





'''simple docstring'''




import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
  from transformers.models.roberta.modeling_flax_roberta import (
      FlaxRobertaForCausalLM,
      FlaxRobertaForMaskedLM,
      FlaxRobertaForMultipleChoice,
      FlaxRobertaForQuestionAnswering,
      FlaxRobertaForSequenceClassification,
      FlaxRobertaForTokenClassification,
      FlaxRobertaModel,
  )


class  SCREAMING_SNAKE_CASE    (unittest.TestCase							):


     def __init__(						self						,	_UpperCAmelCase						,	_UpperCAmelCase=13						,	_UpperCAmelCase=7						,	_UpperCAmelCase=True						,	_UpperCAmelCase=True						,	_UpperCAmelCase=True						,	_UpperCAmelCase=True						,	_UpperCAmelCase=99						,	_UpperCAmelCase=32						,	_UpperCAmelCase=5						,	_UpperCAmelCase=4						,	_UpperCAmelCase=37						,	_UpperCAmelCase="gelu"						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=512						,	_UpperCAmelCase=16						,	_UpperCAmelCase=2						,	_UpperCAmelCase=0.02						,	_UpperCAmelCase=4						,	):

          '''simple docstring'''






          __A						: List[Any]      =       parent
          __A						: List[Any]      =       batch_size
          __A						: Tuple      =       seq_length
          __A						: Optional[int]      =       is_training
          __A						: Optional[Any]      =       use_attention_mask
          __A						: Dict      =       use_token_type_ids
          __A						: List[str]      =       use_labels
          __A						: Optional[int]      =       vocab_size
          __A						: int      =       hidden_size
          __A						: Optional[Any]      =       num_hidden_layers
          __A						: List[Any]      =       num_attention_heads
          __A						: str      =       intermediate_size
          __A						: str      =       hidden_act
          __A						: Optional[int]      =       hidden_dropout_prob
          __A						: Tuple      =       attention_probs_dropout_prob
          __A						: Optional[int]      =       max_position_embeddings
          __A						: List[str]      =       type_vocab_size
          __A						: Tuple      =       type_sequence_label_size
          __A						: Optional[int]      =       initializer_range
          __A						: Optional[Any]      =       num_choices


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size)

          __A						: str      =       None
          if self.use_attention_mask:
               __A						: List[Any]      =       random_attention_mask([self.batch_size, self.seq_length])

          __A						: Tuple      =       None
          if self.use_token_type_ids:
               __A						: Optional[int]      =       ids_tensor([self.batch_size, self.seq_length]						,	self.type_vocab_size)

          __A						: Optional[int]      =       RobertaConfig(
              vocab_size=self.vocab_size						,	hidden_size=self.hidden_size						,	num_hidden_layers=self.num_hidden_layers						,	num_attention_heads=self.num_attention_heads						,	intermediate_size=self.intermediate_size						,	hidden_act=self.hidden_act						,	hidden_dropout_prob=self.hidden_dropout_prob						,	attention_probs_dropout_prob=self.attention_probs_dropout_prob						,	max_position_embeddings=self.max_position_embeddings						,	type_vocab_size=self.type_vocab_size						,	is_decoder=_UpperCAmelCase						,	initializer_range=self.initializer_range						,	)

          return config, input_ids, token_type_ids, attention_mask


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[str]      =       self.prepare_config_and_inputs()
          __A ,__A ,__A ,__A						: List[str]      =       config_and_inputs
          __A						: str      =       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
          return config, inputs_dict


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Dict      =       self.prepare_config_and_inputs()
          __A ,__A ,__A ,__A						: Tuple      =       config_and_inputs

          __A						: Tuple      =       True
          __A						: List[str]      =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
          __A						: List[str]      =       ids_tensor([self.batch_size, self.seq_length]						,	vocab_size=2)

          return (
              config,
              input_ids,
              token_type_ids,
              encoder_hidden_states,
              encoder_attention_mask,
          )




@require_flax
class  SCREAMING_SNAKE_CASE    (a__					,		unittest.TestCase							):
     lowerCAmelCase					=       True

     lowerCAmelCase					=       (
         (
             FlaxRobertaModel,
             FlaxRobertaForCausalLM,
             FlaxRobertaForMaskedLM,
             FlaxRobertaForSequenceClassification,
             FlaxRobertaForTokenClassification,
             FlaxRobertaForMultipleChoice,
             FlaxRobertaForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Dict      =       FlaxRobertaModelTester(self)


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for model_class_name in self.all_model_classes:
               __A						: Optional[Any]      =       model_class_name.from_pretrained('roberta-base'						,	from_pt=_UpperCAmelCase)
               __A						: Any      =       model(np.ones((1, 1)))
               self.assertIsNotNone(_UpperCAmelCase)






'''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)}""")