





'''simple docstring'''






from typing import TYPE_CHECKING

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


snake_case_     			=						{"""configuration_vilt""": ["""VILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViltConfig"""]}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_     			=						["""ViltFeatureExtractor"""]
     snake_case_     			=						["""ViltImageProcessor"""]
     snake_case_     			=						["""ViltProcessor"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_     			=						[
         """VILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """ViltForImageAndTextRetrieval""",
         """ViltForImagesAndTextClassification""",
         """ViltForTokenClassification""",
         """ViltForMaskedLM""",
         """ViltForQuestionAnswering""",
         """ViltLayer""",
         """ViltModel""",
         """ViltPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_vilt import ViltFeatureExtractor
          from .image_processing_vilt import ViltImageProcessor
          from .processing_vilt import ViltProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_vilt import (
              VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
              ViltForImageAndTextRetrieval,
              ViltForImagesAndTextClassification,
              ViltForMaskedLM,
              ViltForQuestionAnswering,
              ViltForTokenClassification,
              ViltLayer,
              ViltModel,
              ViltPreTrainedModel,
          )


else:
     import sys

     snake_case_     			=						_LazyModule(__name__, globals()["""__file__"""], _import_structure)



'''simple docstring'''

from __future__ import annotations

from collections.abc import Callable




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_ = 1_00    ,			):
 UpperCAmelCase			:      Any		   =					x_start
 UpperCAmelCase			:      int		   =					fnc(UpperCAmelCase_      )
 UpperCAmelCase			:      str		   =					0.0

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

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


if __name__ == "__main__":




 def  UpperCamelCase(	UpperCAmelCase_      ):
  return x**3

 print("f(x) = x^3")
 print("The area between the curve, x = -10, x = 10 and the x axis is:")
 lowercase__            =     10
 while i <= 100000:
  lowercase__            =     trapezoidal_area(f, -5, 5, i)
  print(f'''with {i} steps: {area}''')
  i *= 10




