






"""simple docstring"""


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




def 				a (     UpperCamelCase_							:				Any		)  ->		Dict:

 def remove_articles(UpperCamelCase_							:				Union[str, Any]		):
  return re.sub(R'\b(a|an|the)\b'     ,	' '     ,	UpperCamelCase_		)

 def white_space_fix(UpperCamelCase_							:				Dict		):
  return " ".join(text.split()		)

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

 def lower(UpperCamelCase_							:				int		):
  return text.lower()

 return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase_		)		)		)		)




def 				a (     UpperCamelCase_							:				Tuple     ,	UpperCamelCase_							:				str		)  ->		List[str]:
 __A	:       List[Any]       =	normalize_answer(UpperCamelCase_		).split()
 __A	:       List[Any]       =	normalize_answer(UpperCamelCase_		).split()
 __A	:       Union[str, Any]       =	Counter(UpperCamelCase_		) & Counter(UpperCamelCase_		)
 __A	:       Any       =	sum(common.values()		)
 if num_same == 0:
  return 0
 __A	:       Dict       =	1.0 * num_same / len(UpperCamelCase_		)
 __A	:       List[str]       =	1.0 * num_same / len(UpperCamelCase_		)
 __A	:       Tuple       =	(2 * precision * recall) / (precision + recall)
 return fa




def 				a (     UpperCamelCase_							:				Optional[Any]     ,	UpperCamelCase_							:				Tuple		)  ->		Dict:
 return normalize_answer(UpperCamelCase_		) == normalize_answer(UpperCamelCase_		)




def 				a (     UpperCamelCase_							:				Optional[Any]     ,	UpperCamelCase_							:				List[str]     ,	UpperCamelCase_							:				str		)  ->		Optional[Any]:
 __A	:       List[Any]       =	[]
 for ground_truth in ground_truths:
  __A	:       List[str]       =	metric_fn(UpperCamelCase_     ,	UpperCamelCase_		)
  scores_for_ground_truths.append(UpperCamelCase_		)
 return max(UpperCamelCase_		)




def 				a (     UpperCamelCase_							:				Optional[int]     ,	UpperCamelCase_							:				int		)  ->		Tuple:
 __A	:       List[str]       =	0
 __A	:       str       =	[]
 for passage in dataset:
  for qa in passage["qas"]:
   total += 1
   if qa["id"] not in predictions:
    __A	:       Any       =	F"Unanswered question {qa['id']} will receive score 0."
    print(UpperCamelCase_     ,	file=sys.stderr		)
    continue

   __A	:       str       =	[x['text'] for x in qa['answers']]
   __A	:       Any       =	predictions[qa['id']]

   __A	:       str       =	metric_max_over_ground_truths(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_		)
   if int(_exact_match		) == 1:
    correct_ids.append(qa['id']		)
   exact_match += _exact_match

   fa += metric_max_over_ground_truths(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_		)

 __A	:       Dict       =	exact_match / total
 __A	:       Any       =	fa / total

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


if __name__ == "__main__":
   _UpperCamelCase   :						Tuple     						= """1.0"""
   _UpperCamelCase   :						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.""")
   _UpperCamelCase   :						List[str]     						= parser.parse_args()

   with open(args.data_file) as data_file:
      _UpperCamelCase   :						Optional[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,
         )
      _UpperCamelCase   :						Optional[Any]     						= dataset_json["""data"""]

   with open(args.pred_file) as pred_file:
      _UpperCamelCase   :						str     						= json.load(pred_file)

   _UpperCamelCase						,					_UpperCamelCase   :						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)













"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


_UpperCamelCase   :						str     						= logging.get_logger(__name__)






class __a   (   snake_case_				):



  def __init__(self  :		Union[str, Any]				,			*lowercase_  :		Any				,			**lowercase_  :		Optional[Any]   )		->							None:
   warnings.warn(
       'The class LayoutLMv3FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
       ' Please use LayoutLMv3ImageProcessor instead.'				,			lowercase_				,			)
   super().__init__(*lowercase_				,			**lowercase_   )





