



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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_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


_snake_case		=  logging.get_logger(__name__)



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		['pixel_values']







							def __init__(   self,			__a = True,			__a = None,			__a = PIL.Image.BICUBIC,			__a = True,			__a = None,			__a = 1 / 255,			__a = True,			__a = True,			__a = None,			__a = None,			**__a,			):

														'''simple docstring'''

														super().__init__(**__a)
														_lowerCAmelCase     :							Any    =    size if size is not None else {"height": 256, "width": 256}
														_lowerCAmelCase     :							List[Any]    =    get_size_dict(__a)
														_lowerCAmelCase     :							Any    =    crop_size if crop_size is not None else {"height": 224, "width": 224}
														_lowerCAmelCase     :							List[Any]    =    get_size_dict(__a,			param_name="crop_size")

														_lowerCAmelCase     :							int    =    do_resize
														_lowerCAmelCase     :							List[str]    =    size
														_lowerCAmelCase     :							str    =    resample
														_lowerCAmelCase     :							Optional[Any]    =    do_center_crop
														_lowerCAmelCase     :							Tuple    =    crop_size
														_lowerCAmelCase     :							List[str]    =    do_rescale
														_lowerCAmelCase     :							str    =    rescale_factor
														_lowerCAmelCase     :							Optional[Any]    =    do_normalize
														_lowerCAmelCase     :							Tuple    =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														_lowerCAmelCase     :							Optional[int]    =    image_std if image_std is not None else IMAGENET_STANDARD_STD







							def    snake_case__      (   self,			__a,			__a,			__a = PIL.Image.BICUBIC,			__a = None,			**__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    get_size_dict(__a)
														if "height" not in size or "width" not in size:
																					raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}")
														return resize(
														    __a,			size=(size["height"], size["width"]),			resample=__a,			data_format=__a,			**__a)







							def    snake_case__      (   self,			__a,			__a,			__a = None,			**__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    get_size_dict(__a)
														if "height" not in size or "width" not in size:
																					raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}")
														return center_crop(__a,			size=(size["height"], size["width"]),			data_format=__a,			**__a)







							def    snake_case__      (   self,			__a,			__a,			__a = None,			**__a,			):

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a,			__a,			__a,			__a = None,			**__a,			):

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a,			__a = None,			__a = None,			__a=None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = None,			__a = ChannelDimension.FIRST,			**__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    do_resize if do_resize is not None else self.do_resize
														_lowerCAmelCase     :							int    =    resample if resample is not None else self.resample
														_lowerCAmelCase     :							Dict    =    do_center_crop if do_center_crop is not None else self.do_center_crop
														_lowerCAmelCase     :							Tuple    =    do_rescale if do_rescale is not None else self.do_rescale
														_lowerCAmelCase     :							Union[str, Any]    =    rescale_factor if rescale_factor is not None else self.rescale_factor
														_lowerCAmelCase     :							Any    =    do_normalize if do_normalize is not None else self.do_normalize
														_lowerCAmelCase     :							Optional[int]    =    image_mean if image_mean is not None else self.image_mean
														_lowerCAmelCase     :							Dict    =    image_std if image_std is not None else self.image_std

														_lowerCAmelCase     :							Any    =    size if size is not None else self.size
														_lowerCAmelCase     :							int    =    get_size_dict(__a)
														_lowerCAmelCase     :							Optional[int]    =    crop_size if crop_size is not None else self.crop_size
														_lowerCAmelCase     :							Any    =    get_size_dict(__a,			param_name="crop_size")

														_lowerCAmelCase     :							int    =    make_list_of_images(__a)

														if not valid_images(__a):
																					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_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.")

														# All transformations expect numpy arrays.
														_lowerCAmelCase     :							List[Any]    =    [to_numpy_array(__a) for image in images]

														if do_resize:
																					_lowerCAmelCase     :							str    =    [self.resize(image=__a,			size=__a,			resample=__a) for image in images]

														if do_center_crop:
																					_lowerCAmelCase     :							int    =    [self.center_crop(image=__a,			size=__a) for image in images]

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

														if do_normalize:
																					_lowerCAmelCase     :							List[Any]    =    [self.normalize(image=__a,			mean=__a,			std=__a) for image in images]

														_lowerCAmelCase     :							List[Any]    =    [to_channel_dimension_format(__a,			__a) for image in images]

														_lowerCAmelCase     :							str    =    {"pixel_values": images}
														return BatchFeature(data=__a,			tensor_type=__a)







'''simple docstring'''


import math
def 							UpperCAmelCase_  (						__lowerCamelCase					:					int ):
   lowercase_			:Dict    			= []
   lowercase_			:List[Any]    			= 2
   lowercase_			:Optional[Any]    			= int(math.sqrt(__lowerCamelCase ) )  # Size of every segment
   lowercase_			:Optional[Any]    			= [True] * (end + 1)
   lowercase_			:Dict    			= []

   while start <= end:
      if temp[start] is True:
         in_prime.append(__lowerCamelCase )
         for i in range(start * start	,end + 1	,__lowerCamelCase ):
            lowercase_			:List[str]    			= False
      start += 1
   prime += in_prime

   lowercase_			:Dict    			= end + 1
   lowercase_			:Dict    			= min(2 * end	,__lowerCamelCase )

   while low <= n:
      lowercase_			:Any    			= [True] * (high - low + 1)
      for each in in_prime:
         lowercase_			:List[Any]    			= math.floor(low / each ) * each
         if t < low:
            t += each

         for j in range(__lowerCamelCase	,high + 1	,__lowerCamelCase ):
            lowercase_			:str    			= False

      for j in range(len(__lowerCamelCase ) ):
         if temp[j] is True:
            prime.append(j + low )

      lowercase_			:Dict    			= high + 1
      lowercase_			:Dict    			= min(high + end	,__lowerCamelCase )

   return prime


print(sieve(10**6))


