
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


__A							=logging.get_logger(__name__)


if is_vision_available():
			import PIL




class        _SCREAMING_SNAKE_CASE	( _UpperCamelCase		):
	lowerCAmelCase__   =					["""pixel_values"""]



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

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



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



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    lowercase = None		,    **lowercase		,    )							->			np.ndarray:
					lowerCamelCase_         =     get_size_dict(lowercase					)
					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(lowercase		,    size=(size["height"], size["width"])		,    data_format=lowercase		,    **lowercase					)



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    lowercase = None		,    **lowercase		,    )							->			Optional[int]:
					return rescale(lowercase		,    scale=lowercase		,    data_format=lowercase		,    **lowercase					)



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



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase = None		,    lowercase = None		,    lowercase = None		,    lowercase = None		,    lowercase = None		,    lowercase = None		,    lowercase = None		,    lowercase = None		,    lowercase = None		,    lowercase = None		,    lowercase = None		,    lowercase = None		,    lowercase = ChannelDimension.FIRST		,    **lowercase		,    )							->			PIL.Image.Image:
					lowerCamelCase_         =     do_resize if do_resize is not None else self.do_resize
					lowerCamelCase_         =     size if size is not None else self.size
					lowerCamelCase_         =     get_size_dict(lowercase		,    param_name="size"		,    default_to_square=lowercase					)
					lowerCamelCase_         =     resample if resample is not None else self.resample
					lowerCamelCase_         =     do_center_crop if do_center_crop is not None else self.do_center_crop
					lowerCamelCase_         =     crop_size if crop_size is not None else self.crop_size
					lowerCamelCase_         =     get_size_dict(lowercase		,    param_name="crop_size"		,    default_to_square=lowercase					)
					lowerCamelCase_         =     do_rescale if do_rescale is not None else self.do_rescale
					lowerCamelCase_         =     rescale_factor if rescale_factor is not None else self.rescale_factor
					lowerCamelCase_         =     do_normalize if do_normalize is not None else self.do_normalize
					lowerCamelCase_         =     image_mean if image_mean is not None else self.image_mean
					lowerCamelCase_         =     image_std if image_std is not None else self.image_std
					lowerCamelCase_         =     do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

					lowerCamelCase_         =     make_list_of_images(lowercase					)

					if not valid_images(lowercase					):
									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:
									lowerCamelCase_         =     [convert_to_rgb(lowercase					) for image in images]

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

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

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

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

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

					lowerCamelCase_         =     [to_channel_dimension_format(lowercase		,    lowercase					) for image in images]

					lowerCamelCase_         =     {"pixel_values": images}
					return BatchFeature(data=lowercase		,    tensor_type=lowercase					)









def    _A	(   lowerCamelCase = 200 ):
   a__						:    List[str]   		=	[1, 2, 5, 10, 20, 50, 100, 200]
   a__						:    Dict   		=	[0] * (pence + 1)
   a__						:    int   		=	1  # base case: 1 way to make 0 pence

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


if __name__ == "__main__":
  assert solution(2_0_0) == 7_3_6_8_2

