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



def   __A		(			_A							,				_A							,				_A = "x"							,				_A = 10**-10							,				_A = 1							,				):



 """simple docstring"""


 __a       =  symbols(_lowercase					)
 __a       =  lambdify(_lowercase							,				_lowercase					)
 __a       =  lambdify(_lowercase							,				diff(_lowercase							,				_lowercase					)					)

 __a       =  starting_point

 while True:
  if diff_function(_lowercase					) != 0:
   __a       =  prev_guess - multiplicity * func(_lowercase					) / diff_function(
       _lowercase					)
  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       =  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_0_5)}''',
  )

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



import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
  import jax.numpy as jnp

  from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class    A_ (       unittest.TestCase ):

  @slow
  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base"       )
   __a       =  AutoTokenizer.from_pretrained("xlm-roberta-base"       )
   __a       =  "The dog is cute and lives in the garden house"
   __a       =  jnp.array([tokenizer.encode(__SCREAMING_SNAKE_CASE       )]       )

   __a       =  (1, 12, 7_68)  # batch_size, sequence_length, embedding_vector_dim
   __a       =  jnp.array(
       [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]       )

   __a       =  model(__SCREAMING_SNAKE_CASE       )["last_hidden_state"]
   self.assertEqual(output.shape     ,							__SCREAMING_SNAKE_CASE       )
   # compare the actual values for a slice of last dim
   self.assertTrue(jnp.allclose(output[:, :, -1]     ,							__SCREAMING_SNAKE_CASE     ,							atol=1E-3       )       )


