


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 convert_to_rgb, 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


if is_vision_available():
  import PIL


lowerCamelCase						=		logging.get_logger(__name__)





class  _a							(    UpperCamelCase__):
 _a  :  Any				   =							['''pixel_values''']




 def __init__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		bool = True		, _SCREAMING_SNAKE_CASE						:		Dict[str, int] = None		, _SCREAMING_SNAKE_CASE						:		PILImageResampling = PILImageResampling.BICUBIC		, _SCREAMING_SNAKE_CASE						:		bool = True		, _SCREAMING_SNAKE_CASE						:		Union[int, float] = 1 / 255		, _SCREAMING_SNAKE_CASE						:		bool = True		, _SCREAMING_SNAKE_CASE						:		Optional[Union[float, List[float]]] = None		, _SCREAMING_SNAKE_CASE						:		Optional[Union[float, List[float]]] = None		, _SCREAMING_SNAKE_CASE						:		bool = True		, **_SCREAMING_SNAKE_CASE						:		Optional[Any]		, )->	None:
   super().__init__(**UpperCamelCase_		)
   lowerCAmelCase__       :      Tuple   					= size if size is not None else {'''height''': 384, '''width''': 384}
   lowerCAmelCase__       :      Union[str, Any]   					= get_size_dict(UpperCamelCase_		, default_to_square=UpperCamelCase_		)

   lowerCAmelCase__       :      Dict   					= do_resize
   lowerCAmelCase__       :      int   					= size
   lowerCAmelCase__       :      Dict   					= resample
   lowerCAmelCase__       :      Any   					= do_rescale
   lowerCAmelCase__       :      Optional[Any]   					= rescale_factor
   lowerCAmelCase__       :      Any   					= do_normalize
   lowerCAmelCase__       :      Any   					= image_mean if image_mean is not None else OPENAI_CLIP_MEAN
   lowerCAmelCase__       :      Union[str, Any]   					= image_std if image_std is not None else OPENAI_CLIP_STD
   lowerCAmelCase__       :      Union[str, Any]   					= do_convert_rgb




 def 							UpperCAmelCase__( self						:		int		, _SCREAMING_SNAKE_CASE						:		np.ndarray		, _SCREAMING_SNAKE_CASE						:		Dict[str, int]		, _SCREAMING_SNAKE_CASE						:		PILImageResampling = PILImageResampling.BICUBIC		, _SCREAMING_SNAKE_CASE						:		Optional[Union[str, ChannelDimension]] = None		, **_SCREAMING_SNAKE_CASE						:		Any		, )->	np.ndarray:
   lowerCAmelCase__       :      int   					= get_size_dict(UpperCamelCase_		, default_to_square=UpperCamelCase_		)
   if "height" not in size or "width" not in size:
     raise ValueError(F'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'		)
   lowerCAmelCase__       :      int   					= (size['''height'''], size['''width'''])
   return resize(UpperCamelCase_		, size=UpperCamelCase_		, resample=UpperCamelCase_		, data_format=UpperCamelCase_		, **UpperCamelCase_		)




 def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		np.ndarray		, _SCREAMING_SNAKE_CASE						:		Union[int, float]		, _SCREAMING_SNAKE_CASE						:		Optional[Union[str, ChannelDimension]] = None		, **_SCREAMING_SNAKE_CASE						:		Optional[int]		, )->	List[str]:
   return rescale(UpperCamelCase_		, scale=UpperCamelCase_		, data_format=UpperCamelCase_		, **UpperCamelCase_		)




 def 							UpperCAmelCase__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		np.ndarray		, _SCREAMING_SNAKE_CASE						:		Union[float, List[float]]		, _SCREAMING_SNAKE_CASE						:		Union[float, List[float]]		, _SCREAMING_SNAKE_CASE						:		Optional[Union[str, ChannelDimension]] = None		, **_SCREAMING_SNAKE_CASE						:		Optional[int]		, )->	np.ndarray:
   return normalize(UpperCamelCase_		, mean=UpperCamelCase_		, std=UpperCamelCase_		, data_format=UpperCamelCase_		, **UpperCamelCase_		)




 def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		ImageInput		, _SCREAMING_SNAKE_CASE						:		Optional[bool] = None		, _SCREAMING_SNAKE_CASE						:		Optional[Dict[str, int]] = None		, _SCREAMING_SNAKE_CASE						:		PILImageResampling = None		, _SCREAMING_SNAKE_CASE						:		Optional[bool] = None		, _SCREAMING_SNAKE_CASE						:		Optional[float] = None		, _SCREAMING_SNAKE_CASE						:		Optional[bool] = None		, _SCREAMING_SNAKE_CASE						:		Optional[Union[float, List[float]]] = None		, _SCREAMING_SNAKE_CASE						:		Optional[Union[float, List[float]]] = None		, _SCREAMING_SNAKE_CASE						:		Optional[Union[str, TensorType]] = None		, _SCREAMING_SNAKE_CASE						:		bool = None		, _SCREAMING_SNAKE_CASE						:		ChannelDimension = ChannelDimension.FIRST		, **_SCREAMING_SNAKE_CASE						:		Union[str, Any]		, )->	PIL.Image.Image:
   lowerCAmelCase__       :      List[str]   					= do_resize if do_resize is not None else self.do_resize
   lowerCAmelCase__       :      List[str]   					= resample if resample is not None else self.resample
   lowerCAmelCase__       :      Tuple   					= do_rescale if do_rescale is not None else self.do_rescale
   lowerCAmelCase__       :      str   					= rescale_factor if rescale_factor is not None else self.rescale_factor
   lowerCAmelCase__       :      Optional[Any]   					= do_normalize if do_normalize is not None else self.do_normalize
   lowerCAmelCase__       :      Tuple   					= image_mean if image_mean is not None else self.image_mean
   lowerCAmelCase__       :      str   					= image_std if image_std is not None else self.image_std
   lowerCAmelCase__       :      Optional[int]   					= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

   lowerCAmelCase__       :      Optional[int]   					= size if size is not None else self.size
   lowerCAmelCase__       :      Union[str, Any]   					= get_size_dict(UpperCamelCase_		, default_to_square=UpperCamelCase_		)

   lowerCAmelCase__       :      List[Any]   					= 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_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:
     lowerCAmelCase__       :      Dict   					= [convert_to_rgb(UpperCamelCase_		) for image in images]

   # All transformations expect numpy arrays.
   lowerCAmelCase__       :      Optional[Any]   					= [to_numpy_array(UpperCamelCase_		) for image in images]

   if do_resize:
     lowerCAmelCase__       :      Optional[Any]   					= [self.resize(image=UpperCamelCase_		, size=UpperCamelCase_		, resample=UpperCamelCase_		) for image in images]

   if do_rescale:
     lowerCAmelCase__       :      Optional[int]   					= [self.rescale(image=UpperCamelCase_		, scale=UpperCamelCase_		) for image in images]

   if do_normalize:
     lowerCAmelCase__       :      int   					= [self.normalize(image=UpperCamelCase_		, mean=UpperCamelCase_		, std=UpperCamelCase_		) for image in images]

   lowerCAmelCase__       :      Optional[Any]   					= [to_channel_dimension_format(UpperCamelCase_		, UpperCamelCase_		) for image in images]

   lowerCAmelCase__       :      Optional[int]   					= BatchFeature(data={'''pixel_values''': images}		, tensor_type=UpperCamelCase_		)

   return encoded_outputs








from math import isqrt






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Dict   					= [True] * max_number
		for i in range(2     ,       isqrt(max_number - 1	) + 1	):
				if is_prime[i]:
						for j in range(i**2     ,       _a     ,       _a	):
								lowerCAmelCase__       :      int   					= False

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






def 					lowerCamelCase_   (	_a = 10**8	):




		"""simple docstring"""






		lowerCAmelCase__       :      Any   					= calculate_prime_numbers(max_number // 2	)

		lowerCAmelCase__       :      Tuple   					= 0
		lowerCAmelCase__       :      List[Any]   					= 0
		lowerCAmelCase__       :      Optional[int]   					= len(_a	) - 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() = }''')




