





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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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


_lowerCamelCase 					=      logging.get_logger(__name__)






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]       , UpperCamelCase__:				int     ):
		SCREAMING_SNAKE_CASE__								=   b.T
		SCREAMING_SNAKE_CASE__								=   np.sum(np.square(UpperCamelCase__     )       , axis=1     )
		SCREAMING_SNAKE_CASE__								=   np.sum(np.square(UpperCamelCase__     )       , axis=0     )
		SCREAMING_SNAKE_CASE__								=   np.matmul(UpperCamelCase__       , UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   aa[:, None] - 2 * ab + ba[None, :]
		return d






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str       , UpperCamelCase__:				Optional[Any]     ):
		SCREAMING_SNAKE_CASE__								=   x.reshape(-1       , 3     )
		SCREAMING_SNAKE_CASE__								=   squared_euclidean_distance(UpperCamelCase__       , UpperCamelCase__     )
		return np.argmin(UpperCamelCase__       , axis=1     )




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       ["pixel_values"]


							def __init__(				self     :int				,					__A     :Optional[Union[List[List[int]], np.ndarray]] = None				,					__A     :bool = True				,					__A     :Dict[str, int] = None				,					__A     :PILImageResampling = PILImageResampling.BILINEAR				,					__A     :bool = True				,					__A     :bool = True				,					**__A     :int				,					) -> None:



									"""simple docstring"""



									super().__init__(**__A		)
									SCREAMING_SNAKE_CASE__								=   size if size is not None else {"""height""": 256, """width""": 256}
									SCREAMING_SNAKE_CASE__								=   get_size_dict(__A		)
									SCREAMING_SNAKE_CASE__								=   np.array(__A		) if clusters is not None else None
									SCREAMING_SNAKE_CASE__								=   do_resize
									SCREAMING_SNAKE_CASE__								=   size
									SCREAMING_SNAKE_CASE__								=   resample
									SCREAMING_SNAKE_CASE__								=   do_normalize
									SCREAMING_SNAKE_CASE__								=   do_color_quantize


							def   _snake_case	(				self     :str				,					__A     :np.ndarray				,					__A     :Dict[str, int]				,					__A     :PILImageResampling = PILImageResampling.BILINEAR				,					__A     :Optional[Union[str, ChannelDimension]] = None				,					**__A     :List[str]				,					) -> np.ndarray:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   get_size_dict(__A		)
									if "height" not in size or "width" not in size:
											raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}'''		)
									return resize(
									    __A				,					size=(size["""height"""], size["""width"""])				,					resample=__A				,					data_format=__A				,					**__A		)


							def   _snake_case	(				self     :List[Any]				,					__A     :np.ndarray				,					__A     :Optional[Union[str, ChannelDimension]] = None				,					) -> np.ndarray:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   rescale(image=__A				,					scale=1 / 1_2_7.5				,					data_format=__A		)
									SCREAMING_SNAKE_CASE__								=   image - 1
									return image


							def   _snake_case	(				self     :Optional[int]				,					__A     :ImageInput				,					__A     :bool = None				,					__A     :Dict[str, int] = None				,					__A     :PILImageResampling = None				,					__A     :bool = None				,					__A     :Optional[bool] = None				,					__A     :Optional[Union[List[List[int]], np.ndarray]] = None				,					__A     :Optional[Union[str, TensorType]] = None				,					__A     :Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST				,					**__A     :List[str]				,					) -> PIL.Image.Image:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   do_resize if do_resize is not None else self.do_resize
									SCREAMING_SNAKE_CASE__								=   size if size is not None else self.size
									SCREAMING_SNAKE_CASE__								=   get_size_dict(__A		)
									SCREAMING_SNAKE_CASE__								=   resample if resample is not None else self.resample
									SCREAMING_SNAKE_CASE__								=   do_normalize if do_normalize is not None else self.do_normalize
									SCREAMING_SNAKE_CASE__								=   do_color_quantize if do_color_quantize is not None else self.do_color_quantize
									SCREAMING_SNAKE_CASE__								=   clusters if clusters is not None else self.clusters
									SCREAMING_SNAKE_CASE__								=   np.array(__A		)

									SCREAMING_SNAKE_CASE__								=   make_list_of_images(__A		)

									if not valid_images(__A		):
											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_color_quantize and clusters is None:
											raise ValueError("""Clusters must be specified if do_color_quantize is True."""		)

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

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

									if do_normalize:
											SCREAMING_SNAKE_CASE__								=   [self.normalize(image=__A		) for image in images]

									if do_color_quantize:
											SCREAMING_SNAKE_CASE__								=   [to_channel_dimension_format(__A				,					ChannelDimension.LAST		) for image in images]
											# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
											SCREAMING_SNAKE_CASE__								=   np.array(__A		)
											SCREAMING_SNAKE_CASE__								=   color_quantize(__A				,					__A		).reshape(images.shape[:-1]		)

											# flatten to (batch_size, height*width)
											SCREAMING_SNAKE_CASE__								=   images.shape[0]
											SCREAMING_SNAKE_CASE__								=   images.reshape(__A				,					-1		)

											# We need to convert back to a list of images to keep consistent behaviour across processors.
											SCREAMING_SNAKE_CASE__								=   list(__A		)
									else:
											SCREAMING_SNAKE_CASE__								=   [to_channel_dimension_format(__A				,					__A		) for image in images]

									SCREAMING_SNAKE_CASE__								=   {"""input_ids""": images}
									return BatchFeature(data=__A				,					tensor_type=__A		)



'''simple docstring'''



A__						:   dict[tuple[int, int, int], int]				=    {}



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							int				)	->						int:

     # if we are absent twice, or late 3 consecutive days,
     # no further prize strings are possible
     if late == 3 or absent == 2:
          return 0

     # if we have no days left, and have not failed any other rules,
     # we have a prize string
     if days == 0:
          return 1

     # No easy solution, so now we need to do the recursive calculation

     # First, check if the combination is already in the cache, and
     # if yes, return the stored value from there since we already
     # know the number of possible prize strings from this point on
     __lowerCamelCase  :      List[Any]														=						(days, absent, late)
     if key in cache:
          return cache[key]

     # now we calculate the three possible ways that can unfold from
     # this point on, depending on our attendance today

     # 1) if we are late (but not absent), the "absent" counter stays as
     # it is, but the "late" counter increases by one
     __lowerCamelCase  :      Tuple														=						_calculate(days - 1 ,       UpperCAmelCase_ ,       late + 1				)

     # 2) if we are absent, the "absent" counter increases by 1, and the
     # "late" counter resets to 0
     __lowerCamelCase  :      int														=						_calculate(days - 1 ,       absent + 1 ,       0				)

     # 3) if we are on time, this resets the "late" counter and keeps the
     # absent counter
     __lowerCamelCase  :      List[Any]														=						_calculate(days - 1 ,       UpperCAmelCase_ ,       0				)

     __lowerCamelCase  :      Optional[int]														=						state_late + state_absent + state_ontime

     __lowerCamelCase  :      Union[str, Any]														=						prizestrings
     return prizestrings



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int = 30				)	->						int:
     return _calculate(UpperCAmelCase_ ,       absent=0 ,       late=0				)


if __name__ == "__main__":
      print(solution())






