



'''simple docstring'''





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


_lowerCAmelCase										=     logging.get_logger(__name__)

_lowerCAmelCase										=     {
    '''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_(    SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				List[Any]           =				'''detr'''
  __lowercase							:				List[str]           =				['''past_key_values''']
  __lowercase							:				Dict           =				{
      '''hidden_size''': '''d_model''',
      '''num_attention_heads''': '''encoder_attention_heads''',
  }




  def __init__(   self  ,__UpperCAmelCase=True  ,__UpperCAmelCase=None  ,__UpperCAmelCase=3  ,__UpperCAmelCase=100  ,__UpperCAmelCase=6  ,__UpperCAmelCase=2048  ,__UpperCAmelCase=8  ,__UpperCAmelCase=6  ,__UpperCAmelCase=2048  ,__UpperCAmelCase=8  ,__UpperCAmelCase=0.0  ,__UpperCAmelCase=0.0  ,__UpperCAmelCase=True  ,__UpperCAmelCase="relu"  ,__UpperCAmelCase=256  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=0.0  ,__UpperCAmelCase=0.0  ,__UpperCAmelCase=0.0_2  ,__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."""				)
              lowerCAmelCase__			:  Tuple											= CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]				)
          elif isinstance(__UpperCAmelCase  ,__UpperCAmelCase				):
              lowerCAmelCase__			:  int											= backbone_config.get("""model_type"""				)
              lowerCAmelCase__			:  Dict											= CONFIG_MAPPING[backbone_model_type]
              lowerCAmelCase__			:  Tuple											= config_class.from_dict(__UpperCAmelCase				)
          # set timm attributes to None
          lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__			:  Dict											= None, None, None

      lowerCAmelCase__			:  Optional[Any]											= use_timm_backbone
      lowerCAmelCase__			:  Union[str, Any]											= backbone_config
      lowerCAmelCase__			:  List[Any]											= num_channels
      lowerCAmelCase__			:  List[str]											= num_queries
      lowerCAmelCase__			:  List[Any]											= d_model
      lowerCAmelCase__			:  Any											= encoder_ffn_dim
      lowerCAmelCase__			:  Any											= encoder_layers
      lowerCAmelCase__			:  List[str]											= encoder_attention_heads
      lowerCAmelCase__			:  List[Any]											= decoder_ffn_dim
      lowerCAmelCase__			:  List[str]											= decoder_layers
      lowerCAmelCase__			:  Tuple											= decoder_attention_heads
      lowerCAmelCase__			:  Union[str, Any]											= dropout
      lowerCAmelCase__			:  Dict											= attention_dropout
      lowerCAmelCase__			:  Union[str, Any]											= activation_dropout
      lowerCAmelCase__			:  Tuple											= activation_function
      lowerCAmelCase__			:  List[Any]											= init_std
      lowerCAmelCase__			:  int											= init_xavier_std
      lowerCAmelCase__			:  int											= encoder_layerdrop
      lowerCAmelCase__			:  Optional[int]											= decoder_layerdrop
      lowerCAmelCase__			:  str											= encoder_layers
      lowerCAmelCase__			:  List[Any]											= auxiliary_loss
      lowerCAmelCase__			:  Optional[Any]											= position_embedding_type
      lowerCAmelCase__			:  str											= backbone
      lowerCAmelCase__			:  Union[str, Any]											= use_pretrained_backbone
      lowerCAmelCase__			:  Any											= dilation
      # Hungarian matcher
      lowerCAmelCase__			:  Dict											= class_cost
      lowerCAmelCase__			:  List[Any]											= bbox_cost
      lowerCAmelCase__			:  Tuple											= giou_cost
      # Loss coefficients
      lowerCAmelCase__			:  Tuple											= mask_loss_coefficient
      lowerCAmelCase__			:  List[Any]											= dice_loss_coefficient
      lowerCAmelCase__			:  Optional[int]											= bbox_loss_coefficient
      lowerCAmelCase__			:  Dict											= giou_loss_coefficient
      lowerCAmelCase__			:  List[str]											= eos_coefficient
      super().__init__(is_encoder_decoder=__UpperCAmelCase  ,**__UpperCAmelCase				)




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




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




  @classmethod
  def 							UpperCAmelCase_     (   cls  ,__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		Any:
      return cls(backbone_config=__UpperCAmelCase  ,**__UpperCAmelCase				)






  def 							UpperCAmelCase_     (   self				)   ->		Dict[str, any]:
      lowerCAmelCase__			:  Tuple											= copy.deepcopy(self.__dict__				)
      if output["backbone_config"] is not None:
          lowerCAmelCase__			:  Any											= self.backbone_config.to_dict()
      lowerCAmelCase__			:  str											= self.__class__.model_type
      return output







class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Union[str, Any]           =				version.parse('''1.11'''					)




  @property
  def 							UpperCAmelCase_     (   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 							UpperCAmelCase_     (   self				)   ->		float:
      return 1E-5






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







'''simple docstring'''





from PIL import Image
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__				,       lowerCAmelCase__			:  Dict											= image.size
    lowerCAmelCase__			:  int											= 0
    lowerCAmelCase__			:  int											= image.load()
    for i in range(UpperCamelCase      ):
        for j in range(UpperCamelCase      ):
            lowerCAmelCase__			:  int											= pixels[j, i]
            mean += pixel
    mean //= width * height

    for j in range(UpperCamelCase      ):
        for i in range(UpperCamelCase      ):
            lowerCAmelCase__			:  Dict											= 255 if pixels[i, j] > mean else 0
    return image


if __name__ == "__main__":
 _lowerCAmelCase										=     mean_threshold(Image.open('''path_to_image''').convert('''L'''))
 image.save('''output_image_path''')


