
import argparse


lowerCamelCase_						: str         =  """docs/source/_static/js/custom.js"""







def        A__					(						lowerCamelCase						)  ->	Dict:
				with open(__UpperCamelCase     ,    encoding="""utf-8"""     ,    newline="""\n"""						) as f:
								UpperCamelCase_:							Any	=						f.readlines()
				UpperCamelCase_:							str	=						0

				# First let's put the right version
				while not lines[index].startswith("""const stableVersion ="""						):
								index += 1
				UpperCamelCase_:							Union[str, Any]	=						F'''const stableVersion = "v{version}"\n'''

				# Then update the dictionary
				while not lines[index].startswith("""const versionMapping = {"""						):
								index += 1

				# We go until the end
				while not lines[index].startswith("""}"""						):
								index += 1
				# We add the new version at the end
				lines[index - 1] += F'''    "v{version}": "v{version}",\n'''

				with open(__UpperCamelCase     ,    """w"""     ,    encoding="""utf-8"""     ,    newline="""\n"""						) as f:
								f.writelines(__UpperCamelCase						)


if __name__ == "__main__":
			lowerCamelCase_						: List[Any]         =  argparse.ArgumentParser()
			parser.add_argument("""--version""", help="""Release version.""")
			lowerCamelCase_						: Union[str, Any]         =  parser.parse_args()
			update_custom_js(args.version)








import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class        _UpperCamelCase				(					_A     ,       unittest.TestCase  ):






			'''simple docstring'''

			__UpperCamelCase      :						Optional[int]					      = BarthezTokenizer
			__UpperCamelCase      :						str					      = BarthezTokenizerFast
			__UpperCamelCase      :						str					      = True
			__UpperCamelCase      :						List[Any]					      = True

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							super().setUp()

							UpperCamelCase_:							Tuple	=						BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""      )
							tokenizer.save_pretrained(self.tmpdirname      )
							tokenizer.save_pretrained(self.tmpdirname						,	legacy_format=snake_case_      )
							UpperCamelCase_:							Dict	=						tokenizer

			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							str	=						"""<pad>"""
							UpperCamelCase_:							int	=						1

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

			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							Tuple	=						list(self.get_tokenizer().get_vocab().keys()      )

							self.assertEqual(vocab_keys[0]						,	"""<s>"""      )
							self.assertEqual(vocab_keys[1]						,	"""<pad>"""      )
							self.assertEqual(vocab_keys[-1]						,	"""<mask>"""      )
							self.assertEqual(len(snake_case_      )						,	10_1122      )

			def       lowerCAmelCase__	(       self :       Dict      ):
							self.assertEqual(self.get_tokenizer().vocab_size						,	10_1122      )

			@require_torch
			def       lowerCAmelCase__	(       self :       int      ):
							UpperCamelCase_:							Dict	=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
							UpperCamelCase_:							Union[str, Any]	=						[0, 57, 3018, 7_0307, 91, 2]

							UpperCamelCase_:							Union[str, Any]	=						self.tokenizer(
							    snake_case_						,	max_length=len(snake_case_      )						,	padding=snake_case_						,	truncation=snake_case_						,	return_tensors="""pt"""      )
							self.assertIsInstance(snake_case_						,	snake_case_      )

							self.assertEqual((2, 6)						,	batch.input_ids.shape      )
							self.assertEqual((2, 6)						,	batch.attention_mask.shape      )
							UpperCamelCase_:							Any	=						batch.input_ids.tolist()[0]
							self.assertListEqual(snake_case_						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Any      ):
							if not self.test_rust_tokenizer:
											return

							UpperCamelCase_:							Optional[Any]	=						self.get_tokenizer()
							UpperCamelCase_:							Union[str, Any]	=						self.get_rust_tokenizer()

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

							UpperCamelCase_:							str	=						tokenizer.tokenize(snake_case_      )
							UpperCamelCase_:							int	=						rust_tokenizer.tokenize(snake_case_      )
							self.assertListEqual(snake_case_						,	snake_case_      )

							UpperCamelCase_:							int	=						tokenizer.encode(snake_case_						,	add_special_tokens=snake_case_      )
							UpperCamelCase_:							int	=						rust_tokenizer.encode(snake_case_						,	add_special_tokens=snake_case_      )
							self.assertListEqual(snake_case_						,	snake_case_      )

							UpperCamelCase_:							List[str]	=						self.get_rust_tokenizer()
							UpperCamelCase_:							Tuple	=						tokenizer.encode(snake_case_      )
							UpperCamelCase_:							Tuple	=						rust_tokenizer.encode(snake_case_      )
							self.assertListEqual(snake_case_						,	snake_case_      )




			@slow
			def       lowerCAmelCase__	(       self :       int      ):
							# fmt: off
							UpperCamelCase_:							Optional[Any]	=						{"""input_ids""": [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 2, 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, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], """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, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
							# fmt: on

							# moussaKam/mbarthez is a french model. So we also use french texts.
							UpperCamelCase_:							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=snake_case_						,	model_name="""moussaKam/mbarthez"""						,	revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""						,	sequences=snake_case_						,	)






