
"""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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


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


if is_vision_available():
      import PIL

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





     def __init__(     self     ,					__UpperCAmelCase = True     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = PILImageResampling.BICUBIC     ,					__UpperCAmelCase = True     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = True     ,					__UpperCAmelCase = 1 / 255     ,					__UpperCAmelCase = True     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = True     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      super().__init__(**lowercase_				)
      __UpperCamelCase      =	size if size is not None else {"shortest_edge": 224}
      __UpperCamelCase      =	get_size_dict(lowercase_     ,					default_to_square=lowercase_				)
      __UpperCamelCase      =	crop_size if crop_size is not None else {"height": 224, "width": 224}
      __UpperCamelCase      =	get_size_dict(lowercase_     ,					default_to_square=lowercase_     ,					param_name='crop_size'				)

      __UpperCamelCase      =	do_resize
      __UpperCamelCase      =	size
      __UpperCamelCase      =	resample
      __UpperCamelCase      =	do_center_crop
      __UpperCamelCase      =	crop_size
      __UpperCamelCase      =	do_rescale
      __UpperCamelCase      =	rescale_factor
      __UpperCamelCase      =	do_normalize
      __UpperCamelCase      =	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
      __UpperCamelCase      =	image_std if image_std is not None else OPENAI_CLIP_STD
      __UpperCamelCase      =	do_convert_rgb





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

      '''simple docstring'''




      __UpperCamelCase      =	get_size_dict(lowercase_     ,					default_to_square=lowercase_				)
      if "shortest_edge" not in size:
       raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'				)
      __UpperCamelCase      =	get_resize_output_image_size(lowercase_     ,					size=size['shortest_edge']     ,					default_to_square=lowercase_				)
      return resize(lowercase_     ,					size=lowercase_     ,					resample=lowercase_     ,					data_format=lowercase_     ,					**lowercase_				)





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

      '''simple docstring'''




      __UpperCamelCase      =	get_size_dict(lowercase_				)
      if "height" not in size or "width" not in size:
       raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}'				)
      return center_crop(lowercase_     ,					size=(size['height'], size['width'])     ,					data_format=lowercase_     ,					**lowercase_				)





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

      '''simple docstring'''




      return rescale(lowercase_     ,					scale=lowercase_     ,					data_format=lowercase_     ,					**lowercase_				)





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

      '''simple docstring'''




      return normalize(lowercase_     ,					mean=lowercase_     ,					std=lowercase_     ,					data_format=lowercase_     ,					**lowercase_				)







     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__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(lowercase_     ,					param_name='size'     ,					default_to_square=lowercase_				)
      __UpperCamelCase      =	resample if resample is not None else self.resample
      __UpperCamelCase      =	do_center_crop if do_center_crop is not None else self.do_center_crop
      __UpperCamelCase      =	crop_size if crop_size is not None else self.crop_size
      __UpperCamelCase      =	get_size_dict(lowercase_     ,					param_name='crop_size'     ,					default_to_square=lowercase_				)
      __UpperCamelCase      =	do_rescale if do_rescale is not None else self.do_rescale
      __UpperCamelCase      =	rescale_factor if rescale_factor is not None else self.rescale_factor
      __UpperCamelCase      =	do_normalize if do_normalize is not None else self.do_normalize
      __UpperCamelCase      =	image_mean if image_mean is not None else self.image_mean
      __UpperCamelCase      =	image_std if image_std is not None else self.image_std
      __UpperCamelCase      =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

      __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:
       raise ValueError('Size must be specified if do_resize is True.'				)

      if do_center_crop and crop_size is None:
       raise ValueError('Crop size must be specified if do_center_crop is True.'				)

      if do_rescale and rescale_factor is None:
       raise ValueError('Rescale factor must be specified if do_rescale is True.'				)

      if do_normalize and (image_mean is None or image_std is None):
       raise ValueError('Image mean and std must be specified if do_normalize is True.'				)

      # PIL RGBA images are converted to RGB
      if do_convert_rgb:
       __UpperCamelCase      =	[convert_to_rgb(lowercase_				) for image in images]

      # 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_center_crop:
       __UpperCamelCase      =	[self.center_crop(image=lowercase_     ,					size=lowercase_				) for image in images]

      if do_rescale:
       __UpperCamelCase      =	[self.rescale(image=lowercase_     ,					scale=lowercase_				) for image in images]

      if do_normalize:
       __UpperCamelCase      =	[self.normalize(image=lowercase_     ,					mean=lowercase_     ,					std=lowercase_				) for image in images]

      __UpperCamelCase      =	[to_channel_dimension_format(lowercase_     ,					lowercase_				) for image in images]

      __UpperCamelCase      =	{"pixel_values": images}
      return BatchFeature(data=lowercase_     ,					tensor_type=lowercase_				)





"""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() = }''')



