





'''simple docstring'''


from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
 import PIL

if is_torch_available():
 import torch


lowerCAmelCase					:     Dict												=logging.get_logger(__name__)
class       a_   (				_lowerCAmelCase							):
     __A      =			["pixel_values"]





     def __init__( self		:					List[Any]	,      lowercase		:					bool = True	,      lowercase		:					Dict[str, int] = None	,      lowercase		:					PILImageResampling = PILImageResampling.BILINEAR	,      lowercase		:					bool = True	,      lowercase		:					Union[int, float] = 1 / 255	,      lowercase		:					bool = True	,      lowercase		:					Dict[str, int] = None	,      lowercase		:					bool = True	,      **lowercase		:					Dict	,      ):





        """simple docstring"""




        super().__init__(**lowercase						)
        lowercase_			:Any    			= size if size is not None else {"shortest_edge": 224}
        lowercase_			:Dict    			= get_size_dict(lowercase	,      default_to_square=lowercase						)
        lowercase_			:List[str]    			= crop_size if crop_size is not None else {"height": 256, "width": 256}
        lowercase_			:str    			= get_size_dict(lowercase	,      param_name="crop_size"						)

        lowercase_			:Optional[int]    			= do_resize
        lowercase_			:Optional[Any]    			= size
        lowercase_			:Tuple    			= resample
        lowercase_			:Any    			= do_rescale
        lowercase_			:Optional[int]    			= rescale_factor
        lowercase_			:List[str]    			= do_center_crop
        lowercase_			:Optional[int]    			= crop_size
        lowercase_			:str    			= do_flip_channel_order





     def 							lowercase__      ( self		:					Dict	,      lowercase		:					np.ndarray	,      lowercase		:					Dict[str, int]	,      lowercase		:					PILImageResampling = PIL.Image.BILINEAR	,      lowercase		:					Optional[Union[str, ChannelDimension]] = None	,      **lowercase		:					Any	,      ):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= get_size_dict(lowercase	,      default_to_square=lowercase						)
        if "shortest_edge" not in size:
           raise ValueError(F'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'						)
        lowercase_			:List[str]    			= 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 							lowercase__      ( self		:					str	,      lowercase		:					np.ndarray	,      lowercase		:					Dict[str, int]	,      lowercase		:					Optional[Union[str, ChannelDimension]] = None	,      **lowercase		:					Optional[Any]	,      ):





        """simple docstring"""




        lowercase_			:List[str]    			= get_size_dict(lowercase						)
        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()}'						)
        return center_crop(lowercase	,      size=(size["height"], size["width"])	,      data_format=lowercase	,      **lowercase						)





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





        """simple docstring"""




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





     def 							lowercase__      ( self		:					Tuple	,      lowercase		:					np.ndarray	,      lowercase		:					Optional[Union[str, ChannelDimension]] = None						):





        """simple docstring"""




        return flip_channel_order(lowercase	,      data_format=lowercase						)





     def 							lowercase__      ( self		:					List[str]	,      lowercase		:					ImageInput	,      lowercase		:					bool = None	,      lowercase		:					Dict[str, int] = None	,      lowercase		:					PILImageResampling = None	,      lowercase		:					bool = None	,      lowercase		:					float = None	,      lowercase		:					bool = None	,      lowercase		:					Dict[str, int] = None	,      lowercase		:					bool = None	,      lowercase		:					Optional[Union[str, TensorType]] = None	,      lowercase		:					ChannelDimension = ChannelDimension.FIRST	,      **lowercase		:					int	,      ):





        """simple docstring"""




        lowercase_			:Any    			= do_resize if do_resize is not None else self.do_resize
        lowercase_			:List[str]    			= resample if resample is not None else self.resample
        lowercase_			:Optional[Any]    			= do_rescale if do_rescale is not None else self.do_rescale
        lowercase_			:Any    			= rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase_			:Dict    			= do_center_crop if do_center_crop is not None else self.do_center_crop
        lowercase_			:Any    			= (
            do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
        )

        lowercase_			:Union[str, Any]    			= size if size is not None else self.size
        lowercase_			:int    			= get_size_dict(lowercase	,      default_to_square=lowercase						)
        lowercase_			:str    			= crop_size if crop_size is not None else self.crop_size
        lowercase_			:List[Any]    			= get_size_dict(lowercase	,      param_name="crop_size"						)

        lowercase_			:str    			= 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_rescale and rescale_factor is None:
           raise ValueError("Rescale factor must be specified if do_rescale is True."						)

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

        # All transformations expect numpy arrays.
        lowercase_			:Dict    			= [to_numpy_array(lowercase						) for image in images]

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

        if do_center_crop:
           lowercase_			:Dict    			= [self.center_crop(image=lowercase	,      size=lowercase						) for image in images]

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

        # the pretrained checkpoints assume images are BGR, not RGB
        if do_flip_channel_order:
           lowercase_			:Any    			= [self.flip_channel_order(image=lowercase						) for image in images]

        lowercase_			:Dict    			= [to_channel_dimension_format(lowercase	,      lowercase						) for image in images]

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







     def 							lowercase__      ( self		:					str	,      lowercase		:					int	,      lowercase		:					List[Tuple] = None						):





        """simple docstring"""




        lowercase_			:Union[str, Any]    			= outputs.logits

        # Resize logits and compute semantic segmentation maps
        if target_sizes is not None:
           if len(lowercase						) != len(lowercase						):
              raise ValueError(
                  "Make sure that you pass in as many target sizes as the batch dimension of the logits"						)

           if is_torch_tensor(lowercase						):
              lowercase_			:Optional[Any]    			= target_sizes.numpy()

           lowercase_			:Union[str, Any]    			= []

           for idx in range(len(lowercase						)						):
              lowercase_			:Optional[int]    			= torch.nn.functional.interpolate(
                  logits[idx].unsqueeze(dim=0						)	,      size=target_sizes[idx]	,      mode="bilinear"	,      align_corners=lowercase						)
              lowercase_			:Optional[Any]    			= resized_logits[0].argmax(dim=0						)
              semantic_segmentation.append(lowercase						)
        else:
           lowercase_			:List[Any]    			= logits.argmax(dim=1						)
           lowercase_			:Dict    			= [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]						)]

        return semantic_segmentation









'''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))


