






from __future__ import annotations

from collections.abc import Callable




def 				A		(					lowercase__  :  Callable[[int | float], int | float] ,		lowercase__  :  int | float ,		lowercase__  :  int | float ,		lowercase__  :  int = 100 ,		)		-> float:
	UpperCamelCase__ :List[Any]						=      x_start
	UpperCamelCase__ :Any						=      fnc(lowercase__  )
	UpperCamelCase__ :Optional[Any]						=      0.0
	for _ in range(lowercase__  ):
		# Approximates small segments of curve as linear and solve
		# for trapezoidal area
		UpperCamelCase__ :Dict						=      (x_end - x_start) / steps + xa
		UpperCamelCase__ :Dict						=      fnc(lowercase__  )
		area += abs(fxa + fxa  ) * (xa - xa) / 2
		# Increment step
		UpperCamelCase__ :Optional[Any]						=      xa
		UpperCamelCase__ :Optional[Any]						=      fxa
	return area


if __name__ == "__main__":




							def 				A		(					lowercase__  :  Dict  )		-> List[Any]:
								return x**3 + x**2

							print("f(x) = x^3 + x^2")
							print("The area between the curve, x = -5, x = 5 and the x axis is:")
							UpperCamelCase        =				10
							while i <= 100_000:
														print(f'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
														i *= 10



'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase 							=  {
    """configuration_upernet""": ["""UperNetConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCamelCase 							=  [
          """UperNetForSemanticSegmentation""",
          """UperNetPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_upernet import UperNetConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
      import sys

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

