




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






def    snake_case   (				A     )				->				Optional[int]:




	'''simple docstring'''


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

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

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

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

	return white_space_fix(remove_articles(remove_punc(lower(A     )     )     )     )






def    snake_case   (				A		,  A     )				->				Optional[Any]:




	'''simple docstring'''


	lowerCamelCase_				:						List[str]				  =				normalize_answer(A     ).split()
	lowerCamelCase_				:						int				  =				normalize_answer(A     ).split()
	lowerCamelCase_				:						Optional[int]				  =				Counter(A     ) & Counter(A     )
	lowerCamelCase_				:						Optional[Any]				  =				sum(common.values()     )
	if num_same == 0:
		return 0
	lowerCamelCase_				:						Dict				  =				1.0 * num_same / len(A     )
	lowerCamelCase_				:						List[Any]				  =				1.0 * num_same / len(A     )
	lowerCamelCase_				:						Optional[int]				  =				(2 * precision * recall) / (precision + recall)
	return fa






def    snake_case   (				A		,  A     )				->				Tuple:




	'''simple docstring'''


	return normalize_answer(A     ) == normalize_answer(A     )






def    snake_case   (				A		,  A		,  A     )				->				Any:




	'''simple docstring'''


	lowerCamelCase_				:						Any				  =				[]
	for ground_truth in ground_truths:
		lowerCamelCase_				:						Optional[int]				  =				metric_fn(A		,  A     )
		scores_for_ground_truths.append(A     )
	return max(A     )






def    snake_case   (				A		,  A     )				->				str:




	'''simple docstring'''


	lowerCamelCase_				:						Dict				  =				0
	lowerCamelCase_				:						List[Any]				  =				[]
	for passage in dataset:
		for qa in passage["qas"]:
			total += 1
			if qa["id"] not in predictions:
				lowerCamelCase_				:						str				  =				f"Unanswered question {qa['id']} will receive score 0."
				print(A		,  file=sys.stderr     )
				continue

			lowerCamelCase_				:						str				  =				[x['text'] for x in qa['answers']]
			lowerCamelCase_				:						int				  =				predictions[qa['id']]

			lowerCamelCase_				:						Dict				  =				metric_max_over_ground_truths(A		,  A		,  A     )
			if int(_exact_match     ) == 1:
				correct_ids.append(qa['id']     )
			exact_match += _exact_match

			fa += metric_max_over_ground_truths(A		,  A		,  A     )

	lowerCamelCase_				:						str				  =				exact_match / total
	lowerCamelCase_				:						Union[str, Any]				  =				fa / total

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


if __name__ == "__main__":
							UpperCamelCase__						=		'1.0'
							UpperCamelCase__						=		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__						=		parser.parse_args()

							with open(args.data_file) as data_file:
														UpperCamelCase__						=		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__						=		dataset_json['data']

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

							UpperCamelCase__			,					UpperCamelCase__						=		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)


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__						:	List[str]	     = logging.get_logger(__name__)

UpperCAmelCase__						:	str	     = {
    'flaubert/flaubert_small_cased': 'https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json',
    'flaubert/flaubert_base_uncased': 'https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json',
    'flaubert/flaubert_base_cased': 'https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json',
    'flaubert/flaubert_large_cased': 'https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json',
}






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''
   __UpperCamelCase						:       Tuple												=       '''flaubert'''
   __UpperCamelCase						:       str												=       {
       '''hidden_size''': '''emb_dim''',
       '''num_attention_heads''': '''n_heads''',
       '''num_hidden_layers''': '''n_layers''',
       '''n_words''': '''vocab_size''',  # For backward compatibility
   }


   def __init__(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				Union[str, Any]=False     ,     lowerCAmelCase_	:				List[str]=0.0     ,     lowerCAmelCase_	:				Dict=3_0_1_4_5     ,     lowerCAmelCase_	:				Tuple=2_0_4_8     ,     lowerCAmelCase_	:				List[Any]=1_2     ,     lowerCAmelCase_	:				str=1_6     ,     lowerCAmelCase_	:				Union[str, Any]=0.1     ,     lowerCAmelCase_	:				List[str]=0.1     ,     lowerCAmelCase_	:				str=True     ,     lowerCAmelCase_	:				Optional[int]=False     ,     lowerCAmelCase_	:				Any=False     ,     lowerCAmelCase_	:				int=False     ,     lowerCAmelCase_	:				int=1     ,     lowerCAmelCase_	:				List[str]=True     ,     lowerCAmelCase_	:				Dict=5_1_2     ,     lowerCAmelCase_	:				Any=2_0_4_8**-0.5     ,     lowerCAmelCase_	:				List[str]=1e-12     ,     lowerCAmelCase_	:				Any=0.02     ,     lowerCAmelCase_	:				Optional[Any]=0     ,     lowerCAmelCase_	:				Union[str, Any]=1     ,     lowerCAmelCase_	:				List[Any]=2     ,     lowerCAmelCase_	:				Dict=3     ,     lowerCAmelCase_	:				Optional[Any]=5     ,     lowerCAmelCase_	:				Any=True     ,     lowerCAmelCase_	:				Union[str, Any]="first"     ,     lowerCAmelCase_	:				List[Any]=True     ,     lowerCAmelCase_	:				Tuple=None     ,     lowerCAmelCase_	:				Optional[int]=True     ,     lowerCAmelCase_	:				int=0.1     ,     lowerCAmelCase_	:				Optional[int]=5     ,     lowerCAmelCase_	:				Union[str, Any]=5     ,     lowerCAmelCase_	:				List[Any]=0     ,     lowerCAmelCase_	:				Dict=0     ,     lowerCAmelCase_	:				Union[str, Any]=2     ,     lowerCAmelCase_	:				Optional[int]=0     ,     **lowerCAmelCase_	:				Any     ,     ):

      """simple docstring"""
      _A:    List[str]						     =			pre_norm
      _A:    List[Any]						     =			layerdrop
      _A:    Dict						     =			vocab_size
      _A:    List[Any]						     =			emb_dim
      _A:    Optional[Any]						     =			n_layers
      _A:    Union[str, Any]						     =			n_heads
      _A:    Optional[Any]						     =			dropout
      _A:    Optional[Any]						     =			attention_dropout
      _A:    Dict						     =			gelu_activation
      _A:    Tuple						     =			sinusoidal_embeddings
      _A:    Tuple						     =			causal
      _A:    List[Any]						     =			asm
      _A:    Dict						     =			n_langs
      _A:    Optional[int]						     =			use_lang_emb
      _A:    Any						     =			layer_norm_eps
      _A:    str						     =			bos_index
      _A:    Optional[int]						     =			eos_index
      _A:    int						     =			pad_index
      _A:    Tuple						     =			unk_index
      _A:    int						     =			mask_index
      _A:    int						     =			is_encoder
      _A:    Union[str, Any]						     =			max_position_embeddings
      _A:    List[str]						     =			embed_init_std
      _A:    List[str]						     =			init_std
      _A:    Tuple						     =			summary_type
      _A:    List[str]						     =			summary_use_proj
      _A:    Union[str, Any]						     =			summary_activation
      _A:    Union[str, Any]						     =			summary_proj_to_labels
      _A:    int						     =			summary_first_dropout
      _A:    Optional[Any]						     =			start_n_top
      _A:    Optional[Any]						     =			end_n_top
      _A:    Dict						     =			mask_token_id
      _A:    str						     =			lang_id

      if "n_words" in kwargs:
         _A:    int						     =			kwargs['''n_words''']

      super().__init__(pad_token_id=lowerCAmelCase_     ,     bos_token_id=lowerCAmelCase_     ,     **lowerCAmelCase_	)






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''


   @property
   def 					__magic_name__							(   self	:				Tuple	):

      """simple docstring"""
      if self.task == "multiple-choice":
         _A:    int						     =			{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
         _A:    Any						     =			{0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ('''input_ids''', dynamic_axis),
              ('''attention_mask''', dynamic_axis),
          ]	)



