




"""simple docstring"""







import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


__A   :  List[str]					=      get_tests_dir('fixtures/test_sentencepiece.model')
__A   :  List[str]					=      get_tests_dir('fixtures/test_sentencepiece_bpe.model')

__A   :  str					=      'pt' if is_torch_available() else 'tf'




@require_sentencepiece
@require_tokenizers
class       lowerCAmelCase__       (			lowerCAmelCase_  ,			unittest.TestCase    ):


				"""simple docstring"""





				__UpperCAmelCase :     List[Any]       =      CamembertTokenizer
				__UpperCAmelCase :     Optional[Any]       =      CamembertTokenizerFast
				__UpperCAmelCase :     Tuple       =      True
				__UpperCAmelCase :     Any       =      True



				def       snake_case    (     self					:	Union[str, Any]						):
						super().setUp()

						# We have a SentencePiece fixture for testing
						__lowercase				:		Optional[Any]												=				CamembertTokenizer(lowercase__						)
						tokenizer.save_pretrained(self.tmpdirname						)



				def       snake_case    (     self					:	List[Any]						):
						__lowercase				:		Any												=				"<pad>"
						__lowercase				:		Any												=				1

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase__						)    ,   lowercase__						)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase__						)    ,   lowercase__						)



				def       snake_case    (     self					:	Any						):
						__lowercase				:		Dict												=				list(self.get_tokenizer().get_vocab().keys()						)

						self.assertEqual(vocab_keys[0]    ,   "<s>NOTUSED"						)
						self.assertEqual(vocab_keys[1]    ,   "<pad>"						)
						self.assertEqual(vocab_keys[-1]    ,   "<mask>"						)
						self.assertEqual(len(lowercase__						)    ,   1_0_0_4						)



				def       snake_case    (     self					:	Any						):
						self.assertEqual(self.get_tokenizer().vocab_size    ,   1_0_0_5						)



				def       snake_case    (     self					:	Union[str, Any]						):
						__lowercase				:		Tuple												=				CamembertTokenizer(lowercase__						)
						tokenizer.save_pretrained(self.tmpdirname						)
						__lowercase				:		Optional[int]												=				CamembertTokenizerFast.from_pretrained(self.tmpdirname						)

						__lowercase				:		str												=				"I was born in 92000, and this is falsé."

						__lowercase				:		List[Any]												=				tokenizer.encode(lowercase__						)
						__lowercase				:		Tuple												=				rust_tokenizer.encode(lowercase__						)
						self.assertListEqual(lowercase__    ,   lowercase__						)

						__lowercase				:		List[str]												=				tokenizer.encode(lowercase__    ,   add_special_tokens=lowercase__						)
						__lowercase				:		Optional[int]												=				rust_tokenizer.encode(lowercase__    ,   add_special_tokens=lowercase__						)
						self.assertListEqual(lowercase__    ,   lowercase__						)

						# <unk> tokens are not the same for `rust` than for `slow`.
						# Because spm gives back raw token instead of `unk` in EncodeAsPieces
						# tokens = tokenizer.tokenize(sequence)
						__lowercase				:		Dict												=				tokenizer.convert_ids_to_tokens(lowercase__						)
						__lowercase				:		Any												=				rust_tokenizer.tokenize(lowercase__						)
						self.assertListEqual(lowercase__    ,   lowercase__						)



				def       snake_case    (     self					:	Tuple						):
						if not self.test_rust_tokenizer:
								return

						__lowercase				:		List[Any]												=				self.get_tokenizer()
						__lowercase				:		Optional[int]												=				self.get_rust_tokenizer()

						__lowercase				:		Union[str, Any]												=				"I was born in 92000, and this is falsé."

						__lowercase				:		Optional[int]												=				tokenizer.tokenize(lowercase__						)
						__lowercase				:		List[str]												=				rust_tokenizer.tokenize(lowercase__						)
						self.assertListEqual(lowercase__    ,   lowercase__						)

						__lowercase				:		Dict												=				tokenizer.encode(lowercase__    ,   add_special_tokens=lowercase__						)
						__lowercase				:		Optional[int]												=				rust_tokenizer.encode(lowercase__    ,   add_special_tokens=lowercase__						)
						self.assertListEqual(lowercase__    ,   lowercase__						)

						__lowercase				:		Any												=				self.get_rust_tokenizer()
						__lowercase				:		int												=				tokenizer.encode(lowercase__						)
						__lowercase				:		Optional[int]												=				rust_tokenizer.encode(lowercase__						)
						self.assertListEqual(lowercase__    ,   lowercase__						)



				@slow
				def       snake_case    (     self					:	int						):
						# fmt: off
						__lowercase				:		int												=				{"input_ids": [[5, 5_4, 7_1_9_6, 2_9_7, 3_0, 2_3, 7_7_6, 1_8, 1_1, 3_2_1_5, 3_7_0_5, 8_2_5_2, 2_2, 3_1_6_4, 1_1_8_1, 2_1_1_6, 2_9, 1_6, 8_1_3, 2_5, 7_9_1, 3_3_1_4, 2_0, 3_4_4_6, 3_8, 2_7_5_7_5, 1_2_0, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_6_8, 1_7, 1_1, 9_0_8_8, 2_0, 1_5_1_7, 8, 2_2_8_0_4, 1_8_8_1_8, 1_0, 3_8, 6_2_9, 6_0_7, 6_0_7, 1_4_2, 1_9, 7_1_9_6, 8_6_7, 5_6, 1_0_3_2_6, 2_4, 2_2_6_7, 2_0, 4_1_6, 5_0_7_2, 1_5_6_1_2, 2_3_3, 7_3_4, 7, 2_3_9_9, 2_7, 1_6, 3_0_1_5, 1_6_4_9, 7, 2_4, 2_0, 4_3_3_8, 2_3_9_9, 2_7, 1_3, 3_4_0_0, 1_4, 1_3, 6_1_8_9, 8, 9_3_0, 9, 6]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
						# fmt: on

						# camembert is a french model. So we also use french texts.
						__lowercase				:		str												=				[
						    "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
						    "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
						    "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
						    "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
						    "telles que la traduction et la synthèse de texte.",
						]

						self.tokenizer_integration_test_util(
						    expected_encoding=lowercase__    ,   model_name="camembert-base"    ,   revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"    ,   sequences=lowercase__    ,   )






"""simple docstring"""







import argparse
from collections import defaultdict

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->List[str]:






		"""simple docstring"""


		__lowercase				:		Tuple												=				F'{file}_{class_name}_{test_name}'
		done_test[_id] += 1

		with open(_lowerCamelCase,  "r"				) as f:
				__lowercase				:		Union[str, Any]												=				f.readlines()

		__lowercase				:		str												=				F'class {class_name}('
		__lowercase				:		Union[str, Any]												=				F'{4 * " "}def {test_name}('
		__lowercase				:		List[Any]												=				F'{8 * " "}{correct_line.split()[0]}'
		__lowercase				:		Optional[int]												=				F'{16 * " "}{correct_line.split()[0]}'
		__lowercase				:		List[Any]												=				False
		__lowercase				:		int												=				False
		__lowercase				:		int												=				False
		__lowercase				:		List[Any]												=				False
		__lowercase				:		str												=				0
		__lowercase				:		Dict												=				0

		__lowercase				:		Optional[int]												=				[]
		for line in lines:
				if line.startswith(_lowerCamelCase				):
						__lowercase				:		Optional[int]												=				True
				elif in_class and line.startswith(_lowerCamelCase				):
						__lowercase				:		Optional[int]												=				True
				elif in_class and in_func and (line.startswith(_lowerCamelCase				) or line.startswith(_lowerCamelCase				)):
						__lowercase				:		int												=				len(line.split(correct_line.split()[0]				)[0]				)
						count += 1

						if count == done_test[_id]:
								__lowercase				:		Any												=				True

				if in_class and in_func and in_line:
						if ")" not in line:
								continue
						else:
								__lowercase				:		int												=				True

				if in_class and in_func and in_line and insert_line:
						new_lines.append(F'{spaces * " "}{correct_line}'				)
						__lowercase				:		List[Any]												=				False
				else:
						new_lines.append(_lowerCamelCase				)

		with open(_lowerCamelCase,  "w"				) as f:
				for line in new_lines:
						f.write(_lowerCamelCase				)

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase=None				)			->List[Any]:






		"""simple docstring"""


		if fail is not None:
				with open(_lowerCamelCase,  "r"				) as f:
						__lowercase				:		str												=				{l.strip() for l in f.readlines()}
		else:
				__lowercase				:		List[Any]												=				None

		with open(_lowerCamelCase,  "r"				) as f:
				__lowercase				:		str												=				f.readlines()

		__lowercase				:		List[str]												=				defaultdict(_lowerCamelCase				)
		for line in correct_lines:
				__lowercase						,__lowercase						,__lowercase						,__lowercase				:		str												=				line.split(";"				)
				if test_failures is None or "::".join([file, class_name, test_name]				) in test_failures:
						overwrite_file(_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)


if __name__ == "__main__":
						__A   :  str					=      argparse.ArgumentParser()
						parser.add_argument('--correct_filename', help='filename of tests with expected result')
						parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
						__A   :  Union[str, Any]					=      parser.parse_args()

						main(args.correct_filename, args.fail_filename)
