'''simple docstring'''




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







def 			A_   ( _lowerCamelCase			:							Tuple    ):

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

      def white_space_fix(_lowerCamelCase			:							str    ):
            return " ".join(text.split()    )

      def remove_punc(_lowerCamelCase			:							Union[str, Any]    ):
            _lowerCAmelCase		   =   set(string.punctuation    )
            return "".join(ch for ch in text if ch not in exclude    )

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

      return white_space_fix(remove_articles(remove_punc(lower(_lowerCamelCase    )    )    )    )







def 			A_   ( _lowerCamelCase			:							Any  ,  _lowerCamelCase			:							Any    ):
      _lowerCAmelCase		   =   normalize_answer(_lowerCamelCase    ).split()
      _lowerCAmelCase		   =   normalize_answer(_lowerCamelCase    ).split()
      _lowerCAmelCase		   =   Counter(_lowerCamelCase    ) & Counter(_lowerCamelCase    )
      _lowerCAmelCase		   =   sum(common.values()    )
      if num_same == 0:
            return 0
      _lowerCAmelCase		   =   1.0 * num_same / len(_lowerCamelCase    )
      _lowerCAmelCase		   =   1.0 * num_same / len(_lowerCamelCase    )
      _lowerCAmelCase		   =   (2 * precision * recall) / (precision + recall)
      return fa







def 			A_   ( _lowerCamelCase			:							int  ,  _lowerCamelCase			:							Tuple    ):
      return normalize_answer(_lowerCamelCase    ) == normalize_answer(_lowerCamelCase    )







def 			A_   ( _lowerCamelCase			:							List[str]  ,  _lowerCamelCase			:							Any  ,  _lowerCamelCase			:							Dict    ):
      _lowerCAmelCase		   =   []
      for ground_truth in ground_truths:
            _lowerCAmelCase		   =   metric_fn(_lowerCamelCase  ,  _lowerCamelCase    )
            scores_for_ground_truths.append(_lowerCamelCase    )
      return max(_lowerCamelCase    )







def 			A_   ( _lowerCamelCase			:							int  ,  _lowerCamelCase			:							List[Any]    ):
      _lowerCAmelCase		   =   _lowerCAmelCase		   =   _lowerCAmelCase		   =   0
      for article in dataset:
            for paragraph in article["paragraphs"]:
                  for qa in paragraph["qas"]:
                        total += 1
                        if qa["id"] not in predictions:
                              _lowerCAmelCase		   =   'Unanswered question ' + qa['id'] + ' will receive score 0.'
                              print(_lowerCamelCase  ,  file=sys.stderr    )
                              continue
                        _lowerCAmelCase		   =   [x['text'] for x in qa['answers']]
                        _lowerCAmelCase		   =   predictions[qa['id']]
                        exact_match += metric_max_over_ground_truths(_lowerCamelCase  ,  _lowerCamelCase  ,  _lowerCamelCase    )
                        fa += metric_max_over_ground_truths(_lowerCamelCase  ,  _lowerCamelCase  ,  _lowerCamelCase    )

      _lowerCAmelCase		   =   100.0 * exact_match / total
      _lowerCAmelCase		   =   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)))






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


snake_case  =			{
    '''configuration_align''': [
        '''ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''AlignConfig''',
        '''AlignTextConfig''',
        '''AlignVisionConfig''',
    ],
    '''processing_align''': ['''AlignProcessor'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case  =			[
     '''ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''AlignModel''',
     '''AlignPreTrainedModel''',
     '''AlignTextModel''',
     '''AlignVisionModel''',
 ]

if TYPE_CHECKING:
 from .configuration_align import (
     ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
     AlignConfig,
     AlignTextConfig,
     AlignVisionConfig,
 )
 from .processing_align import AlignProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_align import (
      ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
      AlignModel,
      AlignPreTrainedModel,
      AlignTextModel,
      AlignVisionModel,
  )

else:
 import sys

 snake_case  =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





