
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__A				=logging.get_logger(__name__)

__A				={
    '''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json''',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = 'deformable_detr'
	lowerCAmelCase__         = {
	    'hidden_size': 'd_model',
	    'num_attention_heads': 'encoder_attention_heads',
	}



	def __init__(    self						,  lowercase=True						,  lowercase=None						,  lowercase=3						,  lowercase=300						,  lowercase=1024						,  lowercase=6						,  lowercase=1024						,  lowercase=8						,  lowercase=6						,  lowercase=1024						,  lowercase=8						,  lowercase=0.0						,  lowercase=True						,  lowercase="relu"						,  lowercase=256						,  lowercase=0.1						,  lowercase=0.0						,  lowercase=0.0						,  lowercase=0.0_2						,  lowercase=1.0						,  lowercase=True						,  lowercase=False						,  lowercase="sine"						,  lowercase="resnet50"						,  lowercase=True						,  lowercase=False						,  lowercase=4						,  lowercase=4						,  lowercase=4						,  lowercase=False						,  lowercase=300						,  lowercase=False						,  lowercase=1						,  lowercase=5						,  lowercase=2						,  lowercase=1						,  lowercase=1						,  lowercase=5						,  lowercase=2						,  lowercase=0.1						,  lowercase=0.2_5						,  lowercase=False						,  **lowercase						,  )				->       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_						=			CONFIG_MAPPING["resnet"](out_features=["stage4"] )
									elif isinstance(lowercase						,  lowercase ):
													lowerCamelCase_						=			backbone_config.get("model_type" )
													lowerCamelCase_						=			CONFIG_MAPPING[backbone_model_type]
													lowerCamelCase_						=			config_class.from_dict(lowercase )
					lowerCamelCase_						=			use_timm_backbone
					lowerCamelCase_						=			backbone_config
					lowerCamelCase_						=			num_channels
					lowerCamelCase_						=			num_queries
					lowerCamelCase_						=			max_position_embeddings
					lowerCamelCase_						=			d_model
					lowerCamelCase_						=			encoder_ffn_dim
					lowerCamelCase_						=			encoder_layers
					lowerCamelCase_						=			encoder_attention_heads
					lowerCamelCase_						=			decoder_ffn_dim
					lowerCamelCase_						=			decoder_layers
					lowerCamelCase_						=			decoder_attention_heads
					lowerCamelCase_						=			dropout
					lowerCamelCase_						=			attention_dropout
					lowerCamelCase_						=			activation_dropout
					lowerCamelCase_						=			activation_function
					lowerCamelCase_						=			init_std
					lowerCamelCase_						=			init_xavier_std
					lowerCamelCase_						=			encoder_layerdrop
					lowerCamelCase_						=			auxiliary_loss
					lowerCamelCase_						=			position_embedding_type
					lowerCamelCase_						=			backbone
					lowerCamelCase_						=			use_pretrained_backbone
					lowerCamelCase_						=			dilation
					# deformable attributes
					lowerCamelCase_						=			num_feature_levels
					lowerCamelCase_						=			encoder_n_points
					lowerCamelCase_						=			decoder_n_points
					lowerCamelCase_						=			two_stage
					lowerCamelCase_						=			two_stage_num_proposals
					lowerCamelCase_						=			with_box_refine
					if two_stage is True and with_box_refine is False:
									raise ValueError("If two_stage is True, with_box_refine must be True." )
					# Hungarian matcher
					lowerCamelCase_						=			class_cost
					lowerCamelCase_						=			bbox_cost
					lowerCamelCase_						=			giou_cost
					# Loss coefficients
					lowerCamelCase_						=			mask_loss_coefficient
					lowerCamelCase_						=			dice_loss_coefficient
					lowerCamelCase_						=			bbox_loss_coefficient
					lowerCamelCase_						=			giou_loss_coefficient
					lowerCamelCase_						=			eos_coefficient
					lowerCamelCase_						=			focal_alpha
					lowerCamelCase_						=			disable_custom_kernels
					super().__init__(is_encoder_decoder=lowercase						,  **lowercase )



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



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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					lowerCamelCase_						=			copy.deepcopy(self.__dict__ )
					if self.backbone_config is not None:
									lowerCamelCase_						=			self.backbone_config.to_dict()
					lowerCamelCase_						=			self.__class__.model_type
					return output




from abc import ABC, abstractmethod
from argparse import ArgumentParser




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	@staticmethod
	@abstractmethod
	def     SCREAMING_SNAKE_CASE_(    lowercase )				->       int:
					raise NotImplementedError()



	@abstractmethod
	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					raise NotImplementedError()


