


import copy

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


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

SCREAMING_SNAKE_CASE__					:     Dict       =     {
    'SenseTime/deformable-detr': 'https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    '''deformable_detr'''
     __SCREAMING_SNAKE_CASE     					=    {
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
     }


     def __init__(  self							,					A_=True							,					A_=None							,					A_=3							,					A_=3_00							,					A_=10_24							,					A_=6							,					A_=10_24							,					A_=8							,					A_=6							,					A_=10_24							,					A_=8							,					A_=0.0							,					A_=True							,					A_="relu"							,					A_=2_56							,					A_=0.1							,					A_=0.0							,					A_=0.0							,					A_=0.0_2							,					A_=1.0							,					A_=True							,					A_=False							,					A_="sine"							,					A_="resnet50"							,					A_=True							,					A_=False							,					A_=4							,					A_=4							,					A_=4							,					A_=False							,					A_=3_00							,					A_=False							,					A_=1							,					A_=5							,					A_=2							,					A_=1							,					A_=1							,					A_=5							,					A_=2							,					A_=0.1							,					A_=0.2_5							,					A_=False							,					**A_							,					):
       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		:							Union[str, Any]							=     CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]    )
         elif isinstance(A_							,					A_    ):
           _UpperCAmelCase		:							List[str]							=     backbone_config.get("""model_type"""    )
           _UpperCAmelCase		:							Optional[Any]							=     CONFIG_MAPPING[backbone_model_type]
           _UpperCAmelCase		:							Optional[Any]							=     config_class.from_dict(A_    )
       _UpperCAmelCase		:							Optional[Any]							=     use_timm_backbone
       _UpperCAmelCase		:							List[str]							=     backbone_config
       _UpperCAmelCase		:							List[Any]							=     num_channels
       _UpperCAmelCase		:							Tuple							=     num_queries
       _UpperCAmelCase		:							Optional[int]							=     max_position_embeddings
       _UpperCAmelCase		:							Optional[int]							=     d_model
       _UpperCAmelCase		:							str							=     encoder_ffn_dim
       _UpperCAmelCase		:							str							=     encoder_layers
       _UpperCAmelCase		:							int							=     encoder_attention_heads
       _UpperCAmelCase		:							List[str]							=     decoder_ffn_dim
       _UpperCAmelCase		:							Any							=     decoder_layers
       _UpperCAmelCase		:							int							=     decoder_attention_heads
       _UpperCAmelCase		:							Any							=     dropout
       _UpperCAmelCase		:							Tuple							=     attention_dropout
       _UpperCAmelCase		:							int							=     activation_dropout
       _UpperCAmelCase		:							Dict							=     activation_function
       _UpperCAmelCase		:							int							=     init_std
       _UpperCAmelCase		:							Union[str, Any]							=     init_xavier_std
       _UpperCAmelCase		:							int							=     encoder_layerdrop
       _UpperCAmelCase		:							List[str]							=     auxiliary_loss
       _UpperCAmelCase		:							Union[str, Any]							=     position_embedding_type
       _UpperCAmelCase		:							str							=     backbone
       _UpperCAmelCase		:							int							=     use_pretrained_backbone
       _UpperCAmelCase		:							Any							=     dilation
       # deformable attributes
       _UpperCAmelCase		:							Union[str, Any]							=     num_feature_levels
       _UpperCAmelCase		:							Tuple							=     encoder_n_points
       _UpperCAmelCase		:							Optional[int]							=     decoder_n_points
       _UpperCAmelCase		:							Tuple							=     two_stage
       _UpperCAmelCase		:							Any							=     two_stage_num_proposals
       _UpperCAmelCase		:							List[str]							=     with_box_refine
       if two_stage is True and with_box_refine is False:
         raise ValueError("""If two_stage is True, with_box_refine must be True."""    )
       # Hungarian matcher
       _UpperCAmelCase		:							Any							=     class_cost
       _UpperCAmelCase		:							Union[str, Any]							=     bbox_cost
       _UpperCAmelCase		:							Tuple							=     giou_cost
       # Loss coefficients
       _UpperCAmelCase		:							Optional[int]							=     mask_loss_coefficient
       _UpperCAmelCase		:							Tuple							=     dice_loss_coefficient
       _UpperCAmelCase		:							int							=     bbox_loss_coefficient
       _UpperCAmelCase		:							Optional[Any]							=     giou_loss_coefficient
       _UpperCAmelCase		:							Optional[int]							=     eos_coefficient
       _UpperCAmelCase		:							Any							=     focal_alpha
       _UpperCAmelCase		:							int							=     disable_custom_kernels
       super().__init__(is_encoder_decoder=A_							,					**A_    )


     @property
     def        __snake_case(  self    ):
       return self.encoder_attention_heads


     @property
     def        __snake_case(  self    ):
       return self.d_model




     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Optional[Any]							=     copy.deepcopy(self.__dict__    )
       if self.backbone_config is not None:
         _UpperCAmelCase		:							Any							=     self.backbone_config.to_dict()
       _UpperCAmelCase		:							List[Any]							=     self.__class__.model_type
       return output









SCREAMING_SNAKE_CASE__					:     dict[str, float]       =     {
    "joule": 1.0,
    "kilojoule": 1_000,
    "megajoule": 1_000_000,
    "gigajoule": 1_000_000_000,
    "wattsecond": 1.0,
    "watthour": 3_600,
    "kilowatthour": 3_600_000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_186.8,
    "kilocalorie_nutr": 4_186_800.00,
    "electronvolt": 1.602176634e-19,
    "britishthermalunit_it": 1_055.05_585,
    "footpound": 1.355_818,
}




def    a__    (		snake_case__					:							str						,   snake_case__					:							str						,   snake_case__					:							float     ):
  if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
    _UpperCAmelCase		:							Optional[Any]							=     (
        f'''Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
        f'''Valid values are: {", ".join(snake_case__     )}'''
    )
    raise ValueError(snake_case__     )
  return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
 import doctest

 doctest.testmod()





