from __future__ import annotations



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       list ,     __UpperCamelCase    :       int ,     __UpperCamelCase    :       int ,     __UpperCamelCase    :       int	)							->					list:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      []
  SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      input_list[low:mid], input_list[mid : high + 1]
  while left and right:
    result.append((left if left[0] <= right[0] else right).pop(0	)	)
  SCREAMING_SNAKE_CASE__										=      result + left + right
  return input_list



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       list	)							->					list:







  """simple docstring"""





  if len(__UpperCamelCase	) <= 1:
    return input_list
  SCREAMING_SNAKE_CASE__										=      list(__UpperCamelCase	)

  # iteration for two-way merging
  SCREAMING_SNAKE_CASE__										=      2
  while p <= len(__UpperCamelCase	):
    # getting low, high and middle value for merge-sort of single list
    for i in range(0 ,     len(__UpperCamelCase	) ,     __UpperCamelCase	):
      SCREAMING_SNAKE_CASE__										=      i
      SCREAMING_SNAKE_CASE__										=      i + p - 1
      SCREAMING_SNAKE_CASE__										=      (low + high + 1) // 2
      SCREAMING_SNAKE_CASE__										=      merge(__UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase	)
    # final merge of last two parts
    if p * 2 >= len(__UpperCamelCase	):
      SCREAMING_SNAKE_CASE__										=      i
      SCREAMING_SNAKE_CASE__										=      merge(__UpperCamelCase ,     0 ,     __UpperCamelCase ,     len(__UpperCamelCase	) - 1	)
      break
    p *= 2

  return input_list


if __name__ == "__main__":
   __lowerCamelCase			: Tuple							   =		input('''Enter numbers separated by a comma:\n''').strip()
   if user_input == "":
      __lowerCamelCase			: Tuple							   =		[]
   else:
      __lowerCamelCase			: Optional[Any]							   =		[int(item.strip()) for item in user_input.split(''',''')]
   print(iter_merge_sort(unsorted))

from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCamelCase			: Union[str, Any]							   =		logging.get_logger(__name__)







class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							["pixel_values"]




   def __init__(    self			:				Any       ,	_lowercase			:				bool = True       ,	_lowercase			:				int = 32       ,	_lowercase			:				Any=PILImageResampling.BILINEAR       ,	_lowercase			:				bool = True       ,	**_lowercase			:				Union[str, Any]       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      do_resize
     SCREAMING_SNAKE_CASE__										=      do_rescale
     SCREAMING_SNAKE_CASE__										=      size_divisor
     SCREAMING_SNAKE_CASE__										=      resample
     super().__init__(**_lowercase      )




   def 	__a						(    self			:				int       ,	_lowercase			:				np.ndarray       ,	_lowercase			:				int       ,	_lowercase			:				int       ,	_lowercase			:				Optional[ChannelDimension] = None       ,	**_lowercase			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      get_image_size(_lowercase      )
     # Rounds the height and width down to the closest multiple of size_divisor
     SCREAMING_SNAKE_CASE__										=      height // size_divisor * size_divisor
     SCREAMING_SNAKE_CASE__										=      width // size_divisor * size_divisor
     SCREAMING_SNAKE_CASE__										=      resize(_lowercase       ,	(new_h, new_w)       ,	resample=_lowercase       ,	data_format=_lowercase       ,	**_lowercase      )
     return image




   def 	__a						(    self			:				int       ,	_lowercase			:				np.ndarray       ,	_lowercase			:				float       ,	_lowercase			:				Optional[ChannelDimension] = None       ,	**_lowercase			:				str      ):



     """simple docstring"""






     return rescale(image=_lowercase       ,	scale=_lowercase       ,	data_format=_lowercase       ,	**_lowercase      )




   def 	__a						(    self			:				Tuple       ,	_lowercase			:				Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]       ,	_lowercase			:				Optional[bool] = None       ,	_lowercase			:				Optional[int] = None       ,	_lowercase			:				int=None       ,	_lowercase			:				Optional[bool] = None       ,	_lowercase			:				Optional[Union[TensorType, str]] = None       ,	_lowercase			:				ChannelDimension = ChannelDimension.FIRST       ,	**_lowercase			:				Optional[int]       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__										=      do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE__										=      size_divisor if size_divisor is not None else self.size_divisor
     SCREAMING_SNAKE_CASE__										=      resample if resample is not None else self.resample

     if do_resize and size_divisor is None:
       raise ValueError("""size_divisor is required for resizing"""      )

     SCREAMING_SNAKE_CASE__										=      make_list_of_images(_lowercase      )

     if not valid_images(_lowercase      ):
       raise ValueError("""Invalid image(s)"""      )

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE__										=      [to_numpy_array(_lowercase      ) for img in images]

     if do_resize:
       SCREAMING_SNAKE_CASE__										=      [self.resize(_lowercase       ,	size_divisor=_lowercase       ,	resample=_lowercase      ) for image in images]

     if do_rescale:
       SCREAMING_SNAKE_CASE__										=      [self.rescale(_lowercase       ,	scale=1 / 2_55      ) for image in images]

     SCREAMING_SNAKE_CASE__										=      [to_channel_dimension_format(_lowercase       ,	_lowercase      ) for image in images]

     SCREAMING_SNAKE_CASE__										=      {"""pixel_values""": images}
     return BatchFeature(data=_lowercase       ,	tensor_type=_lowercase      )
