


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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowerCamelCase		=						logging.get_logger(__name__)





class    _a				(							_lowercase):
	_a    :    Union[str, Any]        =				['''pixel_values''']




	def __init__(   self		:						Any						,   _SCREAMING_SNAKE_CASE		:						bool = True						,   _SCREAMING_SNAKE_CASE		:						Dict[str, int] = None						,   _SCREAMING_SNAKE_CASE		:						bool = True						,   _SCREAMING_SNAKE_CASE		:						Dict[str, int] = None						,   _SCREAMING_SNAKE_CASE		:						PILImageResampling = PILImageResampling.BICUBIC						,   _SCREAMING_SNAKE_CASE		:						bool = True						,   _SCREAMING_SNAKE_CASE		:						Union[int, float] = 1 / 255						,   _SCREAMING_SNAKE_CASE		:						bool = True						,   _SCREAMING_SNAKE_CASE		:						Optional[Union[float, List[float]]] = None						,   _SCREAMING_SNAKE_CASE		:						Optional[Union[float, List[float]]] = None						,   **_SCREAMING_SNAKE_CASE		:						Optional[Any]						,   )->			None:
			super().__init__(**_SCREAMING_SNAKE_CASE						)
			lowerCAmelCase__  :     int       	=   crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
			lowerCAmelCase__  :     Tuple       	=   get_size_dict(_SCREAMING_SNAKE_CASE						,   param_name='''crop_size'''						)
			lowerCAmelCase__  :     Tuple       	=   size if size is not None else {'''height''': 224, '''width''': 224}
			lowerCAmelCase__  :     List[str]       	=   get_size_dict(_SCREAMING_SNAKE_CASE						)

			lowerCAmelCase__  :     str       	=   do_center_crop
			lowerCAmelCase__  :     List[str]       	=   crop_size
			lowerCAmelCase__  :     Optional[Any]       	=   do_resize
			lowerCAmelCase__  :     Dict       	=   size
			lowerCAmelCase__  :     Optional[int]       	=   resample
			lowerCAmelCase__  :     Optional[int]       	=   do_rescale
			lowerCAmelCase__  :     Tuple       	=   rescale_factor
			lowerCAmelCase__  :     List[Any]       	=   do_normalize
			lowerCAmelCase__  :     Tuple       	=   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
			lowerCAmelCase__  :     str       	=   image_std if image_std is not None else IMAGENET_DEFAULT_STD




	def 				UpperCAmelCase__(   self		:						Union[str, Any]						,   _SCREAMING_SNAKE_CASE		:						np.ndarray						,   _SCREAMING_SNAKE_CASE		:						Dict[str, int]						,   _SCREAMING_SNAKE_CASE		:						Optional[int] = None						,   _SCREAMING_SNAKE_CASE		:						Optional[Union[str, ChannelDimension]] = None						,   **_SCREAMING_SNAKE_CASE		:						Any						,   )->			np.ndarray:
			lowerCAmelCase__  :     Union[str, Any]       	=   self.size if size is None else size
			lowerCAmelCase__  :     str       	=   get_size_dict(_SCREAMING_SNAKE_CASE						)
			lowerCAmelCase__  :     Optional[int]       	=   get_size_dict(_SCREAMING_SNAKE_CASE						,   param_name='''crop_size'''						)

			lowerCAmelCase__	,      lowerCAmelCase__  :     Optional[int]       	=   get_image_size(_SCREAMING_SNAKE_CASE						)
			lowerCAmelCase__  :     Tuple       	=   min(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						)
			lowerCAmelCase__  :     List[Any]       	=   (size['''height'''] / crop_size['''height''']) * min_dim
			lowerCAmelCase__  :     Optional[int]       	=   (size['''width'''] / crop_size['''width''']) * min_dim
			return center_crop(_SCREAMING_SNAKE_CASE						,   size=(cropped_height, cropped_width)						,   data_format=_SCREAMING_SNAKE_CASE						,   **_SCREAMING_SNAKE_CASE						)




	def 				UpperCAmelCase__(   self		:						Optional[Any]						,   _SCREAMING_SNAKE_CASE		:						np.ndarray						,   _SCREAMING_SNAKE_CASE		:						Dict[str, int]						,   _SCREAMING_SNAKE_CASE		:						PILImageResampling = PIL.Image.BICUBIC						,   _SCREAMING_SNAKE_CASE		:						Optional[Union[str, ChannelDimension]] = None						,   **_SCREAMING_SNAKE_CASE		:						List[str]						,   )->			np.ndarray:
			lowerCAmelCase__  :     Any       	=   get_size_dict(_SCREAMING_SNAKE_CASE						)
			if "height" not in size or "width" not in size:
					raise ValueError(F'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'						)
			return resize(
			    _SCREAMING_SNAKE_CASE						,   size=(size['''height'''], size['''width'''])						,   resample=_SCREAMING_SNAKE_CASE						,   data_format=_SCREAMING_SNAKE_CASE						,   **_SCREAMING_SNAKE_CASE						)




	def 				UpperCAmelCase__(   self		:						Optional[int]						,   _SCREAMING_SNAKE_CASE		:						np.ndarray						,   _SCREAMING_SNAKE_CASE		:						Union[int, float]						,   _SCREAMING_SNAKE_CASE		:						Optional[Union[str, ChannelDimension]] = None						,   **_SCREAMING_SNAKE_CASE		:						Tuple						,   )->			Union[str, Any]:
			return rescale(_SCREAMING_SNAKE_CASE						,   scale=_SCREAMING_SNAKE_CASE						,   data_format=_SCREAMING_SNAKE_CASE						,   **_SCREAMING_SNAKE_CASE						)




	def 				UpperCAmelCase__(   self		:						Optional[int]						,   _SCREAMING_SNAKE_CASE		:						np.ndarray						,   _SCREAMING_SNAKE_CASE		:						Union[float, List[float]]						,   _SCREAMING_SNAKE_CASE		:						Union[float, List[float]]						,   _SCREAMING_SNAKE_CASE		:						Optional[Union[str, ChannelDimension]] = None						,   **_SCREAMING_SNAKE_CASE		:						Dict						,   )->			np.ndarray:
			return normalize(_SCREAMING_SNAKE_CASE						,   mean=_SCREAMING_SNAKE_CASE						,   std=_SCREAMING_SNAKE_CASE						,   data_format=_SCREAMING_SNAKE_CASE						,   **_SCREAMING_SNAKE_CASE						)




	def 				UpperCAmelCase__(   self		:						Dict						,   _SCREAMING_SNAKE_CASE		:						ImageInput						,   _SCREAMING_SNAKE_CASE		:						Optional[bool] = None						,   _SCREAMING_SNAKE_CASE		:						Optional[Dict[str, int]] = None						,   _SCREAMING_SNAKE_CASE		:						Optional[bool] = None						,   _SCREAMING_SNAKE_CASE		:						Optional[Dict[str, int]] = None						,   _SCREAMING_SNAKE_CASE		:						PILImageResampling = None						,   _SCREAMING_SNAKE_CASE		:						Optional[bool] = None						,   _SCREAMING_SNAKE_CASE		:						Optional[float] = None						,   _SCREAMING_SNAKE_CASE		:						Optional[bool] = None						,   _SCREAMING_SNAKE_CASE		:						Optional[Union[float, List[float]]] = None						,   _SCREAMING_SNAKE_CASE		:						Optional[Union[float, List[float]]] = None						,   _SCREAMING_SNAKE_CASE		:						Optional[Union[str, TensorType]] = None						,   _SCREAMING_SNAKE_CASE		:						ChannelDimension = ChannelDimension.FIRST						,   **_SCREAMING_SNAKE_CASE		:						Dict						,   )->			PIL.Image.Image:
			lowerCAmelCase__  :     Optional[Any]       	=   do_center_crop if do_center_crop is not None else self.do_center_crop
			lowerCAmelCase__  :     Union[str, Any]       	=   crop_size if crop_size is not None else self.crop_size
			lowerCAmelCase__  :     Any       	=   get_size_dict(_SCREAMING_SNAKE_CASE						,   param_name='''crop_size'''						)
			lowerCAmelCase__  :     Tuple       	=   do_resize if do_resize is not None else self.do_resize
			lowerCAmelCase__  :     Union[str, Any]       	=   size if size is not None else self.size
			lowerCAmelCase__  :     str       	=   get_size_dict(_SCREAMING_SNAKE_CASE						)
			lowerCAmelCase__  :     List[Any]       	=   resample if resample is not None else self.resample
			lowerCAmelCase__  :     Tuple       	=   do_rescale if do_rescale is not None else self.do_rescale
			lowerCAmelCase__  :     List[Any]       	=   rescale_factor if rescale_factor is not None else self.rescale_factor
			lowerCAmelCase__  :     Dict       	=   do_normalize if do_normalize is not None else self.do_normalize
			lowerCAmelCase__  :     Union[str, Any]       	=   image_mean if image_mean is not None else self.image_mean
			lowerCAmelCase__  :     Any       	=   image_std if image_std is not None else self.image_std

			lowerCAmelCase__  :     int       	=   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_center_crop and crop_size is None:
					raise ValueError('''If `do_center_crop` is set to `True`, `crop_size` must be provided.'''						)

			if do_resize and size is None:
					raise ValueError('''Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True.'''						)

			# All transformations expect numpy arrays.
			lowerCAmelCase__  :     Union[str, Any]       	=   [to_numpy_array(_SCREAMING_SNAKE_CASE						) for image in images]

			if do_center_crop:
					lowerCAmelCase__  :     Dict       	=   [self.center_crop(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						,   size=_SCREAMING_SNAKE_CASE						) for image in images]

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

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

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

			lowerCAmelCase__  :     Optional[Any]       	=   [to_channel_dimension_format(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						) for image in images]

			lowerCAmelCase__  :     Optional[Any]       	=   {'''pixel_values''': images}
			return BatchFeature(data=_SCREAMING_SNAKE_CASE						,   tensor_type=_SCREAMING_SNAKE_CASE						)








from itertools import combinations_with_replacement






def 	lowerCamelCase_       (			_a = 100			):




		"""simple docstring"""






		lowerCAmelCase__  :     list[int]       	=   [*list(range(1      ,  21			)			), 25]
		lowerCAmelCase__  :     list[int]       	=   [2 * x for x in range(1      ,  21			)] + [50]
		lowerCAmelCase__  :     list[int]       	=   [3 * x for x in range(1      ,  21			)]
		lowerCAmelCase__  :     list[int]       	=   singles + doubles + triples + [0]

		lowerCAmelCase__  :     int       	=   0
		lowerCAmelCase__  :     int
		lowerCAmelCase__  :     int
		lowerCAmelCase__  :     int
		lowerCAmelCase__  :     int

		for double in doubles:
				for throwa, throwa in combinations_with_replacement(_a      ,  2			):
						lowerCAmelCase__  :     Dict       	=   double + throwa + throwa
						if checkout_total < limit:
								num_checkouts += 1

		return num_checkouts


if __name__ == "__main__":
		print(f'''{solution() = }''')




