code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import argparse
  import json
  import re
  import string
  import sys
  from collections import Counter
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
  
      def remove_articles(lowercase	):
          return re.sub(R'''\b(a|an|the)\b''' ,					''' ''' ,					lowercase	)
  
      def white_space_fix(lowercase	):
          return " ".join(text.split()	)
  
      def remove_punc(lowercase	):
          a					=set(string.punctuation	)
          return "".join(ch for ch in text if ch not in exclude	)
  
      def lower(lowercase	):
          return text.lower()
  
      return white_space_fix(remove_articles(remove_punc(lower(lowercase	)	)	)	)
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=normalize_answer(lowercase	).split()
      a					=normalize_answer(lowercase	).split()
      a					=Counter(lowercase	) & Counter(lowercase	)
      a					=sum(common.values()	)
      if num_same == 0:
          return 0
      a					=1.0 * num_same / len(lowercase	)
      a					=1.0 * num_same / len(lowercase	)
      a					=(2 * precision * recall) / (precision + recall)
      return fa
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      return normalize_answer(lowercase	) == normalize_answer(lowercase	)
  def _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=[]
      for ground_truth in ground_truths:
          a					=metric_fn(lowercase ,					lowercase	)
          scores_for_ground_truths.append(lowercase	)
      return max(lowercase	)
  def _A		(		lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=a					=a					=0
      a					=[]
      for passage in dataset:
          for qa in passage["qas"]:
              total += 1
              if qa["id"] not in predictions:
                  a					=f'''Unanswered question {qa["id"]} will receive score 0.'''
                  print(lowercase ,					file=sys.stderr	)
                  continue
  
              a					=[x['''text'''] for x in qa['''answers''']]
              a					=predictions[qa['''id''']]
  
              a					=metric_max_over_ground_truths(lowercase ,					lowercase ,					lowercase	)
              if int(_exact_match	) == 1:
                  correct_ids.append(qa['''id''']	)
              exact_match += _exact_match
  
              fa += metric_max_over_ground_truths(lowercase ,					lowercase ,					lowercase	)
  
      a					=exact_match / total
      a					=fa / total
  
      return {"exact_match": exact_match, "f1": fa}, correct_ids
  
  
  if __name__ == "__main__":
         lowerCamelCase_     :     List[str]    		=						"""1.0"""
         lowerCamelCase_     :     Tuple    		=						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.""")
         lowerCamelCase_     :     Union[str, Any]    		=						parser.parse_args()
  
         with open(args.data_file) as data_file:
                lowerCamelCase_     :     int    		=						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,
                       )
                lowerCamelCase_     :     str    		=						dataset_json["""data"""]
  
         with open(args.pred_file) as pred_file:
                lowerCamelCase_     :     Tuple    		=						json.load(pred_file)
  
         lowerCamelCase_     :     int    		=						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)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowerCamelCase_     :     int    		=						logging.get_logger(__name__)
  
  lowerCamelCase_     :     Optional[Any]    		=						{
      """dandelin/vilt-b32-mlm""": """https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"""
  }
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						"vilt"
  
  
   def __init__(	self				,					__A=3_0522				,					__A=2				,					__A=2				,					__A=40				,					__A=768				,					__A=12				,					__A=12				,					__A=3072				,					__A="gelu"				,					__A=0.0				,					__A=0.0				,					__A=0.02				,					__A=1E-1_2				,					__A=384				,					__A=32				,					__A=3				,					__A=True				,					__A=-1				,					__A=False				,					__A=-1				,					**__A				,					)      ->  Dict:
       super().__init__(tie_word_embeddings=__A				,					**__A		)
  
       a					=vocab_size
       a					=type_vocab_size
       a					=modality_type_vocab_size
       a					=max_position_embeddings
  
       a					=hidden_size
       a					=num_hidden_layers
       a					=num_attention_heads
       a					=intermediate_size
       a					=hidden_act
       a					=hidden_dropout_prob
       a					=attention_probs_dropout_prob
       a					=initializer_range
       a					=layer_norm_eps
  
       a					=image_size
       a					=patch_size
       a					=num_channels
       a					=qkv_bias
       a					=max_image_length
       a					=num_images