




'''simple docstring'''





from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


UpperCamelCase_						:      str       =						logging.get_logger(__name__)

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   str       =						["""pixel_values"""]






     def __init__(			self ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = 1 / 255 ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = 8 ,**_SCREAMING_SNAKE_CASE ,)							->      None:
          super().__init__(**_SCREAMING_SNAKE_CASE   )

          _snake_case							=				do_rescale
          _snake_case							=				rescale_factor
          _snake_case							=				do_pad
          _snake_case							=				pad_size






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE   )							->      np.ndarray:
          return rescale(_SCREAMING_SNAKE_CASE ,scale=_SCREAMING_SNAKE_CASE ,data_format=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None   )							->      List[Any]:
          _snake_case							, _snake_case							=				get_image_size(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				(old_height // size + 1) * size - old_height
          _snake_case							=				(old_width // size + 1) * size - old_width

          return pad(_SCREAMING_SNAKE_CASE ,((0, pad_height), (0, pad_width)) ,mode="symmetric" ,data_format=_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST ,**_SCREAMING_SNAKE_CASE ,)							->      str:
          _snake_case							=				do_rescale if do_rescale is not None else self.do_rescale
          _snake_case							=				rescale_factor if rescale_factor is not None else self.rescale_factor
          _snake_case							=				do_pad if do_pad is not None else self.do_pad
          _snake_case							=				pad_size if pad_size is not None else self.pad_size

          _snake_case							=				make_list_of_images(_SCREAMING_SNAKE_CASE   )

          if not valid_images(_SCREAMING_SNAKE_CASE   ):
               raise ValueError(
                   "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                   "torch.Tensor, tf.Tensor or jax.ndarray."   )

          if do_rescale and rescale_factor is None:
               raise ValueError("Rescale factor must be specified if do_rescale is True."   )

          # All transformations expect numpy arrays.
          _snake_case							=				[to_numpy_array(_SCREAMING_SNAKE_CASE   ) for image in images]

          if do_rescale:
               _snake_case							=				[self.rescale(image=_SCREAMING_SNAKE_CASE ,scale=_SCREAMING_SNAKE_CASE   ) for image in images]

          if do_pad:
               _snake_case							=				[self.pad(_SCREAMING_SNAKE_CASE ,size=_SCREAMING_SNAKE_CASE   ) for image in images]

          _snake_case							=				[to_channel_dimension_format(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) for image in images]

          _snake_case							=				{"pixel_values": images}
          return BatchFeature(data=_SCREAMING_SNAKE_CASE ,tensor_type=_SCREAMING_SNAKE_CASE   )








'''simple docstring'''





from __future__ import annotations

from collections.abc import Callable





def      __a     (       _UpperCamelCase:   Callable[[int | float], int | float] ,						_UpperCamelCase:   int | float ,						_UpperCamelCase:   int | float ,						_UpperCamelCase:   int = 100 ,						)						->			float:

     """simple docstring"""




     _snake_case							=				x_start
     _snake_case							=				fnc(_UpperCamelCase   )
     _snake_case							=				0.0
     for _ in range(_UpperCamelCase   ):
          # Approximates small segments of curve as linear and solve
          # for trapezoidal area
          _snake_case							=				(x_end - x_start) / steps + xa
          _snake_case							=				fnc(_UpperCamelCase   )
          area += abs(fxa + fxa   ) * (xa - xa) / 2
          # Increment step
          _snake_case							=				xa
          _snake_case							=				fxa
     return area


if __name__ == "__main__":





   def      __a     (       _UpperCamelCase:   Any   )						->			Optional[int]:

        """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_						:      Optional[int]       =						10
   while i <= 100000:
      print(F'with {i} steps: {trapezoidal_area(f, -5, 5, i)}')
      i *= 10


