code: 
  
  
  
  
  
  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					)					)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  from functools import partial
  from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload
  
  import torch
  import torch.nn as nn
  import torch.types
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						torch.Tensor:
         # The first operation in a checkpoint can't be in-place, but it's
         # nice to have in-place addition during inference. Thus...
         if not inplace:
                _snake_case      :							Any     				=						ma + ma
         else:
                ma += ma
  
         return ma
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						torch.Tensor:
         _snake_case      :							str     				=						-1 * len(lowercase_  )
         _snake_case      :							Any     				=						list(range(len(tensor.shape[:zero_index]  )  )  )
         return tensor.permute(first_inds + [zero_index + i for i in inds]  )
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						torch.Tensor:
         return t.reshape(t.shape[:-no_dims] + (-1,)  )
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_ = 1E-4  )			->						torch.Tensor:
         _snake_case      :							Any     				=						mask.expand(*value.shape  )
         return torch.sum(mask * value      ,		dim=lowercase_  ) / (eps + torch.sum(lowercase_      ,		dim=lowercase_  ))
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_ = 2.31_25      ,		lowercase_ = 21.68_75      ,		lowercase_ = 64  )			->						torch.Tensor:
         _snake_case      :							int     				=						torch.linspace(lowercase_      ,		lowercase_      ,		no_bins - 1      ,		device=pts.device  )
         _snake_case      :							Optional[Any]     				=						torch.sqrt(torch.sum((pts.unsqueeze(-2  ) - pts.unsqueeze(-3  )) ** 2      ,		dim=-1  )  )
         return torch.bucketize(lowercase_      ,		lowercase_  )
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						dict:
         _snake_case      :							Dict     				=						dicts[0]
         _snake_case      :							str     				=						{}
         for k, v in first.items():
                _snake_case      :							List[Any]     				=						[d[k] for d in dicts]
                if isinstance(lowercase_      ,		lowercase_  ):
                       _snake_case      :							Any     				=						dict_multimap(lowercase_      ,		lowercase_  )
                else:
                       _snake_case      :							Dict     				=						fn(lowercase_  )
  
         return new_dict
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						torch.Tensor:
         _snake_case      :							str     				=						v_bins.view(((1,) * len(x.shape  )) + (len(lowercase_  ),)  )
         _snake_case      :							Union[str, Any]     				=						x[..., None] - reshaped_bins
         _snake_case      :							Optional[Any]     				=						torch.argmin(torch.abs(lowercase_  )      ,		dim=-1  )
         return nn.functional.one_hot(lowercase_      ,		num_classes=len(lowercase_  )  ).float()
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_ = 0      ,		lowercase_ = 0  )			->						torch.Tensor:
         _snake_case      :							List[Union[slice, torch.Tensor]]     				=						[]
         for i, s in enumerate(data.shape[:no_batch_dims]  ):
                _snake_case      :							Optional[Any]     				=						torch.arange(lowercase_  )
                _snake_case      :							Union[str, Any]     				=						r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape  ) - i - 1)))  )
                ranges.append(lowercase_  )
  
         _snake_case      :							List[Union[slice, torch.Tensor]]     				=						[slice(lowercase_  ) for _ in range(len(data.shape  ) - no_batch_dims  )]
         _snake_case      :							Union[str, Any]     				=						inds
         ranges.extend(lowercase_  )
         # Matt note: Editing this to get around the behaviour of using a list as an array index changing
         # in recent Numpy versions
         return data[tuple(lowercase_  )]
  
  
  lowerCAmelCase_   =   TypeVar("T")
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						Dict[Any, Union[dict, list, tuple, Any]]:
         _snake_case      :							Dict[Any, Union[dict, list, tuple, Any]]     				=						{}
         for k, v in dic.items():
                if isinstance(lowercase_      ,		lowercase_  ):
                       _snake_case      :							Dict     				=						dict_map(lowercase_      ,		lowercase_      ,		lowercase_  )
                else:
                       _snake_case      :							List[Any]     				=						tree_map(lowercase_      ,		lowercase_      ,		lowercase_  )
  
         return new_dict
  
  
  
  
  @overload
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						Any:
         ...
  
  
  
  
  @overload
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						dict:
         ...
  
  
  
  
  @overload
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						list:
         ...
  
  
  
  
  @overload
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						tuple:
         ...
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						Any:
         if isinstance(lowercase_      ,		lowercase_  ):
                return dict_map(lowercase_      ,		lowercase_      ,		lowercase_  )
         elif isinstance(lowercase_      ,		lowercase_  ):
                return [tree_map(lowercase_      ,		lowercase_      ,		lowercase_  ) for x in tree]
         elif isinstance(lowercase_      ,		lowercase_  ):
                return tuple(tree_map(lowercase_      ,		lowercase_      ,		lowercase_  ) for x in tree  )
         elif isinstance(lowercase_      ,		lowercase_  ):
                return fn(lowercase_  )
         else:
                print(type(lowercase_  )  )
                raise ValueError('''Not supported'''  )
  lowerCAmelCase_   =   partial(tree_map, leaf_type=torch.Tensor)
  
  
  
  
  
  
  