






'''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 _lowercase			(       _lowercase       ,   unittest.TestCase	):
							a          = BarthezTokenizer
							a          = BarthezTokenizerFast
							a          = True
							a          = True




							def 	lowerCamelCase_       (							self:       Tuple  ):
														super().setUp()

														lowerCamelCase__      :			Dict 						= BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""  )
														tokenizer.save_pretrained(self.tmpdirname  )
														tokenizer.save_pretrained(self.tmpdirname    ,							legacy_format=UpperCamelCase__  )
														lowerCamelCase__      :			Optional[Any] 						= tokenizer




							def 	lowerCamelCase_       (							self:       Tuple  ):
														lowerCamelCase__      :			Optional[Any] 						= """<pad>"""
														lowerCamelCase__      :			int 						= 1

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




							def 	lowerCamelCase_       (							self:       Dict  ):
														lowerCamelCase__      :			Optional[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__  )    ,							101_122  )




							def 	lowerCamelCase_       (							self:       int  ):
														self.assertEqual(self.get_tokenizer().vocab_size    ,							101_122  )




							@require_torch
							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														lowerCamelCase__      :			List[Any] 						= ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
														lowerCamelCase__      :			str 						= [0, 57, 3_018, 70_307, 91, 2]

														lowerCamelCase__      :			Optional[Any] 						= 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  )
														lowerCamelCase__      :			List[Any] 						= batch.input_ids.tolist()[0]
														self.assertListEqual(UpperCamelCase__    ,							UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       str  ):
														if not self.test_rust_tokenizer:
																					return

														lowerCamelCase__      :			List[str] 						= self.get_tokenizer()
														lowerCamelCase__      :			Optional[int] 						= self.get_rust_tokenizer()

														lowerCamelCase__      :			List[Any] 						= """I was born in 92000, and this is falsé."""

														lowerCamelCase__      :			Union[str, Any] 						= tokenizer.tokenize(UpperCamelCase__  )
														lowerCamelCase__      :			Dict 						= rust_tokenizer.tokenize(UpperCamelCase__  )
														self.assertListEqual(UpperCamelCase__    ,							UpperCamelCase__  )

														lowerCamelCase__      :			Tuple 						= tokenizer.encode(UpperCamelCase__    ,							add_special_tokens=UpperCamelCase__  )
														lowerCamelCase__      :			Dict 						= rust_tokenizer.encode(UpperCamelCase__    ,							add_special_tokens=UpperCamelCase__  )
														self.assertListEqual(UpperCamelCase__    ,							UpperCamelCase__  )

														lowerCamelCase__      :			str 						= self.get_rust_tokenizer()
														lowerCamelCase__      :			Tuple 						= tokenizer.encode(UpperCamelCase__  )
														lowerCamelCase__      :			List[Any] 						= rust_tokenizer.encode(UpperCamelCase__  )
														self.assertListEqual(UpperCamelCase__    ,							UpperCamelCase__  )




							@slow
							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														# fmt: off
														lowerCamelCase__      :			List[str] 						= {"""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.
														lowerCamelCase__      :			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=UpperCamelCase__    ,							model_name="""moussaKam/mbarthez"""    ,							revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""    ,							sequences=UpperCamelCase__    ,							)









'''simple docstring'''







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


_A			:    str					  =logging.get_logger(__name__)

_A			:    int					  ={
    '''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}

class _lowercase			(       _lowercase	):
							a          = """roc_bert"""




							def __init__(							self:       Optional[Any]    ,							UpperCamelCase__:       Any=30_522    ,							UpperCamelCase__:       Optional[Any]=768    ,							UpperCamelCase__:       Union[str, Any]=12    ,							UpperCamelCase__:       Tuple=12    ,							UpperCamelCase__:       Tuple=3_072    ,							UpperCamelCase__:       str="gelu"    ,							UpperCamelCase__:       List[Any]=0.1    ,							UpperCamelCase__:       List[str]=0.1    ,							UpperCamelCase__:       Dict=512    ,							UpperCamelCase__:       str=2    ,							UpperCamelCase__:       str=0.02    ,							UpperCamelCase__:       Tuple=1e-12    ,							UpperCamelCase__:       Any=True    ,							UpperCamelCase__:       Union[str, Any]=0    ,							UpperCamelCase__:       List[Any]="absolute"    ,							UpperCamelCase__:       Any=None    ,							UpperCamelCase__:       Any=True    ,							UpperCamelCase__:       Optional[int]=True    ,							UpperCamelCase__:       Union[str, Any]=768    ,							UpperCamelCase__:       int=910    ,							UpperCamelCase__:       Tuple=512    ,							UpperCamelCase__:       int=24_858    ,							UpperCamelCase__:       Optional[Any]=True    ,							**UpperCamelCase__:       Optional[Any]    ,							):
														lowerCamelCase__      :			Optional[Any] 						= vocab_size
														lowerCamelCase__      :			Tuple 						= max_position_embeddings
														lowerCamelCase__      :			List[Any] 						= hidden_size
														lowerCamelCase__      :			int 						= num_hidden_layers
														lowerCamelCase__      :			Tuple 						= num_attention_heads
														lowerCamelCase__      :			Any 						= intermediate_size
														lowerCamelCase__      :			List[str] 						= hidden_act
														lowerCamelCase__      :			str 						= hidden_dropout_prob
														lowerCamelCase__      :			Dict 						= attention_probs_dropout_prob
														lowerCamelCase__      :			Union[str, Any] 						= initializer_range
														lowerCamelCase__      :			Tuple 						= type_vocab_size
														lowerCamelCase__      :			Optional[Any] 						= layer_norm_eps
														lowerCamelCase__      :			List[Any] 						= use_cache
														lowerCamelCase__      :			Tuple 						= enable_pronunciation
														lowerCamelCase__      :			Union[str, Any] 						= enable_shape
														lowerCamelCase__      :			Union[str, Any] 						= pronunciation_embed_dim
														lowerCamelCase__      :			Any 						= pronunciation_vocab_size
														lowerCamelCase__      :			int 						= shape_embed_dim
														lowerCamelCase__      :			Tuple 						= shape_vocab_size
														lowerCamelCase__      :			Optional[Any] 						= concat_input
														lowerCamelCase__      :			str 						= position_embedding_type
														lowerCamelCase__      :			Dict 						= classifier_dropout
														super().__init__(pad_token_id=UpperCamelCase__    ,							**UpperCamelCase__  )

