Modify arg default

components/load_from_hf_hub/fondant_component.yaml

@@ -15,6 +15,7 @@ args:
   column_name_mapping:
     description: Mapping of the consumed hub dataset to fondant column names
     type: dict
+    default: {}
   image_column_names:
     description: Optional argument, a list containing the original image column names in case the 
       dataset on the hub contains them. Used to format the image from HF hub format to a byte string.
@@ -23,7 +24,7 @@ args:
   n_rows_to_load:
     description: Optional argument that defines the number of rows to load. Useful for testing pipeline runs on a small scale
     type: int
-    default: -1
+    default: None
   index_column:
     description: Column to set index to in the load component, if not specified a default globally unique index will be set
     type: str

components/load_from_hf_hub/src/main.py

@@ -1,5 +1,6 @@
 """This component loads a seed dataset from the hub."""
 import logging
+import typing as t
 
 import dask
 import dask.dataframe as dd
@@ -18,10 +19,10 @@ def __init__(
         spec: ComponentSpec,
         *_,
         dataset_name: str,
-        column_name_mapping: dict,
-        image_column_names: list,
-        n_rows_to_load: int,
-        index_column: str,
+        column_name_mapping: t.Optional[dict],
+        image_column_names: t.Optional[list],
+        n_rows_to_load: t.Optional[int],
+        index_column: t.Optional[str],
     ) -> None:
         """
         Args:
@@ -42,55 +43,45 @@ def __init__(
         self.index_column = index_column
         self.spec = spec
 
-    def load(self) -> dd.DataFrame:
-        # 1) Load data, read as Dask dataframe
-        logger.info("Loading dataset from the hub...")
-
+    def get_columns_to_keep(self) -> t.List[str]:
         # Only read required columns
         columns = []
 
-        invert_column_name_mapping = {v: k for k, v in self.column_name_mapping.items()}
+        if self.column_name_mapping:
+            invert_column_name_mapping = {
+                v: k for k, v in self.column_name_mapping.items()
+            }
+        else:
+            invert_column_name_mapping = {}
+
         for subset_name, subset in self.spec.produces.items():
             for field_name, field in subset.fields.items():
-                subset_field_name = f"{subset_name}_{field_name}"
-                column_name = invert_column_name_mapping.get(
-                    subset_field_name,
-                    subset_field_name,
-                )
-                columns.append(column_name)
-
-        logger.debug(f"Columns to keep: {columns}")
-        dask_df = dd.read_parquet(f"hf://datasets/{self.dataset_name}", columns=columns)
-
-        # 2) Make sure images are bytes instead of dicts
-        if self.image_column_names is not None:
+                column_name = f"{subset_name}_{field_name}"
+                if (
+                    invert_column_name_mapping
+                    and column_name in invert_column_name_mapping
+                ):
+                    columns.append(invert_column_name_mapping[column_name])
+                else:
+                    columns.append(column_name)
+
+        if self.index_column is not None:
+            columns.append(self.index_column)
+
+        return columns
+
+    def convert_images_to_bytes(self, dask_df) -> dd.DataFrame:
+        if self.image_column_names:
             for image_column_name in self.image_column_names:
                 dask_df[image_column_name] = dask_df[image_column_name].map(
                     lambda x: x["bytes"],
                     meta=("bytes", bytes),
                 )
 
-        # 3) Rename columns
-        logger.info("Renaming columns...")
-        dask_df = dask_df.rename(columns=self.column_name_mapping)
-
-        # 4) Optional: only return specific amount of rows
-        if self.n_rows_to_load > 0:
-            partitions_length = 0
-            npartitions = 1
-            for npartitions, partition in enumerate(dask_df.partitions, start=1):
-                if partitions_length >= self.n_rows_to_load:
-                    logger.info(
-                        f"""Required number of partitions to load\n
-                    {self.n_rows_to_load} is {npartitions}""",
-                    )
-                    break
-                partitions_length += len(partition)
-            dask_df = dask_df.head(self.n_rows_to_load, npartitions=npartitions)
-            dask_df = dd.from_pandas(dask_df, npartitions=npartitions)
+        return dask_df
 
-        # 5) Set the index
-        if self.index_column == "None":
+    def set_df_index(self, dask_df: dd.DataFrame) -> dd.DataFrame:
+        if self.index_column is None:
             logger.info(
                 "Index column not specified, setting a globally unique index",
             )
@@ -122,3 +113,43 @@ def _get_meta_df() -> pd.DataFrame:
             dask_df = dask_df.set_index(self.index_column, drop=True)
 
         return dask_df
+
+    def return_subset_of_df(self, dask_df: dd.DataFrame) -> dd.DataFrame:
+        if self.n_rows_to_load is not None:
+            partitions_length = 0
+            npartitions = 1
+            for npartitions, partition in enumerate(dask_df.partitions, start=1):
+                if partitions_length >= self.n_rows_to_load:
+                    logger.info(
+                        f"""Required number of partitions to load\n
+                    {self.n_rows_to_load} is {npartitions}""",
+                    )
+                    break
+                partitions_length += len(partition)
+            dask_df = dask_df.head(self.n_rows_to_load, npartitions=npartitions)
+            dask_df = dd.from_pandas(dask_df, npartitions=npartitions)
+        return dask_df
+
+    def load(self) -> dd.DataFrame:
+        # 1) Load data, read as Dask dataframe
+        logger.info("Loading dataset from the hub...")
+
+        columns = self.get_columns_to_keep()
+
+        logger.debug(f"Columns to keep: {columns}")
+        dask_df = dd.read_parquet(f"hf://datasets/{self.dataset_name}", columns=columns)
+
+        # 2) Make sure images are bytes instead of dicts
+        dask_df = self.convert_images_to_bytes(dask_df)
+
+        # 3) Rename columns
+        logger.info("Renaming columns...")
+        dask_df = dask_df.rename(columns=self.column_name_mapping)
+
+        # 4) Optional: only return specific amount of rows
+        dask_df = self.return_subset_of_df(dask_df)
+
+        # 5) Set the index
+        dask_df = self.set_df_index(dask_df)
+
+        return dask_df

components/load_from_parquet/fondant_component.yaml

@@ -19,7 +19,6 @@ args:
   n_rows_to_load:
     description: Optional argument that defines the number of rows to load. Useful for testing pipeline runs on a small scale
     type: int
-    default: -1
   index_column:
     description: Column to set index to in the load component, if not specified a default globally unique index will be set
     type: str

components/load_from_parquet/src/main.py

@@ -19,8 +19,8 @@ def __init__(
         spec: ComponentSpec,
         *_,
         dataset_uri: str,
-        column_name_mapping: dict,
-        n_rows_to_load: int,
+        column_name_mapping: t.Optional[dict],
+        n_rows_to_load: t.Optional[int],
         index_column: t.Optional[str],
     ) -> None:
         """
@@ -39,49 +39,34 @@ def __init__(
         self.index_column = index_column
         self.spec = spec
 
-    def load(self) -> dd.DataFrame:
-        # 1) Load data, read as Dask dataframe
-        logger.info("Loading dataset from the file...")
-
+    def get_columns_to_keep(self) -> t.List[str]:
         # Only read required columns
         columns = []
-        if self.column_name_mapping is not None:
+
+        if self.column_name_mapping:
             invert_column_name_mapping = {
                 v: k for k, v in self.column_name_mapping.items()
             }
-            for subset_name, subset in self.spec.produces.items():
-                for field_name, field in subset.fields.items():
-                    subset_field_name = f"{subset_name}_{field_name}"
-                    column_name = invert_column_name_mapping.get(
-                        subset_field_name,
-                        subset_field_name,
-                    )
-                    columns.append(column_name)
+        else:
+            invert_column_name_mapping = {}
 
-        logger.debug(f"Columns to keep: {columns}")
-        dask_df = dd.read_parquet(self.dataset_uri, columns=columns)
+        for subset_name, subset in self.spec.produces.items():
+            for field_name, field in subset.fields.items():
+                column_name = f"{subset_name}_{field_name}"
+                if (
+                    invert_column_name_mapping
+                    and column_name in invert_column_name_mapping
+                ):
+                    columns.append(invert_column_name_mapping[column_name])
+                else:
+                    columns.append(column_name)
 
-        # 2) Rename columns
-        if self.column_name_mapping:
-            logger.info("Renaming columns...")
-            dask_df = dask_df.rename(columns=self.column_name_mapping)
+        if self.index_column is not None:
+            columns.append(self.index_column)
 
-        # 3) Optional: only return specific amount of rows
-        if self.n_rows_to_load > 0:
-            partitions_length = 0
-            npartitions = 1
-            for npartitions, partition in enumerate(dask_df.partitions, start=1):
-                if partitions_length >= self.n_rows_to_load:
-                    logger.info(
-                        f"""Required number of partitions to load\n
-                    {self.n_rows_to_load} is {npartitions}""",
-                    )
-                    break
-                partitions_length += len(partition)
-            dask_df = dask_df.head(self.n_rows_to_load, npartitions=npartitions)
-            dask_df = dd.from_pandas(dask_df, npartitions=npartitions)
+        return columns
 
-        # 4) Set the index
+    def set_df_index(self, dask_df: dd.DataFrame) -> dd.DataFrame:
         if self.index_column is None:
             logger.info(
                 "Index column not specified, setting a globally unique index",
@@ -114,3 +99,40 @@ def _get_meta_df() -> pd.DataFrame:
             dask_df = dask_df.set_index(self.index_column, drop=True)
 
         return dask_df
+
+    def return_subset_of_df(self, dask_df: dd.DataFrame) -> dd.DataFrame:
+        if self.n_rows_to_load is not None:
+            partitions_length = 0
+            npartitions = 1
+            for npartitions, partition in enumerate(dask_df.partitions, start=1):
+                if partitions_length >= self.n_rows_to_load:
+                    logger.info(
+                        f"""Required number of partitions to load\n
+                    {self.n_rows_to_load} is {npartitions}""",
+                    )
+                    break
+                partitions_length += len(partition)
+            dask_df = dask_df.head(self.n_rows_to_load, npartitions=npartitions)
+            dask_df = dd.from_pandas(dask_df, npartitions=npartitions)
+        return dask_df
+
+    def load(self) -> dd.DataFrame:
+        # 1) Load data, read as Dask dataframe
+        logger.info("Loading dataset from the hub...")
+
+        columns = self.get_columns_to_keep()
+
+        logger.debug(f"Columns to keep: {columns}")
+        dask_df = dd.read_parquet(self.dataset_uri, columns=columns)
+
+        # 2) Rename columns
+        if self.column_name_mapping:
+            logger.info("Renaming columns...")
+            dask_df = dask_df.rename(columns=self.column_name_mapping)
+
+        # 4) Optional: only return specific amount of rows
+        dask_df = self.return_subset_of_df(dask_df)
+
+        # 5) Set the index
+        dask_df = self.set_df_index(dask_df)
+        return dask_df

components/write_to_hf_hub/src/main.py

@@ -41,8 +41,8 @@ def __init__(
         hf_token: str,
         username: str,
         dataset_name: str,
-        image_column_names: list,
-        column_name_mapping: dict,
+        image_column_names: t.Optional[list],
+        column_name_mapping: t.Optional[dict],
     ):
         """
         Args:
@@ -91,7 +91,7 @@ def write(
         # Map image column to hf data format
         feature_encoder = datasets.Image(decode=True)
 
-        if self.image_column_names:
+        if self.image_column_names is not None:
             for image_column_name in self.image_column_names:
                 dataframe[image_column_name] = dataframe[image_column_name].map(
                     lambda x: convert_bytes_to_image(x, feature_encoder),

examples/pipelines/commoncrawl/components/read_warc_paths/fondant_component.yaml

@@ -15,4 +15,4 @@ args:
   n_records_to_download:
     description: Number of records to download
     type: int
-    default: -1
+    default: None

examples/pipelines/commoncrawl/components/read_warc_paths/src/main.py

@@ -18,7 +18,7 @@ def __init__(
         self,
         *_,
         common_crawl_indices: t.List[str],
-        n_records_to_download: int,
+        n_records_to_download: t.Optional[int] = None,
     ):
         self.index_urls = [
             self.build_index_url(index_name) for index_name in common_crawl_indices
@@ -38,7 +38,7 @@ def load(self) -> dd.DataFrame:
             warc_urls.extend([line.decode() for line in extracted.split(b"\n")])
 
         df = pd.DataFrame(warc_urls, columns=["warc_url"])
-        if self.n_records_to_download > 0:
+        if self.n_records_to_download is not None:
             df = df.head(self.n_records_to_download)
 
         return dd.from_pandas(df, npartitions=len(df) // 100)

examples/pipelines/controlnet-interior-design/components/generate_prompts/fondant_component.yaml

@@ -12,4 +12,4 @@ args:
   n_rows_to_load:
     description: Optional argument that defines the number of rows to load. Useful for testing pipeline runs on a small scale
     type: int
-    default: -1
+    default: None

examples/pipelines/controlnet-interior-design/components/generate_prompts/src/main.py

@@ -114,7 +114,7 @@ def load(self) -> dd.DataFrame:
 
         pandas_df = pd.DataFrame(prompts, columns=["prompts_text"])
 
-        if self.n_rows_to_load > 0:
+        if self.n_rows_to_load:
             pandas_df = pandas_df.head(self.n_rows_to_load)
 
         df = dd.from_pandas(pandas_df, npartitions=1)

examples/pipelines/datacomp/components/load_from_hf_hub/fondant_component.yaml

@@ -46,6 +46,7 @@ args:
   column_name_mapping:
     description: Mapping of the consumed hub dataset to fondant column names
     type: dict
+    default: {}
   image_column_names:
     description: Optional argument, a list containing the original image column names in case the 
       dataset on the hub contains them. Used to format the image from HF hub format to a byte string.
@@ -54,7 +55,7 @@ args:
   n_rows_to_load:
     description: Optional argument that defines the number of rows to load. Useful for testing pipeline runs on a small scale
     type: int
-    default: -1
+    default: None
   index_column:
     description: Column to set index to in the load component, if not specified a default globally unique index will be set
     type: str

examples/pipelines/filter-cc-25m/components/load_from_hf_hub/fondant_component.yaml

@@ -27,6 +27,7 @@ args:
   column_name_mapping:
     description: Mapping of the consumed hub dataset to fondant column names
     type: dict
+    default: {}
   image_column_names:
     description: Optional argument, a list containing the original image column names in case the 
       dataset on the hub contains them. Used to format the image from HF hub format to a byte string.
@@ -35,7 +36,6 @@ args:
   n_rows_to_load:
     description: Optional argument that defines the number of rows to load. Useful for testing pipeline runs on a small scale
     type: int
-    default: -1
   index_column:
     description: Column to set index to in the load component, if not specified a default globally unique index will be set
     type: str