



'''simple docstring'''


from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch

@require_torch
@require_tf
class  SCREAMING_SNAKE_CASE (  _lowerCAmelCase		):






    """simple docstring"""





    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  SMALL_MODEL_IDENTIFIER
       UpperCamelCase					=  'pt'
       UpperCamelCase					=  'tf'






    def  A       (  self      :					Optional[int]  ,						UpperCamelCase__      :					int ):


       """simple docstring"""







       UpperCamelCase					=  AutoModel.from_pretrained(self.test_model )
       model_pt.save_pretrained(_lowerCamelCase )






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase					=  TFAutoModel.from_pretrained(self.test_model  ,						from_pt=_lowerCamelCase )
       model_tf.save_pretrained(_lowerCamelCase )






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  'mock_framework'

       # Framework provided - return whatever the user provides
       UpperCamelCase					=  FeaturesManager.determine_framework(self.test_model  ,						_lowerCamelCase )
       self.assertEqual(_lowerCamelCase  ,						_lowerCamelCase )

       # Local checkpoint and framework provided - return provided framework
       # PyTorch checkpoint
       with TemporaryDirectory() as local_pt_ckpt:
          self._setup_pt_ckpt(_lowerCamelCase )
          UpperCamelCase					=  FeaturesManager.determine_framework(_lowerCamelCase  ,						_lowerCamelCase )
          self.assertEqual(_lowerCamelCase  ,						_lowerCamelCase )

       # TensorFlow checkpoint
       with TemporaryDirectory() as local_tf_ckpt:
          self._setup_tf_ckpt(_lowerCamelCase )
          UpperCamelCase					=  FeaturesManager.determine_framework(_lowerCamelCase  ,						_lowerCamelCase )
          self.assertEqual(_lowerCamelCase  ,						_lowerCamelCase )






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       with TemporaryDirectory() as local_pt_ckpt:
          self._setup_pt_ckpt(_lowerCamelCase )
          UpperCamelCase					=  FeaturesManager.determine_framework(_lowerCamelCase )
          self.assertEqual(_lowerCamelCase  ,						self.framework_pt )

       # TensorFlow checkpoint
       with TemporaryDirectory() as local_tf_ckpt:
          self._setup_tf_ckpt(_lowerCamelCase )
          UpperCamelCase					=  FeaturesManager.determine_framework(_lowerCamelCase )
          self.assertEqual(_lowerCamelCase  ,						self.framework_tf )

       # Invalid local checkpoint
       with TemporaryDirectory() as local_invalid_ckpt:
          with self.assertRaises(_lowerCamelCase ):
             UpperCamelCase					=  FeaturesManager.determine_framework(_lowerCamelCase )






    def  A       (  self      :					str ):


       """simple docstring"""







       UpperCamelCase					=  MagicMock(return_value=_lowerCamelCase )
       with patch('transformers.onnx.features.is_tf_available'  ,						_lowerCamelCase ):
          UpperCamelCase					=  FeaturesManager.determine_framework(self.test_model )
          self.assertEqual(_lowerCamelCase  ,						self.framework_pt )

       # PyTorch not in environment -> use TensorFlow
       UpperCamelCase					=  MagicMock(return_value=_lowerCamelCase )
       with patch('transformers.onnx.features.is_torch_available'  ,						_lowerCamelCase ):
          UpperCamelCase					=  FeaturesManager.determine_framework(self.test_model )
          self.assertEqual(_lowerCamelCase  ,						self.framework_tf )

       # Both in environment -> use PyTorch
       UpperCamelCase					=  MagicMock(return_value=_lowerCamelCase )
       UpperCamelCase					=  MagicMock(return_value=_lowerCamelCase )
       with patch('transformers.onnx.features.is_tf_available'  ,						_lowerCamelCase ), patch(
           'transformers.onnx.features.is_torch_available'  ,						_lowerCamelCase ):
          UpperCamelCase					=  FeaturesManager.determine_framework(self.test_model )
          self.assertEqual(_lowerCamelCase  ,						self.framework_pt )

       # Both not in environment -> raise error
       UpperCamelCase					=  MagicMock(return_value=_lowerCamelCase )
       UpperCamelCase					=  MagicMock(return_value=_lowerCamelCase )
       with patch('transformers.onnx.features.is_tf_available'  ,						_lowerCamelCase ), patch(
           'transformers.onnx.features.is_torch_available'  ,						_lowerCamelCase ):
          with self.assertRaises(_lowerCamelCase ):
             UpperCamelCase					=  FeaturesManager.determine_framework(self.test_model )









"""simple docstring"""


def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  __A									= current_set.copy()
  for row_index, row in enumerate(__UpperCamelCase ):
    __A									= row[0]
    for column_index, column in enumerate(__UpperCamelCase ):
      if magnitude == 0:
        __A									= column
        continue
      __A									= column / magnitude
    # Subtract to cancel term
  __A									= current_set[0]
  __A									= [first_row]
  __A									= current_set[1::]
  for row in current_set:
    __A									= []
    # If first term is 0, it is already in form we want, so we preserve it
    if row[0] == 0:
      final_set.append(__UpperCamelCase )
      continue
    for column_index in range(len(__UpperCamelCase ) ):
      temp_row.append(first_row[column_index] - row[column_index] )
    final_set.append(__UpperCamelCase )
  # Create next recursion iteration set
  if len(final_set[0] ) != 3:
    __A									= final_set[0]
    __A									= []
    __A									= []
    for row in final_set[1::]:
      current_first_column.append(row[0] )
      next_iteration.append(row[1::] )
    __A									= simplify(__UpperCamelCase )
    for i in range(len(__UpperCamelCase ) ):
      resultant[i].insert(0		,       current_first_column[i] )
    resultant.insert(0		,       __UpperCamelCase )
    __A									= resultant
  return final_set



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if len(__UpperCamelCase ) == 0:
    raise IndexError('''solve_simultaneous() requires n lists of length n+1''' )
  __A									= len(__UpperCamelCase ) + 1
  if any(len(__UpperCamelCase ) != _length for item in equations ):
    raise IndexError('''solve_simultaneous() requires n lists of length n+1''' )
  for row in equations:
    if any(not isinstance(__UpperCamelCase		,       (int, float) ) for column in row ):
      raise ValueError('''solve_simultaneous() requires lists of integers''' )
  if len(__UpperCamelCase ) == 1:
    return [equations[0][-1] / equations[0][0]]
  __A									= equations.copy()
  if any(0 in row for row in data_set ):
    __A									= data_set.copy()
    __A									= []
    for row_index, row in enumerate(__UpperCamelCase ):
      if 0 not in row:
        __A									= data_set.pop(__UpperCamelCase )
        break
    if not full_row:
      raise ValueError('''solve_simultaneous() requires at least 1 full equation''' )
    data_set.insert(0		,       __UpperCamelCase )
  __A									= data_set.copy()
  __A									= simplify(__UpperCamelCase )
  __A									= simplified[::-1]
  __A									= []
  for row in simplified:
    __A									= row[-1]
    if not solutions:
      if row[-2] == 0:
        solutions.append(0 )
        continue
      solutions.append(current_solution / row[-2] )
      continue
    __A									= row.copy()[: len(__UpperCamelCase ) - 1 :]
    while temp_row[0] == 0:
      temp_row.pop(0 )
    if len(__UpperCamelCase ) == 0:
      solutions.append(0 )
      continue
    __A									= temp_row[1::]
    __A									= temp_row[::-1]
    for column_index, column in enumerate(__UpperCamelCase ):
      current_solution -= column * solutions[column_index]
    solutions.append(__UpperCamelCase )
  __A									= []
  for item in solutions:
    final.append(float(round(__UpperCamelCase		,       5 ) ) )
  return final[::-1]


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       lowercase_				     =     [
           [2, 1, 1, 1, 1, 4],
           [1, 2, 1, 1, 1, 5],
           [1, 1, 2, 1, 1, 6],
           [1, 1, 1, 2, 1, 7],
           [1, 1, 1, 1, 2, 8],
       ]
       print(solve_simultaneous(eq))
       print(solve_simultaneous([[4, 2]]))




