
import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
		import jax.numpy as jnp

if is_tf_available():
		import tensorflow as tf

if is_torch_available():
		import torch







class        lowerCAmelCase__      (   unittest.TestCase	):

						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase						    =	{
								    """task_specific_params""": {
								        """summarization""": {"""length_penalty""": 1.0, """max_length""": 1_28, """min_length""": 12, """num_beams""": 4},
								        """summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 1_42, """min_length""": 56, """num_beams""": 4},
								        """summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 62, """min_length""": 11, """num_beams""": 6},
								    }
								}
								_UpperCamelCase						    =	{
								    """task_specific_params.summarization.length_penalty""": 1.0,
								    """task_specific_params.summarization.max_length""": 1_28,
								    """task_specific_params.summarization.min_length""": 12,
								    """task_specific_params.summarization.num_beams""": 4,
								    """task_specific_params.summarization_cnn.length_penalty""": 2.0,
								    """task_specific_params.summarization_cnn.max_length""": 1_42,
								    """task_specific_params.summarization_cnn.min_length""": 56,
								    """task_specific_params.summarization_cnn.num_beams""": 4,
								    """task_specific_params.summarization_xsum.length_penalty""": 1.0,
								    """task_specific_params.summarization_xsum.max_length""": 62,
								    """task_specific_params.summarization_xsum.min_length""": 11,
								    """task_specific_params.summarization_xsum.num_beams""": 6,
								}

								self.assertEqual(flatten_dict(a				)       ,							a				)

						def  A_				(			self				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	np.random.randn(3       ,							4				)
								self.assertTrue(np.allclose(transpose(a				)       ,							x.transpose()				)				)

								_UpperCamelCase						    =	np.random.randn(3       ,							4       ,							5				)
								self.assertTrue(np.allclose(transpose(a       ,							axes=(1, 2, 0)				)       ,							x.transpose((1, 2, 0)				)				)				)

						@require_torch
						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								_UpperCamelCase						    =	np.random.randn(3       ,							4				)
								_UpperCamelCase						    =	torch.tensor(a				)
								self.assertTrue(np.allclose(transpose(a				)       ,							transpose(a				).numpy()				)				)

								_UpperCamelCase						    =	np.random.randn(3       ,							4       ,							5				)
								_UpperCamelCase						    =	torch.tensor(a				)
								self.assertTrue(np.allclose(transpose(a       ,							axes=(1, 2, 0)				)       ,							transpose(a       ,							axes=(1, 2, 0)				).numpy()				)				)

						@require_tf
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								_UpperCamelCase						    =	np.random.randn(3       ,							4				)
								_UpperCamelCase						    =	tf.constant(a				)
								self.assertTrue(np.allclose(transpose(a				)       ,							transpose(a				).numpy()				)				)

								_UpperCamelCase						    =	np.random.randn(3       ,							4       ,							5				)
								_UpperCamelCase						    =	tf.constant(a				)
								self.assertTrue(np.allclose(transpose(a       ,							axes=(1, 2, 0)				)       ,							transpose(a       ,							axes=(1, 2, 0)				).numpy()				)				)

						@require_flax
						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	np.random.randn(3       ,							4				)
								_UpperCamelCase						    =	jnp.array(a				)
								self.assertTrue(np.allclose(transpose(a				)       ,							np.asarray(transpose(a				)				)				)				)

								_UpperCamelCase						    =	np.random.randn(3       ,							4       ,							5				)
								_UpperCamelCase						    =	jnp.array(a				)
								self.assertTrue(np.allclose(transpose(a       ,							axes=(1, 2, 0)				)       ,							np.asarray(transpose(a       ,							axes=(1, 2, 0)				)				)				)				)

						def  A_				(			self				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	np.random.randn(3       ,							4				)
								self.assertTrue(np.allclose(reshape(a       ,							(4, 3)				)       ,							np.reshape(a       ,							(4, 3)				)				)				)

								_UpperCamelCase						    =	np.random.randn(3       ,							4       ,							5				)
								self.assertTrue(np.allclose(reshape(a       ,							(12, 5)				)       ,							np.reshape(a       ,							(12, 5)				)				)				)

						@require_torch
						def  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	np.random.randn(3       ,							4				)
								_UpperCamelCase						    =	torch.tensor(a				)
								self.assertTrue(np.allclose(reshape(a       ,							(4, 3)				)       ,							reshape(a       ,							(4, 3)				).numpy()				)				)

								_UpperCamelCase						    =	np.random.randn(3       ,							4       ,							5				)
								_UpperCamelCase						    =	torch.tensor(a				)
								self.assertTrue(np.allclose(reshape(a       ,							(12, 5)				)       ,							reshape(a       ,							(12, 5)				).numpy()				)				)

						@require_tf
						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	np.random.randn(3       ,							4				)
								_UpperCamelCase						    =	tf.constant(a				)
								self.assertTrue(np.allclose(reshape(a       ,							(4, 3)				)       ,							reshape(a       ,							(4, 3)				).numpy()				)				)

								_UpperCamelCase						    =	np.random.randn(3       ,							4       ,							5				)
								_UpperCamelCase						    =	tf.constant(a				)
								self.assertTrue(np.allclose(reshape(a       ,							(12, 5)				)       ,							reshape(a       ,							(12, 5)				).numpy()				)				)

						@require_flax
						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								_UpperCamelCase						    =	np.random.randn(3       ,							4				)
								_UpperCamelCase						    =	jnp.array(a				)
								self.assertTrue(np.allclose(reshape(a       ,							(4, 3)				)       ,							np.asarray(reshape(a       ,							(4, 3)				)				)				)				)

								_UpperCamelCase						    =	np.random.randn(3       ,							4       ,							5				)
								_UpperCamelCase						    =	jnp.array(a				)
								self.assertTrue(np.allclose(reshape(a       ,							(12, 5)				)       ,							np.asarray(reshape(a       ,							(12, 5)				)				)				)				)

						def  A_				(			self				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	np.random.randn(1       ,							3       ,							4				)
								self.assertTrue(np.allclose(squeeze(a				)       ,							np.squeeze(a				)				)				)

								_UpperCamelCase						    =	np.random.randn(1       ,							4       ,							1       ,							5				)
								self.assertTrue(np.allclose(squeeze(a       ,							axis=2				)       ,							np.squeeze(a       ,							axis=2				)				)				)

						@require_torch
						def  A_				(			self				)					->      Union[str, Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	np.random.randn(1       ,							3       ,							4				)
								_UpperCamelCase						    =	torch.tensor(a				)
								self.assertTrue(np.allclose(squeeze(a				)       ,							squeeze(a				).numpy()				)				)

								_UpperCamelCase						    =	np.random.randn(1       ,							4       ,							1       ,							5				)
								_UpperCamelCase						    =	torch.tensor(a				)
								self.assertTrue(np.allclose(squeeze(a       ,							axis=2				)       ,							squeeze(a       ,							axis=2				).numpy()				)				)

						@require_tf
						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase						    =	np.random.randn(1       ,							3       ,							4				)
								_UpperCamelCase						    =	tf.constant(a				)
								self.assertTrue(np.allclose(squeeze(a				)       ,							squeeze(a				).numpy()				)				)

								_UpperCamelCase						    =	np.random.randn(1       ,							4       ,							1       ,							5				)
								_UpperCamelCase						    =	tf.constant(a				)
								self.assertTrue(np.allclose(squeeze(a       ,							axis=2				)       ,							squeeze(a       ,							axis=2				).numpy()				)				)

						@require_flax
						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	np.random.randn(1       ,							3       ,							4				)
								_UpperCamelCase						    =	jnp.array(a				)
								self.assertTrue(np.allclose(squeeze(a				)       ,							np.asarray(squeeze(a				)				)				)				)

								_UpperCamelCase						    =	np.random.randn(1       ,							4       ,							1       ,							5				)
								_UpperCamelCase						    =	jnp.array(a				)
								self.assertTrue(np.allclose(squeeze(a       ,							axis=2				)       ,							np.asarray(squeeze(a       ,							axis=2				)				)				)				)

						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	np.random.randn(3       ,							4				)
								self.assertTrue(np.allclose(expand_dims(a       ,							axis=1				)       ,							np.expand_dims(a       ,							axis=1				)				)				)

						@require_torch
						def  A_				(			self				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	np.random.randn(3       ,							4				)
								_UpperCamelCase						    =	torch.tensor(a				)
								self.assertTrue(np.allclose(expand_dims(a       ,							axis=1				)       ,							expand_dims(a       ,							axis=1				).numpy()				)				)

						@require_tf
						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	np.random.randn(3       ,							4				)
								_UpperCamelCase						    =	tf.constant(a				)
								self.assertTrue(np.allclose(expand_dims(a       ,							axis=1				)       ,							expand_dims(a       ,							axis=1				).numpy()				)				)







						@require_flax
						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase						    =	np.random.randn(3       ,							4				)
								_UpperCamelCase						    =	jnp.array(a				)
								self.assertTrue(np.allclose(expand_dims(a       ,							axis=1				)       ,							np.asarray(expand_dims(a       ,							axis=1				)				)				)				)





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


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase = "x"						,  lowerCAmelCase = 1_0**-1_0						,  lowerCAmelCase = 1						,  )		->				complex:

		"""simple docstring"""






		_UpperCamelCase						    =	symbols(lowerCAmelCase )
		_UpperCamelCase						    =	lambdify(lowerCAmelCase						,  lowerCAmelCase )
		_UpperCamelCase						    =	lambdify(lowerCAmelCase						,  diff(lowerCAmelCase						,  lowerCAmelCase ) )

		_UpperCamelCase						    =	starting_point

		while True:
				if diff_function(lowerCAmelCase ) != 0:
						_UpperCamelCase						    =	prev_guess - multiplicity * func(lowerCAmelCase ) / diff_function(
						    lowerCAmelCase )
				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

				_UpperCamelCase						    =	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=0.0_05)}""",
		)

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



