code: import multiprocessing
  import os
  from typing import BinaryIO, Optional, Union
  
  from .. import Dataset, Features, NamedSplit, config
  from ..formatting import query_table
  from ..packaged_modules.csv.csv import Csv
  from ..utils import logging
  from ..utils.typing import NestedDataStructureLike, PathLike
  from .abc import AbstractDatasetReader
  
  
  
  
  class    __lowerCAmelCase  (       _UpperCAmelCase						):
  
  
  
   '''simple docstring'''
  
   def __init__(			self       :		Any       ,			lowerCamelCase       :		Any       ,			lowerCamelCase       :		Optional[Any] = None       ,			lowerCamelCase       :		str = None       ,			lowerCamelCase       :		List[Any] = None       ,			lowerCamelCase       :		Dict = False       ,			lowerCamelCase       :		int = False       ,			lowerCamelCase       :		Optional[int] = None       ,			**lowerCamelCase       :		List[Any]       ,			) ->							Dict:
          super().__init__(
              lowerCamelCase       ,			split=lowerCamelCase       ,			features=lowerCamelCase       ,			cache_dir=lowerCamelCase       ,			keep_in_memory=lowerCamelCase       ,			streaming=lowerCamelCase       ,			num_proc=lowerCamelCase       ,			**lowerCamelCase       ,			)
          _UpperCamelCase     :       Any  						=  path_or_paths if isinstance(lowerCamelCase       ,			lowerCamelCase  ) else {self.split: path_or_paths}
          _UpperCamelCase     :       List[str]  						=  Csv(
              cache_dir=lowerCamelCase       ,			data_files=lowerCamelCase       ,			features=lowerCamelCase       ,			**lowerCamelCase       ,			)
  
   def 					__snake_case					(			self       :		Union[str, Any]  ) ->							Tuple:
  
  
  
  
  
  
          if self.streaming:
                 _UpperCamelCase     :       Optional[int]  						=  self.builder.as_streaming_dataset(split=self.split  )
          # Build regular (map-style) dataset
          else:
                 _UpperCamelCase     :       Optional[Any]  						=  None
                 _UpperCamelCase     :       Optional[Any]  						=  None
                 _UpperCamelCase     :       Optional[int]  						=  None
                 _UpperCamelCase     :       Optional[int]  						=  None
  
                 self.builder.download_and_prepare(
                     download_config=lowerCamelCase       ,			download_mode=lowerCamelCase       ,			verification_mode=lowerCamelCase       ,			base_path=lowerCamelCase       ,			num_proc=self.num_proc       ,			)
                 _UpperCamelCase     :       str  						=  self.builder.as_dataset(
                     split=self.split       ,			verification_mode=lowerCamelCase       ,			in_memory=self.keep_in_memory  )
          return dataset
  
  
  
  
  
  
  
  class    __lowerCAmelCase  :
  
  
  
   '''simple docstring'''
  
   def __init__(			self       :		Dict       ,			lowerCamelCase       :		List[Any]       ,			lowerCamelCase       :		Optional[Any]       ,			lowerCamelCase       :		Dict = None       ,			lowerCamelCase       :		str = None       ,			**lowerCamelCase       :		Optional[int]       ,			) ->							Dict:
  
  
  
  
  
  
          if num_proc is not None and num_proc <= 0:
                 raise ValueError(f"num_proc {num_proc} must be an integer > 0."  )
  
          _UpperCamelCase     :       Union[str, Any]  						=  dataset
          _UpperCamelCase     :       Optional[Any]  						=  path_or_buf
          _UpperCamelCase     :       Any  						=  batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
          _UpperCamelCase     :       int  						=  num_proc
          _UpperCamelCase     :       Dict  						=  'utf-8'
          _UpperCamelCase     :       Union[str, Any]  						=  to_csv_kwargs
  
   def 					__snake_case					(			self       :		int  ) ->							Dict:
          _UpperCamelCase     :       List[str]  						=  self.to_csv_kwargs.pop('path_or_buf'       ,			lowerCamelCase  )
          _UpperCamelCase     :       Dict  						=  self.to_csv_kwargs.pop('header'       ,			lowerCamelCase  )
          _UpperCamelCase     :       List[str]  						=  self.to_csv_kwargs.pop('index'       ,			lowerCamelCase  )
  
          if isinstance(self.path_or_buf       ,			(str, bytes, os.PathLike)  ):
                 with open(self.path_or_buf       ,			'wb+'  ) as buffer:
                        _UpperCamelCase     :       List[str]  						=  self._write(file_obj=lowerCamelCase       ,			header=lowerCamelCase       ,			index=lowerCamelCase       ,			**self.to_csv_kwargs  )
          else:
                 _UpperCamelCase     :       str  						=  self._write(file_obj=self.path_or_buf       ,			header=lowerCamelCase       ,			index=lowerCamelCase       ,			**self.to_csv_kwargs  )
          return written
  
   def 					__snake_case					(			self       :		List[str]       ,			lowerCamelCase       :		Optional[int]  ) ->							List[Any]:
          _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase     :       Tuple  						=  args
  
          _UpperCamelCase     :       Optional[Any]  						=  query_table(
              table=self.dataset.data       ,			key=slice(lowerCamelCase       ,			offset + self.batch_size  )       ,			indices=self.dataset._indices       ,			)
          _UpperCamelCase     :       List[Any]  						=  batch.to_pandas().to_csv(
              path_or_buf=lowerCamelCase       ,			header=header if (offset == 0) else False       ,			index=lowerCamelCase       ,			**lowerCamelCase  )
          return csv_str.encode(self.encoding  )
  
   def 					__snake_case					(			self       :		Dict       ,			lowerCamelCase       :		List[Any]       ,			lowerCamelCase       :		str       ,			lowerCamelCase       :		Union[str, Any]       ,			**lowerCamelCase       :		Dict  ) ->							Tuple:
          _UpperCamelCase     :       int  						=  0
  
          if self.num_proc is None or self.num_proc == 1:
                 for offset in logging.tqdm(
                     range(0       ,			len(self.dataset  )       ,			self.batch_size  )       ,			unit='ba'       ,			disable=not logging.is_progress_bar_enabled()       ,			desc='Creating CSV from Arrow format'       ,			):
                        _UpperCamelCase     :       int  						=  self._batch_csv((offset, header, index, to_csv_kwargs)  )
                        written += file_obj.write(lowerCamelCase  )
  
          else:
                 _UpperCamelCase						,      _UpperCamelCase     :       List[Any]  						=  len(self.dataset  ), self.batch_size
                 with multiprocessing.Pool(self.num_proc  ) as pool:
                        for csv_str in logging.tqdm(
                            pool.imap(
                                self._batch_csv       ,			[(offset, header, index, to_csv_kwargs) for offset in range(0       ,			lowerCamelCase       ,			lowerCamelCase  )]       ,			)       ,			total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size       ,			unit='ba'       ,			disable=not logging.is_progress_bar_enabled()       ,			desc='Creating CSV from Arrow format'       ,			):
                               written += file_obj.write(lowerCamelCase  )
  
          return written
  
  
  
  
style_context: import argparse
  import json
  import re
  import string
  import sys
  from collections import Counter
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	List[Any]				):
  
         def remove_articles(lowercase_:	Dict				):
                return re.sub(R'\b(a|an|the)\b'		,						' '		,						lowercase_				)
  
         def white_space_fix(lowercase_:	Optional[Any]				):
                return " ".join(text.split()				)
  
         def remove_punc(lowercase_:	List[str]				):
                _UpperCamelCase     :       str  						=  set(string.punctuation				)
                return "".join(ch for ch in text if ch not in exclude				)
  
         def lower(lowercase_:	List[str]				):
                return text.lower()
  
         return white_space_fix(remove_articles(remove_punc(lower(lowercase_				)				)				)				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	List[Any]		,						lowercase_:	Tuple				):
         _UpperCamelCase     :       int  						=  normalize_answer(lowercase_				).split()
         _UpperCamelCase     :       int  						=  normalize_answer(lowercase_				).split()
         _UpperCamelCase     :       Union[str, Any]  						=  Counter(lowercase_				) & Counter(lowercase_				)
         _UpperCamelCase     :       Optional[int]  						=  sum(common.values()				)
         if num_same == 0:
                return 0
         _UpperCamelCase     :       Optional[Any]  						=  1.0 * num_same / len(lowercase_				)
         _UpperCamelCase     :       Optional[int]  						=  1.0 * num_same / len(lowercase_				)
         _UpperCamelCase     :       List[str]  						=  (2 * precision * recall) / (precision + recall)
         return fa
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	str		,						lowercase_:	Union[str, Any]				):
         return normalize_answer(lowercase_				) == normalize_answer(lowercase_				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Optional[int]		,						lowercase_:	int		,						lowercase_:	Union[str, Any]				):
         _UpperCamelCase     :       Any  						=  []
         for ground_truth in ground_truths:
                _UpperCamelCase     :       Optional[Any]  						=  metric_fn(lowercase_		,						lowercase_				)
                scores_for_ground_truths.append(lowercase_				)
         return max(lowercase_				)
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	Union[str, Any]		,						lowercase_:	List[Any]				):
         _UpperCamelCase     :       List[Any]  						=  0
         for article in dataset:
                for paragraph in article["paragraphs"]:
                       for qa in paragraph["qas"]:
                              total += 1
                              if qa["id"] not in predictions:
                                     _UpperCamelCase     :       Union[str, Any]  						=  'Unanswered question ' + qa['id'] + ' will receive score 0.'
                                     print(lowercase_		,						file=sys.stderr				)
                                     continue
                              _UpperCamelCase     :       Dict  						=  [x['text'] for x in qa['answers']]
                              _UpperCamelCase     :       List[str]  						=  predictions[qa['id']]
                              exact_match += metric_max_over_ground_truths(lowercase_		,						lowercase_		,						lowercase_				)
                              fa += metric_max_over_ground_truths(lowercase_		,						lowercase_		,						lowercase_				)
  
         _UpperCamelCase     :       Dict  						=  100.0 * exact_match / total
         _UpperCamelCase     :       Optional[Any]  						=  100.0 * fa / total
  
         return {"exact_match": exact_match, "f1": fa}
  
  
  if __name__ == "__main__":
   UpperCAmelCase					=				'''1.1'''
   UpperCAmelCase					=				argparse.ArgumentParser(description='''Evaluation for SQuAD ''' + expected_version)
   parser.add_argument('''dataset_file''', help='''Dataset file''')
   parser.add_argument('''prediction_file''', help='''Prediction File''')
   UpperCAmelCase					=				parser.parse_args()
   with open(args.dataset_file) as dataset_file:
    UpperCAmelCase					=				json.load(dataset_file)
    if dataset_json["version"] != expected_version:
     print(
         '''Evaluation expects v-''' + expected_version + ''', but got dataset with v-''' + dataset_json['''version'''],
         file=sys.stderr,
     )
    UpperCAmelCase					=				dataset_json['''data''']
   with open(args.prediction_file) as prediction_file:
    UpperCAmelCase					=				json.load(prediction_file)
   print(json.dumps(evaluate(dataset, predictions)))
  
  
  
  