





'''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    (a__							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Any      =       SMALL_MODEL_IDENTIFIER
          __A						: Optional[Any]      =       'pt'
          __A						: List[str]      =       'tf'


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: Union[str, Any]      =       AutoModel.from_pretrained(self.test_model)
          model_pt.save_pretrained(_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: Tuple      =       TFAutoModel.from_pretrained(self.test_model						,	from_pt=_UpperCAmelCase)
          model_tf.save_pretrained(_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Dict      =       'mock_framework'

          # Framework provided - return whatever the user provides
          __A						: Union[str, Any]      =       FeaturesManager.determine_framework(self.test_model						,	_UpperCAmelCase)
          self.assertEqual(_UpperCAmelCase						,	_UpperCAmelCase)

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

          # TensorFlow checkpoint
          with TemporaryDirectory() as local_tf_ckpt:
               self._setup_tf_ckpt(_UpperCAmelCase)
               __A						: int      =       FeaturesManager.determine_framework(_UpperCAmelCase						,	_UpperCAmelCase)
               self.assertEqual(_UpperCAmelCase						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          with TemporaryDirectory() as local_pt_ckpt:
               self._setup_pt_ckpt(_UpperCAmelCase)
               __A						: List[str]      =       FeaturesManager.determine_framework(_UpperCAmelCase)
               self.assertEqual(_UpperCAmelCase						,	self.framework_pt)

          # TensorFlow checkpoint
          with TemporaryDirectory() as local_tf_ckpt:
               self._setup_tf_ckpt(_UpperCAmelCase)
               __A						: Optional[Any]      =       FeaturesManager.determine_framework(_UpperCAmelCase)
               self.assertEqual(_UpperCAmelCase						,	self.framework_tf)

          # Invalid local checkpoint
          with TemporaryDirectory() as local_invalid_ckpt:
               with self.assertRaises(_UpperCAmelCase):
                    __A						: Union[str, Any]      =       FeaturesManager.determine_framework(_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: str      =       MagicMock(return_value=_UpperCAmelCase)
          with patch('transformers.onnx.features.is_tf_available'						,	_UpperCAmelCase):
               __A						: Union[str, Any]      =       FeaturesManager.determine_framework(self.test_model)
               self.assertEqual(_UpperCAmelCase						,	self.framework_pt)

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

          # Both in environment -> use PyTorch
          __A						: Tuple      =       MagicMock(return_value=_UpperCAmelCase)
          __A						: Union[str, Any]      =       MagicMock(return_value=_UpperCAmelCase)
          with patch('transformers.onnx.features.is_tf_available'						,	_UpperCAmelCase), patch(
              'transformers.onnx.features.is_torch_available'						,	_UpperCAmelCase):
               __A						: Any      =       FeaturesManager.determine_framework(self.test_model)
               self.assertEqual(_UpperCAmelCase						,	self.framework_pt)

          # Both not in environment -> raise error
          __A						: Tuple      =       MagicMock(return_value=_UpperCAmelCase)
          __A						: Optional[Any]      =       MagicMock(return_value=_UpperCAmelCase)
          with patch('transformers.onnx.features.is_tf_available'						,	_UpperCAmelCase), patch(
              'transformers.onnx.features.is_torch_available'						,	_UpperCAmelCase):
               with self.assertRaises(_UpperCAmelCase):
                    __A						: Optional[int]      =       FeaturesManager.determine_framework(self.test_model)






'''simple docstring'''




from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403






def        _lowerCAmelCase			(  __snake_case  : str ,							__snake_case  : complex ,							__snake_case  : str = "x" ,							__snake_case  : float = 10**-10 ,							__snake_case  : int = 1 ,							)     ->     complex:
     __A						: int      =       symbols(__snake_case			)
     __A						: Tuple      =       lambdify(__snake_case ,							__snake_case			)
     __A						: Any      =       lambdify(__snake_case ,							diff(__snake_case ,							__snake_case			)			)

     __A						: str      =       starting_point

     while True:
          if diff_function(__snake_case			) != 0:
               __A						: Optional[Any]      =       prev_guess - multiplicity * func(__snake_case			) / diff_function(
                   __snake_case			)
          else:
               raise ZeroDivisionError('Could not find root'			) from None

          # Precision is checked by comparing the difference of consecutive guesses
          if abs(next_guess - prev_guess			) < precision:
               return next_guess

          __A						: Dict      =       next_guess


# Let's Execute
if __name__ == "__main__":
  # Find root of trigonometric function
  # Find value of pi
  print(f"""The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}""")

  # Find root of polynomial
  # Find fourth Root of 5
  print(f"""The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5j)}""")

  # Find value of e
  print(
      '''The root of log(y) - 1 = 0 is ''',
      f"""{newton_raphson("log(y) - 1", 2, variable="y")}""",
  )

  # Exponential Roots
  print(
      '''The root of exp(x) - 1 = 0 is''',
      f"""{newton_raphson("exp(x) - 1", 10, precision=0.005)}""",
  )

  # Find root of cos(x)
  print(f"""The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}""")