






"""simple docstring"""





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







def 					lowerCamelCase__	(							_lowerCamelCase	:		Optional[Any]			)				->    Any:

  def remove_articles(_lowerCamelCase	:		Any			):
    return re.sub(r'\b(a|an|the)\b'		,    ' '		,    _lowerCamelCase			)

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

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

  def lower(_lowerCamelCase	:		Any			):
    return text.lower()

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







def 					lowerCamelCase__	(							_lowerCamelCase	:		Optional[int]		,    _lowerCamelCase	:		Union[str, Any]			)				->    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 					lowerCamelCase__	(							_lowerCamelCase	:		Tuple		,    _lowerCamelCase	:		Optional[Any]			)				->    Tuple:
  return normalize_answer(_lowerCamelCase			) == normalize_answer(_lowerCamelCase			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		Optional[int]		,    _lowerCamelCase	:		Tuple		,    _lowerCamelCase	:		str			)				->    List[str]:
  lowerCamelCase_   					=						[]
  for ground_truth in ground_truths:
    lowerCamelCase_   					=						metric_fn(_lowerCamelCase		,    _lowerCamelCase			)
    scores_for_ground_truths.append(_lowerCamelCase			)
  return max(_lowerCamelCase			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		Dict		,    _lowerCamelCase	:		Dict			)				->    Optional[int]:
  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_   					=						1_00.0 * exact_match / total
  lowerCamelCase_   					=						1_00.0 * fa / total

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


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE :    Dict    =						'''1.1'''
  _SCREAMING_SNAKE_CASE :    List[str]    =						argparse.ArgumentParser(description='''Evaluation for SQuAD ''' + expected_version)
  parser.add_argument('''dataset_file''', help='''Dataset file''')
  parser.add_argument('''prediction_file''', help='''Prediction File''')
  _SCREAMING_SNAKE_CASE :    Optional[int]    =						parser.parse_args()
  with open(args.dataset_file) as dataset_file:
    _SCREAMING_SNAKE_CASE :    Optional[int]    =						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,
      )
    _SCREAMING_SNAKE_CASE :    Tuple    =						dataset_json['''data''']
  with open(args.prediction_file) as prediction_file:
    _SCREAMING_SNAKE_CASE :    Dict    =						json.load(prediction_file)
  print(json.dumps(evaluate(dataset, predictions)))












"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE :    List[Any]    =						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE :    str    =						{
    '''facebook/regnet-y-040''': '''https://huggingface.co/facebook/regnet-y-040/blob/main/config.json''',
}






class 					a						( __snake_case		):
    SCREAMING_SNAKE_CASE						:					str						=			"""regnet"""
    SCREAMING_SNAKE_CASE						:					List[Any]						=			["""x""", """y"""]






    def __init__(	self					:							int	,						__SCREAMING_SNAKE_CASE					:							Any=3	,						__SCREAMING_SNAKE_CASE					:							Any=32	,						__SCREAMING_SNAKE_CASE					:							List[str]=[128, 192, 512, 1088]	,						__SCREAMING_SNAKE_CASE					:							Union[str, Any]=[2, 6, 12, 2]	,						__SCREAMING_SNAKE_CASE					:							str=64	,						__SCREAMING_SNAKE_CASE					:							List[Any]="y"	,						__SCREAMING_SNAKE_CASE					:							Optional[int]="relu"	,						**__SCREAMING_SNAKE_CASE					:							Union[str, Any]	,						)					->		Union[str, Any]:
      super().__init__(**__SCREAMING_SNAKE_CASE )
      if layer_type not in self.layer_types:
        raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types )}''' )
      lowerCamelCase_   					=						num_channels
      lowerCamelCase_   					=						embedding_size
      lowerCamelCase_   					=						hidden_sizes
      lowerCamelCase_   					=						depths
      lowerCamelCase_   					=						groups_width
      lowerCamelCase_   					=						layer_type
      lowerCamelCase_   					=						hidden_act
      # always downsample in the first stage
      lowerCamelCase_   					=						True




