


'''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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


snake_case							: Any      =   logging.get_logger(__name__)







class  snake_case_     (lowerCAmelCase__   ):
					UpperCAmelCase__      :					str					   =    ["pixel_values"]
					def __init__(   self     :Dict    ,__snake_case     :Optional[int] = True    ,__snake_case     :Optional[int] = None    ,__snake_case     :Optional[int] = PILImageResampling.BICUBIC    ,__snake_case     :Union[str, Any] = True    ,__snake_case     :Any = True    ,__snake_case     :int = 1 / 2_55    ,__snake_case     :List[Any] = None    ,__snake_case     :Tuple = True    ,__snake_case     :Dict = None    ,__snake_case     :List[Any] = None    ,**__snake_case     :Tuple    ,)      ->					None:
												super().__init__(**_lowerCamelCase   )
												a__    =  size if size is not None else {'height': 2_24, 'width': 2_24}
												a__    =  get_size_dict(_lowerCamelCase   )
												a__    =  crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
												a__    =  get_size_dict(_lowerCamelCase    ,default_to_square=_lowerCamelCase    ,param_name='crop_size'   )

												a__    =  do_resize
												a__    =  do_rescale
												a__    =  do_normalize
												a__    =  do_center_crop
												a__    =  crop_size
												a__    =  size
												a__    =  resample
												a__    =  rescale_factor
												a__    =  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
												a__    =  image_std if image_std is not None else IMAGENET_DEFAULT_STD
					def 				lowerCamelCase__(   self     :List[str]    ,__snake_case     :List[str]    ,__snake_case     :Dict    ,__snake_case     :Any = PILImageResampling.BILINEAR    ,__snake_case     :List[Any] = None    ,**__snake_case     :Any    ,)      ->					np.ndarray:
												a__    =  get_size_dict(_lowerCamelCase   )

												if "shortest_edge" in size:
																			a__    =  get_resize_output_image_size(_lowerCamelCase    ,size=size['shortest_edge']    ,default_to_square=_lowerCamelCase   )
																			# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
												elif "height" in size and "width" in size:
																			a__    =  (size['height'], size['width'])
												else:
																			raise ValueError(F'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'   )
												return resize(_lowerCamelCase    ,size=_lowerCamelCase    ,resample=_lowerCamelCase    ,data_format=_lowerCamelCase    ,**_lowerCamelCase   )
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :Dict    ,__snake_case     :int    ,__snake_case     :Tuple = None    ,**__snake_case     :Tuple    ,)      ->					np.ndarray:
												a__    =  get_size_dict(_lowerCamelCase   )
												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(_lowerCamelCase    ,size=(size['height'], size['width'])    ,data_format=_lowerCamelCase    ,**_lowerCamelCase   )
					def 				lowerCamelCase__(   self     :Any    ,__snake_case     :Any    ,__snake_case     :str    ,__snake_case     :Any = None    ,**__snake_case     :str   )      ->					np.ndarray:
												return rescale(_lowerCamelCase    ,scale=_lowerCamelCase    ,data_format=_lowerCamelCase    ,**_lowerCamelCase   )
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :Optional[int]    ,__snake_case     :Tuple    ,__snake_case     :Union[str, Any]    ,__snake_case     :Optional[Any] = None    ,**__snake_case     :List[str]    ,)      ->					np.ndarray:
												return normalize(_lowerCamelCase    ,mean=_lowerCamelCase    ,std=_lowerCamelCase    ,data_format=_lowerCamelCase    ,**_lowerCamelCase   )



					def 				lowerCamelCase__(   self     :Dict    ,__snake_case     :Optional[Any]    ,__snake_case     :Optional[Any] = None    ,__snake_case     :Dict = None    ,__snake_case     :Optional[int] = None    ,__snake_case     :Optional[Any] = None    ,__snake_case     :Tuple = None    ,__snake_case     :Optional[int] = None    ,__snake_case     :Tuple = None    ,__snake_case     :Any = None    ,__snake_case     :Union[str, Any] = None    ,__snake_case     :Dict = None    ,__snake_case     :int = None    ,__snake_case     :str = ChannelDimension.FIRST    ,**__snake_case     :Dict    ,)      ->					BatchFeature:
												a__    =  do_resize if do_resize is not None else self.do_resize
												a__    =  do_rescale if do_rescale is not None else self.do_rescale
												a__    =  do_normalize if do_normalize is not None else self.do_normalize
												a__    =  do_center_crop if do_center_crop is not None else self.do_center_crop
												a__    =  crop_size if crop_size is not None else self.crop_size
												a__    =  get_size_dict(_lowerCamelCase    ,param_name='crop_size'    ,default_to_square=_lowerCamelCase   )
												a__    =  resample if resample is not None else self.resample
												a__    =  rescale_factor if rescale_factor is not None else self.rescale_factor
												a__    =  image_mean if image_mean is not None else self.image_mean
												a__    =  image_std if image_std is not None else self.image_std

												a__    =  size if size is not None else self.size
												a__    =  get_size_dict(_lowerCamelCase   )

												if not is_batched(_lowerCamelCase   ):
																			a__    =  [images]

												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:
																			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.'   )

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

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

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

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

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

												a__    =  [to_channel_dimension_format(_lowerCamelCase    ,_lowerCamelCase   ) for image in images]

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








def 							__lowercase     (				__lowerCAmelCase							:       int = 2_0_0     ):
							a__    =  [1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 2_0_0]
							a__    =  [0] * (pence + 1)
							a__    =  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_00) == 7_36_82




