
import argparse
from collections import defaultdict

import yaml


__lowerCAmelCase					:			Any			      =		"docs/source/en/_toctree.yml"


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						Tuple:
    __lowercase		:			Optional[int]	    =       defaultdict(__lowerCAmelCase	)
    __lowercase		:			int	    =       []
    __lowercase		:			Union[str, Any]	    =       []
    for doc in doc_list:
        if "local" in doc:
            counts[doc["local"]] += 1

        if doc["title"].lower() == "overview":
            overview_doc.append({'''local''': doc['''local'''], '''title''': doc['''title''']}	)
        else:
            new_doc_list.append(__lowerCAmelCase	)

    __lowercase		:			Dict	    =       new_doc_list
    __lowercase		:			Optional[int]	    =       [key for key, value in counts.items() if value > 1]

    __lowercase		:			Tuple	    =       []
    for duplicate_key in duplicates:
        __lowercase		:			Optional[Any]	    =       list({doc['''title'''] for doc in doc_list if doc['''local'''] == duplicate_key}	)
        if len(__lowerCAmelCase	) > 1:
            raise ValueError(
                F'{duplicate_key} is present several times in the documentation table of content at '
                '''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
                '''others.'''	)
        # Only add this once
        new_doc.append({'''local''': duplicate_key, '''title''': titles[0]}	)

    # Add none duplicate-keys
    new_doc.extend([doc for doc in doc_list if '''local''' not in counts or counts[doc['''local''']] == 1]	)
    __lowercase		:			Dict	    =       sorted(__lowerCAmelCase	,					key=lambda __lowerCAmelCase	: s["title"].lower()	)

    # "overview" gets special treatment and is always first
    if len(__lowerCAmelCase	) > 1:
        raise ValueError('''{doc_list} has two \'overview\' docs which is not allowed.'''	)

    overview_doc.extend(__lowerCAmelCase	)

    # Sort
    return overview_doc


def     UpperCAmelCase_  (						__lowerCAmelCase=False	) ->						Tuple:
    with open(__lowerCAmelCase	,					encoding='''utf-8'''	) as f:
        __lowercase		:			Union[str, Any]	    =       yaml.safe_load(f.read()	)

    # Get to the API doc
    __lowercase		:			Optional[int]	    =       0
    while content[api_idx]["title"] != "API":
        api_idx += 1
    __lowercase		:			List[Any]	    =       content[api_idx]['''sections''']

    # Then to the model doc
    __lowercase		:			Union[str, Any]	    =       0
    while api_doc[scheduler_idx]["title"] != "Schedulers":
        scheduler_idx += 1

    __lowercase		:			List[str]	    =       api_doc[scheduler_idx]['''sections''']
    __lowercase		:			Dict	    =       clean_doc_toc(__lowerCAmelCase	)

    __lowercase		:			Optional[int]	    =       False
    if new_scheduler_doc != scheduler_doc:
        __lowercase		:			Union[str, Any]	    =       True
        if overwrite:
            __lowercase		:			Optional[Any]	    =       new_scheduler_doc

    if diff:
        if overwrite:
            __lowercase		:			Union[str, Any]	    =       api_doc
            with open(__lowerCAmelCase	,					'''w'''	,					encoding='''utf-8'''	) as f:
                f.write(yaml.dump(__lowerCAmelCase	,					allow_unicode=__lowerCAmelCase	)	)
        else:
            raise ValueError(
                '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''	)


def     UpperCAmelCase_  (						__lowerCAmelCase=False	) ->						List[str]:
    with open(__lowerCAmelCase	,					encoding='''utf-8'''	) as f:
        __lowercase		:			Optional[Any]	    =       yaml.safe_load(f.read()	)

    # Get to the API doc
    __lowercase		:			str	    =       0
    while content[api_idx]["title"] != "API":
        api_idx += 1
    __lowercase		:			Tuple	    =       content[api_idx]['''sections''']

    # Then to the model doc
    __lowercase		:			Optional[int]	    =       0
    while api_doc[pipeline_idx]["title"] != "Pipelines":
        pipeline_idx += 1

    __lowercase		:			Tuple	    =       False
    __lowercase		:			str	    =       api_doc[pipeline_idx]['''sections''']
    __lowercase		:			Union[str, Any]	    =       []

    # sort sub pipeline docs
    for pipeline_doc in pipeline_docs:
        if "section" in pipeline_doc:
            __lowercase		:			Union[str, Any]	    =       pipeline_doc['''section''']
            __lowercase		:			str	    =       clean_doc_toc(__lowerCAmelCase	)
            if overwrite:
                __lowercase		:			List[Any]	    =       new_sub_pipeline_doc
        new_pipeline_docs.append(__lowerCAmelCase	)

    # sort overall pipeline doc
    __lowercase		:			Union[str, Any]	    =       clean_doc_toc(__lowerCAmelCase	)

    if new_pipeline_docs != pipeline_docs:
        __lowercase		:			Union[str, Any]	    =       True
        if overwrite:
            __lowercase		:			str	    =       new_pipeline_docs

    if diff:
        if overwrite:
            __lowercase		:			Union[str, Any]	    =       api_doc
            with open(__lowerCAmelCase	,					'''w'''	,					encoding='''utf-8'''	) as f:
                f.write(yaml.dump(__lowerCAmelCase	,					allow_unicode=__lowerCAmelCase	)	)
        else:
            raise ValueError(
                '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''	)


if __name__ == "__main__":
 __lowerCAmelCase					:			Tuple			      =		argparse.ArgumentParser()
 parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
 __lowerCAmelCase					:			Dict			      =		parser.parse_args()

 check_scheduler_doc(args.fix_and_overwrite)
 check_pipeline_doc(args.fix_and_overwrite)




import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast


@dataclass
class 						__lowerCAmelCase	(	datasets.BuilderConfig      ):



      """simple docstring"""







      A__	:			Optional[datasets.Features]        =    None






class 						__lowerCAmelCase	(	datasets.ArrowBasedBuilder      ):



      """simple docstring"""







      A__	:			List[str]        =    PandasConfig




      def 		snake_case_			(      self		:       int				):
          return datasets.DatasetInfo(features=self.config.features				)




      def 		snake_case_			(      self		:       int				,       _snake_case		:       Optional[int]				):
          if not self.config.data_files:
              raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}'				)
          __lowercase		:			Any	    =       dl_manager.download_and_extract(self.config.data_files				)
          if isinstance(_snake_case				,       (str, list, tuple)				):
              __lowercase		:			str	    =       data_files
              if isinstance(_snake_case				,       _snake_case				):
                  __lowercase		:			Any	    =       [files]
              # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
              __lowercase		:			Optional[int]	    =       [dl_manager.iter_files(_snake_case				) for file in files]
              return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,       gen_kwargs={'''files''': files}				)]
          __lowercase		:			str	    =       []
          for split_name, files in data_files.items():
              if isinstance(_snake_case				,       _snake_case				):
                  __lowercase		:			str	    =       [files]
              # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
              __lowercase		:			int	    =       [dl_manager.iter_files(_snake_case				) for file in files]
              splits.append(datasets.SplitGenerator(name=_snake_case				,       gen_kwargs={'''files''': files}				)				)
          return splits




      def 		snake_case_			(      self		:       Optional[int]				,       _snake_case		:       pa.Table				):
          if self.config.features is not None:
              # more expensive cast to support nested features with keys in a different order
              # allows str <-> int/float or str to Audio for example
              __lowercase		:			List[str]	    =       table_cast(_snake_case				,       self.config.features.arrow_schema				)
          return pa_table




      def 		snake_case_			(      self		:       int				,       _snake_case		:       Union[str, Any]				):
          for i, file in enumerate(itertools.chain.from_iterable(_snake_case				)				):
              with open(_snake_case				,       '''rb'''				) as f:
                  __lowercase		:			Dict	    =       pa.Table.from_pandas(pd.read_pickle(_snake_case				)				)
                  yield i, self._cast_table(_snake_case				)


