





'''simple docstring'''


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


A_				     =							logging.get_logger(__name__)


class 		UpperCamelCase__     (       a							):
      '''simple docstring'''






      _snake_case           =				['''pixel_values''']




      def __init__(     self     , SCREAMING_SNAKE_CASE = True     , SCREAMING_SNAKE_CASE = None     , SCREAMING_SNAKE_CASE = True     , SCREAMING_SNAKE_CASE = None     , SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC     , SCREAMING_SNAKE_CASE = True     , SCREAMING_SNAKE_CASE = 1 / 2_55     , SCREAMING_SNAKE_CASE = True     , SCREAMING_SNAKE_CASE = None     , SCREAMING_SNAKE_CASE = None     , **SCREAMING_SNAKE_CASE     , )				->  None:
        super().__init__(**SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    int			     =  crop_size if crop_size is not None else {'height': 2_56, 'width': 2_56}
        __lowerCAmelCase		:    Optional[Any]			     =  get_size_dict(SCREAMING_SNAKE_CASE     , param_name='crop_size' )
        __lowerCAmelCase		:    Tuple			     =  size if size is not None else {'height': 2_24, 'width': 2_24}
        __lowerCAmelCase		:    Any			     =  get_size_dict(SCREAMING_SNAKE_CASE )

        __lowerCAmelCase		:    Dict			     =  do_center_crop
        __lowerCAmelCase		:    Optional[int]			     =  crop_size
        __lowerCAmelCase		:    Union[str, Any]			     =  do_resize
        __lowerCAmelCase		:    Union[str, Any]			     =  size
        __lowerCAmelCase		:    int			     =  resample
        __lowerCAmelCase		:    int			     =  do_rescale
        __lowerCAmelCase		:    int			     =  rescale_factor
        __lowerCAmelCase		:    List[Any]			     =  do_normalize
        __lowerCAmelCase		:    List[str]			     =  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        __lowerCAmelCase		:    Union[str, Any]			     =  image_std if image_std is not None else IMAGENET_DEFAULT_STD




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE = None     , SCREAMING_SNAKE_CASE = None     , **SCREAMING_SNAKE_CASE     , )				->  np.ndarray:
        __lowerCAmelCase		:    Optional[Any]			     =  self.size if size is None else size
        __lowerCAmelCase		:    str			     =  get_size_dict(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    int			     =  get_size_dict(SCREAMING_SNAKE_CASE     , param_name='crop_size' )

        __lowerCAmelCase     ,					__lowerCAmelCase		:    str			     =  get_image_size(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Optional[Any]			     =  min(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    List[str]			     =  (size['height'] / crop_size['height']) * min_dim
        __lowerCAmelCase		:    Optional[int]			     =  (size['width'] / crop_size['width']) * min_dim
        return center_crop(SCREAMING_SNAKE_CASE     , size=(cropped_height, cropped_width)     , data_format=SCREAMING_SNAKE_CASE     , **SCREAMING_SNAKE_CASE )




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE = PIL.Image.BICUBIC     , SCREAMING_SNAKE_CASE = None     , **SCREAMING_SNAKE_CASE     , )				->  np.ndarray:
        __lowerCAmelCase		:    str			     =  get_size_dict(SCREAMING_SNAKE_CASE )
        if "height" not in size or "width" not in size:
          raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
        return resize(
            SCREAMING_SNAKE_CASE     , size=(size['height'], size['width'])     , resample=SCREAMING_SNAKE_CASE     , data_format=SCREAMING_SNAKE_CASE     , **SCREAMING_SNAKE_CASE )




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE = None     , **SCREAMING_SNAKE_CASE     , )				->  List[str]:
        return rescale(SCREAMING_SNAKE_CASE     , scale=SCREAMING_SNAKE_CASE     , data_format=SCREAMING_SNAKE_CASE     , **SCREAMING_SNAKE_CASE )




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE = None     , **SCREAMING_SNAKE_CASE     , )				->  np.ndarray:
        return normalize(SCREAMING_SNAKE_CASE     , mean=SCREAMING_SNAKE_CASE     , std=SCREAMING_SNAKE_CASE     , data_format=SCREAMING_SNAKE_CASE     , **SCREAMING_SNAKE_CASE )







      def 	snake_case  (     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 = None     , 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     , )				->  PIL.Image.Image:
        __lowerCAmelCase		:    int			     =  do_center_crop if do_center_crop is not None else self.do_center_crop
        __lowerCAmelCase		:    Tuple			     =  crop_size if crop_size is not None else self.crop_size
        __lowerCAmelCase		:    Optional[int]			     =  get_size_dict(SCREAMING_SNAKE_CASE     , param_name='crop_size' )
        __lowerCAmelCase		:    List[Any]			     =  do_resize if do_resize is not None else self.do_resize
        __lowerCAmelCase		:    List[str]			     =  size if size is not None else self.size
        __lowerCAmelCase		:    Tuple			     =  get_size_dict(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Optional[int]			     =  resample if resample is not None else self.resample
        __lowerCAmelCase		:    str			     =  do_rescale if do_rescale is not None else self.do_rescale
        __lowerCAmelCase		:    Dict			     =  rescale_factor if rescale_factor is not None else self.rescale_factor
        __lowerCAmelCase		:    Optional[int]			     =  do_normalize if do_normalize is not None else self.do_normalize
        __lowerCAmelCase		:    Any			     =  image_mean if image_mean is not None else self.image_mean
        __lowerCAmelCase		:    Dict			     =  image_std if image_std is not None else self.image_std

        __lowerCAmelCase		:    Optional[Any]			     =  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_center_crop and crop_size is None:
          raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.' )

        if do_resize and size is None:
          raise ValueError('Size must be specified if do_resize is True.' )

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

        if do_normalize and (image_mean is None or image_std is None):
          raise ValueError('Image mean and image standard deviation must be specified if do_normalize is True.' )

        # All transformations expect numpy arrays.
        __lowerCAmelCase		:    Union[str, Any]			     =  [to_numpy_array(SCREAMING_SNAKE_CASE ) for image in images]

        if do_center_crop:
          __lowerCAmelCase		:    Union[str, Any]			     =  [self.center_crop(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , size=SCREAMING_SNAKE_CASE ) for image in images]

        if do_resize:
          __lowerCAmelCase		:    Optional[int]			     =  [self.resize(image=SCREAMING_SNAKE_CASE     , size=SCREAMING_SNAKE_CASE     , resample=SCREAMING_SNAKE_CASE ) for image in images]

        if do_rescale:
          __lowerCAmelCase		:    Optional[Any]			     =  [self.rescale(image=SCREAMING_SNAKE_CASE     , scale=SCREAMING_SNAKE_CASE ) for image in images]

        if do_normalize:
          __lowerCAmelCase		:    int			     =  [self.normalize(image=SCREAMING_SNAKE_CASE     , mean=SCREAMING_SNAKE_CASE     , std=SCREAMING_SNAKE_CASE ) for image in images]

        __lowerCAmelCase		:    Optional[Any]			     =  [to_channel_dimension_format(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE ) for image in images]

        __lowerCAmelCase		:    Dict			     =  {'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 = 1_0_0						,)  ->				float:


  '''simple docstring'''




  __lowerCAmelCase		:    Dict			     =  x_start
  __lowerCAmelCase		:    Tuple			     =  fnc(_UpperCAmelCase					)
  __lowerCAmelCase		:    Optional[int]			     =  0.0

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

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


if __name__ == "__main__":




    def       A							(      _UpperCAmelCase   :				List[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:")
    A_				     =							10
    while i <= 10_00_00:
        A_				     =							trapezoidal_area(f, -5, 5, i)
        print(F'''with {i} steps: {area}''')
        i *= 10





