



"""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


lowerCamelCase      =		logging.get_logger(__name__)
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							b.T
      UpperCAmelCase_    				=							np.sum(np.square(__SCREAMING_SNAKE_CASE   )			,    axis=1   )
      UpperCAmelCase_    				=							np.sum(np.square(__SCREAMING_SNAKE_CASE   )			,    axis=0   )
      UpperCAmelCase_    				=							np.matmul(__SCREAMING_SNAKE_CASE			,    __SCREAMING_SNAKE_CASE   )
      UpperCAmelCase_    				=							aa[:, None] - 2 * ab + ba[None, :]
      return d
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							x.reshape(-1			,    3   )
      UpperCAmelCase_    				=							squared_euclidean_distance(__SCREAMING_SNAKE_CASE			,    __SCREAMING_SNAKE_CASE   )
      return np.argmin(__SCREAMING_SNAKE_CASE			,    axis=1   )


class    lowercase__     (					snake_case__   ):







       '''simple docstring'''


       UpperCamelCase		=  ['''pixel_values''']



       def __init__(							self    :						Any	,       _UpperCAmelCase    :						Optional[Any] = None	,       _UpperCAmelCase    :						Optional[int] = True	,       _UpperCAmelCase    :						Union[str, Any] = None	,       _UpperCAmelCase    :						int = PILImageResampling.BILINEAR	,       _UpperCAmelCase    :						Optional[int] = True	,       _UpperCAmelCase    :						List[Any] = True	,       **_UpperCAmelCase    :						Optional[int]	,       )				->		str:


             '''simple docstring'''




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



       def   lowercase__       (							self    :						str	,       _UpperCAmelCase    :						str	,       _UpperCAmelCase    :						Any	,       _UpperCAmelCase    :						Union[str, Any] = PILImageResampling.BILINEAR	,       _UpperCAmelCase    :						Dict = None	,       **_UpperCAmelCase    :						Dict	,       )				->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_    				=							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   lowercase__       (							self    :						Optional[Any]	,       _UpperCAmelCase    :						List[Any]	,       _UpperCAmelCase    :						List[str] = None	,       )				->		Dict:


             '''simple docstring'''




             UpperCAmelCase_    				=							rescale(image=lowercase_	,       scale=1 / 127.5	,       data_format=lowercase_					)
             UpperCAmelCase_    				=							image - 1
             return image







       def   lowercase__       (							self    :						str	,       _UpperCAmelCase    :						Optional[Any]	,       _UpperCAmelCase    :						List[str] = None	,       _UpperCAmelCase    :						Any = None	,       _UpperCAmelCase    :						Optional[Any] = None	,       _UpperCAmelCase    :						int = None	,       _UpperCAmelCase    :						Tuple = None	,       _UpperCAmelCase    :						Dict = None	,       _UpperCAmelCase    :						Union[str, Any] = None	,       _UpperCAmelCase    :						Optional[int] = ChannelDimension.FIRST	,       **_UpperCAmelCase    :						Dict	,       )				->		Optional[int]:


             '''simple docstring'''




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

             UpperCAmelCase_    				=							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.
             UpperCAmelCase_    				=							[to_numpy_array(lowercase_					) for image in images]

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

             if do_normalize:
                   UpperCAmelCase_    				=							[self.normalize(image=lowercase_					) for image in images]

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

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

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

             UpperCAmelCase_    				=							{"input_ids": images}
             return BatchFeature(data=lowercase_	,       tensor_type=lowercase_					)









"""simple docstring"""





from __future__ import annotations
def      a__			(   lowerCAmelCase__   ):
      if len(lowerCAmelCase__   ) == 0:
            return []
      UpperCAmelCase_				,					UpperCAmelCase_    				=							min(lowerCAmelCase__   ), max(lowerCAmelCase__   )
      UpperCAmelCase_    				=							int(max_value - min_value   ) + 1
      UpperCAmelCase_    				=							[[] for _ in range(lowerCAmelCase__   )]

      for i in my_list:
            buckets[int(i - min_value   )].append(lowerCAmelCase__   )

      return [v for bucket in buckets for v in sorted(lowerCAmelCase__   )]


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

  testmod()
  assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
  assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]




