
"""simple docstring"""





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				(					_lowercase     ,				unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				BarthezTokenizer
			snake_case	      =				BarthezTokenizerFast
			snake_case	      =				True
			snake_case	      =				True




			def      lowerCamelCase__     (				self						:					List[Any]	)      -> Any:







								"""simple docstring"""
								super().setUp()

								A_				 =						BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez"	)
								tokenizer.save_pretrained(self.tmpdirname	)
								tokenizer.save_pretrained(self.tmpdirname       ,   legacy_format=_snake_case	)
								A_				 =						tokenizer




			def      lowerCamelCase__     (				self						:					int	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						"<pad>"
								A_				 =						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						:					List[str]	)      -> Any:







								"""simple docstring"""
								A_				 =						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	)       ,   101_122	)




			def      lowerCamelCase__     (				self						:					List[Any]	)      -> Tuple:







								"""simple docstring"""
								self.assertEqual(self.get_tokenizer().vocab_size       ,   101_122	)




			@require_torch
			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> Optional[int]:







								"""simple docstring"""
								A_				 =						["A long paragraph for summarization.", "Another paragraph for summarization."]
								A_				 =						[0, 57, 3_018, 70_307, 91, 2]

								A_				 =						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	)
								A_				 =						batch.input_ids.tolist()[0]
								self.assertListEqual(_snake_case       ,   _snake_case	)




			def      lowerCamelCase__     (				self						:					Dict	)      -> Optional[Any]:







								"""simple docstring"""
								if not self.test_rust_tokenizer:
													return

								A_				 =						self.get_tokenizer()
								A_				 =						self.get_rust_tokenizer()

								A_				 =						"I was born in 92000, and this is falsé."

								A_				 =						tokenizer.tokenize(_snake_case	)
								A_				 =						rust_tokenizer.tokenize(_snake_case	)
								self.assertListEqual(_snake_case       ,   _snake_case	)

								A_				 =						tokenizer.encode(_snake_case       ,   add_special_tokens=_snake_case	)
								A_				 =						rust_tokenizer.encode(_snake_case       ,   add_special_tokens=_snake_case	)
								self.assertListEqual(_snake_case       ,   _snake_case	)

								A_				 =						self.get_rust_tokenizer()
								A_				 =						tokenizer.encode(_snake_case	)
								A_				 =						rust_tokenizer.encode(_snake_case	)
								self.assertListEqual(_snake_case       ,   _snake_case	)






			@slow
			def      lowerCamelCase__     (				self						:					Tuple	)      -> Tuple:







								"""simple docstring"""
								# fmt: off
								A_				 =						{"input_ids": [[0, 490, 14_328, 4_507, 354, 47, 43_669, 95, 25, 78_117, 20_215, 19_779, 190, 22, 400, 4, 35_343, 80_310, 603, 86, 24_937, 105, 33_438, 94_762, 196, 39_642, 7, 15, 15_933, 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, 10_534, 87, 25, 66, 3_358, 196, 55_289, 8, 82_961, 81, 2_204, 75_203, 7, 15, 763, 12_956, 216, 178, 14_328, 9_595, 1_377, 69_693, 7, 448, 71_021, 196, 18_106, 1_437, 13_974, 108, 9_083, 4, 49_315, 7, 39, 86, 1_326, 2_793, 46_333, 4, 448, 196, 74_588, 7, 49_315, 7, 39, 21, 822, 38_470, 74, 21, 66_723, 62_480, 8, 22_050, 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.
								A_				 =						[
								    "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       ,   )






"""simple docstring"""





import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
UpperCamelCase_					:						Optional[Any]				= '''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
UpperCamelCase_					:						Union[str, Any]				= direct_transformers_import(PATH_TO_TRANSFORMERS)

UpperCamelCase_					:						List[Any]				= transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
UpperCamelCase_					:						str				= re.compile(R'''\[(.+?)\]\((https://huggingface\.co/.+?)\)''')


UpperCamelCase_					:						Dict				= {
    '''DecisionTransformerConfig''',
    '''EncoderDecoderConfig''',
    '''MusicgenConfig''',
    '''RagConfig''',
    '''SpeechEncoderDecoderConfig''',
    '''TimmBackboneConfig''',
    '''VisionEncoderDecoderConfig''',
    '''VisionTextDualEncoderConfig''',
    '''LlamaConfig''',
}
def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						None

					# source code of `config_class`
					A_				 =						inspect.getsource(__a				)
					A_				 =						_re_checkpoint.findall(__a				)

					# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
					# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
					for ckpt_name, ckpt_link in checkpoints:
										# allow the link to end with `/`
										if ckpt_link.endswith("/"				):
															A_				 =						ckpt_link[:-1]

										# verify the checkpoint name corresponds to the checkpoint link
										A_				 =						f'https://huggingface.co/{ckpt_name}'
										if ckpt_link == ckpt_link_from_name:
															A_				 =						ckpt_name
															break

					return checkpoint
def 							A_     ():

					'''simple docstring'''





					A_				 =						[]

					for config_class in list(CONFIG_MAPPING.values()				):
										# Skip deprecated models
										if "models.deprecated" in config_class.__module__:
															continue
										A_				 =						get_checkpoint_from_config_class(__a				)

										A_				 =						config_class.__name__
										if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
															configs_without_checkpoint.append(__a				)

					if len(__a				) > 0:
										A_				 =						"\n".join(sorted(__a				)				)
										raise ValueError(f'The following configurations don\'t contain any valid checkpoint:\n{message}'				)


if __name__ == "__main__":
		check_config_docstrings_have_checkpoints()




