"""simple docstring"""



import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader



def      _lowerCamelCase(     a							):
       __a	      =  np.inf

       def set_batch_size(a							) -> None:
              nonlocal batch_size
              if isinstance(a					,  a							):
                     __a	      =  min(a					,  config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS							)
              elif isinstance(a					,  a							):
                     __a	      =  min(a					,  config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS							)
              elif isinstance(a					,  a							) and feature.dtype == "binary":
                     __a	      =  min(a					,  config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS							)

       _visit(a					,  a							)

       return None if batch_size is np.inf else batch_size






class       snake_case__	(    _lowerCamelCase    ):


 def __init__(      self  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = False  ,						lowerCamelCase = False  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        super().__init__(
            _UpperCamelCase  ,						split=_UpperCamelCase  ,						features=_UpperCamelCase  ,						cache_dir=_UpperCamelCase  ,						keep_in_memory=_UpperCamelCase  ,						streaming=_UpperCamelCase  ,						num_proc=_UpperCamelCase  ,						**_UpperCamelCase  ,						)
        __a	      =  path_or_paths if isinstance(_UpperCamelCase  ,						_UpperCamelCase		) else {self.split: path_or_paths}
        __a	      =  _PACKAGED_DATASETS_MODULES["parquet"][1]
        __a	      =  Parquet(
            cache_dir=_UpperCamelCase  ,						data_files=_UpperCamelCase  ,						features=_UpperCamelCase  ,						hash=_UpperCamelCase  ,						**_UpperCamelCase  ,						)






 def 		a__					(      self		):
        # Build iterable dataset
        if self.streaming:
               __a	      =  self.builder.as_streaming_dataset(split=self.split		)
        # Build regular (map-style) dataset
        else:
               __a	      =  None
               __a	      =  None
               __a	      =  None
               __a	      =  None

               self.builder.download_and_prepare(
                   download_config=_UpperCamelCase  ,						download_mode=_UpperCamelCase  ,						verification_mode=_UpperCamelCase  ,						base_path=_UpperCamelCase  ,						num_proc=self.num_proc  ,						)
               __a	      =  self.builder.as_dataset(
                   split=self.split  ,						verification_mode=_UpperCamelCase  ,						in_memory=self.keep_in_memory		)
        return dataset






class       snake_case__	:


 def __init__(      self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        __a	      =  dataset
        __a	      =  path_or_buf
        __a	      =  batch_size or get_writer_batch_size(dataset.features		)
        __a	      =  parquet_writer_kwargs


 def 		a__					(      self		):
        __a	      =  self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

        if isinstance(self.path_or_buf  ,						(str, bytes, os.PathLike)		):
               with open(self.path_or_buf  ,						"wb+"		) as buffer:
                      __a	      =  self._write(file_obj=_UpperCamelCase  ,						batch_size=_UpperCamelCase  ,						**self.parquet_writer_kwargs		)
        else:
               __a	      =  self._write(file_obj=self.path_or_buf  ,						batch_size=_UpperCamelCase  ,						**self.parquet_writer_kwargs		)
        return written






 def 		a__					(      self  ,						lowerCamelCase  ,						lowerCamelCase  ,						**lowerCamelCase		):
        __a	      =  0
        __a	      =  parquet_writer_kwargs.pop("path_or_buf"  ,						_UpperCamelCase		)
        __a	      =  self.dataset.features.arrow_schema

        __a	      =  pq.ParquetWriter(_UpperCamelCase  ,						schema=_UpperCamelCase  ,						**_UpperCamelCase		)

        for offset in logging.tqdm(
            range(0  ,						len(self.dataset		)  ,						_UpperCamelCase		)  ,						unit="ba"  ,						disable=not logging.is_progress_bar_enabled()  ,						desc="Creating parquet from Arrow format"  ,						):
               __a	      =  query_table(
                   table=self.dataset._data  ,						key=slice(_UpperCamelCase  ,						offset + batch_size		)  ,						indices=self.dataset._indices if self.dataset._indices is not None else None  ,						)
               writer.write_table(_UpperCamelCase		)
               written += batch.nbytes
        writer.close()
        return written







import requests

snake_case__					:							int													=     'https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey='



def _a				(	lowerCamelCase:   str )	->						None:







      '''simple docstring'''






      __A          =				requests.get(_NEWS_API + bbc_news_api_key ).json()
      # each article in the list is a dict
      for i, article in enumerate(bbc_news_page['''articles''']					,				1 ):
            print(F"""{i}.) {article['title']}""" )


if __name__ == "__main__":
       fetch_bbc_news(bbc_news_api_key='<Your BBC News API key goes here>')





