



from __future__ import annotations



class 		_lowercase		:

   def __init__(   self      :							Any				, __lowerCAmelCase      :							int				)							->					None:


    """simple docstring"""






    a							=		value
    a							=		None
    a							=		None



class 		_lowercase		:
   _UpperCAmelCase  						=      42

   def __init__(   self      :							str				)							->					None:


    """simple docstring"""






    a							=		0

   def 		A				(   self      :							Tuple				, __lowerCAmelCase      :							Node | None				, __lowerCAmelCase      :							int				)							->					None:


    """simple docstring"""






    if node is None:
     return

    if path_sum == self.target:
     self.paths += 1

    if node.left:
     self.depth_first_search(node.left				, path_sum + node.left.value				)
    if node.right:
     self.depth_first_search(node.right				, path_sum + node.right.value				)




   def 		A				(   self      :							Tuple				, __lowerCAmelCase      :							Node | None				, __lowerCAmelCase      :							int | None = None				)							->					int:


    """simple docstring"""






    if node is None:
     return 0
    if target is not None:
     a							=		target

    self.depth_first_search(__lowerCAmelCase				, node.value				)
    self.path_sum(node.left				)
    self.path_sum(node.right				)

    return self.paths


if __name__ == "__main__":
     import doctest

     doctest.testmod()





'''simple docstring'''
from typing import Dict, Optional, Tuple

import torch



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





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



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase    ,       ):






       """simple docstring"""





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



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase = 31    ,       UpperCAmelCase = 64    ,       **UpperCAmelCase    ,       ):






       """simple docstring"""





       snake_case__					:	Dict          =					torch.linspace(0    ,       UpperCAmelCase    ,       steps=(no_bins - 1)    ,       device=logits.device     )

       snake_case__					:	str          =					torch.nn.functional.softmax(UpperCAmelCase    ,       dim=-1     )
       snake_case__       ,   snake_case__					:	List[str]          =					_calculate_expected_aligned_error(
           alignment_confidence_breaks=UpperCAmelCase    ,       aligned_distance_error_probs=UpperCAmelCase    ,       )

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






def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase = None    ,       UpperCAmelCase = 31    ,       UpperCAmelCase = 64    ,       UpperCAmelCase = 1E-8    ,       **UpperCAmelCase    ,       ):






       """simple docstring"""





       if residue_weights is None:
              snake_case__					:	List[Any]          =					logits.new_ones(logits.shape[-2]     )

       snake_case__					:	Optional[Any]          =					torch.linspace(0    ,       UpperCAmelCase    ,       steps=(no_bins - 1)    ,       device=logits.device     )

       snake_case__					:	int          =					_calculate_bin_centers(UpperCAmelCase     )
       torch.sum(UpperCAmelCase     )
       snake_case__					:	Dict          =					logits.shape[-2]
       snake_case__					:	Optional[Any]          =					max(UpperCAmelCase    ,       19     )

       snake_case__					:	Optional[int]          =					1.2_4 * (clipped_n - 15) ** (1.0 / 3) - 1.8

       snake_case__					:	Tuple          =					torch.nn.functional.softmax(UpperCAmelCase    ,       dim=-1     )

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

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

       snake_case__					:	Dict          =					per_alignment * residue_weights

       snake_case__					:	List[str]          =					(weighted == torch.max(UpperCAmelCase     )).nonzero()[0]
       return per_alignment[tuple(UpperCAmelCase     )]




