



'''simple docstring'''

def 						_snake_case						(   _SCREAMING_SNAKE_CASE	:		int = 200				)     ->		Union[str, Any]:







	"""simple docstring"""
	lowerCAmelCase       =      [1, 2, 5, 10, 20, 50, 100, 200]
	lowerCAmelCase       =      [0] * (pence + 1)
	lowerCAmelCase       =      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) == 7_3682



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


lowerCamelCase		   =			logging.get_logger(__name__)


if is_vision_available():
 import PIL




class A    (  UpperCamelCase_	):
      UpperCamelCase__				:    List[str] =['pixel_values']






      def __init__(   self		:	Any     ,  lowercase_		:	bool = True     ,  lowercase_		:	Dict[str, int] = None     ,  lowercase_		:	PILImageResampling = PILImageResampling.BICUBIC     ,  lowercase_		:	bool = True     ,  lowercase_		:	Dict[str, int] = None     ,  lowercase_		:	bool = True     ,  lowercase_		:	Union[int, float] = 1 / 255     ,  lowercase_		:	bool = True     ,  lowercase_		:	Optional[Union[float, List[float]]] = None     ,  lowercase_		:	Optional[Union[float, List[float]]] = None     ,  lowercase_		:	bool = True     ,  **lowercase_		:	str     ,  )   ->		None:
             """simple docstring"""




             super().__init__(**lowercase_					)
             _lowerCamelCase			:						Optional[int]            =size if size is not None else {'shortest_edge': 224}
             _lowerCamelCase			:						List[Any]            =get_size_dict(lowercase_     ,  default_to_square=lowercase_					)
             _lowerCamelCase			:						str            =crop_size if crop_size is not None else {'height': 224, 'width': 224}
             _lowerCamelCase			:						str            =get_size_dict(lowercase_     ,  default_to_square=lowercase_     ,  param_name='crop_size'					)

             _lowerCamelCase			:						Dict            =do_resize
             _lowerCamelCase			:						int            =size
             _lowerCamelCase			:						Optional[Any]            =resample
             _lowerCamelCase			:						Optional[int]            =do_center_crop
             _lowerCamelCase			:						Tuple            =crop_size
             _lowerCamelCase			:						Optional[int]            =do_rescale
             _lowerCamelCase			:						Optional[Any]            =rescale_factor
             _lowerCamelCase			:						Union[str, Any]            =do_normalize
             _lowerCamelCase			:						Optional[int]            =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
             _lowerCamelCase			:						Any            =image_std if image_std is not None else OPENAI_CLIP_STD
             _lowerCamelCase			:						Any            =do_convert_rgb






      def 			lowerCamelCase			(   self		:	int     ,  lowercase_		:	np.ndarray     ,  lowercase_		:	Dict[str, int]     ,  lowercase_		:	PILImageResampling = PILImageResampling.BICUBIC     ,  lowercase_		:	Optional[Union[str, ChannelDimension]] = None     ,  **lowercase_		:	Any     ,  )   ->		np.ndarray:
             """simple docstring"""




             _lowerCamelCase			:						int            =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			:						Union[str, Any]            =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 			lowerCamelCase			(   self		:	Union[str, Any]     ,  lowercase_		:	np.ndarray     ,  lowercase_		:	Dict[str, int]     ,  lowercase_		:	Optional[Union[str, ChannelDimension]] = None     ,  **lowercase_		:	Tuple     ,  )   ->		np.ndarray:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =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 			lowerCamelCase			(   self		:	str     ,  lowercase_		:	np.ndarray     ,  lowercase_		:	Union[int, float]     ,  lowercase_		:	Optional[Union[str, ChannelDimension]] = None     ,  **lowercase_		:	int     ,  )   ->		str:
             """simple docstring"""




             return rescale(lowercase_     ,  scale=lowercase_     ,  data_format=lowercase_     ,  **lowercase_					)






      def 			lowerCamelCase			(   self		:	Optional[int]     ,  lowercase_		:	np.ndarray     ,  lowercase_		:	Union[float, List[float]]     ,  lowercase_		:	Union[float, List[float]]     ,  lowercase_		:	Optional[Union[str, ChannelDimension]] = None     ,  **lowercase_		:	Tuple     ,  )   ->		np.ndarray:
             """simple docstring"""




             return normalize(lowercase_     ,  mean=lowercase_     ,  std=lowercase_     ,  data_format=lowercase_     ,  **lowercase_					)






      def 			lowerCamelCase			(   self		:	List[str]     ,  lowercase_		:	ImageInput     ,  lowercase_		:	bool = None     ,  lowercase_		:	Dict[str, int] = None     ,  lowercase_		:	PILImageResampling = None     ,  lowercase_		:	bool = None     ,  lowercase_		:	int = None     ,  lowercase_		:	bool = None     ,  lowercase_		:	float = None     ,  lowercase_		:	bool = None     ,  lowercase_		:	Optional[Union[float, List[float]]] = None     ,  lowercase_		:	Optional[Union[float, List[float]]] = None     ,  lowercase_		:	bool = None     ,  lowercase_		:	Optional[Union[str, TensorType]] = None     ,  lowercase_		:	Optional[ChannelDimension] = ChannelDimension.FIRST     ,  **lowercase_		:	Union[str, Any]     ,  )   ->		PIL.Image.Image:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, 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			:						Any            =get_size_dict(lowercase_     ,  param_name='size'     ,  default_to_square=lowercase_					)
             _lowerCamelCase			:						str            =resample if resample is not None else self.resample
             _lowerCamelCase			:						List[Any]            =do_center_crop if do_center_crop is not None else self.do_center_crop
             _lowerCamelCase			:						Optional[Any]            =crop_size if crop_size is not None else self.crop_size
             _lowerCamelCase			:						Dict            =get_size_dict(lowercase_     ,  param_name='crop_size'     ,  default_to_square=lowercase_					)
             _lowerCamelCase			:						str            =do_rescale if do_rescale is not None else self.do_rescale
             _lowerCamelCase			:						Tuple            =rescale_factor if rescale_factor is not None else self.rescale_factor
             _lowerCamelCase			:						List[str]            =do_normalize if do_normalize is not None else self.do_normalize
             _lowerCamelCase			:						Tuple            =image_mean if image_mean is not None else self.image_mean
             _lowerCamelCase			:						int            =image_std if image_std is not None else self.image_std
             _lowerCamelCase			:						Tuple            =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

             _lowerCamelCase			:						Any            =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			:						Tuple            =[convert_to_rgb(lowercase_					) for image in images]

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

             if do_resize:
                    _lowerCamelCase			:						Optional[Any]            =[self.resize(image=lowercase_     ,  size=lowercase_     ,  resample=lowercase_					) for image in images]

             if do_center_crop:
                    _lowerCamelCase			:						Optional[int]            =[self.center_crop(image=lowercase_     ,  size=lowercase_					) for image in images]

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

             if do_normalize:
                    _lowerCamelCase			:						List[Any]            =[self.normalize(image=lowercase_     ,  mean=lowercase_     ,  std=lowercase_					) for image in images]

             _lowerCamelCase			:						List[str]            =[to_channel_dimension_format(lowercase_     ,  lowercase_					) for image in images]

             _lowerCamelCase			:						Tuple            ={'pixel_values': images}
             return BatchFeature(data=lowercase_     ,  tensor_type=lowercase_					)


