



"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case							:   Optional[Any]      =    logging.get_logger(__name__)

__snake_case							:   List[Any]      =    {
    'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}



class 			A__   (  _UpperCamelCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	'cvt'

	def __init__(     self:		Union[str, Any]       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=3       ,			_SCREAMING_SNAKE_CASE:		List[str]=[7, 3, 3]       ,			_SCREAMING_SNAKE_CASE:		List[str]=[4, 2, 2]       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=[2, 1, 1]       ,			_SCREAMING_SNAKE_CASE:		List[str]=[64, 192, 384]       ,			_SCREAMING_SNAKE_CASE:		Tuple=[1, 3, 6]       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=[1, 2, 10]       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=[4.0, 4.0, 4.0]       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=[0.0, 0.0, 0.0]       ,			_SCREAMING_SNAKE_CASE:		List[str]=[0.0, 0.0, 0.0]       ,			_SCREAMING_SNAKE_CASE:		List[str]=[0.0, 0.0, 0.1]       ,			_SCREAMING_SNAKE_CASE:		Dict=[True, True, True]       ,			_SCREAMING_SNAKE_CASE:		Dict=[False, False, True]       ,			_SCREAMING_SNAKE_CASE:		str=["dw_bn", "dw_bn", "dw_bn"]       ,			_SCREAMING_SNAKE_CASE:		int=[3, 3, 3]       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=[1, 1, 1]       ,			_SCREAMING_SNAKE_CASE:		List[str]=[2, 2, 2]       ,			_SCREAMING_SNAKE_CASE:		List[str]=[1, 1, 1]       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=[1, 1, 1]       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=0.02       ,			_SCREAMING_SNAKE_CASE:		int=1e-12       ,			**_SCREAMING_SNAKE_CASE:		Union[str, Any]       ,			)				->       Union[str, Any]:

				"""simple docstring"""

				super().__init__(**_UpperCAmelCase)
				__lowerCAmelCase      :  Any												=       num_channels
				__lowerCAmelCase      :  List[Any]												=       patch_sizes
				__lowerCAmelCase      :  int												=       patch_stride
				__lowerCAmelCase      :  List[Any]												=       patch_padding
				__lowerCAmelCase      :  Any												=       embed_dim
				__lowerCAmelCase      :  List[str]												=       num_heads
				__lowerCAmelCase      :  Tuple												=       depth
				__lowerCAmelCase      :  int												=       mlp_ratio
				__lowerCAmelCase      :  List[Any]												=       attention_drop_rate
				__lowerCAmelCase      :  Optional[int]												=       drop_rate
				__lowerCAmelCase      :  str												=       drop_path_rate
				__lowerCAmelCase      :  Tuple												=       qkv_bias
				__lowerCAmelCase      :  str												=       cls_token
				__lowerCAmelCase      :  Optional[int]												=       qkv_projection_method
				__lowerCAmelCase      :  Any												=       kernel_qkv
				__lowerCAmelCase      :  Any												=       padding_kv
				__lowerCAmelCase      :  str												=       stride_kv
				__lowerCAmelCase      :  Tuple												=       padding_q
				__lowerCAmelCase      :  List[str]												=       stride_q
				__lowerCAmelCase      :  List[str]												=       initializer_range
				__lowerCAmelCase      :  Dict												=       layer_norm_eps




'''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 	__magic_name__						(    _UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    Optional[int]   				=					BarthezTokenizer
							lowerCAmelCase       :    int   				=					BarthezTokenizerFast
							lowerCAmelCase       :    Dict   				=					True
							lowerCAmelCase       :    str   				=					True







							def 							__lowercase			(							self					:       List[Any]		):
									super().setUp()

									_a     :      List[Any]        =							BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'		)
									tokenizer.save_pretrained(self.tmpdirname		)
									tokenizer.save_pretrained(self.tmpdirname	,legacy_format=_UpperCAmelCase		)
									_a     :      Union[str, Any]        =							tokenizer







							def 							__lowercase			(							self					:       Tuple		):
									_a     :      Optional[Any]        =							'<pad>'
									_a     :      List[Any]        =							1

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







							def 							__lowercase			(							self					:       str		):
									_a     :      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(_UpperCAmelCase		)	,101122		)







							def 							__lowercase			(							self					:       Dict		):
									self.assertEqual(self.get_tokenizer().vocab_size	,101122		)







							@require_torch
							def 							__lowercase			(							self					:       Dict		):
									_a     :      Any        =							['A long paragraph for summarization.', 'Another paragraph for summarization.']
									_a     :      Dict        =							[0, 57, 3018, 70307, 91, 2]

									_a     :      Dict        =							self.tokenizer(
									    _UpperCAmelCase	,max_length=len(_UpperCAmelCase		)	,padding=_UpperCAmelCase	,truncation=_UpperCAmelCase	,return_tensors='pt'		)
									self.assertIsInstance(_UpperCAmelCase	,_UpperCAmelCase		)

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







							def 							__lowercase			(							self					:       Optional[Any]		):
									if not self.test_rust_tokenizer:
											return

									_a     :      str        =							self.get_tokenizer()
									_a     :      List[str]        =							self.get_rust_tokenizer()

									_a     :      Dict        =							'I was born in 92000, and this is falsé.'

									_a     :      List[Any]        =							tokenizer.tokenize(_UpperCAmelCase		)
									_a     :      Tuple        =							rust_tokenizer.tokenize(_UpperCAmelCase		)
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)

									_a     :      Optional[Any]        =							tokenizer.encode(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase		)
									_a     :      Optional[int]        =							rust_tokenizer.encode(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase		)
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)

									_a     :      Union[str, Any]        =							self.get_rust_tokenizer()
									_a     :      Any        =							tokenizer.encode(_UpperCAmelCase		)
									_a     :      Optional[int]        =							rust_tokenizer.encode(_UpperCAmelCase		)
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)







							@slow
							def 							__lowercase			(							self					:       Optional[int]		):
									# fmt: off
									_a     :      Optional[int]        =							{'input_ids': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 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, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 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     :      Optional[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=_UpperCAmelCase	,model_name='moussaKam/mbarthez'	,revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'	,sequences=_UpperCAmelCase	,)




