



"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase   =	{
    """configuration_git""": ["""GIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GitConfig""", """GitVisionConfig"""],
    """processing_git""": ["""GitProcessor"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase   =	[
       """GIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """GitForCausalLM""",
       """GitModel""",
       """GitPreTrainedModel""",
       """GitVisionModel""",
   ]

if TYPE_CHECKING:
   from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
   from .processing_git import GitProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_git import (
          GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          GitForCausalLM,
          GitModel,
          GitPreTrainedModel,
          GitVisionModel,
      )

else:
   import sys

   _UpperCAmelCase   =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











"""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 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				'''detr'''
	__magic_name__	      =				['''past_key_values''']
	__magic_name__	      =				{
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''encoder_attention_heads''',
	}





	def __init__(      self					: Dict	,	lowerCAmelCase_					: str=True	,	lowerCAmelCase_					: Union[str, Any]=None	,	lowerCAmelCase_					: Dict=3	,	lowerCAmelCase_					: List[Any]=100	,	lowerCAmelCase_					: int=6	,	lowerCAmelCase_					: Union[str, Any]=2_048	,	lowerCAmelCase_					: int=8	,	lowerCAmelCase_					: int=6	,	lowerCAmelCase_					: Union[str, Any]=2_048	,	lowerCAmelCase_					: Any=8	,	lowerCAmelCase_					: Dict=0.0	,	lowerCAmelCase_					: Union[str, Any]=0.0	,	lowerCAmelCase_					: Dict=True	,	lowerCAmelCase_					: List[Any]="relu"	,	lowerCAmelCase_					: List[Any]=256	,	lowerCAmelCase_					: Tuple=0.1	,	lowerCAmelCase_					: Optional[Any]=0.0	,	lowerCAmelCase_					: List[Any]=0.0	,	lowerCAmelCase_					: List[Any]=0.0_2	,	lowerCAmelCase_					: Optional[int]=1.0	,	lowerCAmelCase_					: str=False	,	lowerCAmelCase_					: Tuple="sine"	,	lowerCAmelCase_					: str="resnet50"	,	lowerCAmelCase_					: List[Any]=True	,	lowerCAmelCase_					: int=False	,	lowerCAmelCase_					: Optional[Any]=1	,	lowerCAmelCase_					: int=5	,	lowerCAmelCase_					: Optional[Any]=2	,	lowerCAmelCase_					: List[Any]=1	,	lowerCAmelCase_					: List[Any]=1	,	lowerCAmelCase_					: Dict=5	,	lowerCAmelCase_					: Optional[Any]=2	,	lowerCAmelCase_					: Union[str, Any]=0.1	,	**lowerCAmelCase_					: Dict	,	)						->			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_     : Tuple			     =					CONFIG_MAPPING["resnet"](out_features=["stage4"]						)
							elif isinstance(lowerCAmelCase_	,	lowerCAmelCase_						):
										UpperCAmelCase_     : Dict			     =					backbone_config.get("model_type"						)
										UpperCAmelCase_     : Dict			     =					CONFIG_MAPPING[backbone_model_type]
										UpperCAmelCase_     : Tuple			     =					config_class.from_dict(lowerCAmelCase_						)
							# set timm attributes to None
							UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : Optional[Any]			     =					None, None, None

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





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			int:
				return self.encoder_attention_heads





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





	@classmethod
	def      _SCREAMING_SNAKE_CASE      (      cls					: Union[str, Any]	,	lowerCAmelCase_					: PretrainedConfig	,	**lowerCAmelCase_					: Tuple						)						->			List[Any]:
				return cls(backbone_config=lowerCAmelCase_	,	**lowerCAmelCase_						)





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







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				version.parse('''1.11'''     )





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			Mapping[str, Mapping[int, str]]:
				return OrderedDict(
				    [
				        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
				        ("pixel_mask", {0: "batch"}),
				    ]						)





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			float:
				return 1e-5





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			int:
				return 12



