




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)






import copy

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


UpperCamelCase__						=		logging.get_logger(__name__)

UpperCamelCase__						=		{}


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):
					_lowerCamelCase						    =					"""fsmt_decoder"""

					def __init__(							self					,			__lowercase=0					,			__lowercase=0   ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_				:						Tuple				  =				vocab_size
						lowerCamelCase_				:						Union[str, Any]				  =				bos_token_id


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):
					_lowerCamelCase						    =					"""fsmt"""
					_lowerCamelCase						    =					{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

					def __init__(							self					,			__lowercase=["en", "de"]					,			__lowercase=42_024					,			__lowercase=42_024					,			__lowercase="relu"					,			__lowercase=1_024					,			__lowercase=200					,			__lowercase=1_024					,			__lowercase=4_096					,			__lowercase=12					,			__lowercase=16					,			__lowercase=0.0					,			__lowercase=4_096					,			__lowercase=12					,			__lowercase=16					,			__lowercase=0.0					,			__lowercase=0.0					,			__lowercase=0.1					,			__lowercase=0.0					,			__lowercase=0.02					,			__lowercase=2					,			__lowercase=True					,			__lowercase=True					,			__lowercase=False					,			__lowercase=5					,			__lowercase=1.0					,			__lowercase=False					,			__lowercase=True					,			__lowercase=1					,			__lowercase=0					,			__lowercase=2					,			__lowercase=2					,			**__lowercase					,			):







						'''simple docstring'''




						lowerCamelCase_				:						Any				  =				langs
						lowerCamelCase_				:						Union[str, Any]				  =				src_vocab_size
						lowerCamelCase_				:						Tuple				  =				tgt_vocab_size
						lowerCamelCase_				:						List[Any]				  =				d_model  # encoder_embed_dim and decoder_embed_dim

						lowerCamelCase_				:						str				  =				encoder_ffn_dim
						lowerCamelCase_				:						str				  =				encoder_layers
						lowerCamelCase_				:						Dict				  =				encoder_attention_heads
						lowerCamelCase_				:						Any				  =				encoder_layerdrop
						lowerCamelCase_				:						Union[str, Any]				  =				decoder_layerdrop
						lowerCamelCase_				:						Optional[int]				  =				decoder_ffn_dim
						lowerCamelCase_				:						List[str]				  =				decoder_layers
						lowerCamelCase_				:						int				  =				decoder_attention_heads
						lowerCamelCase_				:						List[str]				  =				max_position_embeddings
						lowerCamelCase_				:						int				  =				init_std  # Normal(0, this parameter)
						lowerCamelCase_				:						Optional[Any]				  =				activation_function

						lowerCamelCase_				:						Union[str, Any]				  =				DecoderConfig(vocab_size=__lowercase					,			bos_token_id=__lowercase   )
						if "decoder" in common_kwargs:
							del common_kwargs["decoder"]

						lowerCamelCase_				:						int				  =				scale_embedding  # scale factor will be sqrt(d_model) if True

						# 3 Types of Dropout
						lowerCamelCase_				:						Optional[int]				  =				attention_dropout
						lowerCamelCase_				:						Union[str, Any]				  =				activation_dropout
						lowerCamelCase_				:						Dict				  =				dropout

						lowerCamelCase_				:						Optional[int]				  =				use_cache
						super().__init__(
						    pad_token_id=__lowercase					,			bos_token_id=__lowercase					,			eos_token_id=__lowercase					,			decoder_start_token_id=__lowercase					,			is_encoder_decoder=__lowercase					,			tie_word_embeddings=__lowercase					,			forced_eos_token_id=__lowercase					,			max_length=__lowercase					,			num_beams=__lowercase					,			length_penalty=__lowercase					,			early_stopping=__lowercase					,			**__lowercase					,			)





					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[Any]				  =				copy.deepcopy(self.__dict__   )
						lowerCamelCase_				:						Optional[int]				  =				self.decoder.to_dict()
						lowerCamelCase_				:						Dict				  =				self.__class__.model_type
						return output
