






from typing import TYPE_CHECKING

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


__a      :       Any				  =   {
    "configuration_rembert": ["REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RemBertConfig", "RemBertOnnxConfig"]
}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a      :       Tuple				  =   ["RemBertTokenizer"]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a      :       Optional[Any]				  =   ["RemBertTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a      :       Optional[Any]				  =   [
      "REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RemBertForCausalLM",
      "RemBertForMaskedLM",
      "RemBertForMultipleChoice",
      "RemBertForQuestionAnswering",
      "RemBertForSequenceClassification",
      "RemBertForTokenClassification",
      "RemBertLayer",
      "RemBertModel",
      "RemBertPreTrainedModel",
      "load_tf_weights_in_rembert",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a      :       int				  =   [
      "TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFRemBertForCausalLM",
      "TFRemBertForMaskedLM",
      "TFRemBertForMultipleChoice",
      "TFRemBertForQuestionAnswering",
      "TFRemBertForSequenceClassification",
      "TFRemBertForTokenClassification",
      "TFRemBertLayer",
      "TFRemBertModel",
      "TFRemBertPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_rembert import RemBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_rembert_fast import RemBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_rembert import (
        REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        RemBertForCausalLM,
        RemBertForMaskedLM,
        RemBertForMultipleChoice,
        RemBertForQuestionAnswering,
        RemBertForSequenceClassification,
        RemBertForTokenClassification,
        RemBertLayer,
        RemBertModel,
        RemBertPreTrainedModel,
        load_tf_weights_in_rembert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_rembert import (
        TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRemBertForCausalLM,
        TFRemBertForMaskedLM,
        TFRemBertForMultipleChoice,
        TFRemBertForQuestionAnswering,
        TFRemBertForSequenceClassification,
        TFRemBertForTokenClassification,
        TFRemBertLayer,
        TFRemBertModel,
        TFRemBertPreTrainedModel,
    )


else:
  import sys

  __a      :       int				  =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)












from __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str			,				__lowercase   :			float | Decimal			,				__lowercase   :			float = 1_0**-1_0    )						->      float:





    """simple docstring"""

    __A         =	a
    while True:
        __A         =	Decimal(__lowercase    ) - (
            Decimal(eval(__lowercase    )    ) / Decimal(eval(str(diff(__lowercase    )    )    )    )  # noqa: S307
        )
        # This number dictates the accuracy of the answer
        if abs(eval(__lowercase    )    ) < precision:  # noqa: S307
            return float(__lowercase    )


# 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
  print(f"""The root of x**2 - 5*x + 2 = 0 is {newton_raphson('x**2 - 5*x + 2', 0.4)}""")
  # Find Square Root of 5
  print(f"""The root of log(x) - 1 = 0 is {newton_raphson('log(x) - 1', 2)}""")
  # Exponential Roots
  print(f"""The root of exp(x) - 1 = 0 is {newton_raphson('exp(x) - 1', 0)}""")




