


'''simple docstring'''

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


lowercase__				     =		logging.get_logger(__name__)

lowercase__				     =		{
    "Intel/dpt-large": "https://huggingface.co/Intel/dpt-large/resolve/main/config.json",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}





class 						__SCREAMING_SNAKE_CASE					(      __lowerCamelCase ):
     UpperCAmelCase 					=				'''dpt'''






     def __init__(			self     ,							__UpperCamelCase=768     ,							__UpperCamelCase=12     ,							__UpperCamelCase=12     ,							__UpperCamelCase=3_072     ,							__UpperCamelCase="gelu"     ,							__UpperCamelCase=0.0     ,							__UpperCamelCase=0.0     ,							__UpperCamelCase=0.02     ,							__UpperCamelCase=1e-12     ,							__UpperCamelCase=384     ,							__UpperCamelCase=16     ,							__UpperCamelCase=3     ,							__UpperCamelCase=False     ,							__UpperCamelCase=True     ,							__UpperCamelCase=[2, 5, 8, 11]     ,							__UpperCamelCase="project"     ,							__UpperCamelCase=[4, 2, 1, 0.5]     ,							__UpperCamelCase=[96, 192, 384, 768]     ,							__UpperCamelCase=256     ,							__UpperCamelCase=-1     ,							__UpperCamelCase=False     ,							__UpperCamelCase=True     ,							__UpperCamelCase=0.4     ,							__UpperCamelCase=255     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=[1, 1_024, 24, 24]     ,							__UpperCamelCase=[0, 1]     ,							__UpperCamelCase=None     ,							**__UpperCamelCase     ,							)       ->    Optional[int]:
          super().__init__(**a_		)

          _a						=		hidden_size
          _a						=		is_hybrid

          if self.is_hybrid:
               if backbone_config is None:
                    logger.info("Initializing the config with a `BiT` backbone."		)
                    _a						=		{
                        "global_padding": "same",
                        "layer_type": "bottleneck",
                        "depths": [3, 4, 9],
                        "out_features": ["stage1", "stage2", "stage3"],
                        "embedding_dynamic_padding": True,
                    }
                    _a						=		BitConfig(**a_		)
               elif isinstance(a_     ,							a_		):
                    logger.info("Initializing the config with a `BiT` backbone."		)
                    _a						=		BitConfig(**a_		)
               elif isinstance(a_     ,							a_		):
                    _a						=		backbone_config
               else:
                    raise ValueError(
                        f"backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}."		)

               _a						=		backbone_featmap_shape
               _a						=		neck_ignore_stages

               if readout_type != "project":
                    raise ValueError("Readout type must be 'project' when using `DPT-hybrid` mode."		)
          else:
               _a						=		None
               _a						=		None
               _a						=		[]

          _a						=		num_hidden_layers
          _a						=		num_attention_heads
          _a						=		intermediate_size
          _a						=		hidden_act
          _a						=		hidden_dropout_prob
          _a						=		attention_probs_dropout_prob
          _a						=		initializer_range
          _a						=		layer_norm_eps
          _a						=		image_size
          _a						=		patch_size
          _a						=		num_channels
          _a						=		qkv_bias
          _a						=		backbone_out_indices
          if readout_type not in ["ignore", "add", "project"]:
               raise ValueError("Readout_type must be one of ['ignore', 'add', 'project']"		)
          _a						=		readout_type
          _a						=		reassemble_factors
          _a						=		neck_hidden_sizes
          _a						=		fusion_hidden_size
          _a						=		head_in_index
          _a						=		use_batch_norm_in_fusion_residual
          # auxiliary head attributes (semantic segmentation)
          _a						=		use_auxiliary_head
          _a						=		auxiliary_loss_weight
          _a						=		semantic_loss_ignore_index
          _a						=		semantic_classifier_dropout






     def  a_					(			self		)       ->    Union[str, Any]:
          _a						=		copy.deepcopy(self.__dict__		)

          if output["backbone_config"] is not None:
               _a						=		self.backbone_config.to_dict()

          _a						=		self.__class__.model_type
          return output





'''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))

