

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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCamelCase__  :      Optional[Any]			=    logging.get_logger(__name__)


if is_vision_available():
       import PIL



class       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	List[Any]						 =  ['pixel_values']

  def __init__(					self						,		_A = True						,		_A = None						,		_A = PILImageResampling.BICUBIC						,		_A = True						,		_A = None						,		_A = True						,		_A = 1 / 255						,		_A = True						,		_A = None						,		_A = None						,		_A = True						,		**_A						,		):
      super().__init__(**_A)
      SCREAMING_SNAKE_CASE_									=						size if size is not None else {'shortest_edge': 224}
      SCREAMING_SNAKE_CASE_									=						get_size_dict(_A						,		default_to_square=_A)
      SCREAMING_SNAKE_CASE_									=						crop_size if crop_size is not None else {'height': 224, 'width': 224}
      SCREAMING_SNAKE_CASE_									=						get_size_dict(_A						,		default_to_square=_A						,		param_name='crop_size')

      SCREAMING_SNAKE_CASE_									=						do_resize
      SCREAMING_SNAKE_CASE_									=						size
      SCREAMING_SNAKE_CASE_									=						resample
      SCREAMING_SNAKE_CASE_									=						do_center_crop
      SCREAMING_SNAKE_CASE_									=						crop_size
      SCREAMING_SNAKE_CASE_									=						do_rescale
      SCREAMING_SNAKE_CASE_									=						rescale_factor
      SCREAMING_SNAKE_CASE_									=						do_normalize
      SCREAMING_SNAKE_CASE_									=						image_mean if image_mean is not None else OPENAI_CLIP_MEAN
      SCREAMING_SNAKE_CASE_									=						image_std if image_std is not None else OPENAI_CLIP_STD
      SCREAMING_SNAKE_CASE_									=						do_convert_rgb

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A = PILImageResampling.BICUBIC						,		_A = None						,		**_A						,		):
      SCREAMING_SNAKE_CASE_									=						get_size_dict(_A						,		default_to_square=_A)
      if "shortest_edge" not in size:
          raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""")
      SCREAMING_SNAKE_CASE_									=						get_resize_output_image_size(_A						,		size=size['shortest_edge']						,		default_to_square=_A)
      return resize(_A						,		size=_A						,		resample=_A						,		data_format=_A						,		**_A)

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A = None						,		**_A						,		):
      SCREAMING_SNAKE_CASE_									=						get_size_dict(_A)
      if "height" not in size or "width" not in size:
          raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""")
      return center_crop(_A						,		size=(size['height'], size['width'])						,		data_format=_A						,		**_A)

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A = None						,		**_A						,		):
      return rescale(_A						,		scale=_A						,		data_format=_A						,		**_A)

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A = None						,		**_A						,		):
      return normalize(_A						,		mean=_A						,		std=_A						,		data_format=_A						,		**_A)


  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None						,		_A = None						,		_A = None						,		_A = None						,		_A = None						,		_A = None						,		_A = None						,		_A = None						,		_A = None						,		_A = None						,		_A = None						,		_A = None						,		_A = ChannelDimension.FIRST						,		**_A						,		):
      SCREAMING_SNAKE_CASE_									=						do_resize if do_resize is not None else self.do_resize
      SCREAMING_SNAKE_CASE_									=						size if size is not None else self.size
      SCREAMING_SNAKE_CASE_									=						get_size_dict(_A						,		param_name='size'						,		default_to_square=_A)
      SCREAMING_SNAKE_CASE_									=						resample if resample is not None else self.resample
      SCREAMING_SNAKE_CASE_									=						do_center_crop if do_center_crop is not None else self.do_center_crop
      SCREAMING_SNAKE_CASE_									=						crop_size if crop_size is not None else self.crop_size
      SCREAMING_SNAKE_CASE_									=						get_size_dict(_A						,		param_name='crop_size'						,		default_to_square=_A)
      SCREAMING_SNAKE_CASE_									=						do_rescale if do_rescale is not None else self.do_rescale
      SCREAMING_SNAKE_CASE_									=						rescale_factor if rescale_factor is not None else self.rescale_factor
      SCREAMING_SNAKE_CASE_									=						do_normalize if do_normalize is not None else self.do_normalize
      SCREAMING_SNAKE_CASE_									=						image_mean if image_mean is not None else self.image_mean
      SCREAMING_SNAKE_CASE_									=						image_std if image_std is not None else self.image_std
      SCREAMING_SNAKE_CASE_									=						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

      SCREAMING_SNAKE_CASE_									=						make_list_of_images(_A)

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

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

      if do_center_crop and crop_size is None:
          raise ValueError('Crop size must be specified if do_center_crop 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 std must be specified if do_normalize is True.')

      # PIL RGBA images are converted to RGB
      if do_convert_rgb:
          SCREAMING_SNAKE_CASE_									=						[convert_to_rgb(_A) for image in images]

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

      if do_resize:
          SCREAMING_SNAKE_CASE_									=						[self.resize(image=_A						,		size=_A						,		resample=_A) for image in images]

      if do_center_crop:
          SCREAMING_SNAKE_CASE_									=						[self.center_crop(image=_A						,		size=_A) for image in images]

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

      if do_normalize:
          SCREAMING_SNAKE_CASE_									=						[self.normalize(image=_A						,		mean=_A						,		std=_A) for image in images]

      SCREAMING_SNAKE_CASE_									=						[to_channel_dimension_format(_A						,		_A) for image in images]

      SCREAMING_SNAKE_CASE_									=						{'pixel_values': images}
      return BatchFeature(data=_A						,		tensor_type=_A)





def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int = 200       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[1, 2, 5, 10, 20, 50, 100, 200]
    SCREAMING_SNAKE_CASE_									=						[0] * (pence + 1)
    SCREAMING_SNAKE_CASE_									=						1  # base case: 1 way to make 0 pence

    for coin in coins:
        for i in range(_SCREAMING_SNAKE_CASE ,   pence + 1 ,   1       ):
            number_of_ways[i] += number_of_ways[i - coin]
    return number_of_ways[pence]


if __name__ == "__main__":
       assert solution(200) == 73_682


