



"""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


SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)



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



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Dict:
		a_     :      List[Any] 						=     x.reshape(-1, 3	)
		a_     :      List[Any] 						=     squared_euclidean_distance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		return np.argmin(SCREAMING_SNAKE_CASE__, axis=1	)




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					["pixel_values"]






							def __init__(   self			,     a_ = None			,     a_ = True			,     a_ = None			,     a_ = PILImageResampling.BILINEAR			,     a_ = True			,     a_ = True			,     **a_			,     ):
									super().__init__(**a_      )
									a_     :      Tuple 						=     size if size is not None else {"height": 2_5_6, "width": 2_5_6}
									a_     :      Any 						=     get_size_dict(a_      )
									a_     :      List[str] 						=     np.array(a_      ) if clusters is not None else None
									a_     :      List[Any] 						=     do_resize
									a_     :      int 						=     size
									a_     :      Optional[Any] 						=     resample
									a_     :      int 						=     do_normalize
									a_     :      Optional[Any] 						=     do_color_quantize






							def snake_case_  (   self			,     a_			,     a_			,     a_ = PILImageResampling.BILINEAR			,     a_ = None			,     **a_			,     ):
									a_     :      Union[str, Any] 						=     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			,     a_			,     a_ = None			,     ):
									a_     :      List[Any] 						=     rescale(image=a_			,     scale=1 / 127.5			,     data_format=a_      )
									a_     :      Union[str, Any] 						=     image - 1
									return image






							def snake_case_  (   self			,     a_			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = ChannelDimension.FIRST			,     **a_			,     ):
									a_     :      List[Any] 						=     do_resize if do_resize is not None else self.do_resize
									a_     :      Optional[Any] 						=     size if size is not None else self.size
									a_     :      List[Any] 						=     get_size_dict(a_      )
									a_     :      Dict 						=     resample if resample is not None else self.resample
									a_     :      str 						=     do_normalize if do_normalize is not None else self.do_normalize
									a_     :      str 						=     do_color_quantize if do_color_quantize is not None else self.do_color_quantize
									a_     :      Tuple 						=     clusters if clusters is not None else self.clusters
									a_     :      Tuple 						=     np.array(a_      )

									a_     :      Optional[int] 						=     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.
									a_     :      Dict 						=     [to_numpy_array(a_      ) for image in images]

									if do_resize:
											a_     :      Union[str, Any] 						=     [self.resize(image=a_			,     size=a_			,     resample=a_      ) for image in images]

									if do_normalize:
											a_     :      List[str] 						=     [self.normalize(image=a_      ) for image in images]

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

											# flatten to (batch_size, height*width)
											a_     :      List[str] 						=     images.shape[0]
											a_     :      Union[str, Any] 						=     images.reshape(a_			,     -1      )

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

									a_     :      str 						=     {"input_ids": images}
									return BatchFeature(data=a_			,     tensor_type=a_      )




"""simple docstring"""
SCREAMING_SNAKE_CASE_				= 0  # The first color of the flag.
SCREAMING_SNAKE_CASE_				= 1  # The second color of the flag.
SCREAMING_SNAKE_CASE_				= 2  # The third color of the flag.
SCREAMING_SNAKE_CASE_				= (red, white, blue)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      list:
		if not sequence:
				return []
		if len(SCREAMING_SNAKE_CASE__	) == 1:
				return list(SCREAMING_SNAKE_CASE__	)
		a_     :      Dict 						=     0
		a_     :      List[Any] 						=     len(SCREAMING_SNAKE_CASE__	) - 1
		a_     :      str 						=     0
		while mid <= high:
				if sequence[mid] == colors[0]:
						a_						,				a_     :      int 						=     sequence[mid], sequence[low]
						low += 1
						mid += 1
				elif sequence[mid] == colors[1]:
						mid += 1
				elif sequence[mid] == colors[2]:
						a_						,				a_     :      List[Any] 						=     sequence[high], sequence[mid]
						high -= 1
				else:
						a_     :      Dict 						=     F"""The elements inside the sequence must contains only {colors} values"""
						raise ValueError(SCREAMING_SNAKE_CASE__	)
		return sequence


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					SCREAMING_SNAKE_CASE_				= input("""Enter numbers separated by commas:\n""").strip()
					SCREAMING_SNAKE_CASE_				= [int(item.strip()) for item in user_input.split(""",""")]
					print(F"""{dutch_national_flag_sort(unsorted)}""")