


'''simple docstring'''



from typing import Dict, Optional, Tuple

import torch



def       UpperCAmelCase__ (     UpperCAmelCase_   :    torch.Tensor							)					->		torch.Tensor:
     __lowerCamelCase       :     Optional[Any]        =		boundaries[1] - boundaries[0]
     __lowerCamelCase       :     Dict        =		boundaries + step / 2
     __lowerCamelCase       :     Optional[Any]        =		torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1							)]				,   dim=0							)
     return bin_centers



def       UpperCAmelCase__ (     UpperCAmelCase_   :    torch.Tensor				,   UpperCAmelCase_   :    torch.Tensor				,   )					->		Tuple[torch.Tensor, torch.Tensor]:
     __lowerCamelCase       :     List[Any]        =		_calculate_bin_centers(UpperCAmelCase_							)
     return (
         torch.sum(aligned_distance_error_probs * bin_centers				,   dim=-1							),
         bin_centers[-1],
     )



def       UpperCAmelCase__ (     UpperCAmelCase_   :    torch.Tensor				,   UpperCAmelCase_   :    int = 31				,   UpperCAmelCase_   :    int = 64				,   **UpperCAmelCase_   :    int				,   )					->		Dict[str, torch.Tensor]:
     __lowerCamelCase       :     Tuple        =		torch.linspace(0				,   UpperCAmelCase_				,   steps=(no_bins - 1)				,   device=logits.device							)

     __lowerCamelCase       :     Any        =		torch.nn.functional.softmax(UpperCAmelCase_				,   dim=-1							)
     __lowerCamelCase       :     int        =		_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       UpperCAmelCase__ (     UpperCAmelCase_   :    torch.Tensor				,   UpperCAmelCase_   :    Optional[torch.Tensor] = None				,   UpperCAmelCase_   :    int = 31				,   UpperCAmelCase_   :    int = 64				,   UpperCAmelCase_   :    float = 1e-8				,   **UpperCAmelCase_   :    int				,   )					->		torch.Tensor:
     if residue_weights is None:
          __lowerCamelCase       :     Any        =		logits.new_ones(logits.shape[-2]							)

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

     __lowerCamelCase       :     Union[str, Any]        =		_calculate_bin_centers(UpperCAmelCase_							)
     torch.sum(UpperCAmelCase_							)
     __lowerCamelCase       :     int        =		logits.shape[-2]
     __lowerCamelCase       :     Any        =		max(UpperCAmelCase_				,   19							)

     __lowerCamelCase       :     int        =		1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

     __lowerCamelCase       :     Optional[int]        =		torch.nn.functional.softmax(UpperCAmelCase_				,   dim=-1							)

     __lowerCamelCase       :     Dict        =		1.0 / (1 + (bin_centers**2) / (da**2))
     __lowerCamelCase       :     Tuple        =		torch.sum(probs * tm_per_bin				,   dim=-1							)

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

     __lowerCamelCase       :     str        =		per_alignment * residue_weights

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










'''simple docstring'''



import torch

from transformers import AutoTokenizer





class        UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    def __init__(							self      ,					SCREAMING_SNAKE_CASE_     )    ->					int:
         __lowerCamelCase       :     Dict        =		AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_     )







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_     )    ->					int:

         if isinstance(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     ) and all(isinstance(_x      ,					SCREAMING_SNAKE_CASE_     ) for _x in x     ):
              __lowerCamelCase       :     Union[str, Any]        =		None
              for l in x:
                   __lowerCamelCase       :     Tuple        =		self.tokenizer(
                       SCREAMING_SNAKE_CASE_      ,					padding='max_length'      ,					max_length=3_84      ,					truncation=SCREAMING_SNAKE_CASE_      ,					return_tensors='pt'      ,					)
                   __lowerCamelCase       :     str        =		torch.tensor([len(SCREAMING_SNAKE_CASE_     )]     )
                   if d is not None:
                        for k in d.keys():
                             __lowerCamelCase       :     List[Any]        =		torch.cat((d[k], t[k])      ,					0     )
                   else:
                        __lowerCamelCase       :     str        =		t

              __lowerCamelCase       :     List[Any]        =		torch.tensor(self.tokenizer.convert_tokens_to_ids('[E]'     )     )
              __lowerCamelCase       :     Tuple        =		torch.tensor(self.tokenizer.convert_tokens_to_ids('[/E]'     )     )

         elif isinstance(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     ) and all(isinstance(_x      ,					SCREAMING_SNAKE_CASE_     ) for _x in x     ):
              __lowerCamelCase       :     Optional[Any]        =		self.tokenizer(
                  SCREAMING_SNAKE_CASE_      ,					padding='max_length'      ,					max_length=3_84      ,					truncation=SCREAMING_SNAKE_CASE_      ,					return_tensors='pt'      ,					)

         else:
              raise Exception(
                  'Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'
                  ' strings` for supports are supported.'     )

         return d







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_=0.7_0     )    ->					str:
         __lowerCamelCase       :     str        =		[]
         for idx in range(len(W_query['input_ids']     )     ):
              __lowerCamelCase       :     Optional[Any]        =		range(p_start.shape[1]     )

              __lowerCamelCase       :     List[Any]        =		[]
              for start_id in start_indexes:
                   for end_id in end_indexes:
                        if start_id < end_id:
                             output.append(
                                 (
                                     start_id,
                                     end_id,
                                     p_start[idx][start_id].item(),
                                     p_end[idx][end_id].item(),
                                 )     )

              output.sort(key=lambda SCREAMING_SNAKE_CASE_     : (tup[2] * tup[3])      ,					reverse=SCREAMING_SNAKE_CASE_     )
              __lowerCamelCase       :     str        =		[]
              for k in range(len(SCREAMING_SNAKE_CASE_     )     ):
                   if output[k][2] * output[k][3] >= thresh:
                        __lowerCamelCase    ,	__lowerCamelCase       :     Union[str, Any]        =		output[k][0], output[k][1]
                        __lowerCamelCase       :     List[str]        =		self.tokenizer.decode(W_query['input_ids'][idx][c_start_pos:c_end_pos]     )
                        temp.append((decoded, output[k][2] * output[k][3])     )

              final_outputs.append(SCREAMING_SNAKE_CASE_     )

         return final_outputs






