code: 
  
  import argparse
  import json
  import re
  import string
  import sys
  from collections import Counter
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Union[str, Any]    ):
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  					def remove_articles(SCREAMING_SNAKE_CASE_	:					List[str]    ):
  										return re.sub(R'\b(a|an|the)\b'			,  ' '			,  SCREAMING_SNAKE_CASE_    )
  
  					def white_space_fix(SCREAMING_SNAKE_CASE_	:					List[str]    ):
  										return " ".join(text.split()    )
  
  					def remove_punc(SCREAMING_SNAKE_CASE_	:					Any    ):
  										lowercase__					:     Optional[int]   = set(string.punctuation    )
  										return "".join(ch for ch in text if ch not in exclude    )
  
  					def lower(SCREAMING_SNAKE_CASE_	:					Union[str, Any]    ):
  										return text.lower()
  
  					return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE_    )    )    )    )
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					List[Any]			,  SCREAMING_SNAKE_CASE_	:					Optional[int]    ):
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  					lowercase__					:     List[str]   = normalize_answer(SCREAMING_SNAKE_CASE_    ).split()
  					lowercase__					:     Any   = normalize_answer(SCREAMING_SNAKE_CASE_    ).split()
  					lowercase__					:     Optional[int]   = Counter(SCREAMING_SNAKE_CASE_    ) & Counter(SCREAMING_SNAKE_CASE_    )
  					lowercase__					:     Optional[int]   = sum(common.values()    )
  					if num_same == 0:
  										return 0
  					lowercase__					:     Optional[Any]   = 1.0 * num_same / len(SCREAMING_SNAKE_CASE_    )
  					lowercase__					:     Any   = 1.0 * num_same / len(SCREAMING_SNAKE_CASE_    )
  					lowercase__					:     int   = (2 * precision * recall) / (precision + recall)
  					return fa
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Any			,  SCREAMING_SNAKE_CASE_	:					Optional[int]    ):
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  					return normalize_answer(SCREAMING_SNAKE_CASE_    ) == normalize_answer(SCREAMING_SNAKE_CASE_    )
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					int			,  SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					List[Any]    ):
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  					lowercase__					:     Union[str, Any]   = []
  					for ground_truth in ground_truths:
  										lowercase__					:     Dict   = metric_fn(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
  										scores_for_ground_truths.append(SCREAMING_SNAKE_CASE_    )
  					return max(SCREAMING_SNAKE_CASE_    )
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Tuple			,  SCREAMING_SNAKE_CASE_	:					str    ):
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  					lowercase__					:     Any   = 0
  					for article in dataset:
  										for paragraph in article["paragraphs"]:
  															for qa in paragraph["qas"]:
  																				total += 1
  																				if qa["id"] not in predictions:
  																									lowercase__					:     Any   = 'Unanswered question ' + qa['id'] + ' will receive score 0.'
  																									print(SCREAMING_SNAKE_CASE_			,  file=sys.stderr    )
  																									continue
  																				lowercase__					:     str   = [x['text'] for x in qa['answers']]
  																				lowercase__					:     Tuple   = predictions[qa['id']]
  																				exact_match += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
  																				fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
  
  					lowercase__					:     List[str]   = 100.0 * exact_match / total
  					lowercase__					:     str   = 100.0 * fa / total
  
  					return {"exact_match": exact_match, "f1": fa}
  
  
  if __name__ == "__main__":
  							snake_case_    						=				'''1.1'''
  							snake_case_    						=				argparse.ArgumentParser(description='''Evaluation for SQuAD ''' + expected_version)
  							parser.add_argument('''dataset_file''', help='''Dataset file''')
  							parser.add_argument('''prediction_file''', help='''Prediction File''')
  							snake_case_    						=				parser.parse_args()
  							with open(args.dataset_file) as dataset_file:
  														snake_case_    						=				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,
  																					)
  														snake_case_    						=				dataset_json['''data''']
  							with open(args.prediction_file) as prediction_file:
  														snake_case_    						=				json.load(prediction_file)
  							print(json.dumps(evaluate(dataset, predictions)))
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  snake_case_    						=				{'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''GPTJForCausalLM''',
             '''GPTJForQuestionAnswering''',
             '''GPTJForSequenceClassification''',
             '''GPTJModel''',
             '''GPTJPreTrainedModel''',
         ]
  
  try:
         if not is_tf_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''TFGPTJForCausalLM''',
             '''TFGPTJForQuestionAnswering''',
             '''TFGPTJForSequenceClassification''',
             '''TFGPTJModel''',
             '''TFGPTJPreTrainedModel''',
         ]
  
  try:
         if not is_flax_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''FlaxGPTJForCausalLM''',
             '''FlaxGPTJModel''',
             '''FlaxGPTJPreTrainedModel''',
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_gptj import (
                    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
                    GPTJForCausalLM,
                    GPTJForQuestionAnswering,
                    GPTJForSequenceClassification,
                    GPTJModel,
                    GPTJPreTrainedModel,
                )
  
         try:
                if not is_tf_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_tf_gptj import (
                    TFGPTJForCausalLM,
                    TFGPTJForQuestionAnswering,
                    TFGPTJForSequenceClassification,
                    TFGPTJModel,
                    TFGPTJPreTrainedModel,
                )
  
         try:
                if not is_flax_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel
  
  else:
         import sys
  
         snake_case_    						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  