


"""simple docstring"""







def _A						(						lowercase = 2_00		):







    """simple docstring"""
    a 		=[1, 2, 5, 10, 20, 50, 1_00, 2_00]
    a 		=[0] * (pence + 1)
    a 		=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(2_0_0) == 7_3_6_8_2

'''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_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


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





class  A				(							__snake_case						):
     __magic_name__						=		['''pixel_values''']





     def __init__(							self						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = 1 / 255						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE						,			) ->				None:






         """simple docstring"""






         super().__init__(**SCREAMING_SNAKE_CASE	)
         A       :   str					=						size if size is not None else {'''shortest_edge''': 384}
         A       :   Tuple					=						get_size_dict(SCREAMING_SNAKE_CASE						,			default_to_square=SCREAMING_SNAKE_CASE	)

         A       :   str					=						do_resize
         A       :   List[Any]					=						size
         # Default value set here for backwards compatibility where the value in config is None
         A       :   List[Any]					=						crop_pct if crop_pct is not None else 224 / 256
         A       :   Optional[int]					=						resample
         A       :   Union[str, Any]					=						do_rescale
         A       :   List[str]					=						rescale_factor
         A       :   Union[str, Any]					=						do_normalize
         A       :   Any					=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         A       :   Optional[int]					=						image_std if image_std is not None else IMAGENET_STANDARD_STD





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE						,			) ->				np.ndarray:






         """simple docstring"""






         A       :   str					=						get_size_dict(SCREAMING_SNAKE_CASE						,			default_to_square=SCREAMING_SNAKE_CASE	)
         if "shortest_edge" not in size:
             raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'	)
         A       :   Any					=						size['''shortest_edge''']

         if shortest_edge < 384:
             # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
             A       :   Dict					=						int(shortest_edge / crop_pct	)
             A       :   str					=						get_resize_output_image_size(SCREAMING_SNAKE_CASE						,			size=SCREAMING_SNAKE_CASE						,			default_to_square=SCREAMING_SNAKE_CASE	)
             A       :   int					=						resize(image=SCREAMING_SNAKE_CASE						,			size=SCREAMING_SNAKE_CASE						,			resample=SCREAMING_SNAKE_CASE						,			data_format=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)
             # then crop to (shortest_edge, shortest_edge)
             return center_crop(image=SCREAMING_SNAKE_CASE						,			size=(shortest_edge, shortest_edge)						,			data_format=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)
         else:
             # warping (no cropping) when evaluated at 384 or larger
             return resize(
                 SCREAMING_SNAKE_CASE						,			size=(shortest_edge, shortest_edge)						,			resample=SCREAMING_SNAKE_CASE						,			data_format=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE						,			) ->				List[str]:






         """simple docstring"""






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





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE						,			) ->				np.ndarray:






         """simple docstring"""






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





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = ChannelDimension.FIRST						,			**SCREAMING_SNAKE_CASE						,			) ->				PIL.Image.Image:






         """simple docstring"""






         A       :   int					=						do_resize if do_resize is not None else self.do_resize
         A       :   Tuple					=						crop_pct if crop_pct is not None else self.crop_pct
         A       :   Optional[Any]					=						resample if resample is not None else self.resample
         A       :   List[Any]					=						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       :   Union[str, Any]					=						do_normalize if do_normalize is not None else self.do_normalize
         A       :   Union[str, Any]					=						image_mean if image_mean is not None else self.image_mean
         A       :   List[str]					=						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[Any]					=						get_size_dict(SCREAMING_SNAKE_CASE						,			default_to_square=SCREAMING_SNAKE_CASE	)

         A       :   Any					=						make_list_of_images(SCREAMING_SNAKE_CASE	)

         if not valid_images(SCREAMING_SNAKE_CASE	):
             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_resize and size["shortest_edge"] < 384 and crop_pct is None:
             raise ValueError('''crop_pct must be specified if size < 384.'''	)

         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[int]					=						[to_numpy_array(SCREAMING_SNAKE_CASE	) for image in images]

         if do_resize:
             A       :   Any					=						[self.resize(image=SCREAMING_SNAKE_CASE						,			size=SCREAMING_SNAKE_CASE						,			crop_pct=SCREAMING_SNAKE_CASE						,			resample=SCREAMING_SNAKE_CASE	) for image in images]

         if do_rescale:
             A       :   str					=						[self.rescale(image=SCREAMING_SNAKE_CASE						,			scale=SCREAMING_SNAKE_CASE	) for image in images]

         if do_normalize:
             A       :   Dict					=						[self.normalize(image=SCREAMING_SNAKE_CASE						,			mean=SCREAMING_SNAKE_CASE						,			std=SCREAMING_SNAKE_CASE	) for image in images]

         A       :   Any					=						[to_channel_dimension_format(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) for image in images]

         A       :   Optional[int]					=						{'''pixel_values''': images}
         return BatchFeature(data=SCREAMING_SNAKE_CASE						,			tensor_type=SCREAMING_SNAKE_CASE	)




