
'''simple docstring'''

from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


__snake_case:  List[str]								= logging.get_logger(__name__)


class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					["pixel_values"]


     def __init__(      self							,		lowerCAmelCase_ = True							,		lowerCAmelCase_ = 1 / 2_55							,		lowerCAmelCase_ = True							,		lowerCAmelCase_ = 8							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       super().__init__(**lowerCAmelCase_ )

       a_      :	Optional[Any]      =							do_rescale
       a_      :	Any      =							rescale_factor
       a_      :	Optional[int]      =							do_pad
       a_      :	Union[str, Any]      =							pad_size


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ = None							,		**lowerCAmelCase_ ):

       '''simple docstring'''

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


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ = None ):

       '''simple docstring'''

       a_      :	Any      =							get_image_size(lowerCAmelCase_ )
       a_      :	Union[str, Any]      =							(old_height // size + 1) * size - old_height
       a_      :	str      =							(old_width // size + 1) * size - old_width

       return pad(lowerCAmelCase_							,		((0, pad_height), (0, pad_width))							,		mode="""symmetric"""							,		data_format=lowerCAmelCase_ )






     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = ChannelDimension.FIRST							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       a_      :	Dict      =							do_rescale if do_rescale is not None else self.do_rescale
       a_      :	Dict      =							rescale_factor if rescale_factor is not None else self.rescale_factor
       a_      :	str      =							do_pad if do_pad is not None else self.do_pad
       a_      :	Optional[Any]      =							pad_size if pad_size is not None else self.pad_size

       a_      :	Dict      =							make_list_of_images(lowerCAmelCase_ )

       if not valid_images(lowerCAmelCase_ ):
         raise ValueError(
             """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
             """torch.Tensor, tf.Tensor or jax.ndarray.""" )

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

       # All transformations expect numpy arrays.
       a_      :	Any      =							[to_numpy_array(lowerCAmelCase_ ) for image in images]

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

       if do_pad:
         a_      :	Any      =							[self.pad(lowerCAmelCase_							,		size=lowerCAmelCase_ ) for image in images]

       a_      :	Dict      =							[to_channel_dimension_format(lowerCAmelCase_							,		lowerCAmelCase_ ) for image in images]

       a_      :	Union[str, Any]      =							{"""pixel_values""": images}
       return BatchFeature(data=lowerCAmelCase_							,		tensor_type=lowerCAmelCase_ )




'''simple docstring'''

def 	_snake_case						(      A_       :    list   ):
  """simple docstring"""







  if len(A_   ) <= 1:
    return [tuple(A_   )]

  a_      :	List[Any]      =							[]

  def generate(A_       :    int     ,		A_       :    list   ):
    a_      :	List[Any]      =							[0] * n
    res.append(tuple(A_   )   )

    a_      :	List[str]      =							0
    while i < n:
      if c[i] < i:
        if i % 2 == 0:
          a_      , a_      :	Union[str, Any]      =							arr[i], arr[0]
        else:
          a_      , a_      :	List[str]      =							arr[i], arr[c[i]]
        res.append(tuple(A_   )   )
        c[i] += 1
        a_      :	Optional[Any]      =							0
      else:
        a_      :	Union[str, Any]      =							0
        i += 1

  generate(len(A_   )     ,		A_   )
  return res


if __name__ == "__main__":
    __snake_case:  Dict								=			input("Enter numbers separated by a comma:\n").strip()
    __snake_case:  Optional[Any]								=			[int(item) for item in user_input.split(",")]
    print(heaps(arr))


