'''simple docstring'''

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


__snake_case       :   Any  		=    logging.get_logger(__name__)


if is_vision_available():
      import PIL
class 							lowercase_    (     _A						):
    a_             =   ["""pixel_values"""]







    def __init__(	self       ,     UpperCamelCase__ = True       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = PILImageResampling.BICUBIC       ,     UpperCamelCase__ = True       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = True       ,     UpperCamelCase__ = 1 / 2_5_5       ,     UpperCamelCase__ = True       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = True       ,     **UpperCamelCase__       ,     )				->       None:





     """simple docstring"""

     super().__init__(**UpperCamelCase__				)
     UpperCAmelCase_				       =					size if size is not None else {"shortest_edge": 2_2_4}
     UpperCAmelCase_				       =					get_size_dict(UpperCamelCase__       ,     default_to_square=UpperCamelCase__				)
     UpperCAmelCase_				       =					crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
     UpperCAmelCase_				       =					get_size_dict(UpperCamelCase__				)

     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   lowerCamelCase_    (	self       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__ = PILImageResampling.BICUBIC       ,     UpperCamelCase__ = None       ,     **UpperCamelCase__       ,     )				->       np.ndarray:





     """simple docstring"""

     UpperCAmelCase_				       =					get_size_dict(UpperCamelCase__       ,     default_to_square=UpperCamelCase__				)
     UpperCAmelCase_				       =					get_resize_output_image_size(
         UpperCamelCase__       ,     size=(size["height"], size["width"])       ,     default_to_square=UpperCamelCase__				)
     return resize(UpperCamelCase__       ,     size=UpperCamelCase__       ,     resample=UpperCamelCase__       ,     data_format=UpperCamelCase__       ,     **UpperCamelCase__				)







    def   lowerCamelCase_    (	self       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__ = None       ,     **UpperCamelCase__       ,     )				->       np.ndarray:





     """simple docstring"""

     UpperCAmelCase_				       =					get_size_dict(UpperCamelCase__				)
     return center_crop(UpperCamelCase__       ,     size=(size["height"], size["width"])       ,     data_format=UpperCamelCase__       ,     **UpperCamelCase__				)







    def   lowerCamelCase_    (	self       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__ = None       ,     **UpperCamelCase__       ,     )				->       int:





     """simple docstring"""

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







    def   lowerCamelCase_    (	self       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__ = None       ,     **UpperCamelCase__       ,     )				->       np.ndarray:





     """simple docstring"""

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







    def   lowerCamelCase_    (	self       ,     UpperCamelCase__       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = None       ,     UpperCamelCase__ = ChannelDimension.FIRST       ,     **UpperCamelCase__       ,     )				->       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(UpperCamelCase__       ,     default_to_square=UpperCamelCase__				)
     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(UpperCamelCase__				)
     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(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:
      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(UpperCamelCase__				) for image in images]

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

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

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

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

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

     UpperCAmelCase_				       =					[to_channel_dimension_format(UpperCamelCase__       ,     UpperCamelCase__				) for image in images]

     UpperCAmelCase_				       =					{"pixel_values": images}
     return BatchFeature(data=UpperCamelCase__       ,     tensor_type=UpperCamelCase__				)

'''simple docstring'''

def lowerCamelCase__						(       A_       ):
 UpperCAmelCase_				       =					len(A_       )
 for _ in range(
     0       ,			int(((no_of_elements - 1) / 2) + 1       )       ):  # we don't need to traverse to end of list as
  for j in range(0       ,			no_of_elements - 1       ):
   if (
       lst[j + 1] < lst[j]
   ):  # applying bubble sort algorithm from left to right (or forwards)
    UpperCAmelCase_				       =					lst[j + 1]
    UpperCAmelCase_				       =					lst[j]
    UpperCAmelCase_				       =					temp
   if (
       lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
   ):  # applying bubble sort algorithm from right to left (or backwards)
    UpperCAmelCase_				       =					lst[no_of_elements - 1 - j]
    UpperCAmelCase_				       =					lst[no_of_elements - 2 - j]
    UpperCAmelCase_				       =					temp
 return lst


if __name__ == "__main__":
      print('''enter the list to be sorted''')
      __snake_case       :   List[str]  		=    [int(x) for x in input().split()]  # inputing elements of the list in one line
      __snake_case       :   Optional[int]  		=    double_sort(lst)
      print('''the sorted list is''')
      print(sorted_lst)
