
"""simple docstring"""




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

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_      ):

     def remove_articles(SCREAMING_SNAKE_CASE_      ):
          return re.sub(R'\b(a|an|the)\b',					' ',					SCREAMING_SNAKE_CASE_      )

     def white_space_fix(SCREAMING_SNAKE_CASE_      ):
          return " ".join(text.split()      )

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

     def lower(SCREAMING_SNAKE_CASE_      ):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE_      )      )      )      )

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
     SCREAMING_SNAKE_CASE           =     normalize_answer(SCREAMING_SNAKE_CASE_      ).split()
     SCREAMING_SNAKE_CASE           =     normalize_answer(SCREAMING_SNAKE_CASE_      ).split()
     SCREAMING_SNAKE_CASE           =     Counter(SCREAMING_SNAKE_CASE_      ) & Counter(SCREAMING_SNAKE_CASE_      )
     SCREAMING_SNAKE_CASE           =     sum(common.values()      )
     if num_same == 0:
          return 0
     SCREAMING_SNAKE_CASE           =     1.0 * num_same / len(SCREAMING_SNAKE_CASE_      )
     SCREAMING_SNAKE_CASE           =     1.0 * num_same / len(SCREAMING_SNAKE_CASE_      )
     SCREAMING_SNAKE_CASE           =     (2 * precision * recall) / (precision + recall)
     return fa

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
     return normalize_answer(SCREAMING_SNAKE_CASE_      ) == normalize_answer(SCREAMING_SNAKE_CASE_      )

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
     SCREAMING_SNAKE_CASE           =     []
     for ground_truth in ground_truths:
          SCREAMING_SNAKE_CASE           =     metric_fn(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )
          scores_for_ground_truths.append(SCREAMING_SNAKE_CASE_      )
     return max(SCREAMING_SNAKE_CASE_      )

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
     SCREAMING_SNAKE_CASE           =     SCREAMING_SNAKE_CASE           =     SCREAMING_SNAKE_CASE           =     0
     for article in dataset:
          for paragraph in article["paragraphs"]:
               for qa in paragraph["qas"]:
                    total += 1
                    if qa["id"] not in predictions:
                         SCREAMING_SNAKE_CASE           =     'Unanswered question ' + qa['id'] + ' will receive score 0.'
                         print(SCREAMING_SNAKE_CASE_,					file=sys.stderr      )
                         continue
                    SCREAMING_SNAKE_CASE           =     [x['text'] for x in qa['answers']]
                    SCREAMING_SNAKE_CASE           =     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_      )

     SCREAMING_SNAKE_CASE           =     100.0 * exact_match / total
     SCREAMING_SNAKE_CASE           =     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"""




import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


snake_case			       =		TypeVar('T', bound='TaskTemplate')




@dataclass(frozen=__magic_name__				)
class 					UpperCamelCase     (	abc.ABC				):

				"""simple docstring"""



				UpperCAmelCase_    :				str
				UpperCAmelCase_    :				ClassVar[Features]
				UpperCAmelCase_    :				ClassVar[Features]


				def 							A (    self		,     lowercase__      )   ->   T:
									"""simple docstring"""







									return copy.deepcopy(self      )


				@property
				def 							A (    self      )   ->   Features:
									"""simple docstring"""







									return Features(**self.input_schema		,     **self.label_schema      )


				@property
				@abc.abstractmethod
				def 							A (    self      )   ->   Dict[str, str]:
									"""simple docstring"""







									raise NotImplementedError




				@classmethod
				def 							A (    cls		,     lowercase__      )   ->   T:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     {f.name for f in dataclasses.fields(cls      )}
									return cls(**{k: v for k, v in template_dict.items() if k in field_names}      )
