


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


__UpperCamelCase       :       List[Any]						       =				logging.get_logger(__name__)

__UpperCamelCase       :       Optional[int]						       =				{
    "microsoft/table-transformer-detection": (
        "https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
    ),
}

class       __magic_name__	(     __lowerCAmelCase):
     A:		Tuple			=      "table-transformer"
     A:		Dict			=      ["past_key_values"]
     A:		Optional[int]			=      {
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
     }


     def __init__(      self							:     str							,	lowerCamelCase__							:     Union[str, Any]=True							,	lowerCamelCase__							:     Optional[Any]=None							,	lowerCamelCase__							:     Union[str, Any]=3							,	lowerCamelCase__							:     Optional[int]=100							,	lowerCamelCase__							:     Dict=6							,	lowerCamelCase__							:     Tuple=2048							,	lowerCamelCase__							:     Optional[Any]=8							,	lowerCamelCase__							:     List[Any]=6							,	lowerCamelCase__							:     int=2048							,	lowerCamelCase__							:     List[Any]=8							,	lowerCamelCase__							:     str=0.0							,	lowerCamelCase__							:     List[str]=0.0							,	lowerCamelCase__							:     Union[str, Any]=True							,	lowerCamelCase__							:     str="relu"							,	lowerCamelCase__							:     List[str]=256							,	lowerCamelCase__							:     Any=0.1							,	lowerCamelCase__							:     Dict=0.0							,	lowerCamelCase__							:     str=0.0							,	lowerCamelCase__							:     Any=0.02							,	lowerCamelCase__							:     Union[str, Any]=1.0							,	lowerCamelCase__							:     List[str]=False							,	lowerCamelCase__							:     Optional[Any]="sine"							,	lowerCamelCase__							:     Any="resnet50"							,	lowerCamelCase__							:     Union[str, Any]=True							,	lowerCamelCase__							:     Tuple=False							,	lowerCamelCase__							:     Optional[int]=1							,	lowerCamelCase__							:     Any=5							,	lowerCamelCase__							:     Tuple=2							,	lowerCamelCase__							:     List[Any]=1							,	lowerCamelCase__							:     int=1							,	lowerCamelCase__							:     Any=5							,	lowerCamelCase__							:     str=2							,	lowerCamelCase__							:     Optional[Any]=0.1							,	**lowerCamelCase__							:     Optional[int]							,	)  ->      Optional[int]:





      '''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.'''			)
        UpperCamelCase__					:    Optional[int]          =   CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']			)
       elif isinstance(lowerCamelCase__							,	lowerCamelCase__			):
        UpperCamelCase__					:    Optional[int]          =   backbone_config.get('''model_type'''			)
        UpperCamelCase__					:    Union[str, Any]          =   CONFIG_MAPPING[backbone_model_type]
        UpperCamelCase__					:    Optional[int]          =   config_class.from_dict(lowerCamelCase__			)
       # set timm attributes to None
       UpperCamelCase__       ,				UpperCamelCase__       ,				UpperCamelCase__					:    Any          =   None, None, None

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


     @property
     def      UpperCAmelCase__	(      self							:     Dict			)  ->      int:





      '''simple docstring'''






      return self.encoder_attention_heads


     @property
     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      int:





      '''simple docstring'''






      return self.d_model



class       __magic_name__	(     __lowerCAmelCase):
     A:		List[Any]			=      version.parse("1.11")


     @property
     def      UpperCAmelCase__	(      self							:     Any			)  ->      Mapping[str, Mapping[int, str]]:





      '''simple docstring'''






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


     @property
     def      UpperCAmelCase__	(      self							:     List[Any]			)  ->      float:





      '''simple docstring'''






      return 1E-5


     @property
     def      UpperCAmelCase__	(      self							:     List[Any]			)  ->      int:





      '''simple docstring'''






      return 12






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__	(     __lowerCAmelCase					,				unittest.TestCase):
     A:		Optional[int]			=      BarthezTokenizer
     A:		Optional[int]			=      BarthezTokenizerFast
     A:		str			=      True
     A:		Optional[int]			=      True


     def      UpperCAmelCase__	(      self							:     List[Any]			)  ->      str:





      '''simple docstring'''






      super().setUp()

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


     def      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   '''<pad>'''
      UpperCamelCase__					:    List[str]          =   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							:     int			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   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__			)							,	101122			)


     def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      str:





      '''simple docstring'''






      self.assertEqual(self.get_tokenizer().vocab_size							,	101122			)


     @require_torch
     def      UpperCAmelCase__	(      self							:     Dict			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
      UpperCamelCase__					:    Any          =   [0, 57, 3018, 70307, 91, 2]

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


     def      UpperCAmelCase__	(      self							:     str			)  ->      List[Any]:





      '''simple docstring'''






      if not self.test_rust_tokenizer:
       return

      UpperCamelCase__					:    List[Any]          =   self.get_tokenizer()
      UpperCamelCase__					:    Optional[int]          =   self.get_rust_tokenizer()

      UpperCamelCase__					:    Any          =   '''I was born in 92000, and this is falsé.'''

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

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

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


     @slow
     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__					:    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.
      UpperCamelCase__					:    int          =   [
          '''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__							,	)


