




"""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  _SCREAMING_SNAKE_CASE       (     unittest.TestCase			,				SCREAMING_SNAKE_CASE__				):



 '''simple docstring'''





 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	List[str]					      =  load_tool("translation"   )
  self.tool.setup()
  _UpperCamelCase      :	str					      =  load_tool("translation"					,     remote=lowerCAmelCase__   )






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	Optional[int]					      =  self.tool("Hey, what's up?"					,     src_lang="English"					,     tgt_lang="French"   )
  self.assertEqual(lowerCAmelCase__					,     "- Hé, comment ça va?"   )






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	int					      =  self.remote_tool("Hey, what's up?"					,     src_lang="English"					,     tgt_lang="French"   )
  self.assertEqual(lowerCAmelCase__					,     "- Hé, comment ça va?"   )






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	Optional[int]					      =  self.tool(text="Hey, what's up?"					,     src_lang="English"					,     tgt_lang="French"   )
  self.assertEqual(lowerCAmelCase__					,     "- Hé, comment ça va?"   )






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	List[str]					      =  self.remote_tool(text="Hey, what's up?"					,     src_lang="English"					,     tgt_lang="French"   )
  self.assertEqual(lowerCAmelCase__					,     "- Hé, comment ça va?"   )






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	Optional[int]					      =  ["Hey, what's up?", "English", "Spanish"]
  _UpperCamelCase      :	List[str]					      =  self.tool(*lowerCAmelCase__   )

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

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






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	List[Any]					      =  ["Hey, what's up?", "English", "Spanish"]
  _UpperCamelCase      :	str					      =  self.tool(*lowerCAmelCase__   )

  if not isinstance(lowerCAmelCase__					,     lowerCAmelCase__   ):
   _UpperCamelCase      :	Optional[int]					      =  [outputs]

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

  for output, output_type in zip(lowerCAmelCase__					,     self.tool.outputs   ):
   _UpperCamelCase      :	Tuple					      =  AGENT_TYPE_MAPPING[output_type]
   self.assertTrue(isinstance(lowerCAmelCase__					,     lowerCAmelCase__   )   )






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	Optional[int]					      =  ["Hey, what's up?", "English", "Spanish"]

  _UpperCamelCase      :	Optional[int]					      =  []

  for _input, input_type in zip(lowerCAmelCase__					,     self.tool.inputs   ):
   if isinstance(lowerCAmelCase__					,     lowerCAmelCase__   ):
    _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
  _UpperCamelCase      :	Union[str, Any]					      =  self.tool(*lowerCAmelCase__   )

  if not isinstance(lowerCAmelCase__					,     lowerCAmelCase__   ):
   _UpperCamelCase      :	Dict					      =  [outputs]

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









"""simple docstring"""
import numpy as np
from numpy import floataa
from numpy.typing import NDArray






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		NDArray[floataa]  ,	__lowerCAmelCase			:		NDArray[floataa]						)    ->       NDArray[floataa]:
 _UpperCamelCase      ,	_UpperCamelCase      :	Optional[Any]					      =  np.shape(__lowerCAmelCase						)

 _UpperCamelCase      :	NDArray[floataa]					      =  np.zeros((rows, 1)  ,	dtype=__lowerCAmelCase						)
 for row in reversed(range(__lowerCAmelCase						)						):
  _UpperCamelCase      :	List[str]					      =  0
  for col in range(row + 1  ,	__lowerCAmelCase						):
   total += coefficients[row, col] * x[col]

  _UpperCamelCase      :	Dict					      =  (vector[row] - total) / coefficients[row, row]

 return x






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		NDArray[floataa]  ,	__lowerCAmelCase			:		NDArray[floataa]						)    ->       NDArray[floataa]:
 _UpperCamelCase      ,	_UpperCamelCase      :	Optional[Any]					      =  np.shape(__lowerCAmelCase						)
 if rows != columns:
  return np.array(()  ,	dtype=__lowerCAmelCase						)

 # augmented matrix
 _UpperCamelCase      :	NDArray[floataa]					      =  np.concatenate((coefficients, vector)  ,	axis=1						)
 _UpperCamelCase      :	List[str]					      =  augmented_mat.astype("float64"						)

 # scale the matrix leaving it triangular
 for row in range(rows - 1						):
  _UpperCamelCase      :	Optional[Any]					      =  augmented_mat[row, row]
  for col in range(row + 1  ,	__lowerCAmelCase						):
   _UpperCamelCase      :	Union[str, Any]					      =  augmented_mat[col, row] / pivot
   augmented_mat[col, :] -= factor * augmented_mat[row, :]

 _UpperCamelCase      :	Any					      =  retroactive_resolution(
     augmented_mat[:, 0:columns]  ,	augmented_mat[:, columns : columns + 1]						)

 return x


if __name__ == "__main__":
    import doctest

    doctest.testmod()



