




"""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 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


lowercase__     						=						logging.get_logger(__name__)







def      _snake_case			(				lowercase__							,    lowercase__		):
				_lowerCamelCase     :			Any    =    b.T
				_lowerCamelCase     :			str    =    np.sum(np.square(lowerCamelCase_		)							,    axis=1		)
				_lowerCamelCase     :			List[str]    =    np.sum(np.square(lowerCamelCase_		)							,    axis=0		)
				_lowerCamelCase     :			List[Any]    =    np.matmul(lowerCamelCase_							,    lowerCamelCase_		)
				_lowerCamelCase     :			Any    =    aa[:, None] - 2 * ab + ba[None, :]
				return d







def      _snake_case			(				lowercase__							,    lowercase__		):
				_lowerCamelCase     :			List[Any]    =    x.reshape(-1							,    3		)
				_lowerCamelCase     :			int    =    squared_euclidean_distance(lowerCamelCase_							,    lowerCamelCase_		)
				return np.argmin(lowerCamelCase_							,    axis=1		)






class lowerCAmelCase__	(     A__						):






			'''simple docstring'''



			lowerCamelCase__      =			["""pixel_values"""]
			def __init__(  self  ,      lowercase = None  ,      lowercase = True  ,      lowercase = None  ,      lowercase = PILImageResampling.BILINEAR  ,      lowercase = True  ,      lowercase = True  ,      **lowercase  ,      ):
							super().__init__(**lowercase  )
							_lowerCamelCase     :			Optional[int]    =    size if size is not None else {'height': 256, 'width': 256}
							_lowerCamelCase     :			str    =    get_size_dict(lowercase  )
							_lowerCamelCase     :			Optional[int]    =    np.array(lowercase  ) if clusters is not None else None
							_lowerCamelCase     :			int    =    do_resize
							_lowerCamelCase     :			Tuple    =    size
							_lowerCamelCase     :			List[Any]    =    resample
							_lowerCamelCase     :			Any    =    do_normalize
							_lowerCamelCase     :			int    =    do_color_quantize
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase = PILImageResampling.BILINEAR  ,      lowercase = None  ,      **lowercase  ,      ):
							_lowerCamelCase     :			Optional[Any]    =    get_size_dict(lowercase  )
							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(
							    lowercase  ,      size=(size['height'], size['width'])  ,      resample=lowercase  ,      data_format=lowercase  ,      **lowercase  )
			def 					A_    (  self  ,      lowercase  ,      lowercase = None  ,      ):
							_lowerCamelCase     :			Any    =    rescale(image=lowercase  ,      scale=1 / 1_27.5  ,      data_format=lowercase  )
							_lowerCamelCase     :			Dict    =    image - 1
							return image






			def 					A_    (  self  ,      lowercase  ,      lowercase = None  ,      lowercase = None  ,      lowercase = None  ,      lowercase = None  ,      lowercase = None  ,      lowercase = None  ,      lowercase = None  ,      lowercase = ChannelDimension.FIRST  ,      **lowercase  ,      ):
							_lowerCamelCase     :			int    =    do_resize if do_resize is not None else self.do_resize
							_lowerCamelCase     :			int    =    size if size is not None else self.size
							_lowerCamelCase     :			Dict    =    get_size_dict(lowercase  )
							_lowerCamelCase     :			Any    =    resample if resample is not None else self.resample
							_lowerCamelCase     :			Any    =    do_normalize if do_normalize is not None else self.do_normalize
							_lowerCamelCase     :			str    =    do_color_quantize if do_color_quantize is not None else self.do_color_quantize
							_lowerCamelCase     :			List[Any]    =    clusters if clusters is not None else self.clusters
							_lowerCamelCase     :			Union[str, Any]    =    np.array(lowercase  )

							_lowerCamelCase     :			Optional[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 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.
							_lowerCamelCase     :			int    =    [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_normalize:
											_lowerCamelCase     :			Optional[Any]    =    [self.normalize(image=lowercase  ) for image in images]

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

											# flatten to (batch_size, height*width)
											_lowerCamelCase     :			str    =    images.shape[0]
											_lowerCamelCase     :			Dict    =    images.reshape(lowercase  ,      -1  )

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

							_lowerCamelCase     :			Optional[int]    =    {'input_ids': images}
							return BatchFeature(data=lowercase  ,      tensor_type=lowercase  )







def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			[1]

				lowercase__ ,						lowercase__ ,						lowercase__    =			0, 0, 0
				lowercase__    =			ugly_nums[ia] * 2
				lowercase__    =			ugly_nums[ia] * 3
				lowercase__    =			ugly_nums[ia] * 5

				for _ in range(1		,   lowerCamelCase_				):
								lowercase__    =			min(lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				)
								ugly_nums.append(lowerCamelCase_				)
								if next_num == next_a:
												ia += 1
												lowercase__    =			ugly_nums[ia] * 2
								if next_num == next_a:
												ia += 1
												lowercase__    =			ugly_nums[ia] * 3
								if next_num == next_a:
												ia += 1
												lowercase__    =			ugly_nums[ia] * 5
				return ugly_nums[-1]


if __name__ == "__main__":
							from doctest import testmod

							testmod(verbose=True)
							print(F"{ugly_numbers(2_00) = }")




