
'''simple docstring'''


import copy

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


__lowercase							:      Optional[int]		 =   logging.get_logger(__name__)

__lowercase							:      Dict		 =   {
    "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 		__lowercase (     _lowercase			):
			lowerCamelCase	:						Any							=	'''deformable_detr'''
			lowerCamelCase	:						List[Any]							=	{
			    '''hidden_size''': '''d_model''',
			    '''num_attention_heads''': '''encoder_attention_heads''',
			}

			def __init__(self	,      A=True	,      A=None	,      A=3	,      A=3_0_0	,      A=1_0_2_4	,      A=6	,      A=1_0_2_4	,      A=8	,      A=6	,      A=1_0_2_4	,      A=8	,      A=0.0	,      A=True	,      A="relu"	,      A=2_5_6	,      A=0.1	,      A=0.0	,      A=0.0	,      A=0.02	,      A=1.0	,      A=True	,      A=False	,      A="sine"	,      A="resnet50"	,      A=True	,      A=False	,      A=4	,      A=4	,      A=4	,      A=False	,      A=3_0_0	,      A=False	,      A=1	,      A=5	,      A=2	,      A=1	,      A=1	,      A=5	,      A=2	,      A=0.1	,      A=0.25	,      A=False	,      **A	,      ):


								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_    :					Union[str, Any]   =						CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']					)
													elif isinstance(_snake_case	,      _snake_case					):
																		lowerCamelCase_    :					str   =						backbone_config.get('''model_type'''					)
																		lowerCamelCase_    :					str   =						CONFIG_MAPPING[backbone_model_type]
																		lowerCamelCase_    :					Tuple   =						config_class.from_dict(_snake_case					)
								lowerCamelCase_    :					List[str]   =						use_timm_backbone
								lowerCamelCase_    :					Optional[Any]   =						backbone_config
								lowerCamelCase_    :					Union[str, Any]   =						num_channels
								lowerCamelCase_    :					Optional[Any]   =						num_queries
								lowerCamelCase_    :					Tuple   =						max_position_embeddings
								lowerCamelCase_    :					Any   =						d_model
								lowerCamelCase_    :					Optional[Any]   =						encoder_ffn_dim
								lowerCamelCase_    :					List[Any]   =						encoder_layers
								lowerCamelCase_    :					List[Any]   =						encoder_attention_heads
								lowerCamelCase_    :					Any   =						decoder_ffn_dim
								lowerCamelCase_    :					List[str]   =						decoder_layers
								lowerCamelCase_    :					Union[str, Any]   =						decoder_attention_heads
								lowerCamelCase_    :					Union[str, Any]   =						dropout
								lowerCamelCase_    :					str   =						attention_dropout
								lowerCamelCase_    :					Optional[int]   =						activation_dropout
								lowerCamelCase_    :					Dict   =						activation_function
								lowerCamelCase_    :					Any   =						init_std
								lowerCamelCase_    :					Dict   =						init_xavier_std
								lowerCamelCase_    :					Dict   =						encoder_layerdrop
								lowerCamelCase_    :					Tuple   =						auxiliary_loss
								lowerCamelCase_    :					Optional[Any]   =						position_embedding_type
								lowerCamelCase_    :					Tuple   =						backbone
								lowerCamelCase_    :					List[Any]   =						use_pretrained_backbone
								lowerCamelCase_    :					Union[str, Any]   =						dilation
								# deformable attributes
								lowerCamelCase_    :					int   =						num_feature_levels
								lowerCamelCase_    :					Union[str, Any]   =						encoder_n_points
								lowerCamelCase_    :					Optional[Any]   =						decoder_n_points
								lowerCamelCase_    :					Optional[Any]   =						two_stage
								lowerCamelCase_    :					Optional[Any]   =						two_stage_num_proposals
								lowerCamelCase_    :					List[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
								lowerCamelCase_    :					Optional[int]   =						class_cost
								lowerCamelCase_    :					Optional[Any]   =						bbox_cost
								lowerCamelCase_    :					Union[str, Any]   =						giou_cost
								# Loss coefficients
								lowerCamelCase_    :					Optional[Any]   =						mask_loss_coefficient
								lowerCamelCase_    :					Union[str, Any]   =						dice_loss_coefficient
								lowerCamelCase_    :					Optional[Any]   =						bbox_loss_coefficient
								lowerCamelCase_    :					str   =						giou_loss_coefficient
								lowerCamelCase_    :					Optional[int]   =						eos_coefficient
								lowerCamelCase_    :					Union[str, Any]   =						focal_alpha
								lowerCamelCase_    :					Dict   =						disable_custom_kernels
								super().__init__(is_encoder_decoder=_snake_case	,      **_snake_case					)

			@property
			def 				UpperCAmelCase__       (self					):
								return self.encoder_attention_heads

			@property
			def 				UpperCAmelCase__       (self					):
								return self.d_model






			def 				UpperCAmelCase__       (self					):
								lowerCamelCase_    :					str   =						copy.deepcopy(self.__dict__					)
								if self.backbone_config is not None:
													lowerCamelCase_    :					Tuple   =						self.backbone_config.to_dict()
								lowerCamelCase_    :					int   =						self.__class__.model_type
								return output








snake_case_ : Dict    		=       {
    "Pillow": "Pillow",
    "accelerate": "accelerate>=0.11.0",
    "compel": "compel==0.1.8",
    "black": "black~=23.1",
    "datasets": "datasets",
    "filelock": "filelock",
    "flax": "flax>=0.4.1",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.13.2",
    "requests-mock": "requests-mock==1.10.0",
    "importlib_metadata": "importlib_metadata",
    "invisible-watermark": "invisible-watermark",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2",
    "jaxlib": "jaxlib>=0.1.65",
    "Jinja2": "Jinja2",
    "k-diffusion": "k-diffusion>=0.0.12",
    "torchsde": "torchsde",
    "note_seq": "note_seq",
    "librosa": "librosa",
    "numpy": "numpy",
    "omegaconf": "omegaconf",
    "parameterized": "parameterized",
    "protobuf": "protobuf>=3.20.3,<4",
    "pytest": "pytest",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "ruff": "ruff>=0.0.241",
    "safetensors": "safetensors",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "scipy": "scipy",
    "onnx": "onnx",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "tensorboard": "tensorboard",
    "torch": "torch>=1.4",
    "torchvision": "torchvision",
    "transformers": "transformers>=4.25.1",
    "urllib3": "urllib3<=2.0.0",
}






