


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


__a     =			logging.get_logger(__name__)


if is_vision_available():
 import PIL






class   __lowercase				(  __snake_case  ):
      UpperCamelCase								=       ['''pixel_values''']





      def __init__(		self						:					List[str]					,       __lowerCamelCase						:					bool = True					,       __lowerCamelCase						:					Dict[str, int] = None					,       __lowerCamelCase						:					PILImageResampling = PILImageResampling.BICUBIC					,       __lowerCamelCase						:					bool = True					,       __lowerCamelCase						:					Dict[str, int] = None					,       __lowerCamelCase						:					bool = True					,       __lowerCamelCase						:					Union[int, float] = 1 / 2_5_5					,       __lowerCamelCase						:					bool = True					,       __lowerCamelCase						:					Optional[Union[float, List[float]]] = None					,       __lowerCamelCase						:					Optional[Union[float, List[float]]] = None					,       __lowerCamelCase						:					bool = True					,       **__lowerCamelCase						:					Optional[Any]					,       )    ->   None:






         """simple docstring"""







         super().__init__(**__lowerCamelCase       )
         UpperCAmelCase							=     size if size is not None else {"""shortest_edge""": 2_2_4}
         UpperCAmelCase							=     get_size_dict(__lowerCamelCase					,       default_to_square=__lowerCamelCase       )
         UpperCAmelCase							=     crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
         UpperCAmelCase							=     get_size_dict(__lowerCamelCase					,       default_to_square=__lowerCamelCase					,       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        _lowercase				(		self						:					Dict					,       __lowerCamelCase						:					np.ndarray					,       __lowerCamelCase						:					Dict[str, int]					,       __lowerCamelCase						:					PILImageResampling = PILImageResampling.BICUBIC					,       __lowerCamelCase						:					Optional[Union[str, ChannelDimension]] = None					,       **__lowerCamelCase						:					Tuple					,       )    ->   np.ndarray:






         """simple docstring"""







         UpperCAmelCase							=     get_size_dict(__lowerCamelCase					,       default_to_square=__lowerCamelCase       )
         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(__lowerCamelCase					,       size=size["""shortest_edge"""]					,       default_to_square=__lowerCamelCase       )
         return resize(__lowerCamelCase					,       size=__lowerCamelCase					,       resample=__lowerCamelCase					,       data_format=__lowerCamelCase					,       **__lowerCamelCase       )





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






         """simple docstring"""







         UpperCAmelCase							=     get_size_dict(__lowerCamelCase       )
         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(__lowerCamelCase					,       size=(size["""height"""], size["""width"""])					,       data_format=__lowerCamelCase					,       **__lowerCamelCase       )





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






         """simple docstring"""







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





      def        _lowercase				(		self						:					Optional[Any]					,       __lowerCamelCase						:					np.ndarray					,       __lowerCamelCase						:					Union[float, List[float]]					,       __lowerCamelCase						:					Union[float, List[float]]					,       __lowerCamelCase						:					Optional[Union[str, ChannelDimension]] = None					,       **__lowerCamelCase						:					Dict					,       )    ->   np.ndarray:






         """simple docstring"""







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





      def        _lowercase				(		self						:					str					,       __lowerCamelCase						:					ImageInput					,       __lowerCamelCase						:					bool = None					,       __lowerCamelCase						:					Dict[str, int] = None					,       __lowerCamelCase						:					PILImageResampling = None					,       __lowerCamelCase						:					bool = None					,       __lowerCamelCase						:					int = None					,       __lowerCamelCase						:					bool = None					,       __lowerCamelCase						:					float = None					,       __lowerCamelCase						:					bool = None					,       __lowerCamelCase						:					Optional[Union[float, List[float]]] = None					,       __lowerCamelCase						:					Optional[Union[float, List[float]]] = None					,       __lowerCamelCase						:					bool = None					,       __lowerCamelCase						:					Optional[Union[str, TensorType]] = None					,       __lowerCamelCase						:					Optional[ChannelDimension] = ChannelDimension.FIRST					,       **__lowerCamelCase						:					Dict					,       )    ->   PIL.Image.Image:






         """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(__lowerCamelCase					,       param_name="""size"""					,       default_to_square=__lowerCamelCase       )
         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(__lowerCamelCase					,       param_name="""crop_size"""					,       default_to_square=__lowerCamelCase       )
         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(__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:
            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(__lowerCamelCase       ) for image in images]

         # All transformations expect numpy arrays.
         UpperCAmelCase							=     [to_numpy_array(__lowerCamelCase       ) for image in images]

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

         if do_center_crop:
            UpperCAmelCase							=     [self.center_crop(image=__lowerCamelCase					,       size=__lowerCamelCase       ) for image in images]

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

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

         UpperCAmelCase							=     [to_channel_dimension_format(__lowerCamelCase					,       __lowerCamelCase       ) for image in images]

         UpperCAmelCase							=     {"""pixel_values""": images}
         return BatchFeature(data=__lowerCamelCase					,       tensor_type=__lowerCamelCase       )







from math import isqrt
def   _UpperCamelCase				(						lowerCAmelCase_   )						->bool:
   return all(number % divisor != 0 for divisor in range(2  ,						isqrt(lowerCAmelCase_   ) + 1   )   )
def   _UpperCamelCase				(						lowerCAmelCase_ = 1_0**6   )						->int:
   UpperCAmelCase							=     0
   UpperCAmelCase							=     1
   UpperCAmelCase							=     7
   while prime_candidate < max_prime:
      primes_count += is_prime(lowerCAmelCase_   )

      cube_index += 1
      prime_candidate += 6 * cube_index

   return primes_count


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



