


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

lowerCAmelCase__			:      List[str]								={
    '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    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







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






							def __init__(      self			,						lowerCAmelCase__=True			,						lowerCAmelCase__=None			,						lowerCAmelCase__=3			,						lowerCAmelCase__=1_0_0			,						lowerCAmelCase__=6			,						lowerCAmelCase__=2_0_4_8			,						lowerCAmelCase__=8			,						lowerCAmelCase__=6			,						lowerCAmelCase__=2_0_4_8			,						lowerCAmelCase__=8			,						lowerCAmelCase__=0.0			,						lowerCAmelCase__=0.0			,						lowerCAmelCase__=True			,						lowerCAmelCase__="relu"			,						lowerCAmelCase__=2_5_6			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=0.0			,						lowerCAmelCase__=0.0			,						lowerCAmelCase__=0.02			,						lowerCAmelCase__=1.0			,						lowerCAmelCase__=False			,						lowerCAmelCase__="sine"			,						lowerCAmelCase__="resnet50"			,						lowerCAmelCase__=True			,						lowerCAmelCase__=False			,						lowerCAmelCase__=1			,						lowerCAmelCase__=5			,						lowerCAmelCase__=2			,						lowerCAmelCase__=1			,						lowerCAmelCase__=1			,						lowerCAmelCase__=5			,						lowerCAmelCase__=2			,						lowerCAmelCase__=0.1			,						**lowerCAmelCase__			,						):





										"""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.'       )
																SCREAMING_SNAKE_CASE_		:     Dict					=					CONFIG_MAPPING['resnet'](out_features=['stage4']       )
													elif isinstance(lowerCAmelCase__			,						lowerCAmelCase__       ):
																SCREAMING_SNAKE_CASE_		:     Tuple					=					backbone_config.get('model_type'       )
																SCREAMING_SNAKE_CASE_		:     Optional[int]					=					CONFIG_MAPPING[backbone_model_type]
																SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					config_class.from_dict(lowerCAmelCase__       )
													# set timm attributes to None
													SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					None, None, None

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






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return self.encoder_attention_heads






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return self.d_model






							@classmethod
							def 					UpperCamelCase__     (      cls			,						lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										return cls(backbone_config=lowerCAmelCase__			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					copy.deepcopy(self.__dict__       )
										if output["backbone_config"] is not None:
													SCREAMING_SNAKE_CASE_		:     str					=					self.backbone_config.to_dict()
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.__class__.model_type
										return output







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







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






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




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






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return 1E-5






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return 1_2







import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union




@dataclass
class   __lowercase						:
	_A       =   None
	_A       =   False
	_A       =   False
	_A       =   False
	_A       =   None
	_A       =   None
	_A       =   False
	_A       =   False
	_A       =   False
	_A       =   True
	_A       =   None
	_A       =   1
	_A       =   None
	_A       =   False
	_A       =   None
	_A       =   None






	def       _a(self				: str    ) ->			"DownloadConfig":
							return self.__class__(**{k: copy.deepcopy(snake_case    ) for k, v in self.__dict__.items()}    )




