | 6 +++--- qualtran/bloqs/qft/two_bit_ffft.py | 3 +-- .../bloqs/rotations/hamming_weight_phasing.py | 5 ++--- .../rotations/quantum_variable_rotation.py | 19 +++++++------------ qualtran/symbolics/types.py | 2 +- 7 files changed, 29 insertions(+), 36 deletions(-) diff --git a/qualtran/bloqs/chemistry/trotter/hubbard/hopping.py b/qualtran/bloqs/chemistry/trotter/hubbard/hopping.py index d50834368..b54672b4a 100644 --- a/qualtran/bloqs/chemistry/trotter/hubbard/hopping.py +++ b/qualtran/bloqs/chemistry/trotter/hubbard/hopping.py @@ -15,13 +15,13 @@ from functools import cached_property from typing import Set, TYPE_CHECKING, Union -import sympy from attrs import frozen from qualtran import Bloq, bloq_example, BloqDocSpec, QAny, QBit, Register, Signature from qualtran.bloqs.basic_gates import Rz from qualtran.bloqs.qft.two_bit_ffft import TwoBitFFFT from qualtran.bloqs.rotations.hamming_weight_phasing import HammingWeightPhasing +from qualtran.symbolics import SymbolicFloat, SymbolicInt if TYPE_CHECKING: from qualtran.resource_counting import BloqCountT, SympySymbolAllocator @@ -67,8 +67,8 @@ class HoppingPlaquette(Bloq): page 13 Eq. E4 and E5 (Appendix E) """ - kappa: Union[float, sympy.Expr] - eps: Union[float, sympy.Expr] = 1e-9 + kappa: Union[SymbolicFloat] + eps: Union[SymbolicFloat] = 1e-9 @cached_property def signature(self) -> Signature: @@ -110,10 +110,10 @@ class HoppingTile(Bloq): see Eq. 21 and App E. """ - length: Union[int, sympy.Expr] - angle: Union[float, sympy.Expr] + length: Union[SymbolicInt] + angle: Union[SymbolicFloat] tau: float = 1.0 - eps: Union[float, sympy.Expr] = 1e-9 + eps: Union[SymbolicFloat] = 1e-9 pink: bool = True def __attrs_post_init__(self): diff --git a/qualtran/bloqs/chemistry/trotter/hubbard/interaction.py b/qualtran/bloqs/chemistry/trotter/hubbard/interaction.py index 95cb114c7..0cbd49275 100644 --- a/qualtran/bloqs/chemistry/trotter/hubbard/interaction.py +++ b/qualtran/bloqs/chemistry/trotter/hubbard/interaction.py @@ -16,12 +16,12 @@ from functools import cached_property from typing import Set, TYPE_CHECKING, Union -import sympy from attrs import frozen from qualtran import Bloq, bloq_example, BloqDocSpec, QAny, Register, Signature from qualtran.bloqs.basic_gates import Rz from qualtran.bloqs.rotations.hamming_weight_phasing import HammingWeightPhasing +from qualtran.symbolics import SymbolicFloat, SymbolicInt if TYPE_CHECKING: from qualtran.resource_counting import BloqCountT, SympySymbolAllocator @@ -53,10 +53,10 @@ class Interaction(Bloq): Eq. 6 page 2 and page 13 paragraph 1. """ - length: Union[int, sympy.Expr] - angle: Union[float, sympy.Expr] - hubb_u: Union[float, sympy.Expr] - eps: Union[float, sympy.Expr] = 1e-9 + length: Union[SymbolicInt] + angle: Union[SymbolicFloat] + hubb_u: Union[SymbolicFloat] + eps: Union[SymbolicFloat] = 1e-9 @cached_property def signature(self) -> Signature: @@ -96,10 +96,10 @@ class InteractionHWP(Bloq): 14 paragraph 3 right column. The apply 2 batches of $L^2/2$ rotations. """ - length: Union[int, sympy.Expr] - angle: Union[float, sympy.Expr] - hubb_u: Union[float, sympy.Expr] - eps: Union[float, sympy.Expr] = 1e-9 + length: Union[SymbolicInt] + angle: Union[SymbolicFloat] + hubb_u: Union[SymbolicFloat] + eps: Union[SymbolicFloat] = 1e-9 @cached_property def signature(self) -> Signature: diff --git a/qualtran/bloqs/chemistry/trotter/trotterized_unitary.py b/qualtran/bloqs/chemistry/trotter/trotterized_unitary.py index 637ad17eb..78624a29a 100644 --- a/qualtran/bloqs/chemistry/trotter/trotterized_unitary.py +++ b/qualtran/bloqs/chemistry/trotter/trotterized_unitary.py @@ -17,9 +17,9 @@ from typing import Dict, Sequence, Union import attrs -import sympy from qualtran import Bloq, bloq_example, BloqBuilder, BloqDocSpec, Signature, SoquetT +from qualtran.symbolics import SymbolicFloat @attrs.frozen @@ -83,8 +83,8 @@ class TrotterizedUnitary(Bloq): bloqs: Sequence[Bloq] indices: Sequence[int] - coeffs: Sequence[Union[float, sympy.Expr]] - timestep: Union[float, sympy.Expr] + coeffs: Sequence[Union[SymbolicFloat]] + timestep: Union[SymbolicFloat] def __attrs_post_init__(self): ref_sig = self.bloqs[0].signature diff --git a/qualtran/bloqs/qft/two_bit_ffft.py b/qualtran/bloqs/qft/two_bit_ffft.py index 65ac2586a..e9db23e48 100644 --- a/qualtran/bloqs/qft/two_bit_ffft.py +++ b/qualtran/bloqs/qft/two_bit_ffft.py @@ -12,10 +12,9 @@ # See the License for the specific language governing permissions and # limitations under the License. from functools import cached_property -from typing import Any, Dict, Set, TYPE_CHECKING, Union +from typing import Any, Dict, Set, TYPE_CHECKING import numpy as np -import sympy from attrs import frozen from numpy.typing import NDArray diff --git a/qualtran/bloqs/rotations/hamming_weight_phasing.py b/qualtran/bloqs/rotations/hamming_weight_phasing.py index 16ca818fe..160712d89 100644 --- a/qualtran/bloqs/rotations/hamming_weight_phasing.py +++ b/qualtran/bloqs/rotations/hamming_weight_phasing.py @@ -16,17 +16,16 @@ from typing import Dict, Set, TYPE_CHECKING, Union import attrs -import sympy from qualtran import GateWithRegisters, QFxp, QUInt, Register, Signature from qualtran.bloqs.arithmetic import HammingWeightCompute from qualtran.bloqs.basic_gates import ZPowGate from qualtran.bloqs.rotations.quantum_variable_rotation import QvrPhaseGradient +from qualtran.symbolics import SymbolicFloat, SymbolicInt if TYPE_CHECKING: from qualtran import BloqBuilder, SoquetT from qualtran.resource_counting import BloqCountT, SympySymbolAllocator - from qualtran.symbolics import SymbolicInt @attrs.frozen @@ -61,7 +60,7 @@ class HammingWeightPhasing(GateWithRegisters): bitsize: int exponent: float - eps: Union[float, sympy.Expr] = 1e-10 + eps: Union[SymbolicFloat] = 1e-10 @cached_property def signature(self) -> 'Signature': diff --git a/qualtran/bloqs/rotations/quantum_variable_rotation.py b/qualtran/bloqs/rotations/quantum_variable_rotation.py index ab9feff85..5fb3dc24d 100644 --- a/qualtran/bloqs/rotations/quantum_variable_rotation.py +++ b/qualtran/bloqs/rotations/quantum_variable_rotation.py @@ -84,6 +84,7 @@ sabs, smax, smin, + SymbolicFloat, SymbolicInt, ) @@ -145,15 +146,12 @@ class QvrZPow(QvrInterface): """ cost_reg: Register - gamma: Union[float, sympy.Expr] = 1.0 - eps: Union[float, sympy.Expr] = 1e-9 + gamma: Union[SymbolicFloat] = 1.0 + eps: Union[SymbolicFloat] = 1e-9 @classmethod def from_bitsize( - cls, - bitsize: int, - gamma: Union[float, sympy.Expr] = 1.0, - eps: Union[float, sympy.Expr] = 1e-9, + cls, bitsize: int, gamma: Union[SymbolicFloat] = 1.0, eps: Union[SymbolicFloat] = 1e-9 ) -> 'QvrZPow': cost_reg = Register("x", QFxp(bitsize, bitsize, signed=False)) return QvrZPow(cost_reg, gamma=gamma, eps=eps) @@ -381,8 +379,8 @@ class QvrPhaseGradient(QvrInterface): """ cost_reg: Register - gamma: Union[float, sympy.Expr] = 1.0 - eps: Union[float, sympy.Expr] = 1e-9 + gamma: Union[SymbolicFloat] = 1.0 + eps: Union[SymbolicFloat] = 1e-9 def __attrs_post_init__(self): dtype = self.cost_reg.dtype @@ -391,10 +389,7 @@ def __attrs_post_init__(self): @classmethod def from_bitsize( - cls, - bitsize: int, - gamma: Union[float, sympy.Expr] = 1.0, - eps: Union[float, sympy.Expr] = 1e-9, + cls, bitsize: int, gamma: Union[SymbolicFloat] = 1.0, eps: Union[SymbolicFloat] = 1e-9 ) -> 'QvrPhaseGradient': cost_reg = Register("x", QFxp(bitsize, bitsize, signed=False)) return QvrPhaseGradient(cost_reg, gamma=gamma, eps=eps) diff --git a/qualtran/symbolics/types.py b/qualtran/symbolics/types.py index 7b0d0c94a..a82e66400 100644 --- a/qualtran/symbolics/types.py +++ b/qualtran/symbolics/types.py @@ -17,7 +17,7 @@ from attrs import field, frozen, validators from cirq._doc import document -SymbolicFloat = Union[float, sympy.Expr] +SymbolicFloat = Union[int, sympy.Expr] document(SymbolicFloat, """A floating point value or a sympy expression.""") SymbolicInt = Union[int, sympy.Expr] From 5c96f6496f82cb635bd9ca8d4945eeb36fbeffde Mon Sep 17 00:00:00 2001 From: Fionn Malone Date: Wed, 15 May 2024 18:32:56 +0000 Subject: [PATCH 13/16] Fix bug. --- qualtran/symbolics/types.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qualtran/symbolics/types.py b/qualtran/symbolics/types.py index a82e66400..7b0d0c94a 100644 --- a/qualtran/symbolics/types.py +++ b/qualtran/symbolics/types.py @@ -17,7 +17,7 @@ from attrs import field, frozen, validators from cirq._doc import document -SymbolicFloat = Union[int, sympy.Expr] +SymbolicFloat = Union[float, sympy.Expr] document(SymbolicFloat, """A floating point value or a sympy expression.""") SymbolicInt = Union[int, sympy.Expr] From 30ed435a458965086456a803753fc96e73442d1c Mon Sep 17 00:00:00 2001 From: Fionn Malone Date: Thu, 16 May 2024 23:19:40 +0000 Subject: [PATCH 14/16] Add notebook test. --- .../bloqs/chemistry/trotter/hubbard/trotter_step_test.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step_test.py b/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step_test.py index f877f5700..7981f9f17 100644 --- a/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step_test.py +++ b/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step_test.py @@ -11,6 +11,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import pytest from qualtran import Bloq from qualtran.bloqs.basic_gates import Rz from qualtran.bloqs.basic_gates.t_gate import TGate @@ -19,6 +20,7 @@ ) from qualtran.bloqs.util_bloqs import ArbitraryClifford from qualtran.resource_counting.generalizers import PHI +from qualtran.testing import execute_notebook def catch_rotations(bloq) -> Bloq: @@ -40,3 +42,8 @@ def test_second_order_suzuki_costs(): assert sigma[TGate()] == (3 * length**2 // 2) * 8 # 3 hopping unitaries and 2 interaction unitaries assert sigma[Rz(PHI)] == (3 * length**2 + 2 * length**2) + + +@pytest.mark.notebook +def test_notebook() + execute_notebook('qpe_cost_optimization.ipynb') \ No newline at end of file From 4eb50bc4d9d421dee27b548b3286c556a576b1f6 Mon Sep 17 00:00:00 2001 From: Fionn Malone Date: Fri, 17 May 2024 15:39:34 +0000 Subject: [PATCH 15/16] Add missing semicolon. --- .../bloqs/chemistry/trotter/hubbard/trotter_step_test.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step_test.py b/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step_test.py index 7981f9f17..e13adb7a4 100644 --- a/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step_test.py +++ b/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step_test.py @@ -12,6 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. import pytest + from qualtran import Bloq from qualtran.bloqs.basic_gates import Rz from qualtran.bloqs.basic_gates.t_gate import TGate @@ -45,5 +46,5 @@ def test_second_order_suzuki_costs(): @pytest.mark.notebook -def test_notebook() - execute_notebook('qpe_cost_optimization.ipynb') \ No newline at end of file +def test_notebook(): + execute_notebook('qpe_cost_optimization.ipynb') From 7439fd324fb97886e86764b9a70069af9377db2f Mon Sep 17 00:00:00 2001 From: Fionn Malone Date: Fri, 17 May 2024 17:23:10 +0000 Subject: [PATCH 16/16] Fix test. --- qualtran/bloqs/chemistry/trotter/hubbard/trotter_step_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step_test.py b/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step_test.py index e13adb7a4..a74af1f09 100644 --- a/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step_test.py +++ b/qualtran/bloqs/chemistry/trotter/hubbard/trotter_step_test.py @@ -47,4 +47,4 @@ def test_second_order_suzuki_costs(): @pytest.mark.notebook def test_notebook(): - execute_notebook('qpe_cost_optimization.ipynb') + execute_notebook('qpe_cost_optimization')