'''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,
    get_resize_output_image_size,
    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


SCREAMING_SNAKE_CASE_	   =			logging.get_logger(__name__)




class    a (       __lowerCAmelCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					["""pixel_values"""]







  def __init__(							self   ,    snake_case_ = True   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = PILImageResampling.BILINEAR   ,    snake_case_ = True   ,    snake_case_ = 1 / 255   ,    snake_case_ = True   ,    snake_case_ = None   ,    snake_case_ = None   ,    **snake_case_   ,    ):

    '''simple docstring'''







    super().__init__(**snake_case_					)
    __UpperCAmelCase:					str		= size if size is not None else {"""shortest_edge""": 384}
    __UpperCAmelCase:					Optional[int]		= get_size_dict(snake_case_   ,    default_to_square=snake_case_					)

    __UpperCAmelCase:					int		= do_resize
    __UpperCAmelCase:					Dict		= size
    # Default value set here for backwards compatibility where the value in config is None
    __UpperCAmelCase:					str		= crop_pct if crop_pct is not None else 224 / 256
    __UpperCAmelCase:					Dict		= resample
    __UpperCAmelCase:					Dict		= do_rescale
    __UpperCAmelCase:					Union[str, Any]		= rescale_factor
    __UpperCAmelCase:					List[str]		= do_normalize
    __UpperCAmelCase:					Optional[Any]		= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    __UpperCAmelCase:					List[str]		= image_std if image_std is not None else IMAGENET_STANDARD_STD







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_ = PILImageResampling.BICUBIC   ,    snake_case_ = None   ,    **snake_case_   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					Dict		= get_size_dict(snake_case_   ,    default_to_square=snake_case_					)
    if "shortest_edge" not in size:
      raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''					)
    __UpperCAmelCase:					List[Any]		= size["""shortest_edge"""]

    if shortest_edge < 384:
      # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
      __UpperCAmelCase:					Union[str, Any]		= int(shortest_edge / crop_pct					)
      __UpperCAmelCase:					int		= get_resize_output_image_size(snake_case_   ,    size=snake_case_   ,    default_to_square=snake_case_					)
      __UpperCAmelCase:					Optional[Any]		= resize(image=snake_case_   ,    size=snake_case_   ,    resample=snake_case_   ,    data_format=snake_case_   ,    **snake_case_					)
      # then crop to (shortest_edge, shortest_edge)
      return center_crop(image=snake_case_   ,    size=(shortest_edge, shortest_edge)   ,    data_format=snake_case_   ,    **snake_case_					)
    else:
      # warping (no cropping) when evaluated at 384 or larger
      return resize(
          snake_case_   ,    size=(shortest_edge, shortest_edge)   ,    resample=snake_case_   ,    data_format=snake_case_   ,    **snake_case_					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_ = None   ,    **snake_case_   ,    ):

    '''simple docstring'''







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







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_ = None   ,    **snake_case_   ,    ):

    '''simple docstring'''







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







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = None   ,    snake_case_ = ChannelDimension.FIRST   ,    **snake_case_   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= do_resize if do_resize is not None else self.do_resize
    __UpperCAmelCase:					Tuple		= crop_pct if crop_pct is not None else self.crop_pct
    __UpperCAmelCase:					List[Any]		= resample if resample is not None else self.resample
    __UpperCAmelCase:					Optional[Any]		= do_rescale if do_rescale is not None else self.do_rescale
    __UpperCAmelCase:					Optional[Any]		= rescale_factor if rescale_factor is not None else self.rescale_factor
    __UpperCAmelCase:					str		= do_normalize if do_normalize is not None else self.do_normalize
    __UpperCAmelCase:					int		= image_mean if image_mean is not None else self.image_mean
    __UpperCAmelCase:					Any		= image_std if image_std is not None else self.image_std

    __UpperCAmelCase:					str		= size if size is not None else self.size
    __UpperCAmelCase:					Optional[int]		= get_size_dict(snake_case_   ,    default_to_square=snake_case_					)

    __UpperCAmelCase:					Dict		= make_list_of_images(snake_case_					)

    if not valid_images(snake_case_					):
      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_resize and size["shortest_edge"] < 384 and crop_pct is None:
      raise ValueError("""crop_pct must be specified if size < 384."""					)

    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.
    __UpperCAmelCase:					Optional[int]		= [to_numpy_array(snake_case_					) for image in images]

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

    if do_rescale:
      __UpperCAmelCase:					Optional[Any]		= [self.rescale(image=snake_case_   ,    scale=snake_case_					) for image in images]

    if do_normalize:
      __UpperCAmelCase:					Union[str, Any]		= [self.normalize(image=snake_case_   ,    mean=snake_case_   ,    std=snake_case_					) for image in images]

    __UpperCAmelCase:					List[str]		= [to_channel_dimension_format(snake_case_   ,    snake_case_					) for image in images]

    __UpperCAmelCase:					Any		= {"""pixel_values""": images}
    return BatchFeature(data=snake_case_   ,    tensor_type=snake_case_					)
'''simple docstring'''






def  UpperCamelCase__					(					_lowercase    :				list     )							->	list:
  if len(_lowercase     ) <= 1:
    return lst

  __UpperCAmelCase:					List[str]		= 1

  while i < len(_lowercase     ):
    if lst[i - 1] <= lst[i]:
      i += 1
    else:
      __UpperCAmelCase, __UpperCAmelCase:					Tuple		= lst[i], lst[i - 1]
      i -= 1
      if i == 0:
        __UpperCAmelCase:					List[str]		= 1

  return lst


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_	   =			input('Enter numbers separated by a comma:\n').strip()
    SCREAMING_SNAKE_CASE_	   =			[int(item) for item in user_input.split(',')]
    print(gnome_sort(unsorted))