
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			:     int           =   logging.get_logger(__name__)

__lowerCAmelCase			:     Optional[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        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		"""detr"""
  a__           =		["""past_key_values"""]
  a__           =		{
      """hidden_size""": """d_model""",
      """num_attention_heads""": """encoder_attention_heads""",
  }
  def __init__( self			: List[Any]			,							UpperCamelCase__			: Tuple=True			,							UpperCamelCase__			: Dict=None			,							UpperCamelCase__			: Optional[Any]=3			,							UpperCamelCase__			: int=100			,							UpperCamelCase__			: Dict=6			,							UpperCamelCase__			: str=2048			,							UpperCamelCase__			: Optional[int]=8			,							UpperCamelCase__			: List[str]=6			,							UpperCamelCase__			: List[str]=2048			,							UpperCamelCase__			: Dict=8			,							UpperCamelCase__			: Union[str, Any]=0.0			,							UpperCamelCase__			: Dict=0.0			,							UpperCamelCase__			: int=True			,							UpperCamelCase__			: List[str]="relu"			,							UpperCamelCase__			: Dict=256			,							UpperCamelCase__			: Any=0.1			,							UpperCamelCase__			: Dict=0.0			,							UpperCamelCase__			: int=0.0			,							UpperCamelCase__			: Tuple=0.02			,							UpperCamelCase__			: Tuple=1.0			,							UpperCamelCase__			: Dict=False			,							UpperCamelCase__			: Any="sine"			,							UpperCamelCase__			: Optional[Any]="resnet50"			,							UpperCamelCase__			: Tuple=True			,							UpperCamelCase__			: Tuple=False			,							UpperCamelCase__			: Dict=1			,							UpperCamelCase__			: Union[str, Any]=5			,							UpperCamelCase__			: List[str]=2			,							UpperCamelCase__			: Any=1			,							UpperCamelCase__			: List[Any]=1			,							UpperCamelCase__			: Union[str, Any]=5			,							UpperCamelCase__			: Union[str, Any]=2			,							UpperCamelCase__			: List[str]=0.1			,							**UpperCamelCase__			: Any			,							)			->	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."""    )
        __magic_name__				     =       CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]    )
      elif isinstance(UpperCamelCase__			,							UpperCamelCase__    ):
        __magic_name__				     =       backbone_config.get("""model_type"""    )
        __magic_name__				     =       CONFIG_MAPPING[backbone_model_type]
        __magic_name__				     =       config_class.from_dict(UpperCamelCase__    )
      # set timm attributes to None
      __magic_name__     ,    __magic_name__     ,    __magic_name__				     =       None, None, None

    __magic_name__				     =       use_timm_backbone
    __magic_name__				     =       backbone_config
    __magic_name__				     =       num_channels
    __magic_name__				     =       num_queries
    __magic_name__				     =       d_model
    __magic_name__				     =       encoder_ffn_dim
    __magic_name__				     =       encoder_layers
    __magic_name__				     =       encoder_attention_heads
    __magic_name__				     =       decoder_ffn_dim
    __magic_name__				     =       decoder_layers
    __magic_name__				     =       decoder_attention_heads
    __magic_name__				     =       dropout
    __magic_name__				     =       attention_dropout
    __magic_name__				     =       activation_dropout
    __magic_name__				     =       activation_function
    __magic_name__				     =       init_std
    __magic_name__				     =       init_xavier_std
    __magic_name__				     =       encoder_layerdrop
    __magic_name__				     =       decoder_layerdrop
    __magic_name__				     =       encoder_layers
    __magic_name__				     =       auxiliary_loss
    __magic_name__				     =       position_embedding_type
    __magic_name__				     =       backbone
    __magic_name__				     =       use_pretrained_backbone
    __magic_name__				     =       dilation
    # Hungarian matcher
    __magic_name__				     =       class_cost
    __magic_name__				     =       bbox_cost
    __magic_name__				     =       giou_cost
    # Loss coefficients
    __magic_name__				     =       mask_loss_coefficient
    __magic_name__				     =       dice_loss_coefficient
    __magic_name__				     =       bbox_loss_coefficient
    __magic_name__				     =       giou_loss_coefficient
    __magic_name__				     =       eos_coefficient
    super().__init__(is_encoder_decoder=UpperCamelCase__			,							**UpperCamelCase__    )
  @property
  def 							_lowercase							( self			: List[Any]    )			->	int:







    """simple docstring"""






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







    """simple docstring"""






    return self.d_model
  @classmethod
  def 							_lowercase							( cls			: Optional[Any]			,							UpperCamelCase__			: PretrainedConfig			,							**UpperCamelCase__			: str    )			->	int:







    """simple docstring"""






    return cls(backbone_config=UpperCamelCase__			,							**UpperCamelCase__    )





  def 							_lowercase							( self			: Dict    )			->	Dict[str, any]:







    """simple docstring"""






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







class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		version.parse("""1.11"""     )
  @property
  def 							_lowercase							( self			: Any    )			->	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 							_lowercase							( self			: Tuple    )			->	float:







    """simple docstring"""






    return 1E-5





  @property
  def 							_lowercase							( self			: Optional[int]    )			->	int:







    """simple docstring"""






    return 12



from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

__lowerCAmelCase			:     Tuple           =   {
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}




class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		"""pegasus"""
  a__           =		["""past_key_values"""]
  a__           =		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
  def __init__( self			: Optional[int]			,							UpperCamelCase__			: Optional[int]=5_0265			,							UpperCamelCase__			: Optional[int]=1024			,							UpperCamelCase__			: Any=12			,							UpperCamelCase__			: Union[str, Any]=4096			,							UpperCamelCase__			: Any=16			,							UpperCamelCase__			: Union[str, Any]=12			,							UpperCamelCase__			: List[str]=4096			,							UpperCamelCase__			: Tuple=16			,							UpperCamelCase__			: Optional[int]=0.0			,							UpperCamelCase__			: List[Any]=0.0			,							UpperCamelCase__			: List[str]=True			,							UpperCamelCase__			: List[Any]=True			,							UpperCamelCase__			: List[Any]="gelu"			,							UpperCamelCase__			: List[Any]=1024			,							UpperCamelCase__			: Optional[Any]=0.1			,							UpperCamelCase__			: str=0.0			,							UpperCamelCase__			: Any=0.0			,							UpperCamelCase__			: Union[str, Any]=0.02			,							UpperCamelCase__			: Any=0			,							UpperCamelCase__			: int=False			,							UpperCamelCase__			: Any=0			,							UpperCamelCase__			: List[str]=1			,							UpperCamelCase__			: Tuple=1			,							**UpperCamelCase__			: Union[str, Any]			,							)			->	str:







    """simple docstring"""






    __magic_name__				     =       vocab_size
    __magic_name__				     =       max_position_embeddings
    __magic_name__				     =       d_model
    __magic_name__				     =       encoder_ffn_dim
    __magic_name__				     =       encoder_layers
    __magic_name__				     =       encoder_attention_heads
    __magic_name__				     =       decoder_ffn_dim
    __magic_name__				     =       decoder_layers
    __magic_name__				     =       decoder_attention_heads
    __magic_name__				     =       dropout
    __magic_name__				     =       attention_dropout
    __magic_name__				     =       activation_dropout
    __magic_name__				     =       activation_function
    __magic_name__				     =       init_std
    __magic_name__				     =       encoder_layerdrop
    __magic_name__				     =       decoder_layerdrop
    __magic_name__				     =       use_cache
    __magic_name__				     =       encoder_layers
    __magic_name__				     =       scale_embedding  # scale factor will be sqrt(d_model) if True
    super().__init__(
        pad_token_id=UpperCamelCase__			,							eos_token_id=UpperCamelCase__			,							is_encoder_decoder=UpperCamelCase__			,							decoder_start_token_id=UpperCamelCase__			,							forced_eos_token_id=UpperCamelCase__			,							**UpperCamelCase__			,							)
  @property
  def 							_lowercase							( self			: List[Any]    )			->	int:







    """simple docstring"""






    return self.encoder_attention_heads





  @property
  def 							_lowercase							( self			: Dict    )			->	int:







    """simple docstring"""






    return self.d_model

