





"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


a_			=      logging.get_logger(__name__)

a_			=      {
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}






class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 _lowerCamelCase				=					"""table-transformer"""
 _lowerCamelCase				=					["""past_key_values"""]
 _lowerCamelCase				=					{
     """hidden_size""": """d_model""",
     """num_attention_heads""": """encoder_attention_heads""",
 }

 def __init__(   self ,						__lowerCamelCase=True ,						__lowerCamelCase=None ,						__lowerCamelCase=3 ,						__lowerCamelCase=100 ,						__lowerCamelCase=6 ,						__lowerCamelCase=2048 ,						__lowerCamelCase=8 ,						__lowerCamelCase=6 ,						__lowerCamelCase=2048 ,						__lowerCamelCase=8 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=True ,						__lowerCamelCase="relu" ,						__lowerCamelCase=256 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=1.0 ,						__lowerCamelCase=False ,						__lowerCamelCase="sine" ,						__lowerCamelCase="resnet50" ,						__lowerCamelCase=True ,						__lowerCamelCase=False ,						__lowerCamelCase=1 ,						__lowerCamelCase=5 ,						__lowerCamelCase=2 ,						__lowerCamelCase=1 ,						__lowerCamelCase=1 ,						__lowerCamelCase=5 ,						__lowerCamelCase=2 ,						__lowerCamelCase=0.1 ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     if backbone_config is not None and use_timm_backbone:
         raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''  )

     if not use_timm_backbone:
         if backbone_config is None:
             logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''  )
             __A   :						Dict									=					CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']  )
         elif isinstance(__lowerCamelCase ,						__lowerCamelCase  ):
             __A   :						Tuple									=					backbone_config.get('''model_type'''  )
             __A   :						Any									=					CONFIG_MAPPING[backbone_model_type]
             __A   :						Optional[Any]									=					config_class.from_dict(__lowerCamelCase  )
         # set timm attributes to None
         __A   :						Optional[int]									=					None, None, None

     __A   :						str									=					use_timm_backbone
     __A   :						Union[str, Any]									=					backbone_config
     __A   :						str									=					num_channels
     __A   :						int									=					num_queries
     __A   :						Any									=					d_model
     __A   :						List[Any]									=					encoder_ffn_dim
     __A   :						Optional[Any]									=					encoder_layers
     __A   :						Union[str, Any]									=					encoder_attention_heads
     __A   :						Union[str, Any]									=					decoder_ffn_dim
     __A   :						str									=					decoder_layers
     __A   :						List[Any]									=					decoder_attention_heads
     __A   :						int									=					dropout
     __A   :						Union[str, Any]									=					attention_dropout
     __A   :						Tuple									=					activation_dropout
     __A   :						Dict									=					activation_function
     __A   :						List[Any]									=					init_std
     __A   :						List[str]									=					init_xavier_std
     __A   :						List[str]									=					encoder_layerdrop
     __A   :						Union[str, Any]									=					decoder_layerdrop
     __A   :						Any									=					encoder_layers
     __A   :						Optional[int]									=					auxiliary_loss
     __A   :						List[Any]									=					position_embedding_type
     __A   :						Tuple									=					backbone
     __A   :						Union[str, Any]									=					use_pretrained_backbone
     __A   :						str									=					dilation
     # Hungarian matcher
     __A   :						Union[str, Any]									=					class_cost
     __A   :						Any									=					bbox_cost
     __A   :						List[Any]									=					giou_cost
     # Loss coefficients
     __A   :						Dict									=					mask_loss_coefficient
     __A   :						Optional[int]									=					dice_loss_coefficient
     __A   :						Union[str, Any]									=					bbox_loss_coefficient
     __A   :						Optional[int]									=					giou_loss_coefficient
     __A   :						str									=					eos_coefficient
     super().__init__(is_encoder_decoder=__lowerCamelCase ,						**__lowerCamelCase  )

 @property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return self.encoder_attention_heads







 @property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return self.d_model






class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 _lowerCamelCase				=					version.parse("""1.11"""					)

 @property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return OrderedDict(
         [
             ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ('''pixel_mask''', {0: '''batch'''}),
         ]  )

 @property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return 1e-5







 @property
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return 12








"""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 		__snake_case (		SCREAMING_SNAKE_CASE__							,							unittest.TestCase					):





 """simple docstring"""


 _lowerCamelCase				=					BarthezTokenizer
 _lowerCamelCase				=					BarthezTokenizerFast
 _lowerCamelCase				=					True
 _lowerCamelCase				=					True

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     super().setUp()

     __A   :						List[Any]									=					BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''  )
     tokenizer.save_pretrained(self.tmpdirname  )
     tokenizer.save_pretrained(self.tmpdirname ,						legacy_format=__lowerCamelCase  )
     __A   :						Any									=					tokenizer

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Any									=					'''<pad>'''
     __A   :						Union[str, Any]									=					1

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

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						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(__lowerCamelCase  ) ,						10_1122  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     self.assertEqual(self.get_tokenizer().vocab_size ,						10_1122  )

 @require_torch
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[Any]									=					['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     __A   :						Any									=					[0, 57, 3018, 7_0307, 91, 2]

     __A   :						List[Any]									=					self.tokenizer(
         __lowerCamelCase ,						max_length=len(__lowerCamelCase  ) ,						padding=__lowerCamelCase ,						truncation=__lowerCamelCase ,						return_tensors='''pt'''  )
     self.assertIsInstance(__lowerCamelCase ,						__lowerCamelCase  )

     self.assertEqual((2, 6) ,						batch.input_ids.shape  )
     self.assertEqual((2, 6) ,						batch.attention_mask.shape  )
     __A   :						Any									=					batch.input_ids.tolist()[0]
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     if not self.test_rust_tokenizer:
         return

     __A   :						Tuple									=					self.get_tokenizer()
     __A   :						Dict									=					self.get_rust_tokenizer()

     __A   :						Union[str, Any]									=					'''I was born in 92000, and this is falsé.'''

     __A   :						List[Any]									=					tokenizer.tokenize(__lowerCamelCase  )
     __A   :						Optional[int]									=					rust_tokenizer.tokenize(__lowerCamelCase  )
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

     __A   :						str									=					tokenizer.encode(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase  )
     __A   :						Union[str, Any]									=					rust_tokenizer.encode(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase  )
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

     __A   :						str									=					self.get_rust_tokenizer()
     __A   :						Optional[int]									=					tokenizer.encode(__lowerCamelCase  )
     __A   :						Any									=					rust_tokenizer.encode(__lowerCamelCase  )
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )







 @slow
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[str]									=					{'''input_ids''': [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 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, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 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   :						Union[str, 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=__lowerCamelCase ,						model_name='''moussaKam/mbarthez''' ,						revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6''' ,						sequences=__lowerCamelCase ,						)




