

from typing import TYPE_CHECKING

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


lowerCamelCase 	=   {
    '''configuration_openai''': ['''OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OpenAIGPTConfig'''],
    '''tokenization_openai''': ['''OpenAIGPTTokenizer'''],
}

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase 	=   [
           '''OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''OpenAIGPTDoubleHeadsModel''',
           '''OpenAIGPTForSequenceClassification''',
           '''OpenAIGPTLMHeadModel''',
           '''OpenAIGPTModel''',
           '''OpenAIGPTPreTrainedModel''',
           '''load_tf_weights_in_openai_gpt''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase 	=   [
           '''TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFOpenAIGPTDoubleHeadsModel''',
           '''TFOpenAIGPTForSequenceClassification''',
           '''TFOpenAIGPTLMHeadModel''',
           '''TFOpenAIGPTMainLayer''',
           '''TFOpenAIGPTModel''',
           '''TFOpenAIGPTPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
       from .tokenization_openai import OpenAIGPTTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_openai_fast import OpenAIGPTTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_openai import (
                  OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  OpenAIGPTDoubleHeadsModel,
                  OpenAIGPTForSequenceClassification,
                  OpenAIGPTLMHeadModel,
                  OpenAIGPTModel,
                  OpenAIGPTPreTrainedModel,
                  load_tf_weights_in_openai_gpt,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_openai import (
                  TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFOpenAIGPTDoubleHeadsModel,
                  TFOpenAIGPTForSequenceClassification,
                  TFOpenAIGPTLMHeadModel,
                  TFOpenAIGPTMainLayer,
                  TFOpenAIGPTModel,
                  TFOpenAIGPTPreTrainedModel,
              )

else:
       import sys

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




"""simple docstring"""





import math as m


def   _snake_case    (    _snake_case					:      List[Any]			, _snake_case					:      Tuple			, _snake_case					:      Tuple=0.001      ):
      return (f(a + h      ) - f(a - h      )) / (2 * h)


def   _snake_case    (    _snake_case					:      int			, _snake_case					:      List[Any]=0			, _snake_case					:      Any=100			, _snake_case					:      Any=0.0001			, _snake_case					:      Optional[Any]=1E-6			, _snake_case					:      Any=False      ):
      lowerCAmelCase       :						Optional[Any]							= xa  # set the initial guess
      lowerCAmelCase       :						str							= [a]
      lowerCAmelCase       :						List[str]							= abs(f(_snake_case      )      )
      lowerCAmelCase       :						Dict							= lambda _snake_case      : calc_derivative(_snake_case			, _snake_case			, h=_snake_case      )  # noqa: E731  Derivative of f(x)
      for _ in range(_snake_case      ):
            if fa(_snake_case      ) == 0:
                  raise ValueError('''No converging solution found'''      )
            lowerCAmelCase       :						Tuple							= a - f(_snake_case      ) / fa(_snake_case      )  # Calculate the next estimate
            if logsteps:
                  steps.append(_snake_case      )
            if error < maxerror:
                  break
      else:
            raise ValueError('''Iteration limit reached, no converging solution found'''      )
      if logsteps:
            # If logstep is true, then log intermediate steps
            return a, error, steps
      return a, error


if __name__ == "__main__":
 from matplotlib import pyplot as plt

 snake_case__		:  Any     					=					lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
 snake_case__					,  snake_case__					,  snake_case__		:  List[str]     					=					newton_raphson(
     f, xa=10, maxiter=1_000, step=1e-6, logsteps=True
 )
 plt.plot([abs(f(x)) for x in steps])
 plt.xlabel('''step''')
 plt.ylabel('''error''')
 plt.show()
 print(f"""solution = {{{solution:f}}}, error = {{{error:f}}}""")


