from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCamelCase    :				List[str]							     =	logging.get_logger(__name__)







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							["pixel_values"]




   def __init__(					self    :       Tuple ,			_lowercase    :       bool = True ,			_lowercase    :       int = 32 ,			_lowercase    :       Tuple=PILImageResampling.BILINEAR ,			_lowercase    :       bool = True ,			**_lowercase    :       Tuple ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   do_resize
     SCREAMING_SNAKE_CASE__    							=   do_rescale
     SCREAMING_SNAKE_CASE__    							=   size_divisor
     SCREAMING_SNAKE_CASE__    							=   resample
     super().__init__(**_lowercase   )




   def 			__a		(					self    :       Dict ,			_lowercase    :       np.ndarray ,			_lowercase    :       int ,			_lowercase    :       Union[str, Any] ,			_lowercase    :       Optional[ChannelDimension] = None ,			**_lowercase    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   get_image_size(_lowercase   )
     # Rounds the height and width down to the closest multiple of size_divisor
     SCREAMING_SNAKE_CASE__    							=   height // size_divisor * size_divisor
     SCREAMING_SNAKE_CASE__    							=   width // size_divisor * size_divisor
     SCREAMING_SNAKE_CASE__    							=   resize(_lowercase ,			(new_h, new_w) ,			resample=_lowercase ,			data_format=_lowercase ,			**_lowercase   )
     return image




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       np.ndarray ,			_lowercase    :       float ,			_lowercase    :       Optional[ChannelDimension] = None ,			**_lowercase    :       Optional[Any]   ):



     """simple docstring"""






     return rescale(image=_lowercase ,			scale=_lowercase ,			data_format=_lowercase ,			**_lowercase   )




   def 			__a		(					self    :       List[str] ,			_lowercase    :       Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]] ,			_lowercase    :       Optional[bool] = None ,			_lowercase    :       Optional[int] = None ,			_lowercase    :       Any=None ,			_lowercase    :       Optional[bool] = None ,			_lowercase    :       Optional[Union[TensorType, str]] = None ,			_lowercase    :       ChannelDimension = ChannelDimension.FIRST ,			**_lowercase    :       Any ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__    							=   do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE__    							=   size_divisor if size_divisor is not None else self.size_divisor
     SCREAMING_SNAKE_CASE__    							=   resample if resample is not None else self.resample

     if do_resize and size_divisor is None:
       raise ValueError("""size_divisor is required for resizing"""   )

     SCREAMING_SNAKE_CASE__    							=   make_list_of_images(_lowercase   )

     if not valid_images(_lowercase   ):
       raise ValueError("""Invalid image(s)"""   )

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE__    							=   [to_numpy_array(_lowercase   ) for img in images]

     if do_resize:
       SCREAMING_SNAKE_CASE__    							=   [self.resize(_lowercase ,			size_divisor=_lowercase ,			resample=_lowercase   ) for image in images]

     if do_rescale:
       SCREAMING_SNAKE_CASE__    							=   [self.rescale(_lowercase ,			scale=1 / 2_55   ) for image in images]

     SCREAMING_SNAKE_CASE__    							=   [to_channel_dimension_format(_lowercase ,			_lowercase   ) for image in images]

     SCREAMING_SNAKE_CASE__    							=   {"""pixel_values""": images}
     return BatchFeature(data=_lowercase ,			tensor_type=_lowercase   )




from math import isqrt



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int			)							->      bool:







  """simple docstring"""





  return all(number % divisor != 0 for divisor in range(2				,  isqrt(__UpperCamelCase			) + 1			)			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int = 10**6			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   0
  SCREAMING_SNAKE_CASE__    							=   1
  SCREAMING_SNAKE_CASE__    							=   7
  while prime_candidate < max_prime:
    primes_count += is_prime(__UpperCamelCase			)

    cube_index += 1
    prime_candidate += 6 * cube_index

  return primes_count


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