



'''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


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

A_							: int						 =	{
    """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 							lowercase       (  __A				):


 """simple docstring"""


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







 def __init__(							self  ,a_=True  ,a_=None  ,a_=3  ,a_=100  ,a_=6  ,a_=2_048  ,a_=8  ,a_=6  ,a_=2_048  ,a_=8  ,a_=0.0  ,a_=0.0  ,a_=True  ,a_="relu"  ,a_=256  ,a_=0.1  ,a_=0.0  ,a_=0.0  ,a_=0.02  ,a_=1.0  ,a_=False  ,a_="sine"  ,a_="resnet50"  ,a_=True  ,a_=False  ,a_=1  ,a_=5  ,a_=2  ,a_=1  ,a_=1  ,a_=5  ,a_=2  ,a_=0.1  ,**a_  ,)		->   Optional[int]:
        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  :						List[Any]			     = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]							)
               elif isinstance(_UpperCamelCase  ,_UpperCamelCase							):
                      _UpperCAmelCase  :						List[Any]			     = backbone_config.get("""model_type"""							)
                      _UpperCAmelCase  :						Optional[Any]			     = CONFIG_MAPPING[backbone_model_type]
                      _UpperCAmelCase  :						List[str]			     = config_class.from_dict(_UpperCamelCase							)
               # set timm attributes to None
               _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase  :						str			     = None, None, None

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







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







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







 @classmethod
 def _snake_case (							cls  ,a_  ,**a_							)		->   Optional[int]:
        return cls(backbone_config=_UpperCamelCase  ,**_UpperCamelCase							)







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







class 							lowercase       (  __A				):


 """simple docstring"""


 UpperCAmelCase							=  version.parse("""1.11"""				)







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







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






from __future__ import annotations


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						return [ord(SCREAMING_SNAKE_CASE__       ) - 96 for elem in plain]


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						return "".join(chr(elem + 96       ) for elem in encoded       )


def   __SCREAMING_SNAKE_CASE					():
						snake_case_   =	encode(input('''-> '''       ).strip().lower()       )
						print('''Encoded: '''  ,		SCREAMING_SNAKE_CASE__       )
						print('''Decoded:'''  ,		decode(SCREAMING_SNAKE_CASE__       )       )


if __name__ == "__main__":
					main()