






"""simple docstring"""

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






def _lowerCAmelCase						(	__lowerCamelCase:int ):

						'''simple docstring'''






						def remove_articles(__lowerCamelCase:Dict ):
												return re.sub(r"\b(a|an|the)\b"			,    " "			,    __lowerCamelCase )

						def white_space_fix(__lowerCamelCase:List[str] ):
												return " ".join(text.split() )

						def remove_punc(__lowerCamelCase:List[str] ):
												__magic_name__      =  set(string.punctuation )
												return "".join(ch for ch in text if ch not in exclude )

						def lower(__lowerCamelCase:str ):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(__lowerCamelCase ) ) ) )






def _lowerCAmelCase						(	__lowerCamelCase:Optional[int]			,    __lowerCamelCase:Optional[Any] ):

						'''simple docstring'''






						__magic_name__      =  normalize_answer(__lowerCamelCase ).split()
						__magic_name__      =  normalize_answer(__lowerCamelCase ).split()
						__magic_name__      =  Counter(__lowerCamelCase ) & Counter(__lowerCamelCase )
						__magic_name__      =  sum(common.values() )
						if num_same == 0:
												return 0
						__magic_name__      =  1.0 * num_same / len(__lowerCamelCase )
						__magic_name__      =  1.0 * num_same / len(__lowerCamelCase )
						__magic_name__      =  (2 * precision * recall) / (precision + recall)
						return fa






def _lowerCAmelCase						(	__lowerCamelCase:Dict			,    __lowerCamelCase:List[Any] ):

						'''simple docstring'''






						return normalize_answer(__lowerCamelCase ) == normalize_answer(__lowerCamelCase )






def _lowerCAmelCase						(	__lowerCamelCase:Union[str, Any]			,    __lowerCamelCase:Any			,    __lowerCamelCase:Tuple ):

						'''simple docstring'''






						__magic_name__      =  []
						for ground_truth in ground_truths:
												__magic_name__      =  metric_fn(__lowerCamelCase			,    __lowerCamelCase )
												scores_for_ground_truths.append(__lowerCamelCase )
						return max(__lowerCamelCase )






def _lowerCAmelCase						(	__lowerCamelCase:int			,    __lowerCamelCase:str ):

						'''simple docstring'''






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

																		__magic_name__      =  [x["text"] for x in qa["answers"]]
																		__magic_name__      =  predictions[qa["id"]]

																		__magic_name__      =  metric_max_over_ground_truths(__lowerCamelCase			,    __lowerCamelCase			,    __lowerCamelCase )
																		if int(_exact_match ) == 1:
																								correct_ids.append(qa["id"] )
																		exact_match += _exact_match

																		fa += metric_max_over_ground_truths(__lowerCamelCase			,    __lowerCamelCase			,    __lowerCamelCase )

						__magic_name__      =  exact_match / total
						__magic_name__      =  fa / total

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


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

			with open(args.data_file) as data_file:
						lowercase           =							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,
									)
						lowercase           =							dataset_json['''data''']

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

			lowercase      ,   lowercase           =							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 unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
			import torch

			from transformers import CamembertModel

@require_torch
@require_sentencepiece
@require_tokenizers
class     A_				(   unittest.TestCase	):







				@slow
				def      _snake_case       (				self				:    Tuple						)					->       Dict:
										__magic_name__      =  CamembertModel.from_pretrained("camembert-base"						)
										model.to(__lowerCamelCase						)

										__magic_name__      =  torch.tensor(
										    [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]    ,     device=__lowerCamelCase    ,     dtype=torch.long    ,     )  # J'aime le camembert !
										with torch.no_grad():
																__magic_name__      =  model(__lowerCamelCase						)["last_hidden_state"]
										__magic_name__      =  torch.Size((1, 1_0, 7_6_8)						)
										self.assertEqual(output.shape    ,     __lowerCamelCase						)
										# compare the actual values for a slice.
										__magic_name__      =  torch.tensor(
										    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]    ,     device=__lowerCamelCase    ,     dtype=torch.float    ,     )
										# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
										# camembert.eval()
										# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

										self.assertTrue(torch.allclose(output[:, :3, :3]    ,     __lowerCamelCase    ,     atol=1e-4						)						)



