
"""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     :    Union[str, Any]       =			logging.get_logger(__name__)






def 							A						(    snake_case				:Tuple    , snake_case				:Dict		)				->     Any:
 __UpperCamelCase      =	b.T
 __UpperCamelCase      =	np.sum(np.square(snake_case		)    , axis=1		)
 __UpperCamelCase      =	np.sum(np.square(snake_case		)    , axis=0		)
 __UpperCamelCase      =	np.matmul(snake_case    , snake_case		)
 __UpperCamelCase      =	aa[:, None] - 2 * ab + ba[None, :]
 return d






def 							A						(    snake_case				:List[Any]    , snake_case				:List[str]		)				->     int:
 __UpperCamelCase      =	x.reshape(-1    , 3		)
 __UpperCamelCase      =	squared_euclidean_distance(snake_case    , snake_case		)
 return np.argmin(snake_case    , axis=1		)





class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):
     lowercase						       =						["pixel_values"]





     def __init__(     self     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = True     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = PILImageResampling.BILINEAR     ,					__UpperCAmelCase = True     ,					__UpperCAmelCase = True     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




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





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase = PILImageResampling.BILINEAR     ,					__UpperCAmelCase = None     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	get_size_dict(__UpperCAmelCase				)
      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(
          __UpperCAmelCase     ,					size=(size['height'], size['width'])     ,					resample=__UpperCAmelCase     ,					data_format=__UpperCAmelCase     ,					**__UpperCAmelCase				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	rescale(image=__UpperCAmelCase     ,					scale=1 / 1_2_7.5     ,					data_format=__UpperCAmelCase				)
      __UpperCamelCase      =	image - 1
      return image







     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = ChannelDimension.FIRST     ,					**__UpperCAmelCase     ,					):

      '''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(__UpperCAmelCase				)
      __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(__UpperCAmelCase				)

      __UpperCamelCase      =	make_list_of_images(__UpperCAmelCase				)

      if not valid_images(__UpperCAmelCase				):
       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(__UpperCAmelCase				) for image in images]

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

      if do_normalize:
       __UpperCamelCase      =	[self.normalize(image=__UpperCAmelCase				) for image in images]

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

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

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

      __UpperCamelCase      =	{'input_ids': images}
      return BatchFeature(data=__UpperCAmelCase     ,					tensor_type=__UpperCAmelCase				)





"""simple docstring"""

from math import isqrt






def 							A						(    snake_case				:int		)				->     list[int]:
 __UpperCamelCase      =	[True] * max_number
 for i in range(2    , isqrt(max_number - 1		) + 1		):
  if is_prime[i]:
   for j in range(i**2    , snake_case    , snake_case		):
    __UpperCamelCase      =	False

 return [i for i in range(2    , snake_case		) if is_prime[i]]






def 							A						(    snake_case				:int = 1_0**8		)				->     int:
 __UpperCamelCase      =	calculate_prime_numbers(max_number // 2		)

 __UpperCamelCase      =	0
 __UpperCamelCase      =	0
 __UpperCamelCase      =	len(snake_case		) - 1
 while left <= right:
  while prime_numbers[left] * prime_numbers[right] >= max_number:
   right -= 1
  semiprimes_count += right - left + 1
  left += 1

 return semiprimes_count


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



