






'''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


UpperCamelCase    :      List[str]  			=		logging.get_logger(__name__)
def 						A__			(				__lowerCAmelCase		:			List[str]   ,    __lowerCAmelCase		:			Union[str, Any]						):
      lowerCamelCase__         =  b.T
      lowerCamelCase__         =  np.sum(np.square(__lowerCAmelCase						)   ,    axis=1						)
      lowerCamelCase__         =  np.sum(np.square(__lowerCAmelCase						)   ,    axis=0						)
      lowerCamelCase__         =  np.matmul(__lowerCAmelCase   ,    __lowerCAmelCase						)
      lowerCamelCase__         =  aa[:, None] - 2 * ab + ba[None, :]
      return d
def 						A__			(				__lowerCAmelCase		:			List[Any]   ,    __lowerCAmelCase		:			str						):
      lowerCamelCase__         =  x.reshape(-1   ,    3						)
      lowerCamelCase__         =  squared_euclidean_distance(__lowerCAmelCase   ,    __lowerCAmelCase						)
      return np.argmin(__lowerCAmelCase   ,    axis=1						)





class 		UpperCamelCase__    (a			):




  '''simple docstring'''





  _UpperCamelCase				  =					['pixel_values']






  def __init__(      self   ,_lowerCAmelCase = None   ,_lowerCAmelCase = True   ,_lowerCAmelCase = None   ,_lowerCAmelCase = PILImageResampling.BILINEAR   ,_lowerCAmelCase = True   ,_lowerCAmelCase = True   ,**_lowerCAmelCase   ,):
        super().__init__(**_lowerCAmelCase       )
        lowerCamelCase__         =  size if size is not None else {"""height""": 2_56, """width""": 2_56}
        lowerCamelCase__         =  get_size_dict(_lowerCAmelCase       )
        lowerCamelCase__         =  np.array(_lowerCAmelCase       ) if clusters is not None else None
        lowerCamelCase__         =  do_resize
        lowerCamelCase__         =  size
        lowerCamelCase__         =  resample
        lowerCamelCase__         =  do_normalize
        lowerCamelCase__         =  do_color_quantize






  def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase = PILImageResampling.BILINEAR   ,_lowerCAmelCase = None   ,**_lowerCAmelCase   ,):
        lowerCamelCase__         =  get_size_dict(_lowerCAmelCase       )
        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(
            _lowerCAmelCase   ,size=(size["""height"""], size["""width"""])   ,resample=_lowerCAmelCase   ,data_format=_lowerCAmelCase   ,**_lowerCAmelCase       )






  def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = None   ,):
        lowerCamelCase__         =  rescale(image=_lowerCAmelCase   ,scale=1 / 127.5   ,data_format=_lowerCAmelCase       )
        lowerCamelCase__         =  image - 1
        return image






  def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = None   ,_lowerCAmelCase = None   ,_lowerCAmelCase = None   ,_lowerCAmelCase = None   ,_lowerCAmelCase = None   ,_lowerCAmelCase = None   ,_lowerCAmelCase = None   ,_lowerCAmelCase = ChannelDimension.FIRST   ,**_lowerCAmelCase   ,):
        lowerCamelCase__         =  do_resize if do_resize is not None else self.do_resize
        lowerCamelCase__         =  size if size is not None else self.size
        lowerCamelCase__         =  get_size_dict(_lowerCAmelCase       )
        lowerCamelCase__         =  resample if resample is not None else self.resample
        lowerCamelCase__         =  do_normalize if do_normalize is not None else self.do_normalize
        lowerCamelCase__         =  do_color_quantize if do_color_quantize is not None else self.do_color_quantize
        lowerCamelCase__         =  clusters if clusters is not None else self.clusters
        lowerCamelCase__         =  np.array(_lowerCAmelCase       )

        lowerCamelCase__         =  make_list_of_images(_lowerCAmelCase       )

        if not valid_images(_lowerCAmelCase       ):
              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__         =  [to_numpy_array(_lowerCAmelCase       ) for image in images]

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

        if do_normalize:
              lowerCamelCase__         =  [self.normalize(image=_lowerCAmelCase       ) for image in images]

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

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

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

        lowerCamelCase__         =  {"""input_ids""": images}
        return BatchFeature(data=_lowerCAmelCase   ,tensor_type=_lowerCAmelCase       )









'''simple docstring'''







from __future__ import annotations

import math
def 						A__			(				__lowerCAmelCase		:			int						):

						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5   ,    int(math.sqrt(__lowerCAmelCase						) + 1						)   ,    6						):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True


UpperCamelCase    :      str  			=		[num for num in range(3, 10_00_01, 2) if not is_prime(num)]
def 						A__			(				__lowerCAmelCase		:			int						):
						if not isinstance(__lowerCAmelCase   ,    __lowerCAmelCase						):
												raise ValueError("""n must be an integer"""						)
						if n <= 0:
												raise ValueError("""n must be >= 0"""						)

						lowerCamelCase__         =  []
						for num in range(len(__lowerCAmelCase						)						):
												lowerCamelCase__         =  0
												while 2 * i * i <= odd_composites[num]:
																		lowerCamelCase__         =  odd_composites[num] - 2 * i * i
																		if is_prime(__lowerCAmelCase						):
																								break
																		i += 1
												else:
																		list_nums.append(odd_composites[num]						)
																		if len(__lowerCAmelCase						) == n:
																								return list_nums

						return []
def 						A__			(				):
						return compute_nums(1						)[0]


if __name__ == "__main__":
							print(F'{solution() = }')

