"""simple docstring"""







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






class 							__snake_case(					enum.Enum	):


    '''simple docstring'''





    _UpperCAmelCase       =			"timm"
    _UpperCAmelCase       =			"transformers"







def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Optional[Iterable[str]]  ,       _SCREAMING_SNAKE_CASE				:				Optional[Iterable[int]]  ,       _SCREAMING_SNAKE_CASE				:				Optional[Iterable[str]]						)    ->     Optional[Any]:







     '''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(_SCREAMING_SNAKE_CASE  ,       (list,)						):
               raise ValueError(F'out_features must be a list {type(_SCREAMING_SNAKE_CASE						)}'						)
          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(_SCREAMING_SNAKE_CASE  ,       (list, tuple)						):
               raise ValueError(F'out_indices must be a list or tuple, got {type(_SCREAMING_SNAKE_CASE						)}'						)
          if any(idx >= len(_SCREAMING_SNAKE_CASE						) 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(_SCREAMING_SNAKE_CASE						) != len(_SCREAMING_SNAKE_CASE						):
               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				(  _SCREAMING_SNAKE_CASE				:				Optional[List[str]]  ,       _SCREAMING_SNAKE_CASE				:				Optional[Union[List[int], Tuple[int]]]  ,       _SCREAMING_SNAKE_CASE				:				List[str]  ,       )    ->     Optional[Any]:







     '''simple docstring'''


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




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Optional[List[str]]  ,       _SCREAMING_SNAKE_CASE				:				Optional[Union[List[int], Tuple[int]]]  ,       _SCREAMING_SNAKE_CASE				:				List[str]  ,       )    ->     Tuple[List[str], List[int]]:







     '''simple docstring'''


     verify_out_features_out_indices(out_features=_SCREAMING_SNAKE_CASE  ,       out_indices=_SCREAMING_SNAKE_CASE  ,       stage_names=_SCREAMING_SNAKE_CASE						)
     __A						,		__A					:					Any								=		_align_output_features_output_indices(
         out_features=_SCREAMING_SNAKE_CASE  ,       out_indices=_SCREAMING_SNAKE_CASE  ,       stage_names=_SCREAMING_SNAKE_CASE						)
     # Verify that the aligned out_features and out_indices are valid
     verify_out_features_out_indices(out_features=_SCREAMING_SNAKE_CASE  ,       out_indices=_SCREAMING_SNAKE_CASE  ,       stage_names=_SCREAMING_SNAKE_CASE						)
     return output_features, output_indices






class 							__snake_case:


    '''simple docstring'''





    _UpperCAmelCase       =			None
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         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']
         __A					:					List[str]								=		[stage['module'] for stage in self._backbone.feature_info.info]
         __A					:					List[Any]								=		[stage['num_chs'] for stage in self._backbone.feature_info.info]
         __A					:					Optional[Any]								=		self._backbone.feature_info.out_indices
         __A					:					Union[str, Any]								=		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	)
         __A						,		__A					:					List[Any]								=		out_features, out_indices
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					List[Any]								=		getattr(__lowerCamelCase					,       'stage_names'	)
         __A					:					Any								=		getattr(__lowerCamelCase					,       'out_features'					,       __lowerCamelCase	)
         __A					:					int								=		getattr(__lowerCamelCase					,       'out_indices'					,       __lowerCamelCase	)

         __A					:					Dict								=		stage_names
         __A						,		__A					:					Tuple								=		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
         __A					:					Any								=		None
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					Dict								=		config

         __A					:					Optional[int]								=		getattr(__lowerCamelCase					,       'use_timm_backbone'					,       __lowerCamelCase	)
         __A					:					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	):




         '''simple docstring'''



         return self._out_features
    @out_features.setter
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A						,		__A					:					str								=		get_aligned_output_features_output_indices(
             out_features=__lowerCamelCase					,       out_indices=__lowerCamelCase					,       stage_names=self.stage_names	)
    @property
    def 						_a							(  self	):




         '''simple docstring'''



         return self._out_indices
    @out_indices.setter
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A						,		__A					:					Dict								=		get_aligned_output_features_output_indices(
             out_features=__lowerCamelCase					,       out_indices=__lowerCamelCase					,       stage_names=self.stage_names	)
    @property
    def 						_a							(  self	):




         '''simple docstring'''



         return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names	)}
    @property
    def 						_a							(  self	):




         '''simple docstring'''



         return [self.out_feature_channels[name] for name in self.out_features]
    def 						_a							(  self					,       *__lowerCamelCase					,       **__lowerCamelCase	):




         '''simple docstring'''



         __A					:					Optional[Any]								=		dict(inspect.signature(self.forward	).parameters	)
         __A					:					Tuple								=		{k: v for k, v in kwargs.items() if k in signature}
         return self(*__lowerCamelCase					,       **__lowerCamelCase	)
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase = None					,       __lowerCamelCase = None					,       __lowerCamelCase = None					,       ):




         '''simple docstring'''



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

    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Dict								=		super().to_dict()
         __A					:					Any								=		output.pop('_out_features'	)
         __A					:					Any								=		output.pop('_out_indices'	)
         return output







class 							__snake_case:


    '''simple docstring'''





    @property
    def 						_a							(  self	):




         '''simple docstring'''



         return self._out_features
    @out_features.setter
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A						,		__A					:					Optional[Any]								=		get_aligned_output_features_output_indices(
             out_features=__lowerCamelCase					,       out_indices=__lowerCamelCase					,       stage_names=self.stage_names	)
    @property
    def 						_a							(  self	):




         '''simple docstring'''



         return self._out_indices
    @out_indices.setter
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A						,		__A					:					Tuple								=		get_aligned_output_features_output_indices(
             out_features=__lowerCamelCase					,       out_indices=__lowerCamelCase					,       stage_names=self.stage_names	)

    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					List[str]								=		super().to_dict()
         __A					:					List[str]								=		output.pop('_out_features'	)
         __A					:					List[Any]								=		output.pop('_out_indices'	)
         return output




"""simple docstring"""







from typing import Dict, Optional, Tuple

import torch




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				torch.Tensor						)    ->     torch.Tensor:







     '''simple docstring'''


     __A					:					List[str]								=		boundaries[1] - boundaries[0]
     __A					:					int								=		boundaries + step / 2
     __A					:					Union[str, Any]								=		torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1						)]  ,       dim=0						)
     return bin_centers




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				torch.Tensor  ,       _SCREAMING_SNAKE_CASE				:				torch.Tensor  ,       )    ->     Tuple[torch.Tensor, torch.Tensor]:







     '''simple docstring'''


     __A					:					str								=		_calculate_bin_centers(_SCREAMING_SNAKE_CASE						)
     return (
         torch.sum(aligned_distance_error_probs * bin_centers  ,       dim=-1						),
         bin_centers[-1],
     )




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				torch.Tensor  ,       _SCREAMING_SNAKE_CASE				:				int = 31  ,       _SCREAMING_SNAKE_CASE				:				int = 64  ,       **_SCREAMING_SNAKE_CASE				:				Optional[Any]  ,       )    ->     Dict[str, torch.Tensor]:







     '''simple docstring'''


     __A					:					int								=		torch.linspace(0  ,       _SCREAMING_SNAKE_CASE  ,       steps=(no_bins - 1)  ,       device=logits.device						)

     __A					:					Any								=		torch.nn.functional.softmax(_SCREAMING_SNAKE_CASE  ,       dim=-1						)
     __A						,		__A					:					Tuple								=		_calculate_expected_aligned_error(
         alignment_confidence_breaks=_SCREAMING_SNAKE_CASE  ,       aligned_distance_error_probs=_SCREAMING_SNAKE_CASE  ,       )

     return {
         "aligned_confidence_probs": aligned_confidence_probs,
         "predicted_aligned_error": predicted_aligned_error,
         "max_predicted_aligned_error": max_predicted_aligned_error,
     }




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				torch.Tensor  ,       _SCREAMING_SNAKE_CASE				:				Optional[torch.Tensor] = None  ,       _SCREAMING_SNAKE_CASE				:				int = 31  ,       _SCREAMING_SNAKE_CASE				:				int = 64  ,       _SCREAMING_SNAKE_CASE				:				float = 1E-8  ,       **_SCREAMING_SNAKE_CASE				:				Union[str, Any]  ,       )    ->     torch.Tensor:







     '''simple docstring'''


     if residue_weights is None:
          __A					:					Optional[int]								=		logits.new_ones(logits.shape[-2]						)

     __A					:					List[str]								=		torch.linspace(0  ,       _SCREAMING_SNAKE_CASE  ,       steps=(no_bins - 1)  ,       device=logits.device						)

     __A					:					Tuple								=		_calculate_bin_centers(_SCREAMING_SNAKE_CASE						)
     torch.sum(_SCREAMING_SNAKE_CASE						)
     __A					:					int								=		logits.shape[-2]
     __A					:					str								=		max(_SCREAMING_SNAKE_CASE  ,       19						)

     __A					:					Tuple								=		1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

     __A					:					Tuple								=		torch.nn.functional.softmax(_SCREAMING_SNAKE_CASE  ,       dim=-1						)

     __A					:					List[str]								=		1.0 / (1 + (bin_centers**2) / (da**2))
     __A					:					int								=		torch.sum(probs * tm_per_bin  ,       dim=-1						)

     __A					:					Any								=		residue_weights / (eps + residue_weights.sum())
     __A					:					List[Any]								=		torch.sum(predicted_tm_term * normed_residue_mask  ,       dim=-1						)

     __A					:					int								=		per_alignment * residue_weights

     __A					:					Optional[int]								=		(weighted == torch.max(_SCREAMING_SNAKE_CASE						)).nonzero()[0]
     return per_alignment[tuple(_SCREAMING_SNAKE_CASE						)]



