





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    __lowerCAmelCase			(     a_,	unittest.TestCase	):
			lowerCamelCase_	:	Optional[int]			     = BarthezTokenizer
			lowerCamelCase_	:	str			     = BarthezTokenizerFast
			lowerCamelCase_	:	Optional[Any]			     = True
			lowerCamelCase_	:	List[str]			     = True






			def      lowerCamelCase    (self       )	->  Optional[int]:


							'''simple docstring'''



							super().setUp()

							snake_case_		:       Union[str, Any]           =    BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''       )
							tokenizer.save_pretrained(self.tmpdirname       )
							tokenizer.save_pretrained(self.tmpdirname   ,				legacy_format=__magic_name__       )
							snake_case_		:       Optional[Any]           =    tokenizer






			def      lowerCamelCase    (self       )	->  Optional[int]:


							'''simple docstring'''



							snake_case_		:       Any           =    '''<pad>'''
							snake_case_		:       str           =    1

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






			def      lowerCamelCase    (self       )	->  Union[str, Any]:


							'''simple docstring'''



							snake_case_		:       Union[str, Any]           =    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(__magic_name__       )   ,				10_1122       )






			def      lowerCamelCase    (self       )	->  Optional[int]:


							'''simple docstring'''



							self.assertEqual(self.get_tokenizer().vocab_size   ,				10_1122       )






			@require_torch
			def      lowerCamelCase    (self       )	->  str:


							'''simple docstring'''



							snake_case_		:       Tuple           =    ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
							snake_case_		:       List[Any]           =    [0, 57, 3018, 7_0307, 91, 2]

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

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






			def      lowerCamelCase    (self       )	->  Optional[Any]:


							'''simple docstring'''



							if not self.test_rust_tokenizer:
											return

							snake_case_		:       Optional[Any]           =    self.get_tokenizer()
							snake_case_		:       Tuple           =    self.get_rust_tokenizer()

							snake_case_		:       Optional[int]           =    '''I was born in 92000, and this is falsé.'''

							snake_case_		:       List[Any]           =    tokenizer.tokenize(__magic_name__       )
							snake_case_		:       str           =    rust_tokenizer.tokenize(__magic_name__       )
							self.assertListEqual(__magic_name__   ,				__magic_name__       )

							snake_case_		:       Any           =    tokenizer.encode(__magic_name__   ,				add_special_tokens=__magic_name__       )
							snake_case_		:       Tuple           =    rust_tokenizer.encode(__magic_name__   ,				add_special_tokens=__magic_name__       )
							self.assertListEqual(__magic_name__   ,				__magic_name__       )

							snake_case_		:       Dict           =    self.get_rust_tokenizer()
							snake_case_		:       str           =    tokenizer.encode(__magic_name__       )
							snake_case_		:       Optional[Any]           =    rust_tokenizer.encode(__magic_name__       )
							self.assertListEqual(__magic_name__   ,				__magic_name__       )






			@slow
			def      lowerCamelCase    (self       )	->  Tuple:


							'''simple docstring'''



							snake_case_		:       Optional[int]           =    {'''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.
							snake_case_		:       Any           =    [
							    '''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=__magic_name__   ,				model_name='''moussaKam/mbarthez'''   ,				revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''   ,				sequences=__magic_name__   ,				)




import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar







class        __lowercase       (							unittest.TestCase  ):



		"""simple docstring"""

		def 					__A   ( self	)  -> Dict:







					'''simple docstring'''







					lowerCamelCase    		=  logging.get_logger()

					# the current default level is logging.WARNING
					lowerCamelCase    		=  logging.get_verbosity()

					logging.set_verbosity_error()
					self.assertEqual(logger.getEffectiveLevel()		,      logging.get_verbosity()	)

					logging.set_verbosity_warning()
					self.assertEqual(logger.getEffectiveLevel()		,      logging.get_verbosity()	)

					logging.set_verbosity_info()
					self.assertEqual(logger.getEffectiveLevel()		,      logging.get_verbosity()	)

					logging.set_verbosity_debug()
					self.assertEqual(logger.getEffectiveLevel()		,      logging.get_verbosity()	)

					# restore to the original level
					logging.set_verbosity(A	)







		def 					__A   ( self	)  -> Tuple:







					'''simple docstring'''







					lowerCamelCase    		=  logging.get_verbosity()

					lowerCamelCase    		=  logging.get_logger("""transformers.models.bart.tokenization_bart"""	)
					lowerCamelCase    		=  """Testing 1, 2, 3"""

					# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
					if level_origin <= logging.WARNING:
								with CaptureLogger(A	) as cl:
											logger.warning(A	)
								self.assertEqual(cl.out		,      msg + """\n"""	)

					# this is setting the level for all of `transformers.*` loggers
					logging.set_verbosity_error()

					# should not be able to log warnings
					with CaptureLogger(A	) as cl:
								logger.warning(A	)
					self.assertEqual(cl.out		,      """"""	)

					# should be able to log warnings again
					logging.set_verbosity_warning()
					with CaptureLogger(A	) as cl:
								logger.warning(A	)
					self.assertEqual(cl.out		,      msg + """\n"""	)

					# restore to the original level
					logging.set_verbosity(A	)







		@mockenv(TRANSFORMERS_VERBOSITY="""error"""	)
		def 					__A   ( self	)  -> Union[str, Any]:







					'''simple docstring'''







					transformers.utils.logging._reset_library_root_logger()
					# this action activates the env var
					lowerCamelCase    		=  logging.get_logger("""transformers.models.bart.tokenization_bart"""	)

					lowerCamelCase    		=  os.getenv("""TRANSFORMERS_VERBOSITY"""		,      A	)
					lowerCamelCase    		=  logging.log_levels[env_level_str]

					lowerCamelCase    		=  logging.get_verbosity()
					self.assertEqual(
					    A		,      A		,      F'TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'		,      )

					# restore to the original level
					lowerCamelCase    		=  """"""
					transformers.utils.logging._reset_library_root_logger()







		@mockenv(TRANSFORMERS_VERBOSITY="""super-error"""	)
		def 					__A   ( self	)  -> Union[str, Any]:







					'''simple docstring'''







					transformers.utils.logging._reset_library_root_logger()
					lowerCamelCase    		=  logging.logging.getLogger()
					with CaptureLogger(A	) as cl:
								# this action activates the env var
								logging.get_logger("""transformers.models.bart.tokenization_bart"""	)
					self.assertIn("""Unknown option TRANSFORMERS_VERBOSITY=super-error"""		,      cl.out	)

					# no need to restore as nothing was changed







		def 					__A   ( self	)  -> Optional[Any]:







					'''simple docstring'''







					transformers.utils.logging._reset_library_root_logger()

					lowerCamelCase    		=  logging.get_logger("""transformers.models.bart.tokenization_bart"""	)
					lowerCamelCase    		=  """Testing 1, 2, 3"""

					with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="""1"""	):
								# nothing should be logged as env var disables this method
								with CaptureLogger(A	) as cl:
											logger.warning_advice(A	)
								self.assertEqual(cl.out		,      """"""	)

					with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=""""""	):
								# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
								with CaptureLogger(A	) as cl:
											logger.warning_advice(A	)
								self.assertEqual(cl.out		,      msg + """\n"""	)






def 	__lowerCamelCase						(							):





			'''simple docstring'''




			disable_progress_bar()
			assert are_progress_bars_disabled()

			enable_progress_bar()
			assert not are_progress_bars_disabled()

