



from typing import Dict, Optional, Tuple

import torch



def   a_	(			__a      ):
     A__				=   boundaries[1] - boundaries[0]
     A__				=   boundaries + step / 2
     A__				=   torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1      )]		,							dim=0      )
     return bin_centers



def   a_	(			__a		,							__a		,							):
     A__				=   _calculate_bin_centers(_UpperCAmelCase      )
     return (
         torch.sum(aligned_distance_error_probs * bin_centers		,							dim=-1      ),
         bin_centers[-1],
     )



def   a_	(			__a		,							__a = 31		,							__a = 64		,							**__a		,							):
     A__				=   torch.linspace(0		,							_UpperCAmelCase		,							steps=(no_bins - 1)		,							device=logits.device      )

     A__				=   torch.nn.functional.softmax(_UpperCAmelCase		,							dim=-1      )
     A__				=   _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   a_	(			__a		,							__a = None		,							__a = 31		,							__a = 64		,							__a = 1e-8		,							**__a		,							):






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

     A__				=   torch.linspace(0		,							_UpperCAmelCase		,							steps=(no_bins - 1)		,							device=logits.device      )

     A__				=   _calculate_bin_centers(_UpperCAmelCase      )
     torch.sum(_UpperCAmelCase      )
     A__				=   logits.shape[-2]
     A__				=   max(_UpperCAmelCase		,							19      )

     A__				=   1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

     A__				=   torch.nn.functional.softmax(_UpperCAmelCase		,							dim=-1      )

     A__				=   1.0 / (1 + (bin_centers**2) / (da**2))
     A__				=   torch.sum(probs * tm_per_bin		,							dim=-1      )

     A__				=   residue_weights / (eps + residue_weights.sum())
     A__				=   torch.sum(predicted_tm_term * normed_residue_mask		,							dim=-1      )

     A__				=   per_alignment * residue_weights

     A__				=   (weighted == torch.max(_UpperCAmelCase      )).nonzero()[0]
     return per_alignment[tuple(_UpperCAmelCase      )]








"""simple docstring"""







import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__snake_case	:				Optional[Any]  					=			500_000

__snake_case					,  __snake_case	:				Any  					=			os.path.split(__file__)
__snake_case	:				Optional[Any]  					=			os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))



@get_duration
def   a_	(			__a      ):
     A__				=   dataset.select(range(0		,							len(__a      )		,							2      )      )



@get_duration
def   a_	(			__a      ):
     A__				=   dataset.sort('''numbers'''      )



@get_duration
def   a_	(			__a      ):
     A__				=   dataset.shuffle()



@get_duration
def   a_	(			__a      ):
     A__				=   dataset.train_test_split(0.1      )



@get_duration
def   a_	(			__a		,							__a=10      ):
     for shard_id in range(__a      ):
          A__				=   dataset.shard(__a		,							__a      )



def   a_	(			):
     A__				=   {'''num examples''': SPEED_TEST_N_EXAMPLES}
     A__				=   (select, sort, shuffle, train_test_split, shard)
     with tempfile.TemporaryDirectory() as tmp_dir:
          print('''generating dataset'''      )
          A__				=   datasets.Features({'''text''': datasets.Value('''string'''      ), '''numbers''': datasets.Value('''float32'''      )}      )
          A__				=   generate_example_dataset(
              os.path.join(__a		,							'''dataset.arrow'''      )		,							__a		,							num_examples=__a      )
          print('''Functions'''      )
          for func in functions:
               print(func.__name__      )
               A__				=   func(__a      )

     with open(__a		,							'''wb'''      ) as f:
          f.write(json.dumps(__a      ).encode('''utf-8'''      )      )


if __name__ == "__main__":  # useful to run the profiler
 benchmark_indices_mapping()



