





from __future__ import annotations

from collections.abc import Callable







def 						_lowercase       (     __lowerCamelCase			:					Callable[[int | float], int | float]		,__lowerCamelCase			:					int | float		,__lowerCamelCase			:					int | float		,__lowerCamelCase			:					int = 100		,)  ->   float:





     '''simple docstring'''





     UpperCamelCase__							:  Tuple								=     x_start
     UpperCamelCase__							:  Any								=     fnc(__lowerCamelCase			)
     UpperCamelCase__							:  List[str]								=     0.0

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

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


if __name__ == "__main__":







   def 						_lowercase       (     __lowerCamelCase			:					Union[str, Any]			)  ->   List[Any]:





        '''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:""")
   _SCREAMING_SNAKE_CASE :					List[str]			=	10
   while i <= 100000:
      _SCREAMING_SNAKE_CASE :					Dict			=	trapezoidal_area(f, -5, 5, i)
      print(F'with {i} steps: {area}')
      i *= 10








from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE :					str			=	{
    """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 :					List[Any]			=	[
       """DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """DonutSwinModel""",
       """DonutSwinPreTrainedModel""",
   ]

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					List[Any]			=	["""DonutFeatureExtractor"""]
   _SCREAMING_SNAKE_CASE :					Tuple			=	["""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 :					Optional[Any]			=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




