
"""simple docstring"""

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 			_lowercase  (							unittest.TestCase							,			__UpperCAmelCase						):




	def      _UpperCamelCase       (					self   )						->				Union[str, Any]:
			lowerCamelCase	:     str            =       load_tool('translation'   )
			self.tool.setup()
			lowerCamelCase	:     Optional[int]            =       load_tool('translation'						,					remote=_snake_case   )




	def      _UpperCamelCase       (					self   )						->				Dict:
			lowerCamelCase	:     Optional[Any]            =       self.tool('Hey, what\'s up?'						,					src_lang='English'						,					tgt_lang='French'   )
			self.assertEqual(_snake_case						,					'- Hé, comment ça va?'   )




	def      _UpperCamelCase       (					self   )						->				Dict:
			lowerCamelCase	:     Optional[int]            =       self.remote_tool('Hey, what\'s up?'						,					src_lang='English'						,					tgt_lang='French'   )
			self.assertEqual(_snake_case						,					'- Hé, comment ça va?'   )




	def      _UpperCamelCase       (					self   )						->				Optional[int]:
			lowerCamelCase	:     Union[str, Any]            =       self.tool(text='Hey, what\'s up?'						,					src_lang='English'						,					tgt_lang='French'   )
			self.assertEqual(_snake_case						,					'- Hé, comment ça va?'   )




	def      _UpperCamelCase       (					self   )						->				Tuple:
			lowerCamelCase	:     Optional[int]            =       self.remote_tool(text='Hey, what\'s up?'						,					src_lang='English'						,					tgt_lang='French'   )
			self.assertEqual(_snake_case						,					'- Hé, comment ça va?'   )




	def      _UpperCamelCase       (					self   )						->				Any:
			lowerCamelCase	:     str            =       ['Hey, what\'s up?', 'English', 'Spanish']
			lowerCamelCase	:     Any            =       self.tool(*_snake_case   )

			# There is a single output
			if len(self.tool.outputs   ) == 1:
					lowerCamelCase	:     Any            =       [outputs]

			self.assertListEqual(output_types(_snake_case   )						,					self.tool.outputs   )




	def      _UpperCamelCase       (					self   )						->				Optional[Any]:
			lowerCamelCase	:     List[str]            =       ['Hey, what\'s up?', 'English', 'Spanish']
			lowerCamelCase	:     Optional[Any]            =       self.tool(*_snake_case   )

			if not isinstance(_snake_case						,					_snake_case   ):
					lowerCamelCase	:     List[str]            =       [outputs]

			self.assertEqual(len(_snake_case   )						,					len(self.tool.outputs   )   )

			for output, output_type in zip(_snake_case						,					self.tool.outputs   ):
					lowerCamelCase	:     Optional[int]            =       AGENT_TYPE_MAPPING[output_type]
					self.assertTrue(isinstance(_snake_case						,					_snake_case   )   )




	def      _UpperCamelCase       (					self   )						->				Optional[Any]:
			lowerCamelCase	:     Dict            =       ['Hey, what\'s up?', 'English', 'Spanish']

			lowerCamelCase	:     List[str]            =       []

			for _input, input_type in zip(_snake_case						,					self.tool.inputs   ):
					if isinstance(_snake_case						,					_snake_case   ):
							_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
			lowerCamelCase	:     Any            =       self.tool(*_snake_case   )

			if not isinstance(_snake_case						,					_snake_case   ):
					lowerCamelCase	:     Optional[Any]            =       [outputs]

			self.assertEqual(len(_snake_case   )						,					len(self.tool.outputs   )   )














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   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      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:
  lowerCamelCase_							:    Tuple							=     ma + ma
 else:
  ma += ma

 return ma




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__				)  ->      torch.Tensor:
 lowerCamelCase_							:    Union[str, Any]							=     -1 * len(lowerCamelCase__				)
 lowerCamelCase_							:    Optional[Any]							=     list(range(len(tensor.shape[:zero_index]				)				)				)
 return tensor.permute(first_inds + [zero_index + i for i in inds]				)




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__				)  ->      torch.Tensor:
 return t.reshape(t.shape[:-no_dims] + (-1,)				)




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__ = 1e-4				)  ->      torch.Tensor:
 lowerCamelCase_							:    Optional[int]							=     mask.expand(*value.shape				)
 return torch.sum(mask * value ,			dim=lowerCamelCase__				) / (eps + torch.sum(lowerCamelCase__ ,			dim=lowerCamelCase__				))




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ = 2.3125 ,			lowerCamelCase__ = 21.6875 ,			lowerCamelCase__ = 64				)  ->      torch.Tensor:
 lowerCamelCase_							:    int							=     torch.linspace(lowerCamelCase__ ,			lowerCamelCase__ ,			no_bins - 1 ,			device=pts.device				)
 lowerCamelCase_							:    Tuple							=     torch.sqrt(torch.sum((pts.unsqueeze(-2				) - pts.unsqueeze(-3				)) ** 2 ,			dim=-1				)				)
 return torch.bucketize(lowerCamelCase__ ,			lowerCamelCase__				)




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__				)  ->      dict:
 lowerCamelCase_							:    Optional[Any]							=     dicts[0]
 lowerCamelCase_							:    Optional[Any]							=     {}
 for k, v in first.items():
  lowerCamelCase_							:    Tuple							=     [d[k] for d in dicts]
  if isinstance(lowerCamelCase__ ,			lowerCamelCase__				):
   lowerCamelCase_							:    Optional[int]							=     dict_multimap(lowerCamelCase__ ,			lowerCamelCase__				)
  else:
   lowerCamelCase_							:    List[Any]							=     fn(lowerCamelCase__				)

 return new_dict




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__				)  ->      torch.Tensor:
 lowerCamelCase_							:    str							=     v_bins.view(((1,) * len(x.shape				)) + (len(lowerCamelCase__				),)				)
 lowerCamelCase_							:    List[Any]							=     x[..., None] - reshaped_bins
 lowerCamelCase_							:    List[Any]							=     torch.argmin(torch.abs(lowerCamelCase__				) ,			dim=-1				)
 return nn.functional.one_hot(lowerCamelCase__ ,			num_classes=len(lowerCamelCase__				)				).float()




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__ = 0 ,			lowerCamelCase__ = 0				)  ->      torch.Tensor:
 lowerCamelCase_							:    List[Union[slice, torch.Tensor]]							=     []
 for i, s in enumerate(data.shape[:no_batch_dims]				):
  lowerCamelCase_							:    int							=     torch.arange(lowerCamelCase__				)
  lowerCamelCase_							:    Any							=     r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape				) - i - 1)))				)
  ranges.append(lowerCamelCase__				)

 lowerCamelCase_							:    List[Union[slice, torch.Tensor]]							=     [slice(lowerCamelCase__				) for _ in range(len(data.shape				) - no_batch_dims				)]
 lowerCamelCase_							:    Optional[int]							=     inds
 ranges.extend(lowerCamelCase__				)
 # 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(lowerCamelCase__				)]


UpperCamelCase											=      TypeVar('''T''')




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      Dict[Any, Union[dict, list, tuple, Any]]:
 lowerCamelCase_							:    Dict[Any, Union[dict, list, tuple, Any]]							=     {}
 for k, v in dic.items():
  if isinstance(lowerCamelCase__ ,			lowerCamelCase__				):
   lowerCamelCase_							:    Optional[Any]							=     dict_map(lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)
  else:
   lowerCamelCase_							:    Dict							=     tree_map(lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)

 return new_dict




@overload
def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      Any:
 ...




@overload
def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      dict:
 ...




@overload
def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      list:
 ...




@overload
def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      tuple:
 ...




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      Any:
 if isinstance(lowerCamelCase__ ,			lowerCamelCase__				):
  return dict_map(lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)
 elif isinstance(lowerCamelCase__ ,			lowerCamelCase__				):
  return [tree_map(lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				) for x in tree]
 elif isinstance(lowerCamelCase__ ,			lowerCamelCase__				):
  return tuple(tree_map(lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				) for x in tree				)
 elif isinstance(lowerCamelCase__ ,			lowerCamelCase__				):
  return fn(lowerCamelCase__				)
 else:
  print(type(lowerCamelCase__				)				)
  raise ValueError('Not supported'				)





UpperCamelCase											=      partial(tree_map, leaf_type=torch.Tensor)
