
'''simple docstring'''





import math as m







def snake_case_    (				lowercase__      ,					lowercase__      ,					lowercase__=0.001		):
  return (f(a + h		) - f(a - h		)) / (2 * h)







def snake_case_    (				lowercase__      ,					lowercase__=0      ,					lowercase__=1_0_0      ,					lowercase__=0.0001      ,					lowercase__=1E-6      ,					lowercase__=False		):
  UpperCAmelCase__			:				Dict												=       xa  # set the initial guess
  UpperCAmelCase__			:				Tuple												=       [a]
  UpperCAmelCase__			:				List[str]												=       abs(f(lowercase__		)		)
  UpperCAmelCase__			:				Any												=       lambda lowercase__		: calc_derivative(lowercase__      ,					lowercase__      ,					h=lowercase__		)  # noqa: E731  Derivative of f(x)
  for _ in range(lowercase__		):
    if fa(lowercase__		) == 0:
      raise ValueError("No converging solution found"		)
    UpperCAmelCase__			:				Union[str, Any]												=       a - f(lowercase__		) / fa(lowercase__		)  # Calculate the next estimate
    if logsteps:
      steps.append(lowercase__		)
    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

      SCREAMING_SNAKE_CASE    =					lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
      SCREAMING_SNAKE_CASE,	SCREAMING_SNAKE_CASE,	SCREAMING_SNAKE_CASE    =					newton_raphson(
          f, xa=1_0, maxiter=1_0_0_0, 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}}}')





'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE    =					{
    """configuration_donut_swin""": ["""DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DonutSwinConfig"""],
    """processing_donut""": ["""DonutProcessor"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =					[
          """DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """DonutSwinModel""",
          """DonutSwinPreTrainedModel""",
      ]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =					["""DonutFeatureExtractor"""]
      SCREAMING_SNAKE_CASE    =					["""DonutImageProcessor"""]


if TYPE_CHECKING:
      from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
      from .processing_donut import DonutProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_donut_swin import (
                DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
                DonutSwinModel,
                DonutSwinPreTrainedModel,
            )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_donut import DonutFeatureExtractor
            from .image_processing_donut import DonutImageProcessor

else:
      import sys

      SCREAMING_SNAKE_CASE    =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



