



'''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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


UpperCAmelCase     		=   logging.get_logger(__name__)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Tuple							)      ->						List[List[ImageInput]]:







	"""simple docstring"""
	if isinstance(_SCREAMING_SNAKE_CASE							,		(list, tuple)							) and isinstance(videos[0]							,		(list, tuple)							) and is_valid_image(videos[0][0]							):
		return videos

	elif isinstance(_SCREAMING_SNAKE_CASE							,		(list, tuple)							) and is_valid_image(videos[0]							):
		return [videos]

	elif is_valid_image(_SCREAMING_SNAKE_CASE							):
		return [[videos]]

	raise ValueError(f'Could not make batched video from {videos}'							)





class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   str									=		["pixel_values"]


						def __init__( self     ,	A_ = True     ,	A_ = None     ,	A_ = PILImageResampling.BILINEAR     ,	A_ = True     ,	A_ = None     ,	A_ = True     ,	A_ = 1 / 255     ,	A_ = True     ,	A_ = None     ,	A_ = None     ,	**A_     ,	)     ->     None:
							super().__init__(**A_				)
							lowerCAmelCase   =     size if size is not None else {"""shortest_edge""": 224}
							lowerCAmelCase   =     get_size_dict(A_     ,	default_to_square=A_				)
							lowerCAmelCase   =     crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
							lowerCAmelCase   =     get_size_dict(A_     ,	param_name="""crop_size"""				)

							lowerCAmelCase   =     do_resize
							lowerCAmelCase   =     size
							lowerCAmelCase   =     do_center_crop
							lowerCAmelCase   =     crop_size
							lowerCAmelCase   =     resample
							lowerCAmelCase   =     do_rescale
							lowerCAmelCase   =     rescale_factor
							lowerCAmelCase   =     do_normalize
							lowerCAmelCase   =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							lowerCAmelCase   =     image_std if image_std is not None else IMAGENET_STANDARD_STD


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_ = PILImageResampling.BILINEAR     ,	A_ = None     ,	**A_     ,	)     ->     np.ndarray:
							lowerCAmelCase   =     get_size_dict(A_     ,	default_to_square=A_				)
							if "shortest_edge" in size:
								lowerCAmelCase   =     get_resize_output_image_size(A_     ,	size["""shortest_edge"""]     ,	default_to_square=A_				)
							elif "height" in size and "width" in size:
								lowerCAmelCase   =     (size["""height"""], size["""width"""])
							else:
								raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'				)
							return resize(A_     ,	size=A_     ,	resample=A_     ,	data_format=A_     ,	**A_				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_ = None     ,	**A_     ,	)     ->     np.ndarray:
							lowerCAmelCase   =     get_size_dict(A_				)
							if "height" not in size or "width" not in size:
								raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'				)
							return center_crop(A_     ,	size=(size["""height"""], size["""width"""])     ,	data_format=A_     ,	**A_				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_ = None     ,	**A_     ,	)     ->     List[Any]:
							return rescale(A_     ,	scale=A_     ,	data_format=A_     ,	**A_				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_ = None     ,	**A_     ,	)     ->     np.ndarray:
							return normalize(A_     ,	mean=A_     ,	std=A_     ,	data_format=A_     ,	**A_				)


						def     __snake_case   ( self     ,	A_     ,	A_ = None     ,	A_ = None     ,	A_ = None     ,	A_ = None     ,	A_ = None     ,	A_ = None     ,	A_ = None     ,	A_ = None     ,	A_ = None     ,	A_ = None     ,	A_ = ChannelDimension.FIRST     ,	)     ->     np.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_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."""				)

							# All transformations expect numpy arrays.
							lowerCAmelCase   =     to_numpy_array(A_				)

							if do_resize:
								lowerCAmelCase   =     self.resize(image=A_     ,	size=A_     ,	resample=A_				)

							if do_center_crop:
								lowerCAmelCase   =     self.center_crop(A_     ,	size=A_				)

							if do_rescale:
								lowerCAmelCase   =     self.rescale(image=A_     ,	scale=A_				)

							if do_normalize:
								lowerCAmelCase   =     self.normalize(image=A_     ,	mean=A_     ,	std=A_				)

							lowerCAmelCase   =     to_channel_dimension_format(A_     ,	A_				)
							return image




						def     __snake_case   ( self     ,	A_     ,	A_ = None     ,	A_ = None     ,	A_ = None     ,	A_ = None     ,	A_ = None     ,	A_ = None     ,	A_ = None     ,	A_ = None     ,	A_ = None     ,	A_ = None     ,	A_ = None     ,	A_ = ChannelDimension.FIRST     ,	**A_     ,	)     ->     PIL.Image.Image:
							lowerCAmelCase   =     do_resize if do_resize is not None else self.do_resize
							lowerCAmelCase   =     resample if resample is not None else self.resample
							lowerCAmelCase   =     do_center_crop if do_center_crop is not None else self.do_center_crop
							lowerCAmelCase   =     do_rescale if do_rescale is not None else self.do_rescale
							lowerCAmelCase   =     rescale_factor if rescale_factor is not None else self.rescale_factor
							lowerCAmelCase   =     do_normalize if do_normalize is not None else self.do_normalize
							lowerCAmelCase   =     image_mean if image_mean is not None else self.image_mean
							lowerCAmelCase   =     image_std if image_std is not None else self.image_std

							lowerCAmelCase   =     size if size is not None else self.size
							lowerCAmelCase   =     get_size_dict(A_     ,	default_to_square=A_				)
							lowerCAmelCase   =     crop_size if crop_size is not None else self.crop_size
							lowerCAmelCase   =     get_size_dict(A_     ,	param_name="""crop_size"""				)

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

							lowerCAmelCase   =     make_batched(A_				)

							lowerCAmelCase   =     [
							    [
							        self._preprocess_image(
							            image=A_     ,	do_resize=A_     ,	size=A_     ,	resample=A_     ,	do_center_crop=A_     ,	crop_size=A_     ,	do_rescale=A_     ,	rescale_factor=A_     ,	do_normalize=A_     ,	image_mean=A_     ,	image_std=A_     ,	data_format=A_     ,	)
							        for img in video
							    ]
							    for video in videos
							]

							lowerCAmelCase   =     {"""pixel_values""": videos}
							return BatchFeature(data=A_     ,	tensor_type=A_				)




'''simple docstring'''

def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list							)      ->						list:







	"""simple docstring"""

	if len(_SCREAMING_SNAKE_CASE							) < 2:
		return collection

	def circle_sort_util(_SCREAMING_SNAKE_CASE					:						list							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						int							) -> bool:
		lowerCAmelCase   =     False

		if low == high:
			return swapped

		lowerCAmelCase   =     low
		lowerCAmelCase   =     high

		while left < right:
			if collection[left] > collection[right]:
				lowerCAmelCase,       lowerCAmelCase   =     (
				    collection[right],
				    collection[left],
				)
				lowerCAmelCase   =     True

			left += 1
			right -= 1

		if left == right and collection[left] > collection[right + 1]:
			lowerCAmelCase,       lowerCAmelCase   =     (
			    collection[right + 1],
			    collection[left],
			)

			lowerCAmelCase   =     True

		lowerCAmelCase   =     low + int((high - low) / 2							)
		lowerCAmelCase   =     circle_sort_util(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
		lowerCAmelCase   =     circle_sort_util(_SCREAMING_SNAKE_CASE							,		mid + 1							,		_SCREAMING_SNAKE_CASE							)

		return swapped or left_swap or right_swap

	lowerCAmelCase   =     True

	while is_not_sorted is True:
		lowerCAmelCase   =     circle_sort_util(_SCREAMING_SNAKE_CASE							,		0							,		len(_SCREAMING_SNAKE_CASE							) - 1							)

	return collection


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