



'''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


lowercase_		       =  logging.get_logger(__name__)

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






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       str          =       'table-transformer'
 __lowerCamelCase    :       Tuple          =       ['past_key_values']
 __lowerCamelCase    :       Union[str, Any]          =       {
     'hidden_size': 'd_model',
     'num_attention_heads': 'encoder_attention_heads',
 }



 def __init__(self ,   A=True ,   A=None ,   A=3 ,   A=100 ,   A=6 ,   A=2_048 ,   A=8 ,   A=6 ,   A=2_048 ,   A=8 ,   A=0.0 ,   A=0.0 ,   A=True ,   A="relu" ,   A=256 ,   A=0.1 ,   A=0.0 ,   A=0.0 ,   A=0.02 ,   A=1.0 ,   A=False ,   A="sine" ,   A="resnet50" ,   A=True ,   A=False ,   A=1 ,   A=5 ,   A=2 ,   A=1 ,   A=1 ,   A=5 ,   A=2 ,   A=0.1 ,   **A ,   )    ->     List[str]:


      """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      =  CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']       )
           elif isinstance(_snake_case ,   _snake_case       ):
                _a      =  backbone_config.get('''model_type'''       )
                _a      =  CONFIG_MAPPING[backbone_model_type]
                _a      =  config_class.from_dict(_snake_case       )
           # set timm attributes to None
           _a  ,    _a  ,    _a      =  None, None, None

      _a      =  use_timm_backbone
      _a      =  backbone_config
      _a      =  num_channels
      _a      =  num_queries
      _a      =  d_model
      _a      =  encoder_ffn_dim
      _a      =  encoder_layers
      _a      =  encoder_attention_heads
      _a      =  decoder_ffn_dim
      _a      =  decoder_layers
      _a      =  decoder_attention_heads
      _a      =  dropout
      _a      =  attention_dropout
      _a      =  activation_dropout
      _a      =  activation_function
      _a      =  init_std
      _a      =  init_xavier_std
      _a      =  encoder_layerdrop
      _a      =  decoder_layerdrop
      _a      =  encoder_layers
      _a      =  auxiliary_loss
      _a      =  position_embedding_type
      _a      =  backbone
      _a      =  use_pretrained_backbone
      _a      =  dilation
      # Hungarian matcher
      _a      =  class_cost
      _a      =  bbox_cost
      _a      =  giou_cost
      # Loss coefficients
      _a      =  mask_loss_coefficient
      _a      =  dice_loss_coefficient
      _a      =  bbox_loss_coefficient
      _a      =  giou_loss_coefficient
      _a      =  eos_coefficient
      super().__init__(is_encoder_decoder=_snake_case ,   **_snake_case       )



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return self.encoder_attention_heads



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return self.d_model






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Dict          =       version.parse('1.11'						)



 @property
 def 					a__  (self       )    ->     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 					a__  (self       )    ->     float:


      """simple docstring"""



      return 1E-5



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return 12





def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  list:
				__a										=    len(a__  )
				__a										=    [[0] * n for i in range(a__  )]
				for i in range(a__  ):
								__a										=    y_points[i]

				for i in range(2						,    a__  ):
								for j in range(a__						,    a__  ):
												__a										=    (
												    (xa - x_points[j - i + 1]) * q[j][i - 1]
												    - (xa - x_points[j]) * q[j - 1][i - 1]
												) / (x_points[j] - x_points[j - i + 1])

				return [q[n - 1][n - 1], q]


if __name__ == "__main__":
					import doctest

					doctest.testmod()