





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


UpperCAmelCase	:	Any		     =       logging.get_logger(__name__)

UpperCAmelCase	:	Optional[int]		     =       {
    """uclanlp/visualbert-vqa""": """https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json""",
    """uclanlp/visualbert-vqa-pre""": """https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json""",
    """uclanlp/visualbert-vqa-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"""
    ),
    """uclanlp/visualbert-vcr""": """https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json""",
    """uclanlp/visualbert-vcr-pre""": """https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json""",
    """uclanlp/visualbert-vcr-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"""
    ),
    """uclanlp/visualbert-nlvr2""": """https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json""",
    """uclanlp/visualbert-nlvr2-pre""": """https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json""",
    """uclanlp/visualbert-nlvr2-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"""
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Union[str, Any]						=			"""visual_bert"""







      def __init__(    self		,      lowerCAmelCase__=3_0_5_2_2		,      lowerCAmelCase__=7_6_8		,      lowerCAmelCase__=5_1_2		,      lowerCAmelCase__=1_2		,      lowerCAmelCase__=1_2		,      lowerCAmelCase__=3_0_7_2		,      lowerCAmelCase__="gelu"		,      lowerCAmelCase__=0.1		,      lowerCAmelCase__=0.1		,      lowerCAmelCase__=5_1_2		,      lowerCAmelCase__=2		,      lowerCAmelCase__=0.02		,      lowerCAmelCase__=1E-12		,      lowerCAmelCase__=False		,      lowerCAmelCase__=True		,      lowerCAmelCase__=1		,      lowerCAmelCase__=0		,      lowerCAmelCase__=2		,      **lowerCAmelCase__		,      )   ->						Dict:



        '''simple docstring'''




        super().__init__(pad_token_id=lowerCAmelCase__		,      bos_token_id=lowerCAmelCase__		,      eos_token_id=lowerCAmelCase__		,      **lowerCAmelCase__							)

        a__	:					Any										=vocab_size
        a__	:					Optional[Any]										=max_position_embeddings
        a__	:					str										=hidden_size
        a__	:					str										=visual_embedding_dim
        a__	:					Optional[int]										=num_hidden_layers
        a__	:					Dict										=num_attention_heads
        a__	:					Tuple										=intermediate_size
        a__	:					List[str]										=hidden_act
        a__	:					int										=hidden_dropout_prob
        a__	:					Dict										=attention_probs_dropout_prob
        a__	:					int										=initializer_range
        a__	:					Dict										=type_vocab_size
        a__	:					List[Any]										=layer_norm_eps
        a__	:					Optional[int]										=bypass_transformer
        a__	:					Any										=special_visual_initialize











'''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	,)




