






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 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				Optional[int]:
      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 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				Tuple:
      UpperCamelCase       						=      x.reshape(-1	,		3					)
      UpperCamelCase       						=      squared_euclidean_distance(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE					)
      return np.argmin(__SCREAMING_SNAKE_CASE	,		axis=1					)






class        a_		(    snake_case__       ):
  lowercase  	=   ["""pixel_values"""]

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = True    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR    ,	_SCREAMING_SNAKE_CASE = True    ,	_SCREAMING_SNAKE_CASE = True    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   Dict:



        """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 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR    ,	_SCREAMING_SNAKE_CASE = None    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   Tuple:



        """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 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = None    ,	)						->   Dict:



        """simple docstring"""






        UpperCamelCase       						=      rescale(image=lowercase_    ,	scale=1 / 1_2_7.5    ,	data_format=lowercase_   )
        UpperCamelCase       						=      image - 1
        return image


  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   List[Any]:



        """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 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				tuple[float, list[float]]:
      UpperCamelCase       						=      list(range(len(__UpperCamelCase					)					)					)
      UpperCamelCase       						=      [v / w for v, w in zip(__UpperCamelCase	,		__UpperCamelCase					)]
      index.sort(key=lambda __UpperCamelCase					: ratio[i]	,		reverse=__UpperCamelCase					)

      UpperCamelCase       						=      0
      UpperCamelCase       						=      [0] * len(__UpperCamelCase					)
      for i in index:
            if weight[i] <= capacity:
                  UpperCamelCase       						=      1
                  max_value += value[i]
                  capacity -= weight[i]
            else:
                  UpperCamelCase       						=      capacity / weight[i]
                  max_value += value[i] * capacity / weight[i]
                  break

      return max_value, fractions


if __name__ == "__main__":
     import doctest

     doctest.testmod()
