code: 
  
  
  
  
  
  from typing import Dict, Optional, Tuple
  
  import torch
  
  
  
  
  def 						A_			(  lowercase_  )			->						torch.Tensor:
         _snake_case      :							Optional[Any]     				=						boundaries[1] - boundaries[0]
         _snake_case      :							Union[str, Any]     				=						boundaries + step / 2
         _snake_case      :							List[str]     				=						torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1  )]      ,		dim=0  )
         return bin_centers
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		)			->						Tuple[torch.Tensor, torch.Tensor]:
         _snake_case      :							Optional[int]     				=						_calculate_bin_centers(lowercase_  )
         return (
             torch.sum(aligned_distance_error_probs * bin_centers      ,		dim=-1  ),
             bin_centers[-1],
         )
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_ = 31      ,		lowercase_ = 64      ,		**lowercase_      ,		)			->						Dict[str, torch.Tensor]:
         _snake_case      :							List[str]     				=						torch.linspace(0      ,		lowercase_      ,		steps=(no_bins - 1)      ,		device=logits.device  )
  
         _snake_case      :							Optional[Any]     				=						torch.nn.functional.softmax(lowercase_      ,		dim=-1  )
         _snake_case				,    _snake_case      :							Optional[int]     				=						_calculate_expected_aligned_error(
             alignment_confidence_breaks=lowercase_      ,		aligned_distance_error_probs=lowercase_      ,		)
  
         return {
             "aligned_confidence_probs": aligned_confidence_probs,
             "predicted_aligned_error": predicted_aligned_error,
             "max_predicted_aligned_error": max_predicted_aligned_error,
         }
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_ = None      ,		lowercase_ = 31      ,		lowercase_ = 64      ,		lowercase_ = 1E-8      ,		**lowercase_      ,		)			->						torch.Tensor:
         if residue_weights is None:
                _snake_case      :							int     				=						logits.new_ones(logits.shape[-2]  )
  
         _snake_case      :							Dict     				=						torch.linspace(0      ,		lowercase_      ,		steps=(no_bins - 1)      ,		device=logits.device  )
  
         _snake_case      :							Dict     				=						_calculate_bin_centers(lowercase_  )
         torch.sum(lowercase_  )
         _snake_case      :							Dict     				=						logits.shape[-2]
         _snake_case      :							Optional[Any]     				=						max(lowercase_      ,		19  )
  
         _snake_case      :							Optional[int]     				=						1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8
  
         _snake_case      :							List[Any]     				=						torch.nn.functional.softmax(lowercase_      ,		dim=-1  )
  
         _snake_case      :							Any     				=						1.0 / (1 + (bin_centers**2) / (da**2))
         _snake_case      :							Optional[int]     				=						torch.sum(probs * tm_per_bin      ,		dim=-1  )
  
         _snake_case      :							int     				=						residue_weights / (eps + residue_weights.sum())
         _snake_case      :							str     				=						torch.sum(predicted_tm_term * normed_residue_mask      ,		dim=-1  )
  
         _snake_case      :							Union[str, Any]     				=						per_alignment * residue_weights
  
         _snake_case      :							str     				=						(weighted == torch.max(lowercase_  )).nonzero()[0]
         return per_alignment[tuple(lowercase_  )]
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import unittest
  
  from transformers import load_tool
  from transformers.tools.agent_types import AGENT_TYPE_MAPPING
  
  from .test_tools_common import ToolTesterMixin, output_types
  
  class 				A    (unittest.TestCase       ,__UpperCAmelCase				):
  
  
  
        def    __a (					self					)      ->   str:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						load_tool('''translation'''					)
               self.tool.setup()
               _snake_case      :							Any     				=						load_tool('''translation'''     ,  remote=lowercase_					)
  
  
  
        def    __a (					self					)      ->   Optional[int]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Tuple     				=						self.tool('''Hey, what\'s up?'''     ,  src_lang='''English'''     ,  tgt_lang='''French'''					)
               self.assertEqual(lowercase_     ,  '''- Hé, comment ça va?'''					)
  
  
  
        def    __a (					self					)      ->   Union[str, Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[int]     				=						self.remote_tool('''Hey, what\'s up?'''     ,  src_lang='''English'''     ,  tgt_lang='''French'''					)
               self.assertEqual(lowercase_     ,  '''- Hé, comment ça va?'''					)
  
  
  
        def    __a (					self					)      ->   List[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Dict     				=						self.tool(text='''Hey, what\'s up?'''     ,  src_lang='''English'''     ,  tgt_lang='''French'''					)
               self.assertEqual(lowercase_     ,  '''- Hé, comment ça va?'''					)
  
  
  
        def    __a (					self					)      ->   Dict:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						self.remote_tool(text='''Hey, what\'s up?'''     ,  src_lang='''English'''     ,  tgt_lang='''French'''					)
               self.assertEqual(lowercase_     ,  '''- Hé, comment ça va?'''					)
  
  
  
        def    __a (					self					)      ->   str:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[int]     				=						['''Hey, what\'s up?''', '''English''', '''Spanish''']
               _snake_case      :							Dict     				=						self.tool(*lowercase_					)
  
               # There is a single output
               if len(self.tool.outputs					) == 1:
                      _snake_case      :							Optional[Any]     				=						[outputs]
  
               self.assertListEqual(output_types(lowercase_					)     ,  self.tool.outputs					)
  
  
  
        def    __a (					self					)      ->   Tuple:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Union[str, Any]     				=						['''Hey, what\'s up?''', '''English''', '''Spanish''']
               _snake_case      :							Dict     				=						self.tool(*lowercase_					)
  
               if not isinstance(lowercase_     ,  lowercase_					):
                      _snake_case      :							Optional[Any]     				=						[outputs]
  
               self.assertEqual(len(lowercase_					)     ,  len(self.tool.outputs					)					)
  
               for output, output_type in zip(lowercase_     ,  self.tool.outputs					):
                      _snake_case      :							List[str]     				=						AGENT_TYPE_MAPPING[output_type]
                      self.assertTrue(isinstance(lowercase_     ,  lowercase_					)					)
  
  
  
        def    __a (					self					)      ->   List[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Dict     				=						['''Hey, what\'s up?''', '''English''', '''Spanish''']
  
               _snake_case      :							Any     				=						[]
  
               for _input, input_type in zip(lowercase_     ,  self.tool.inputs					):
                      if isinstance(lowercase_     ,  lowercase_					):
                             _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input					) for _input_type in input_type]					)
                      else:
                             _inputs.append(AGENT_TYPE_MAPPING[input_type](_input					)					)
  
          # Should not raise an error
               _snake_case      :							List[Any]     				=						self.tool(*lowercase_					)
  
               if not isinstance(lowercase_     ,  lowercase_					):
                      _snake_case      :							Any     				=						[outputs]
  
               self.assertEqual(len(lowercase_					)     ,  len(self.tool.outputs					)					)
  
  
  
  
  
  
  