

'''simple docstring'''



from typing import TYPE_CHECKING

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


UpperCAmelCase				: Optional[int]         =  {'configuration_regnet': ['REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RegNetConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				: str         =  [
      'REGNET_PRETRAINED_MODEL_ARCHIVE_LIST',
      'RegNetForImageClassification',
      'RegNetModel',
      'RegNetPreTrainedModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				: Union[str, Any]         =  [
      'TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFRegNetForImageClassification',
      'TFRegNetModel',
      'TFRegNetPreTrainedModel',
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				: str         =  [
      'FlaxRegNetForImageClassification',
      'FlaxRegNetModel',
      'FlaxRegNetPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_regnet import (
        REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        RegNetForImageClassification,
        RegNetModel,
        RegNetPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_regnet import (
        TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRegNetForImageClassification,
        TFRegNetModel,
        TFRegNetPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_regnet import (
        FlaxRegNetForImageClassification,
        FlaxRegNetModel,
        FlaxRegNetPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase				: Dict         =  _LazyModule(__name__, globals()['__file__'], _import_structure)









'''simple docstring'''



from __future__ import annotations

from collections.abc import Callable







def      a__ (						a__   ,					a__   ,					a__   ,					a__ = 1_00   ,					):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   x_start
     __SCREAMING_SNAKE_CASE						     =   fnc(a__		)
     __SCREAMING_SNAKE_CASE						     =   0.0

     for _ in range(a__		):
          # Approximates small segments of curve as linear and solve
          # for trapezoidal area
          __SCREAMING_SNAKE_CASE						     =   (x_end - x_start) / steps + xa
          __SCREAMING_SNAKE_CASE						     =   fnc(a__		)
          area += abs(fxa + fxa		) * (xa - xa) / 2

          # Increment step
          __SCREAMING_SNAKE_CASE						     =   xa
          __SCREAMING_SNAKE_CASE						     =   fxa
     return area


if __name__ == "__main__":







  def      a__ (						a__		):




       """simple docstring"""


       return x**3

  print('f(x) = x^3')
  print('The area between the curve, x = -10, x = 10 and the x axis is:')
  UpperCAmelCase				: Optional[Any]         =  1_0
  while i <= 1_0_0_0_0_0:
    UpperCAmelCase				: Union[str, Any]         =  trapezoidal_area(f, -5, 5, i)
    print(f"""with {i} steps: {area}""")
    i *= 1_0






