'''simple docstring'''






import argparse
import json
import re
import string
import sys
from collections import Counter





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Union[str, Any]							):

     def remove_articles(__snake_case       : Optional[int]							):
          return re.sub(R'\b(a|an|the)\b'      ,		' '      ,		__snake_case							)

     def white_space_fix(__snake_case       : List[Any]							):
          return " ".join(text.split()							)

     def remove_punc(__snake_case       : int							):
          _A								=     set(string.punctuation							)
          return "".join(ch for ch in text if ch not in exclude							)

     def lower(__snake_case       : List[Any]							):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(__snake_case							)							)							)							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Union[str, Any]      ,		__snake_case       : List[str]							):
     _A								=     normalize_answer(__snake_case							).split()
     _A								=     normalize_answer(__snake_case							).split()
     _A								=     Counter(__snake_case							) & Counter(__snake_case							)
     _A								=     sum(common.values()							)
     if num_same == 0:
          return 0
     _A								=     1.0 * num_same / len(__snake_case							)
     _A								=     1.0 * num_same / len(__snake_case							)
     _A								=     (2 * precision * recall) / (precision + recall)
     return fa





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[int]      ,		__snake_case       : Optional[Any]							):
     return normalize_answer(__snake_case							) == normalize_answer(__snake_case							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Any      ,		__snake_case       : Tuple      ,		__snake_case       : Dict							):
     _A								=     []
     for ground_truth in ground_truths:
          _A								=     metric_fn(__snake_case      ,		__snake_case							)
          scores_for_ground_truths.append(__snake_case							)
     return max(__snake_case							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Any      ,		__snake_case       : Dict							):
     _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(__snake_case      ,		file=sys.stderr							)
                    continue

               _A								=     [x['text'] for x in qa['answers']]
               _A								=     predictions[qa['id']]

               _A								=     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							)

     _A								=     exact_match / total
     _A								=     fa / total

     return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
   _UpperCAmelCase						:    Optional[int]     					=					'''1.0'''
   _UpperCAmelCase						:    Optional[int]     					=					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.''')
   _UpperCAmelCase						:    Any     					=					parser.parse_args()

   with open(args.data_file) as data_file:
      _UpperCAmelCase						:    List[str]     					=					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,
         )
      _UpperCAmelCase						:    Any     					=					dataset_json['''data''']

   with open(args.pred_file) as pred_file:
      _UpperCAmelCase						:    Tuple     					=					json.load(pred_file)

   _UpperCAmelCase					,     _UpperCAmelCase						:    Dict     					=					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)




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase						:    Dict     					=					{
    '''configuration_decision_transformer''': [
        '''DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DecisionTransformerConfig''',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase						:    Union[str, Any]     					=					[
       '''DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''DecisionTransformerGPT2Model''',
       '''DecisionTransformerGPT2PreTrainedModel''',
       '''DecisionTransformerModel''',
       '''DecisionTransformerPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_decision_transformer import (
       DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DecisionTransformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_decision_transformer import (
          DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DecisionTransformerGPTaModel,
          DecisionTransformerGPTaPreTrainedModel,
          DecisionTransformerModel,
          DecisionTransformerPreTrainedModel,
      )


else:
   import sys

   _UpperCAmelCase						:    Dict     					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



