
import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union


class 		__magic_name__   (						enum.Enum    ):
		_lowerCAmelCase		=	"timm"
		_lowerCAmelCase		=	"transformers"



def     UpperCAmelCase__							(       __magic_name__						:     Optional[Iterable[str]]							,  __magic_name__						:     Optional[Iterable[int]]							,  __magic_name__						:     Optional[Iterable[str]]  ):


					'''simple docstring'''

					if stage_names is None:
										raise ValueError('''Stage_names must be set for transformers backbones'''  )

					if out_features is not None:
										if not isinstance(__magic_name__							,  (list,)  ):
															raise ValueError(f'''out_features must be a list {type(__magic_name__  )}'''  )
										if any(feat not in stage_names for feat in out_features  ):
															raise ValueError(f'''out_features must be a subset of stage_names: {stage_names} got {out_features}'''  )

					if out_indices is not None:
										if not isinstance(__magic_name__							,  (list, tuple)  ):
															raise ValueError(f'''out_indices must be a list or tuple, got {type(__magic_name__  )}'''  )
										if any(idx >= len(__magic_name__  ) for idx in out_indices  ):
															raise ValueError('''out_indices must be valid indices for stage_names {stage_names}, got {out_indices}'''  )

					if out_features is not None and out_indices is not None:
										if len(__magic_name__  ) != len(__magic_name__  ):
															raise ValueError('''out_features and out_indices should have the same length if both are set'''  )
										if out_features != [stage_names[idx] for idx in out_indices]:
															raise ValueError('''out_features and out_indices should correspond to the same stages if both are set'''  )
def     UpperCAmelCase__							(       __magic_name__						:     Optional[List[str]]							,  __magic_name__						:     Optional[Union[List[int], Tuple[int]]]							,  __magic_name__						:     List[str]							,  ):


					'''simple docstring'''

					if out_indices is None and out_features is None:
										lowerCAmelCase		:					str  	=     [len(__magic_name__  ) - 1]
										lowerCAmelCase		:					Any  	=     [stage_names[-1]]
					elif out_indices is None and out_features is not None:
										lowerCAmelCase		:					List[Any]  	=     [stage_names.index(__magic_name__  ) for layer in out_features]
					elif out_features is None and out_indices is not None:
										lowerCAmelCase		:					List[Any]  	=     [stage_names[idx] for idx in out_indices]
					return out_features, out_indices
def     UpperCAmelCase__							(       __magic_name__						:     Optional[List[str]]							,  __magic_name__						:     Optional[Union[List[int], Tuple[int]]]							,  __magic_name__						:     List[str]							,  ):


					'''simple docstring'''

					verify_out_features_out_indices(out_features=__magic_name__							,  out_indices=__magic_name__							,  stage_names=__magic_name__  )
					lowerCAmelCase	,							lowerCAmelCase		:					Dict  	=     _align_output_features_output_indices(
					    out_features=__magic_name__							,  out_indices=__magic_name__							,  stage_names=__magic_name__  )
					# Verify that the aligned out_features and out_indices are valid
					verify_out_features_out_indices(out_features=__magic_name__							,  out_indices=__magic_name__							,  stage_names=__magic_name__  )
					return output_features, output_indices


class 		__magic_name__   :
		_lowerCAmelCase		=	None





		def       _A							(							self    :	Union[str, Any]						,  lowerCamelCase__    :	Union[str, Any]				):
							if getattr(self						,  '''_backbone'''						,  lowerCamelCase__				) is None:
												raise ValueError('''self._backbone must be set before calling _init_timm_backbone'''				)

							# These will diagree with the defaults for the transformers models e.g. for resnet50
							# the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
							# the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
							lowerCAmelCase		:					Optional[Any]  	=     [stage['''module'''] for stage in self._backbone.feature_info.info]
							lowerCAmelCase		:					Dict  	=     [stage['''num_chs'''] for stage in self._backbone.feature_info.info]
							lowerCAmelCase		:					Union[str, Any]  	=     self._backbone.feature_info.out_indices
							lowerCAmelCase		:					Optional[int]  	=     self._backbone.feature_info.module_name()

							# We verify the out indices and out features are valid
							verify_out_features_out_indices(
							    out_features=lowerCamelCase__						,  out_indices=lowerCamelCase__						,  stage_names=self.stage_names				)
							lowerCAmelCase	,							lowerCAmelCase		:					Any  	=     out_features, out_indices





		def       _A							(							self    :	Optional[Any]						,  lowerCamelCase__    :	Union[str, Any]				):
							lowerCAmelCase		:					Any  	=     getattr(lowerCamelCase__						,  '''stage_names'''				)
							lowerCAmelCase		:					Dict  	=     getattr(lowerCamelCase__						,  '''out_features'''						,  lowerCamelCase__				)
							lowerCAmelCase		:					int  	=     getattr(lowerCamelCase__						,  '''out_indices'''						,  lowerCamelCase__				)

							lowerCAmelCase		:					Tuple  	=     stage_names
							lowerCAmelCase	,							lowerCAmelCase		:					Any  	=     get_aligned_output_features_output_indices(
							    out_features=lowerCamelCase__						,  out_indices=lowerCamelCase__						,  stage_names=lowerCamelCase__				)
							# Number of channels for each stage. This is set in the transformer backbone model init
							lowerCAmelCase		:					List[Any]  	=     None





		def       _A							(							self    :	Tuple						,  lowerCamelCase__    :	int				):
							lowerCAmelCase		:					Union[str, Any]  	=     config

							lowerCAmelCase		:					Optional[Any]  	=     getattr(lowerCamelCase__						,  '''use_timm_backbone'''						,  lowerCamelCase__				)
							lowerCAmelCase		:					Optional[Any]  	=     BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

							if self.backbone_type == BackboneType.TIMM:
												self._init_timm_backbone(lowerCamelCase__				)
							elif self.backbone_type == BackboneType.TRANSFORMERS:
												self._init_transformers_backbone(lowerCamelCase__				)
							else:
												raise ValueError(f'''backbone_type {self.backbone_type} not supported.'''				)





		@property
		def       _A							(							self    :	Union[str, Any]				):
							return self._out_features





		@out_features.setter
		def       _A							(							self    :	Optional[int]						,  lowerCamelCase__    :	List[str]				):
							lowerCAmelCase	,							lowerCAmelCase		:					Dict  	=     get_aligned_output_features_output_indices(
							    out_features=lowerCamelCase__						,  out_indices=lowerCamelCase__						,  stage_names=self.stage_names				)





		@property
		def       _A							(							self    :	str				):
							return self._out_indices





		@out_indices.setter
		def       _A							(							self    :	Union[str, Any]						,  lowerCamelCase__    :	Union[Tuple[int], List[int]]				):
							lowerCAmelCase	,							lowerCAmelCase		:					Any  	=     get_aligned_output_features_output_indices(
							    out_features=lowerCamelCase__						,  out_indices=lowerCamelCase__						,  stage_names=self.stage_names				)





		@property
		def       _A							(							self    :	int				):
							# the current backbones will output the number of channels for each stage
							# even if that stage is not in the out_features list.
							return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names				)}





		@property
		def       _A							(							self    :	List[Any]				):
							return [self.out_feature_channels[name] for name in self.out_features]





		def       _A							(							self    :	List[str]						,  *lowerCamelCase__    :	Tuple						,  **lowerCamelCase__    :	Union[str, Any]				):
							lowerCAmelCase		:					List[str]  	=     dict(inspect.signature(self.forward				).parameters				)
							lowerCAmelCase		:					Any  	=     {k: v for k, v in kwargs.items() if k in signature}
							return self(*lowerCamelCase__						,  **lowerCamelCase__				)





		def       _A							(							self    :	Tuple						,  lowerCamelCase__    :	str						,  lowerCamelCase__    :	Optional[bool] = None						,  lowerCamelCase__    :	Optional[bool] = None						,  lowerCamelCase__    :	Optional[bool] = None						,  ):
							raise NotImplementedError('''This method should be implemented by the derived class.'''				)





		def       _A							(							self    :	str				):
							lowerCAmelCase		:					Optional[Any]  	=     super().to_dict()
							lowerCAmelCase		:					Optional[Any]  	=     output.pop('''_out_features'''				)
							lowerCAmelCase		:					int  	=     output.pop('''_out_indices'''				)
							return output



class 		__magic_name__   :





		@property
		def       _A							(							self    :	Any				):
							return self._out_features





		@out_features.setter
		def       _A							(							self    :	Union[str, Any]						,  lowerCamelCase__    :	List[str]				):
							lowerCAmelCase	,							lowerCAmelCase		:					Optional[Any]  	=     get_aligned_output_features_output_indices(
							    out_features=lowerCamelCase__						,  out_indices=lowerCamelCase__						,  stage_names=self.stage_names				)





		@property
		def       _A							(							self    :	Any				):
							return self._out_indices





		@out_indices.setter
		def       _A							(							self    :	Any						,  lowerCamelCase__    :	Union[Tuple[int], List[int]]				):
							lowerCAmelCase	,							lowerCAmelCase		:					List[str]  	=     get_aligned_output_features_output_indices(
							    out_features=lowerCamelCase__						,  out_indices=lowerCamelCase__						,  stage_names=self.stage_names				)





		def       _A							(							self    :	Any				):
							lowerCAmelCase		:					Any  	=     super().to_dict()
							lowerCAmelCase		:					Any  	=     output.pop('''_out_features'''				)
							lowerCAmelCase		:					Tuple  	=     output.pop('''_out_indices'''				)
							return output


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__SCREAMING_SNAKE_CASE			:   str         =     {
    'configuration_deta': ['DETA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetaConfig'],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE			:   Optional[int]         =     ['DetaImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE			:   str         =     [
		    'DETA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'DetaForObjectDetection',
		    'DetaModel',
		    'DetaPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_deta import DetaImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deta import (
				    DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DetaForObjectDetection,
				    DetaModel,
				    DetaPreTrainedModel,
				)

else:
		import sys

		__SCREAMING_SNAKE_CASE			:   Dict         =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
