


'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__magic_name__			=      {
    'configuration_albert': ['ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'AlbertConfig', 'AlbertOnnxConfig'],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      ['AlbertTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      ['AlbertTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      [
         'ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'AlbertForMaskedLM',
         'AlbertForMultipleChoice',
         'AlbertForPreTraining',
         'AlbertForQuestionAnswering',
         'AlbertForSequenceClassification',
         'AlbertForTokenClassification',
         'AlbertModel',
         'AlbertPreTrainedModel',
         'load_tf_weights_in_albert',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      [
         'TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFAlbertForMaskedLM',
         'TFAlbertForMultipleChoice',
         'TFAlbertForPreTraining',
         'TFAlbertForQuestionAnswering',
         'TFAlbertForSequenceClassification',
         'TFAlbertForTokenClassification',
         'TFAlbertMainLayer',
         'TFAlbertModel',
         'TFAlbertPreTrainedModel',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      [
         'FlaxAlbertForMaskedLM',
         'FlaxAlbertForMultipleChoice',
         'FlaxAlbertForPreTraining',
         'FlaxAlbertForQuestionAnswering',
         'FlaxAlbertForSequenceClassification',
         'FlaxAlbertForTokenClassification',
         'FlaxAlbertModel',
         'FlaxAlbertPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_albert import AlbertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_albert_fast import AlbertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_albert import (
              ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              AlbertForMaskedLM,
              AlbertForMultipleChoice,
              AlbertForPreTraining,
              AlbertForQuestionAnswering,
              AlbertForSequenceClassification,
              AlbertForTokenClassification,
              AlbertModel,
              AlbertPreTrainedModel,
              load_tf_weights_in_albert,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_albert import (
              TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFAlbertForMaskedLM,
              TFAlbertForMultipleChoice,
              TFAlbertForPreTraining,
              TFAlbertForQuestionAnswering,
              TFAlbertForSequenceClassification,
              TFAlbertForTokenClassification,
              TFAlbertMainLayer,
              TFAlbertModel,
              TFAlbertPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_albert import (
              FlaxAlbertForMaskedLM,
              FlaxAlbertForMultipleChoice,
              FlaxAlbertForPreTraining,
              FlaxAlbertForQuestionAnswering,
              FlaxAlbertForSequenceClassification,
              FlaxAlbertForTokenClassification,
              FlaxAlbertModel,
              FlaxAlbertPreTrainedModel,
          )
else:
     import sys

     __magic_name__			=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




'''simple docstring'''


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




def      lowerCamelCase					(      lowerCamelCase  :      str							,						lowerCamelCase  :      complex							,						lowerCamelCase  :      str = "x"							,						lowerCamelCase  :      float = 10**-10							,						lowerCamelCase  :      int = 1							,						):
      A_						:  int          =				symbols(lowerCamelCase)
      A_						:  List[Any]          =				lambdify(lowerCamelCase							,						lowerCamelCase)
      A_						:  List[str]          =				lambdify(lowerCamelCase							,						diff(lowerCamelCase							,						lowerCamelCase))

      A_						:  str          =				starting_point

      while True:
            if diff_function(lowerCamelCase) != 0:
                  A_						:  int          =				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

            A_						:  Union[str, Any]          =				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)}""")
