






"""simple docstring"""
import copy

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


lowerCAmelCase__    						=			logging.get_logger(__name__)

lowerCAmelCase__    						=			{
    '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 _lowerCAmelCase						(    __UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					List[str] 					=				'deformable_detr'
						SCREAMING_SNAKE_CASE_:					Tuple 					=				{
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'encoder_attention_heads',
						}







						def __init__(    self   ,				lowerCAmelCase_=True   ,				lowerCAmelCase_=None   ,				lowerCAmelCase_=3   ,				lowerCAmelCase_=3_0_0   ,				lowerCAmelCase_=1_0_2_4   ,				lowerCAmelCase_=6   ,				lowerCAmelCase_=1_0_2_4   ,				lowerCAmelCase_=8   ,				lowerCAmelCase_=6   ,				lowerCAmelCase_=1_0_2_4   ,				lowerCAmelCase_=8   ,				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_=True   ,				lowerCAmelCase_=False   ,				lowerCAmelCase_="sine"   ,				lowerCAmelCase_="resnet50"   ,				lowerCAmelCase_=True   ,				lowerCAmelCase_=False   ,				lowerCAmelCase_=4   ,				lowerCAmelCase_=4   ,				lowerCAmelCase_=4   ,				lowerCAmelCase_=False   ,				lowerCAmelCase_=3_0_0   ,				lowerCAmelCase_=False   ,				lowerCAmelCase_=1   ,				lowerCAmelCase_=5   ,				lowerCAmelCase_=2   ,				lowerCAmelCase_=1   ,				lowerCAmelCase_=1   ,				lowerCAmelCase_=5   ,				lowerCAmelCase_=2   ,				lowerCAmelCase_=0.1   ,				lowerCAmelCase_=0.25   ,				lowerCAmelCase_=False   ,				**lowerCAmelCase_   ,				)		->							Optional[Any]:
										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   :		Union[str, Any]       		=    CONFIG_MAPPING['resnet'](out_features=['stage4']							)
														elif isinstance(lowerCAmelCase_   ,				lowerCAmelCase_							):
																		_SCREAMING_SNAKE_CASE   :		str       		=    backbone_config.get('model_type'							)
																		_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    CONFIG_MAPPING[backbone_model_type]
																		_SCREAMING_SNAKE_CASE   :		Dict       		=    config_class.from_dict(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    use_timm_backbone
										_SCREAMING_SNAKE_CASE   :		str       		=    backbone_config
										_SCREAMING_SNAKE_CASE   :		int       		=    num_channels
										_SCREAMING_SNAKE_CASE   :		str       		=    num_queries
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    max_position_embeddings
										_SCREAMING_SNAKE_CASE   :		int       		=    d_model
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    encoder_ffn_dim
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    encoder_layers
										_SCREAMING_SNAKE_CASE   :		int       		=    encoder_attention_heads
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    decoder_ffn_dim
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    decoder_layers
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    decoder_attention_heads
										_SCREAMING_SNAKE_CASE   :		int       		=    dropout
										_SCREAMING_SNAKE_CASE   :		str       		=    attention_dropout
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    activation_dropout
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    activation_function
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    init_std
										_SCREAMING_SNAKE_CASE   :		Dict       		=    init_xavier_std
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    encoder_layerdrop
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    auxiliary_loss
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    position_embedding_type
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    backbone
										_SCREAMING_SNAKE_CASE   :		str       		=    use_pretrained_backbone
										_SCREAMING_SNAKE_CASE   :		Dict       		=    dilation
										# deformable attributes
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    num_feature_levels
										_SCREAMING_SNAKE_CASE   :		Dict       		=    encoder_n_points
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    decoder_n_points
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    two_stage
										_SCREAMING_SNAKE_CASE   :		Any       		=    two_stage_num_proposals
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    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
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    class_cost
										_SCREAMING_SNAKE_CASE   :		Any       		=    bbox_cost
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    giou_cost
										# Loss coefficients
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    mask_loss_coefficient
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    dice_loss_coefficient
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    bbox_loss_coefficient
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    giou_loss_coefficient
										_SCREAMING_SNAKE_CASE   :		Any       		=    eos_coefficient
										_SCREAMING_SNAKE_CASE   :		int       		=    focal_alpha
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    disable_custom_kernels
										super().__init__(is_encoder_decoder=lowerCAmelCase_   ,				**lowerCAmelCase_							)







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







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







						def 			A      (    self							)		->							Tuple:
										_SCREAMING_SNAKE_CASE   :		str       		=    copy.deepcopy(self.__dict__							)
										if self.backbone_config is not None:
														_SCREAMING_SNAKE_CASE   :		Dict       		=    self.backbone_config.to_dict()
										_SCREAMING_SNAKE_CASE   :		Dict       		=    self.__class__.model_type
										return output









"""simple docstring"""
lowerCAmelCase__    						=			'\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

lowerCAmelCase__    						=			[{'type': 'code', 'content': INSTALL_CONTENT}]
lowerCAmelCase__    						=			{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}

