
'''simple docstring'''
from __future__ import annotations

from collections.abc import Callable

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE = 1_0_0,						):
    """simple docstring"""






    snake_case_	:      Optional[Any]            =  x_start
    snake_case_	:      str            =  fnc(__SCREAMING_SNAKE_CASE    )
    snake_case_	:      List[str]            =  0.0

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

        # Increment step
        snake_case_	:      List[Any]            =  xa
        snake_case_	:      Optional[Any]            =  fxa
    return area


if __name__ == "__main__":

  def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
      """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:")
  a_     =  10
  while i <= 10_0000:
    a_     =  trapezoidal_area(f, -5, 5, i)
    print(f"""with {i} steps: {area}""")
    i *= 10






'''simple docstring'''
from typing import TYPE_CHECKING

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


a_     =  {
    "configuration_splinter": ["SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SplinterConfig"],
    "tokenization_splinter": ["SplinterTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_     =  ["SplinterTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_     =  [
      "SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "SplinterForQuestionAnswering",
      "SplinterForPreTraining",
      "SplinterLayer",
      "SplinterModel",
      "SplinterPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
  from .tokenization_splinter import SplinterTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_splinter_fast import SplinterTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_splinter import (
        SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
        SplinterForPreTraining,
        SplinterForQuestionAnswering,
        SplinterLayer,
        SplinterModel,
        SplinterPreTrainedModel,
    )


else:
  import sys

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




