


import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


UpperCAmelCase    =					logging.get_logger(__name__)

UpperCAmelCase    =					{
    """facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
    # See all DETR models at https://huggingface.co/models?filter=detr
}







class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						"detr"
 __snake_case				=						["past_key_values"]
 __snake_case				=						{
     "hidden_size": "d_model",
     "num_attention_heads": "encoder_attention_heads",
 }



 def __init__(			self	,   _UpperCAmelCase=True	,   _UpperCAmelCase=None	,   _UpperCAmelCase=3	,   _UpperCAmelCase=1_00	,   _UpperCAmelCase=6	,   _UpperCAmelCase=20_48	,   _UpperCAmelCase=8	,   _UpperCAmelCase=6	,   _UpperCAmelCase=20_48	,   _UpperCAmelCase=8	,   _UpperCAmelCase=0.0	,   _UpperCAmelCase=0.0	,   _UpperCAmelCase=True	,   _UpperCAmelCase="relu"	,   _UpperCAmelCase=2_56	,   _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	,   ):
       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.'''      )
                   snake_case_ =   CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']      )
             elif isinstance(_UpperCAmelCase	,   _UpperCAmelCase      ):
                   snake_case_ =   backbone_config.get('''model_type'''      )
                   snake_case_ =   CONFIG_MAPPING[backbone_model_type]
                   snake_case_ =   config_class.from_dict(_UpperCAmelCase      )
             # set timm attributes to None
             snake_case_ ,					snake_case_ ,					snake_case_ =   None, None, None

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



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



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



 @classmethod
 def 		UpperCamelCase__  (			cls	,   _UpperCAmelCase	,   **_UpperCAmelCase      ):
       return cls(backbone_config=_UpperCAmelCase	,   **_UpperCAmelCase      )





 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   copy.deepcopy(self.__dict__      )
       if output["backbone_config"] is not None:
             snake_case_ =   self.backbone_config.to_dict()
       snake_case_ =   self.__class__.model_type
       return output







class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						version.parse("1.11" )



 @property
 def 		UpperCamelCase__  (			self      ):
       return OrderedDict(
           [
               ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
               ('''pixel_mask''', {0: '''batch'''}),
           ]      )



 @property
 def 		UpperCamelCase__  (			self      ):
       return 1E-5





 @property
 def 		UpperCamelCase__  (			self      ):
       return 12



def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE = 6008_5147_5143      )->						int:




      """simple docstring"""

      try:
            snake_case_ =   int(SCREAMING_SNAKE_CASE      )
      except (TypeError, ValueError):
            raise TypeError('''Parameter n must be int or castable to int.'''      )
      if n <= 0:
            raise ValueError('''Parameter n must be greater than or equal to one.'''      )
      snake_case_ =   1
      snake_case_ =   2
      while i * i <= n:
            while n % i == 0:
                  snake_case_ =   i
                  n //= i
            i += 1
      if n > 1:
            snake_case_ =   n
      return int(SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
   print(f'''{solution() = }''')