code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  import os
  import warnings
  from pathlib import Path
  from typing import Optional, Union
  
  from .dynamic_module_utils import custom_object_save
  from .tokenization_utils_base import PreTrainedTokenizerBase
  from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging
  
  
  lowercase__			=	logging.get_logger(__name__)
  
  # Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
  lowercase__			=	direct_transformers_import(Path(__file__).parent)
  
  
  lowercase__			=	{
      '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
      '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
      '''AutoImageProcessor''': '''ImageProcessingMixin''',
  }
  
  
  
  
  
  
  class 						lowercase     (   UpperCAmelCase_):
        __lowercase 		=  ['''feature_extractor''', '''tokenizer''']
        # Names need to be attr_class for attr in attributes
        __lowercase 		=  None
        __lowercase 		=  None
        __lowercase 		=  None
  
  
  
  
  
  
        def __init__(self ,  *a__ ,  **a__					)       ->   Any:
           # Sanitize args and kwargs
           for key in kwargs:
              if key not in self.attributes:
                 raise TypeError(f"""Unexpected keyword argument {key}."""					)
           for arg, attribute_name in zip(a__ ,  self.attributes					):
              if attribute_name in kwargs:
                 raise TypeError(f"""Got multiple values for argument {attribute_name}."""					)
              else:
                 SCREAMING_SNAKE_CASE__			:				int       						= arg
  
           if len(a__					) != len(self.attributes					):
              raise ValueError(
                  f"""This processor requires {len(self.attributes					)} arguments: {", ".join(self.attributes					)}. Got """
                  f"""{len(a__					)} arguments instead."""					)
  
           # Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
           for attribute_name, arg in kwargs.items():
              SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= getattr(self ,  f"""{attribute_name}_class"""					)
              # Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
              SCREAMING_SNAKE_CASE__			:				str       						= AUTO_TO_BASE_CLASS_MAPPING.get(a__ ,  a__					)
              if isinstance(a__ ,  a__					):
                 SCREAMING_SNAKE_CASE__			:				Tuple       						= tuple(getattr(a__ ,  a__					) for n in class_name if n is not None					)
              else:
                 SCREAMING_SNAKE_CASE__			:				Tuple       						= getattr(a__ ,  a__					)
  
              if not isinstance(a__ ,  a__					):
                 raise ValueError(
                     f"""Received a {type(a__					).__name__} for argument {attribute_name}, but a {class_name} was expected."""					)
  
              setattr(self ,  a__ ,  a__					)
  
  
  
  
  
  
        def __repr__(self					)       ->   str:
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= [f"""- {name}: {repr(getattr(self ,  a__					)					)}""" for name in self.attributes]
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= '''\n'''.join(a__					)
           return f"""{self.__class__.__name__}:\n{attributes_repr}"""
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ = False ,  **a__					)       ->   Dict:
           os.makedirs(a__ ,  exist_ok=a__					)
  
           if push_to_hub:
              SCREAMING_SNAKE_CASE__			:				str       						= kwargs.pop('''commit_message''' ,  a__					)
              SCREAMING_SNAKE_CASE__			:				Any       						= kwargs.pop('''repo_id''' ,  save_directory.split(os.path.sep					)[-1]					)
              SCREAMING_SNAKE_CASE__			:				Optional[int]       						= self._create_repo(a__ ,  **a__					)
              SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= self._get_files_timestamps(a__					)
           # If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
           # loaded from the Hub.
           if self._auto_class is not None:
              SCREAMING_SNAKE_CASE__			:				List[str]       						= [getattr(self ,  a__					) for attribute_name in self.attributes]
              SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= [(a.init_kwargs if isinstance(a__ ,  a__					) else a) for a in attrs]
              custom_object_save(self ,  a__ ,  config=a__					)
  
           for attribute_name in self.attributes:
              SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= getattr(self ,  a__					)
              # Include the processor class in the attribute config so this processor can then be reloaded with the
              # `AutoProcessor` API.
              if hasattr(a__ ,  '''_set_processor_class'''					):
                 attribute._set_processor_class(self.__class__.__name__					)
              attribute.save_pretrained(a__					)
  
           if self._auto_class is not None:
              # We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
              for attribute_name in self.attributes:
                 SCREAMING_SNAKE_CASE__			:				Any       						= getattr(self ,  a__					)
                 if isinstance(a__ ,  a__					):
                    del attribute.init_kwargs["auto_map"]
  
           if push_to_hub:
              self._upload_modified_files(
                  a__ ,  a__ ,  a__ ,  commit_message=a__ ,  token=kwargs.get('''use_auth_token'''					) ,  )
  
  
  
  
  
  
        @classmethod
        def    A__(cls ,  a__ ,  a__ = None ,  a__ = False ,  a__ = False ,  a__ = None ,  a__ = "main" ,  **a__ ,  )       ->   List[Any]:
           SCREAMING_SNAKE_CASE__			:				Tuple       						= cache_dir
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= force_download
           SCREAMING_SNAKE_CASE__			:				str       						= local_files_only
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= revision
  
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= kwargs.pop('''use_auth_token''' ,  a__					)
           if use_auth_token is not None:
              warnings.warn(
                  '''The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.''' ,  a__					)
              if token is not None:
                 raise ValueError(
                     '''`token` and `use_auth_token` are both specified. Please set only the argument `token`.'''					)
              SCREAMING_SNAKE_CASE__			:				Dict       						= use_auth_token
  
           if token is not None:
              # change to `token` in a follow-up PR
              SCREAMING_SNAKE_CASE__			:				Any       						= token
  
           SCREAMING_SNAKE_CASE__			:				List[str]       						= cls._get_arguments_from_pretrained(a__ ,  **a__					)
           return cls(*a__					)
  
  
  
  
  
  
        @classmethod
        def    A__(cls ,  a__="AutoProcessor"					)       ->   List[str]:
           if not isinstance(a__ ,  a__					):
              SCREAMING_SNAKE_CASE__			:				List[Any]       						= auto_class.__name__
  
           import transformers.models.auto as auto_module
  
           if not hasattr(a__ ,  a__					):
              raise ValueError(f"""{auto_class} is not a valid auto class."""					)
  
           SCREAMING_SNAKE_CASE__			:				Tuple       						= auto_class
  
  
  
  
  
  
        @classmethod
        def    A__(cls ,  a__ ,  **a__					)       ->   Any:
           SCREAMING_SNAKE_CASE__			:				int       						= []
           for attribute_name in cls.attributes:
              SCREAMING_SNAKE_CASE__			:				Optional[int]       						= getattr(cls ,  f"""{attribute_name}_class"""					)
              if isinstance(a__ ,  a__					):
                 SCREAMING_SNAKE_CASE__			:				List[Any]       						= tuple(getattr(a__ ,  a__					) if n is not None else None for n in class_name					)
                 SCREAMING_SNAKE_CASE__			:				Dict       						= kwargs.get('''use_fast''' ,  a__					)
                 if use_fast and classes[1] is not None:
                    SCREAMING_SNAKE_CASE__			:				Optional[int]       						= classes[1]
                 else:
                    SCREAMING_SNAKE_CASE__			:				str       						= classes[0]
              else:
                 SCREAMING_SNAKE_CASE__			:				Any       						= getattr(a__ ,  a__					)
  
              args.append(attribute_class.from_pretrained(a__ ,  **a__					)					)
           return args
  
  
  
  
  
  
        @property
        def    A__(self					)       ->   Tuple:
           SCREAMING_SNAKE_CASE__			:				List[str]       						= getattr(self ,  self.attributes[0]					)
           return getattr(a__ ,  '''model_input_names''' ,  a__					)
  
  lowercase__			=	copy_func(ProcessorMixin.push_to_hub)
  if ProcessorMixin.push_to_hub.__doc__ is not None:
         lowercase__			=	ProcessorMixin.push_to_hub.__doc__.format(
             object='''processor''', object_class='''AutoProcessor''', object_files='''processor files'''
         )
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  import argparse
  import json
  import re
  import string
  import sys
  from collections import Counter
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     def remove_articles(snake_case   ):
        return re.sub(R'''\b(a|an|the)\b'''	,     ''' '''	,     snake_case   )
  
     def white_space_fix(snake_case   ):
        return " ".join(text.split()   )
  
     def remove_punc(snake_case   ):
        SCREAMING_SNAKE_CASE__			:				Optional[int]       						= set(string.punctuation   )
        return "".join(ch for ch in text if ch not in exclude   )
  
     def lower(snake_case   ):
        return text.lower()
  
     return white_space_fix(remove_articles(remove_punc(lower(snake_case   )   )   )   )
  
  
  def 				UpperCamelCase	(  snake_case	,     snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Tuple       						= normalize_answer(snake_case   ).split()
     SCREAMING_SNAKE_CASE__			:				List[Any]       						= normalize_answer(snake_case   ).split()
     SCREAMING_SNAKE_CASE__			:				Dict       						= Counter(snake_case   ) & Counter(snake_case   )
     SCREAMING_SNAKE_CASE__			:				int       						= sum(common.values()   )
     if num_same == 0:
        return 0
     SCREAMING_SNAKE_CASE__			:				Tuple       						= 1.0 * num_same / len(snake_case   )
     SCREAMING_SNAKE_CASE__			:				Tuple       						= 1.0 * num_same / len(snake_case   )
     SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= (2 * precision * recall) / (precision + recall)
     return fa
  
  
  def 				UpperCamelCase	(  snake_case	,     snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     return normalize_answer(snake_case   ) == normalize_answer(snake_case   )
  
  
  def 				UpperCamelCase	(  snake_case	,     snake_case	,     snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Any       						= []
     for ground_truth in ground_truths:
        SCREAMING_SNAKE_CASE__			:				List[Any]       						= metric_fn(snake_case	,     snake_case   )
        scores_for_ground_truths.append(snake_case   )
     return max(snake_case   )
  
  
  def 				UpperCamelCase	(  snake_case	,     snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				str       						= 0
     SCREAMING_SNAKE_CASE__			:				Optional[int]       						= []
     for passage in dataset:
        for qa in passage["qas"]:
           total += 1
           if qa["id"] not in predictions:
              SCREAMING_SNAKE_CASE__			:				Any       						= f"""Unanswered question {qa["id"]} will receive score 0."""
              print(snake_case	,     file=sys.stderr   )
              continue
  
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= [x['''text'''] for x in qa['''answers''']]
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= predictions[qa['''id''']]
  
           SCREAMING_SNAKE_CASE__			:				List[str]       						= metric_max_over_ground_truths(snake_case	,     snake_case	,     snake_case   )
           if int(_exact_match   ) == 1:
              correct_ids.append(qa['''id''']   )
           exact_match += _exact_match
  
           fa += metric_max_over_ground_truths(snake_case	,     snake_case	,     snake_case   )
  
     SCREAMING_SNAKE_CASE__			:				int       						= exact_match / total
     SCREAMING_SNAKE_CASE__			:				Optional[int]       						= fa / total
  
     return {"exact_match": exact_match, "f1": fa}, correct_ids
  
  
  if __name__ == "__main__":
         lowercase__			=	'''1.0'''
         lowercase__			=	argparse.ArgumentParser('''Official evaluation script for ReCoRD v1.0.''')
         parser.add_argument('''data_file''', help='''The dataset file in JSON format.''')
         parser.add_argument('''pred_file''', help='''The model prediction file in JSON format.''')
         parser.add_argument('''--output_correct_ids''', action='''store_true''', help='''Output the correctly answered query IDs.''')
         lowercase__			=	parser.parse_args()
  
         with open(args.data_file) as data_file:
                lowercase__			=	json.load(data_file)
                if dataset_json["version"] != expected_version:
                       print(
                           F"Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json['version']}",
                           file=sys.stderr,
                       )
                lowercase__			=	dataset_json['''data''']
  
         with open(args.pred_file) as pred_file:
                lowercase__			=	json.load(pred_file)
  
         lowercase__	,     lowercase__			=	evaluate(dataset, predictions)
  
         if args.output_correct_ids:
                print(F"Output {len(correct_ids)} correctly answered question IDs.")
                with open('''correct_ids.json''', '''w''') as f:
                       json.dump(correct_ids, f)
  
  