


"""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__						(   UpperCAmelCase_							,    unittest.TestCase			):
      lowerCAmelCase       =     BarthezTokenizer
      lowerCAmelCase       =     BarthezTokenizerFast
      lowerCAmelCase       =     True
      lowerCAmelCase       =     True







      def __a						(							self   :    int		):
        super().setUp()

        A					       = BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'		)
        tokenizer.save_pretrained(self.tmpdirname		)
        tokenizer.save_pretrained(self.tmpdirname			,			legacy_format=_lowercase		)
        A					       = tokenizer







      def __a						(							self   :    Any		):
        A					       = '<pad>'
        A					       = 1

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







      def __a						(							self   :    List[str]		):
        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(_lowercase		)			,			101_122		)







      def __a						(							self   :    List[Any]		):
        self.assertEqual(self.get_tokenizer().vocab_size			,			101_122		)







      @require_torch
      def __a						(							self   :    str		):
        A					       = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
        A					       = [0, 57, 3_018, 70_307, 91, 2]

        A					       = self.tokenizer(
            _lowercase			,			max_length=len(_lowercase		)			,			padding=_lowercase			,			truncation=_lowercase			,			return_tensors='pt'		)
        self.assertIsInstance(_lowercase			,			_lowercase		)

        self.assertEqual((2, 6)			,			batch.input_ids.shape		)
        self.assertEqual((2, 6)			,			batch.attention_mask.shape		)
        A					       = batch.input_ids.tolist()[0]
        self.assertListEqual(_lowercase			,			_lowercase		)







      def __a						(							self   :    Union[str, Any]		):
        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(_lowercase		)
        A					       = rust_tokenizer.tokenize(_lowercase		)
        self.assertListEqual(_lowercase			,			_lowercase		)

        A					       = tokenizer.encode(_lowercase			,			add_special_tokens=_lowercase		)
        A					       = rust_tokenizer.encode(_lowercase			,			add_special_tokens=_lowercase		)
        self.assertListEqual(_lowercase			,			_lowercase		)

        A					       = self.get_rust_tokenizer()
        A					       = tokenizer.encode(_lowercase		)
        A					       = rust_tokenizer.encode(_lowercase		)
        self.assertListEqual(_lowercase			,			_lowercase		)







      @slow
      def __a						(							self   :    int		):
        # 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=_lowercase			,			model_name='moussaKam/mbarthez'			,			revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'			,			sequences=_lowercase			,			)









"""simple docstring"""


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


UpperCamelCase						:				Tuple             =      logging.get_logger(__name__)

UpperCamelCase						:				Union[str, Any]             =      {
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}



class 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     """lilt"""







						def __init__(							self   :    Optional[Any]			,			_lowercase   :    Dict=30_522			,			_lowercase   :    Any=768			,			_lowercase   :    Union[str, Any]=12			,			_lowercase   :    List[str]=12			,			_lowercase   :    str=3_072			,			_lowercase   :    int="gelu"			,			_lowercase   :    Union[str, Any]=0.1			,			_lowercase   :    Dict=0.1			,			_lowercase   :    Optional[Any]=512			,			_lowercase   :    Optional[Any]=2			,			_lowercase   :    Tuple=0.0_2			,			_lowercase   :    int=1e-12			,			_lowercase   :    Any=0			,			_lowercase   :    List[str]="absolute"			,			_lowercase   :    Dict=None			,			_lowercase   :    Optional[int]=4			,			_lowercase   :    Optional[int]=1_024			,			**_lowercase   :    Union[str, Any]			,			):
								super().__init__(pad_token_id=_lowercase			,			**_lowercase		)

								A					       = vocab_size
								A					       = hidden_size
								A					       = num_hidden_layers
								A					       = num_attention_heads
								A					       = hidden_act
								A					       = intermediate_size
								A					       = hidden_dropout_prob
								A					       = attention_probs_dropout_prob
								A					       = max_position_embeddings
								A					       = type_vocab_size
								A					       = initializer_range
								A					       = layer_norm_eps
								A					       = position_embedding_type
								A					       = classifier_dropout
								A					       = channel_shrink_ratio
								A					       = max_ad_position_embeddings





