


'''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 						__SCREAMING_SNAKE_CASE					(      _UpperCamelCase ):
     UpperCAmelCase 					=				"table-transformer"
     UpperCAmelCase 					=				["past_key_values"]
     UpperCAmelCase 					=				{
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
     }






     def __init__(			self     ,							__UpperCamelCase=True     ,							__UpperCamelCase=None     ,							__UpperCamelCase=3     ,							__UpperCamelCase=100     ,							__UpperCamelCase=6     ,							__UpperCamelCase=2_048     ,							__UpperCamelCase=8     ,							__UpperCamelCase=6     ,							__UpperCamelCase=2_048     ,							__UpperCamelCase=8     ,							__UpperCamelCase=0.0     ,							__UpperCamelCase=0.0     ,							__UpperCamelCase=True     ,							__UpperCamelCase="relu"     ,							__UpperCamelCase=256     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=0.0     ,							__UpperCamelCase=0.0     ,							__UpperCamelCase=0.02     ,							__UpperCamelCase=1.0     ,							__UpperCamelCase=False     ,							__UpperCamelCase="sine"     ,							__UpperCamelCase="resnet50"     ,							__UpperCamelCase=True     ,							__UpperCamelCase=False     ,							__UpperCamelCase=1     ,							__UpperCamelCase=5     ,							__UpperCamelCase=2     ,							__UpperCamelCase=1     ,							__UpperCamelCase=1     ,							__UpperCamelCase=5     ,							__UpperCamelCase=2     ,							__UpperCamelCase=0.1     ,							**__UpperCamelCase     ,							)       ->    List[str]:
          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(__a     ,							__a		):
                    _a						=		backbone_config.get("model_type"		)
                    _a						=		CONFIG_MAPPING[backbone_model_type]
                    _a						=		config_class.from_dict(__a		)
               # set timm attributes to None
               _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=__a     ,							**__a		)






     @property
     def  a_					(			self		)       ->    int:
          return self.encoder_attention_heads






     @property
     def  a_					(			self		)       ->    int:
          return self.d_model







class 						__SCREAMING_SNAKE_CASE					(      _UpperCamelCase ):
     UpperCAmelCase 					=				version.parse('''1.11''' )






     @property
     def  a_					(			self		)       ->    Mapping[str, Mapping[int, str]]:
          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                  ("pixel_mask", {0: "batch"}),
              ]		)






     @property
     def  a_					(			self		)       ->    float:
          return 1e-5






     @property
     def  a_					(			self		)       ->    int:
          return 12





'''simple docstring'''

from math import factorial, pi






def __UpperCamelCase   (			__lowerCamelCase		: float      ,  __lowerCamelCase		: int = 30				)      ->     float:







     '''simple docstring'''






     if not isinstance(__lowerCamelCase      ,  (int, float)				):
          raise ValueError("maclaurin_sin() requires either an int or float for theta"				)

     if not isinstance(__lowerCamelCase      ,  __lowerCamelCase				) or accuracy <= 0:
          raise ValueError("maclaurin_sin() requires a positive int for accuracy"				)

     _a						=		float(__lowerCamelCase				)
     _a						=		theta // (2 * pi)
     theta -= 2 * div * pi
     return sum(
         (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1				) for r in range(__lowerCamelCase				)				)






def __UpperCamelCase   (			__lowerCamelCase		: float      ,  __lowerCamelCase		: int = 30				)      ->     float:







     '''simple docstring'''






     if not isinstance(__lowerCamelCase      ,  (int, float)				):
          raise ValueError("maclaurin_cos() requires either an int or float for theta"				)

     if not isinstance(__lowerCamelCase      ,  __lowerCamelCase				) or accuracy <= 0:
          raise ValueError("maclaurin_cos() requires a positive int for accuracy"				)

     _a						=		float(__lowerCamelCase				)
     _a						=		theta // (2 * pi)
     theta -= 2 * div * pi
     return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r				) for r in range(__lowerCamelCase				)				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   print(maclaurin_sin(10))
   print(maclaurin_sin(-10))
   print(maclaurin_sin(10, 15))
   print(maclaurin_sin(-10, 15))

   print(maclaurin_cos(5))
   print(maclaurin_cos(-5))
   print(maclaurin_cos(10, 15))
   print(maclaurin_cos(-10, 15))

