
import requests







def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	None:
				UpperCamelCase_:							Union[str, Any]	=						{"""Content-Type""": """application/json"""}
				UpperCamelCase_:							List[Any]	=						requests.post(lowerCamelCase     ,    json={"""text""": message_body}     ,    headers=lowerCamelCase						)
				if response.status_code != 2_00:
								UpperCamelCase_:							Optional[Any]	=						(
								    """Request to slack returned an error """
								    F'''{response.status_code}, the response is:\n{response.text}'''
								)
								raise ValueError(lowerCamelCase						)


if __name__ == "__main__":
			# Set the slack url to the one provided by Slack when you create the webhook at
			# https://my.slack.com/services/new/incoming-webhook/
			send_slack_message("""<YOUR MESSAGE BODY>""", """<SLACK CHANNEL URL>""")








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_						,	)






