


def 						_lowercase       (     __lowerCamelCase			:					Optional[Any]		,__lowerCamelCase			:					Tuple			)  ->   int:





     '''simple docstring'''





     UpperCamelCase__							:  Tuple								=     (boundary[1] - boundary[0]) / steps
     UpperCamelCase__							:  List[str]								=     boundary[0]
     UpperCamelCase__							:  Optional[int]								=     boundary[1]
     UpperCamelCase__							:  Optional[int]								=     make_points(__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)
     UpperCamelCase__							:  Optional[Any]								=     0.0
     y += (h / 3.0) * f(__lowerCamelCase			)
     UpperCamelCase__							:  str								=     2
     for i in x_i:
          y += (h / 3) * (4 - 2 * (cnt % 2)) * f(__lowerCamelCase			)
          cnt += 1
     y += (h / 3.0) * f(__lowerCamelCase			)
     return y







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





     '''simple docstring'''





     UpperCamelCase__							:  Dict								=     a + h
     while x < (b - h):
          yield x
          UpperCamelCase__							:  List[Any]								=     x + h







def 						_lowercase       (     __lowerCamelCase			:					str			)  ->   List[Any]:  # enter your function here





     '''simple docstring'''





     UpperCamelCase__							:  Dict								=     (x - 0) * (x - 0)
     return y







def 						_lowercase       (     )  ->   List[Any]:





     '''simple docstring'''





     UpperCamelCase__							:  Union[str, Any]								=     0.0  # Lower bound of integration
     UpperCamelCase__							:  Tuple								=     1.0  # Upper bound of integration
     UpperCamelCase__							:  Tuple								=     1_0.0  # define number of steps or resolution
     UpperCamelCase__							:  Optional[int]								=     [a, b]  # define boundary of integration
     UpperCamelCase__							:  Dict								=     method_a(__lowerCamelCase		,__lowerCamelCase			)
     print(F'y = {y}'			)


if __name__ == "__main__":
   main()








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__)




