


"""simple docstring"""
from __future__ import annotations

from collections.abc import Callable

def 				_snake_case      (   __snake_case							:      Callable[[int | float], int | float]						, __snake_case							:      int | float						, __snake_case							:      int | float						, __snake_case							:      int = 100						, ):




   """simple docstring"""







   _lowerCamelCase	:					Optional[int]   =		x_start
   _lowerCamelCase	:					Optional[int]   =		fnc(__snake_case   )
   _lowerCamelCase	:					Optional[int]   =		0.0
   for _ in range(__snake_case   ):
      # Approximates small segments of curve as linear and solve
      # for trapezoidal area
      _lowerCamelCase	:					int   =		(x_end - x_start) / steps + xa
      _lowerCamelCase	:					List[Any]   =		fnc(__snake_case   )
      area += abs(fxa + fxa   ) * (xa - xa) / 2
      # Increment step
      _lowerCamelCase	:					List[Any]   =		xa
      _lowerCamelCase	:					Any   =		fxa
   return area


if __name__ == "__main__":

 def 				_snake_case      (   __snake_case							:      List[Any]   ):




    """simple docstring"""







    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 <= 10_0000:
  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_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase				=						{
    """configuration_vision_encoder_decoder""": ["""VisionEncoderDecoderConfig""", """VisionEncoderDecoderOnnxConfig"""]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase				=						["""VisionEncoderDecoderModel"""]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase				=						["""TFVisionEncoderDecoderModel"""]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase				=						["""FlaxVisionEncoderDecoderModel"""]

if TYPE_CHECKING:
 from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
 import sys

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

