





"""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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


a		:				Any  			=    logging.get_logger(__name__)


class  __UpperCamelCase       (		UpperCAmelCase__		):
							lowerCamelCase     :      Tuple         =['pixel_values']






							def __init__(     self   ,      lowerCAmelCase__ = True   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = PIL.Image.BICUBIC   ,      lowerCAmelCase__ = True   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = 1 / 255   ,      lowerCAmelCase__ = True   ,      lowerCAmelCase__ = True   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				None:
								super().__init__(**lowerCAmelCase__   )
								a   :	str			=       size if size is not None else {"""height""": 256, """width""": 256}
								a   :	Union[str, Any]			=       get_size_dict(lowerCAmelCase__   )
								a   :	List[Any]			=       crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
								a   :	Union[str, Any]			=       get_size_dict(lowerCAmelCase__   ,      param_name="crop_size"   )

								a   :	List[str]			=       do_resize
								a   :	List[str]			=       size
								a   :	Optional[int]			=       resample
								a   :	Any			=       do_center_crop
								a   :	Optional[Any]			=       crop_size
								a   :	Any			=       do_rescale
								a   :	Union[str, Any]			=       rescale_factor
								a   :	List[str]			=       do_normalize
								a   :	Tuple			=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								a   :	Union[str, Any]			=       image_std if image_std is not None else IMAGENET_STANDARD_STD






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = PIL.Image.BICUBIC   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				np.ndarray:
								a   :	Any			=       get_size_dict(lowerCAmelCase__   )
								if "height" not in size or "width" not in size:
									raise ValueError(f"""The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}"""   )
								return resize(
								    lowerCAmelCase__   ,      size=(size["height"], size["width"])   ,      resample=lowerCAmelCase__   ,      data_format=lowerCAmelCase__   ,      **lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				np.ndarray:
								a   :	List[Any]			=       get_size_dict(lowerCAmelCase__   )
								if "height" not in size or "width" not in size:
									raise ValueError(f"""The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}"""   )
								return center_crop(lowerCAmelCase__   ,      size=(size["height"], size["width"])   ,      data_format=lowerCAmelCase__   ,      **lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				Tuple:
								return rescale(lowerCAmelCase__   ,      scale=lowerCAmelCase__   ,      data_format=lowerCAmelCase__   ,      **lowerCAmelCase__   )






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






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = ChannelDimension.FIRST   ,      **lowerCAmelCase__   ,      )			->				PIL.Image.Image:
								a   :	Any			=       do_resize if do_resize is not None else self.do_resize
								a   :	Optional[Any]			=       resample if resample is not None else self.resample
								a   :	Optional[int]			=       do_center_crop if do_center_crop is not None else self.do_center_crop
								a   :	Dict			=       do_rescale if do_rescale is not None else self.do_rescale
								a   :	List[Any]			=       rescale_factor if rescale_factor is not None else self.rescale_factor
								a   :	Dict			=       do_normalize if do_normalize is not None else self.do_normalize
								a   :	Optional[Any]			=       image_mean if image_mean is not None else self.image_mean
								a   :	Union[str, Any]			=       image_std if image_std is not None else self.image_std

								a   :	Union[str, Any]			=       size if size is not None else self.size
								a   :	List[str]			=       get_size_dict(lowerCAmelCase__   )
								a   :	List[str]			=       crop_size if crop_size is not None else self.crop_size
								a   :	List[Any]			=       get_size_dict(lowerCAmelCase__   ,      param_name="crop_size"   )

								a   :	int			=       make_list_of_images(lowerCAmelCase__   )

								if not valid_images(lowerCAmelCase__   ):
									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 or resample is None:
									raise ValueError("Size and resample 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."   )

								# All transformations expect numpy arrays.
								a   :	Optional[Any]			=       [to_numpy_array(lowerCAmelCase__   ) for image in images]

								if do_resize:
									a   :	List[str]			=       [self.resize(image=lowerCAmelCase__   ,      size=lowerCAmelCase__   ,      resample=lowerCAmelCase__   ) for image in images]

								if do_center_crop:
									a   :	Optional[Any]			=       [self.center_crop(image=lowerCAmelCase__   ,      size=lowerCAmelCase__   ) for image in images]

								if do_rescale:
									a   :	Tuple			=       [self.rescale(image=lowerCAmelCase__   ,      scale=lowerCAmelCase__   ) for image in images]

								if do_normalize:
									a   :	str			=       [self.normalize(image=lowerCAmelCase__   ,      mean=lowerCAmelCase__   ,      std=lowerCAmelCase__   ) for image in images]

								a   :	List[str]			=       [to_channel_dimension_format(lowerCAmelCase__   ,      lowerCAmelCase__   ) for image in images]

								a   :	List[Any]			=       {"""pixel_values""": images}
								return BatchFeature(data=lowerCAmelCase__   ,      tensor_type=lowerCAmelCase__   )









"""simple docstring"""




def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int = 200      )				->int:






	'''simple docstring'''






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

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


if __name__ == "__main__":
		assert solution(200) == 73682


