



'''simple docstring'''
import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union



class _A	(     enum.Enum     ):
		_SCREAMING_SNAKE_CASE       :     Dict       =      "timm"
		_SCREAMING_SNAKE_CASE       :     Optional[Any]       =      "transformers"


def 	lowercase_						( lowerCAmelCase__						:	Optional[Iterable[str]]      ,				lowerCAmelCase__						:	Optional[Iterable[int]]      ,				lowerCAmelCase__						:	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(lowerCAmelCase__      ,				(list,)			):
									raise ValueError(f'out_features must be a list {type(lowerCAmelCase__			)}'			)
						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(lowerCAmelCase__      ,				(list, tuple)			):
									raise ValueError(f'out_indices must be a list or tuple, got {type(lowerCAmelCase__			)}'			)
						if any(idx >= len(lowerCAmelCase__			) 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(lowerCAmelCase__			) != len(lowerCAmelCase__			):
									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 	lowercase_						( lowerCAmelCase__						:	Optional[List[str]]      ,				lowerCAmelCase__						:	Optional[Union[List[int], Tuple[int]]]      ,				lowerCAmelCase__						:	List[str]      ,				):

			"""simple docstring"""





			if out_indices is None and out_features is None:
						__UpperCAmelCase :   Dict    					=  [len(lowerCAmelCase__			) - 1]
						__UpperCAmelCase :   int    					=  [stage_names[-1]]
			elif out_indices is None and out_features is not None:
						__UpperCAmelCase :   List[Any]    					=  [stage_names.index(lowerCAmelCase__			) for layer in out_features]
			elif out_features is None and out_indices is not None:
						__UpperCAmelCase :   Dict    					=  [stage_names[idx] for idx in out_indices]
			return out_features, out_indices


def 	lowercase_						( lowerCAmelCase__						:	Optional[List[str]]      ,				lowerCAmelCase__						:	Optional[Union[List[int], Tuple[int]]]      ,				lowerCAmelCase__						:	List[str]      ,				):

			"""simple docstring"""





			verify_out_features_out_indices(out_features=lowerCAmelCase__      ,				out_indices=lowerCAmelCase__      ,				stage_names=lowerCAmelCase__			)
			__UpperCAmelCase					,	__UpperCAmelCase :   str    					=  _align_output_features_output_indices(
			    out_features=lowerCAmelCase__      ,				out_indices=lowerCAmelCase__      ,				stage_names=lowerCAmelCase__			)
			# Verify that the aligned out_features and out_indices are valid
			verify_out_features_out_indices(out_features=lowerCAmelCase__      ,				out_indices=lowerCAmelCase__      ,				stage_names=lowerCAmelCase__			)
			return output_features, output_indices



class _A	:
		_SCREAMING_SNAKE_CASE       :     Optional[BackboneType]       =      None





		def 				__A							(  self			,							__UpperCAmelCase					)							->  None:

					'''simple docstring'''
					if getattr(self			,							"""_backbone"""			,							__UpperCAmelCase					) 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']
					__UpperCAmelCase :   Union[str, Any]    					=  [stage["""module"""] for stage in self._backbone.feature_info.info]
					__UpperCAmelCase :   str    					=  [stage["""num_chs"""] for stage in self._backbone.feature_info.info]
					__UpperCAmelCase :   int    					=  self._backbone.feature_info.out_indices
					__UpperCAmelCase :   List[Any]    					=  self._backbone.feature_info.module_name()

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





		def 				__A							(  self			,							__UpperCAmelCase					)							->  None:

					'''simple docstring'''
					__UpperCAmelCase :   Tuple    					=  getattr(__UpperCAmelCase			,							"""stage_names"""					)
					__UpperCAmelCase :   List[str]    					=  getattr(__UpperCAmelCase			,							"""out_features"""			,							__UpperCAmelCase					)
					__UpperCAmelCase :   Optional[Any]    					=  getattr(__UpperCAmelCase			,							"""out_indices"""			,							__UpperCAmelCase					)

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





		def 				__A							(  self			,							__UpperCAmelCase					)							->  None:

					'''simple docstring'''
					__UpperCAmelCase :   Union[str, Any]    					=  config

					__UpperCAmelCase :   Tuple    					=  getattr(__UpperCAmelCase			,							"""use_timm_backbone"""			,							__UpperCAmelCase					)
					__UpperCAmelCase :   str    					=  BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

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





		@property
		def 				__A							(  self					)							->  List[str]:

					'''simple docstring'''
					return self._out_features





		@out_features.setter
		def 				__A							(  self			,							__UpperCAmelCase					)							->  str:

					'''simple docstring'''
					__UpperCAmelCase					,	__UpperCAmelCase :   List[str]    					=  get_aligned_output_features_output_indices(
					    out_features=__UpperCAmelCase			,							out_indices=__UpperCAmelCase			,							stage_names=self.stage_names					)





		@property
		def 				__A							(  self					)							->  int:

					'''simple docstring'''
					return self._out_indices





		@out_indices.setter
		def 				__A							(  self			,							__UpperCAmelCase					)							->  int:

					'''simple docstring'''
					__UpperCAmelCase					,	__UpperCAmelCase :   Optional[Any]    					=  get_aligned_output_features_output_indices(
					    out_features=__UpperCAmelCase			,							out_indices=__UpperCAmelCase			,							stage_names=self.stage_names					)





		@property
		def 				__A							(  self					)							->  Union[str, Any]:

					'''simple docstring'''
					# 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					)							->  Any:

					'''simple docstring'''
					return [self.out_feature_channels[name] for name in self.out_features]





		def 				__A							(  self			,							*__UpperCAmelCase			,							**__UpperCAmelCase					)							->  List[str]:

					'''simple docstring'''
					__UpperCAmelCase :   int    					=  dict(inspect.signature(self.forward					).parameters					)
					__UpperCAmelCase :   str    					=  {k: v for k, v in kwargs.items() if k in signature}
					return self(*__UpperCAmelCase			,							**__UpperCAmelCase					)





		def 				__A							(  self			,							__UpperCAmelCase			,							__UpperCAmelCase = None			,							__UpperCAmelCase = None			,							__UpperCAmelCase = None			,							)							->  Tuple:

					'''simple docstring'''
					raise NotImplementedError("""This method should be implemented by the derived class."""					)





		def 				__A							(  self					)							->  Optional[Any]:

					'''simple docstring'''
					__UpperCAmelCase :   Tuple    					=  super().to_dict()
					__UpperCAmelCase :   Optional[Any]    					=  output.pop("""_out_features"""					)
					__UpperCAmelCase :   str    					=  output.pop("""_out_indices"""					)
					return output



class _A	:





		@property
		def 				__A							(  self					)							->  List[str]:

					'''simple docstring'''
					return self._out_features





		@out_features.setter
		def 				__A							(  self			,							__UpperCAmelCase					)							->  Tuple:

					'''simple docstring'''
					__UpperCAmelCase					,	__UpperCAmelCase :   Union[str, Any]    					=  get_aligned_output_features_output_indices(
					    out_features=__UpperCAmelCase			,							out_indices=__UpperCAmelCase			,							stage_names=self.stage_names					)





		@property
		def 				__A							(  self					)							->  Union[str, Any]:

					'''simple docstring'''
					return self._out_indices





		@out_indices.setter
		def 				__A							(  self			,							__UpperCAmelCase					)							->  Any:

					'''simple docstring'''
					__UpperCAmelCase					,	__UpperCAmelCase :   List[str]    					=  get_aligned_output_features_output_indices(
					    out_features=__UpperCAmelCase			,							out_indices=__UpperCAmelCase			,							stage_names=self.stage_names					)





		def 				__A							(  self					)							->  List[Any]:

					'''simple docstring'''
					__UpperCAmelCase :   Optional[int]    					=  super().to_dict()
					__UpperCAmelCase :   Any    					=  output.pop("""_out_features"""					)
					__UpperCAmelCase :   Tuple    					=  output.pop("""_out_indices"""					)
					return output









'''simple docstring'''
from .hash_table import HashTable



class _A	(     __SCREAMING_SNAKE_CASE     ):





		def __init__(  self			,							*__UpperCAmelCase			,							**__UpperCAmelCase					)							->  List[Any]:

					'''simple docstring'''
					super().__init__(*__UpperCAmelCase			,							**__UpperCAmelCase					)





		def 				__A							(  self			,							__UpperCAmelCase			,							__UpperCAmelCase=None					)							->  str:

					'''simple docstring'''
					__UpperCAmelCase :   List[str]    					=  1
					__UpperCAmelCase :   Optional[Any]    					=  self.hash_function(key + i * i					)

					while self.values[new_key] is not None and self.values[new_key] != key:
								i += 1
								__UpperCAmelCase :   str    					=  (
								    self.hash_function(key + i * i					)
								    if not self.balanced_factor() >= self.lim_charge
								    else None
								)

								if new_key is None:
											break

					return new_key




