




import copy
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


lowerCamelCase							:						Union[str, Any]					       =  logging.get_logger(__name__)

lowerCamelCase							:						List[str]					       =  {
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}







class     A(		UpperCamelCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		'''conditional_detr'''
 UpperCamelCase		  =		['''past_key_values''']
 UpperCamelCase		  =		{
     '''hidden_size''': '''d_model''',
     '''num_attention_heads''': '''encoder_attention_heads''',
 }

 def __init__(							self   :    Union[str, Any]		,				A_   :    List[Any]=True		,				A_   :    Dict=None		,				A_   :    Any=3		,				A_   :    int=300		,				A_   :    Dict=6		,				A_   :    Any=2048		,				A_   :    Tuple=8		,				A_   :    Union[str, Any]=6		,				A_   :    Optional[Any]=2048		,				A_   :    Optional[int]=8		,				A_   :    List[Any]=0.0		,				A_   :    Any=0.0		,				A_   :    Tuple=True		,				A_   :    Dict="relu"		,				A_   :    Dict=256		,				A_   :    Optional[int]=0.1		,				A_   :    Tuple=0.0		,				A_   :    Any=0.0		,				A_   :    List[str]=0.02		,				A_   :    int=1.0		,				A_   :    Optional[int]=False		,				A_   :    int="sine"		,				A_   :    Tuple="resnet50"		,				A_   :    Optional[Any]=True		,				A_   :    Dict=False		,				A_   :    Union[str, Any]=2		,				A_   :    str=5		,				A_   :    Union[str, Any]=2		,				A_   :    List[Any]=1		,				A_   :    List[Any]=1		,				A_   :    List[Any]=2		,				A_   :    Optional[Any]=5		,				A_   :    Optional[Any]=2		,				A_   :    Optional[int]=0.25		,				**A_   :    Optional[Any]		,				)  ->      List[Any]:

       """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.'    )
                   lowerCamelCase_							=       CONFIG_MAPPING['resnet'](out_features=['stage4']    )
             elif isinstance(A_		,				A_    ):
                   lowerCamelCase_							=       backbone_config.get('model_type'    )
                   lowerCamelCase_							=       CONFIG_MAPPING[backbone_model_type]
                   lowerCamelCase_							=       config_class.from_dict(A_    )

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

 @property
 def 							a__					(							self   :    Tuple    )  ->      int:

       """simple docstring"""






       return self.encoder_attention_heads

 @property
 def 							a__					(							self   :    Union[str, Any]    )  ->      int:

       """simple docstring"""






       return self.d_model





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

       """simple docstring"""






       lowerCamelCase_							=       copy.deepcopy(self.__dict__    )
       if self.backbone_config is not None:
             lowerCamelCase_							=       self.backbone_config.to_dict()
       lowerCamelCase_							=       self.__class__.model_type
       return output







class     A(		UpperCamelCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		version.parse('''1.11'''  )

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

       """simple docstring"""






       return 1E-5





 @property
 def 							a__					(							self   :    Union[str, Any]    )  ->      int:

       """simple docstring"""






       return 12







'''simple docstring'''

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A			)			->			list:






       """simple docstring"""







       lowercase__      =					len(A			)
       lowercase__      =					[[0] * n for i in range(A			)]
       for i in range(A			):
              lowercase__      =					y_points[i]

       for i in range(2    ,					A			):
              for j in range(A    ,					A			):
                     lowercase__      =					(
                         (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()




