

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 convert_to_rgb, 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


if is_vision_available():
					import PIL


_lowerCAmelCase  :      Dict			 =logging.get_logger(__name__)

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   ["pixel_values"]





					def __init__(				self				,      lowerCamelCase__ = True				,      lowerCamelCase__ = None				,      lowerCamelCase__ = PILImageResampling.BICUBIC				,      lowerCamelCase__ = True				,      lowerCamelCase__ = 1 / 2_5_5				,      lowerCamelCase__ = True				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__ = True				,      **lowerCamelCase__				,      ):
						super().__init__(**lowerCamelCase__						)
						UpperCAmelCase__:	List[str]									= size if size is not None else {"height": 3_8_4, "width": 3_8_4}
						UpperCAmelCase__:	str									= get_size_dict(lowerCamelCase__				,      default_to_square=lowerCamelCase__						)

						UpperCAmelCase__:	Tuple									= do_resize
						UpperCAmelCase__:	List[str]									= size
						UpperCAmelCase__:	Dict									= resample
						UpperCAmelCase__:	str									= do_rescale
						UpperCAmelCase__:	int									= rescale_factor
						UpperCAmelCase__:	List[Any]									= do_normalize
						UpperCAmelCase__:	Union[str, Any]									= image_mean if image_mean is not None else OPENAI_CLIP_MEAN
						UpperCAmelCase__:	str									= image_std if image_std is not None else OPENAI_CLIP_STD
						UpperCAmelCase__:	Any									= do_convert_rgb





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__ = PILImageResampling.BICUBIC				,      lowerCamelCase__ = None				,      **lowerCamelCase__				,      ):
						UpperCAmelCase__:	Dict									= get_size_dict(lowerCamelCase__				,      default_to_square=lowerCamelCase__						)
						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()}"						)
						UpperCAmelCase__:	Any									= (size["height"], size["width"])
						return resize(lowerCamelCase__				,      size=lowerCamelCase__				,      resample=lowerCamelCase__				,      data_format=lowerCamelCase__				,      **lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__ = None				,      **lowerCamelCase__				,      ):
						return rescale(lowerCamelCase__				,      scale=lowerCamelCase__				,      data_format=lowerCamelCase__				,      **lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__ = None				,      **lowerCamelCase__				,      ):
						return normalize(lowerCamelCase__				,      mean=lowerCamelCase__				,      std=lowerCamelCase__				,      data_format=lowerCamelCase__				,      **lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__ = ChannelDimension.FIRST				,      **lowerCamelCase__				,      ):
						UpperCAmelCase__:	Any									= do_resize if do_resize is not None else self.do_resize
						UpperCAmelCase__:	Optional[Any]									= resample if resample is not None else self.resample
						UpperCAmelCase__:	List[Any]									= do_rescale if do_rescale is not None else self.do_rescale
						UpperCAmelCase__:	int									= rescale_factor if rescale_factor is not None else self.rescale_factor
						UpperCAmelCase__:	Optional[int]									= do_normalize if do_normalize is not None else self.do_normalize
						UpperCAmelCase__:	Tuple									= image_mean if image_mean is not None else self.image_mean
						UpperCAmelCase__:	Tuple									= image_std if image_std is not None else self.image_std
						UpperCAmelCase__:	str									= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

						UpperCAmelCase__:	List[Any]									= size if size is not None else self.size
						UpperCAmelCase__:	Any									= get_size_dict(lowerCamelCase__				,      default_to_square=lowerCamelCase__						)

						UpperCAmelCase__:	Optional[Any]									= 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_resize and size is None or resample is None:
							raise ValueError("Size and resample 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_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__:	Union[str, Any]									= [convert_to_rgb(lowerCamelCase__						) for image in images]

						# All transformations expect numpy arrays.
						UpperCAmelCase__:	str									= [to_numpy_array(lowerCamelCase__						) for image in images]

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

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

						if do_normalize:
							UpperCAmelCase__:	Tuple									= [self.normalize(image=lowerCamelCase__				,      mean=lowerCamelCase__				,      std=lowerCamelCase__						) for image in images]

						UpperCAmelCase__:	List[str]									= [to_channel_dimension_format(lowerCamelCase__				,      lowerCamelCase__						) for image in images]

						UpperCAmelCase__:	Optional[int]									= BatchFeature(data={"pixel_values": images}				,      tensor_type=lowerCamelCase__						)

						return encoded_outputs


def 			_A	(      SCREAMING_SNAKE_CASE      ):
	stooge(SCREAMING_SNAKE_CASE	,0	,len(SCREAMING_SNAKE_CASE      ) - 1      )
	return arr



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	if i >= h:
		return

	# If first element is smaller than the last then swap them
	if arr[i] > arr[h]:
		UpperCAmelCase__							,						UpperCAmelCase__:	int									= arr[h], arr[i]

	# If there are more than 2 elements in the array
	if h - i + 1 > 2:
		UpperCAmelCase__:	str									= (int)((h - i + 1) / 3      )

		# Recursively sort first 2/3 elements
		stooge(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,(h - t)      )

		# Recursively sort last 2/3 elements
		stooge(SCREAMING_SNAKE_CASE	,i + t	,(SCREAMING_SNAKE_CASE)      )

		# Recursively sort first 2/3 elements
		stooge(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,(h - t)      )


if __name__ == "__main__":
					_lowerCAmelCase  :      List[str]			 =input("""Enter numbers separated by a comma:\n""").strip()
					_lowerCAmelCase  :      Any			 =[int(item) for item in user_input.split(""",""")]
					print(stooge_sort(unsorted))