

"""simple docstring"""


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A						   =			{"configuration_timm_backbone": ["TimmBackboneConfig"]}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A						   =			["TimmBackbone"]


if TYPE_CHECKING:
			from .configuration_timm_backbone import TimmBackboneConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_timm_backbone import TimmBackbone

else:
			import sys

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





"""simple docstring"""


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     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      str         =  """deformable_detr"""
				SCREAMING_SNAKE_CASE_   :      int         =  {
				    """hidden_size""": """d_model""",
				    """num_attention_heads""": """encoder_attention_heads""",
				}

				def __init__(	self							:				int     ,  UpperCamelCase__							:				int=True     ,  UpperCamelCase__							:				str=None     ,  UpperCamelCase__							:				int=3     ,  UpperCamelCase__							:				Dict=3_0_0     ,  UpperCamelCase__							:				Optional[int]=1_0_2_4     ,  UpperCamelCase__							:				int=6     ,  UpperCamelCase__							:				List[Any]=1_0_2_4     ,  UpperCamelCase__							:				List[Any]=8     ,  UpperCamelCase__							:				str=6     ,  UpperCamelCase__							:				str=1_0_2_4     ,  UpperCamelCase__							:				Union[str, Any]=8     ,  UpperCamelCase__							:				Dict=0.0     ,  UpperCamelCase__							:				str=True     ,  UpperCamelCase__							:				List[Any]="relu"     ,  UpperCamelCase__							:				Tuple=2_5_6     ,  UpperCamelCase__							:				Tuple=0.1     ,  UpperCamelCase__							:				Optional[int]=0.0     ,  UpperCamelCase__							:				Dict=0.0     ,  UpperCamelCase__							:				Optional[Any]=0.02     ,  UpperCamelCase__							:				Dict=1.0     ,  UpperCamelCase__							:				Dict=True     ,  UpperCamelCase__							:				Optional[Any]=False     ,  UpperCamelCase__							:				List[str]="sine"     ,  UpperCamelCase__							:				Any="resnet50"     ,  UpperCamelCase__							:				List[Any]=True     ,  UpperCamelCase__							:				Any=False     ,  UpperCamelCase__							:				Optional[int]=4     ,  UpperCamelCase__							:				List[str]=4     ,  UpperCamelCase__							:				Tuple=4     ,  UpperCamelCase__							:				List[str]=False     ,  UpperCamelCase__							:				Optional[int]=3_0_0     ,  UpperCamelCase__							:				int=False     ,  UpperCamelCase__							:				List[Any]=1     ,  UpperCamelCase__							:				Optional[Any]=5     ,  UpperCamelCase__							:				List[Any]=2     ,  UpperCamelCase__							:				str=1     ,  UpperCamelCase__							:				int=1     ,  UpperCamelCase__							:				Dict=5     ,  UpperCamelCase__							:				Tuple=2     ,  UpperCamelCase__							:				Optional[Any]=0.1     ,  UpperCamelCase__							:				Union[str, Any]=0.25     ,  UpperCamelCase__							:				List[Any]=False     ,  **UpperCamelCase__							:				Dict     ,  )->   Optional[int]:



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

				@property
				def 							lowercase_     (	self							:				List[Any])->   int:



						'''simple docstring'''


						return self.encoder_attention_heads

				@property
				def 							lowercase_     (	self							:				Optional[Any])->   int:



						'''simple docstring'''


						return self.d_model




				def 							lowercase_     (	self							:				Union[str, Any])->   List[str]:



						'''simple docstring'''


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


