




'''simple docstring'''


def 	snake_case_					(UpperCamelCase :						list   ):





							'''simple docstring'''


							if len(UpperCamelCase   ) < 2:
														return collection

							def circle_sort_util(UpperCamelCase :						list				,		UpperCamelCase :						int				,		UpperCamelCase :						int   ) -> bool:
														_a			  =       False

														if low == high:
																					return swapped

														_a			  =       low
														_a			  =       high

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

																					left += 1
																					right -= 1

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

																					_a			  =       True

														_a			  =       low + int((high - low) / 2   )
														_a			  =       circle_sort_util(UpperCamelCase				,		UpperCamelCase				,		UpperCamelCase   )
														_a			  =       circle_sort_util(UpperCamelCase				,		mid + 1				,		UpperCamelCase   )

														return swapped or left_swap or right_swap

							_a			  =       True

							while is_not_sorted is True:
														_a			  =       circle_sort_util(UpperCamelCase				,		0				,		len(UpperCamelCase   ) - 1   )

							return collection


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





'''simple docstring'''


from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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 logging


if is_vision_available():
				import PIL

_snake_case   :   str     							=	logging.get_logger(__name__)
def 	snake_case_					(UpperCamelCase :						Optional[int]   ):





							'''simple docstring'''


							if isinstance(UpperCamelCase				,		(list, tuple)   ) and isinstance(videos[0]				,		(list, tuple)   ) and is_valid_image(videos[0][0]   ):
														return videos

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

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

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

class     A					(	_a					):
							lowercase_           =       ['pixel_values']







							def __init__(						self :						List[Any]							,    lowerCAmelCase_ :						bool = True							,    lowerCAmelCase_ :						Dict[str, int] = None							,    lowerCAmelCase_ :						PILImageResampling = PILImageResampling.BILINEAR							,    lowerCAmelCase_ :						bool = True							,    lowerCAmelCase_ :						Dict[str, int] = None							,    lowerCAmelCase_ :						bool = True							,    lowerCAmelCase_ :						Union[int, float] = 1 / 2_55							,    lowerCAmelCase_ :						bool = True							,    lowerCAmelCase_ :						bool = True							,    lowerCAmelCase_ :						Optional[Union[float, List[float]]] = None							,    lowerCAmelCase_ :						Optional[Union[float, List[float]]] = None							,    **lowerCAmelCase_ :						List[str]							,    )    ->							None:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_      )
														_a			  =       size if size is not None else {'''shortest_edge''': 2_56}
														_a			  =       get_size_dict(lowerCAmelCase_							,    default_to_square=lowerCAmelCase_      )
														_a			  =       crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
														_a			  =       get_size_dict(lowerCAmelCase_							,    param_name='''crop_size'''      )

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







							def      __lowerCAmelCase    (						self :						List[str]							,    lowerCAmelCase_ :						np.ndarray							,    lowerCAmelCase_ :						Dict[str, int]							,    lowerCAmelCase_ :						PILImageResampling = PILImageResampling.BILINEAR							,    lowerCAmelCase_ :						Optional[Union[str, ChannelDimension]] = None							,    **lowerCAmelCase_ :						Union[str, Any]							,    )    ->							np.ndarray:


														"""simple docstring"""


														_a			  =       get_size_dict(lowerCAmelCase_							,    default_to_square=lowerCAmelCase_      )
														if "shortest_edge" in size:
																					_a			  =       get_resize_output_image_size(lowerCAmelCase_							,    size['''shortest_edge''']							,    default_to_square=lowerCAmelCase_      )
														elif "height" in size and "width" in size:
																					_a			  =       (size['''height'''], size['''width'''])
														else:
																					raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'      )
														return resize(lowerCAmelCase_							,    size=lowerCAmelCase_							,    resample=lowerCAmelCase_							,    data_format=lowerCAmelCase_							,    **lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Optional[Any]							,    lowerCAmelCase_ :						np.ndarray							,    lowerCAmelCase_ :						Dict[str, int]							,    lowerCAmelCase_ :						Optional[Union[str, ChannelDimension]] = None							,    **lowerCAmelCase_ :						Tuple							,    )    ->							np.ndarray:


														"""simple docstring"""


														_a			  =       get_size_dict(lowerCAmelCase_      )
														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(lowerCAmelCase_							,    size=(size['''height'''], size['''width'''])							,    data_format=lowerCAmelCase_							,    **lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						List[Any]							,    lowerCAmelCase_ :						np.ndarray							,    lowerCAmelCase_ :						Union[int, float]							,    lowerCAmelCase_ :						bool = True							,    lowerCAmelCase_ :						Optional[Union[str, ChannelDimension]] = None							,    **lowerCAmelCase_ :						Any							,    )    ->							Optional[Any]:


														"""simple docstring"""


														_a			  =       image.astype(np.floataa      )
														if offset:
																					_a			  =       image - (scale / 2)
														return rescale(lowerCAmelCase_							,    scale=lowerCAmelCase_							,    data_format=lowerCAmelCase_							,    **lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						List[Any]							,    lowerCAmelCase_ :						np.ndarray							,    lowerCAmelCase_ :						Union[float, List[float]]							,    lowerCAmelCase_ :						Union[float, List[float]]							,    lowerCAmelCase_ :						Optional[Union[str, ChannelDimension]] = None							,    **lowerCAmelCase_ :						Union[str, Any]							,    )    ->							np.ndarray:


														"""simple docstring"""


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







							def      __lowerCAmelCase    (						self :						Tuple							,    lowerCAmelCase_ :						ImageInput							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						Dict[str, int] = None							,    lowerCAmelCase_ :						PILImageResampling = None							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						Dict[str, int] = None							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						float = None							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						Optional[Union[float, List[float]]] = None							,    lowerCAmelCase_ :						Optional[Union[float, List[float]]] = None							,    lowerCAmelCase_ :						Optional[ChannelDimension] = ChannelDimension.FIRST							,    )    ->							np.ndarray:


														"""simple docstring"""


														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.'''      )

														if offset and not do_rescale:
																					raise ValueError('''For offset, do_rescale must also be set to True.'''      )

														# All transformations expect numpy arrays.
														_a			  =       to_numpy_array(lowerCAmelCase_      )

														if do_resize:
																					_a			  =       self.resize(image=lowerCAmelCase_							,    size=lowerCAmelCase_							,    resample=lowerCAmelCase_      )

														if do_center_crop:
																					_a			  =       self.center_crop(lowerCAmelCase_							,    size=lowerCAmelCase_      )

														if do_rescale:
																					_a			  =       self.rescale(image=lowerCAmelCase_							,    scale=lowerCAmelCase_							,    offset=lowerCAmelCase_      )

														if do_normalize:
																					_a			  =       self.normalize(image=lowerCAmelCase_							,    mean=lowerCAmelCase_							,    std=lowerCAmelCase_      )

														_a			  =       to_channel_dimension_format(lowerCAmelCase_							,    lowerCAmelCase_      )
														return image







							def      __lowerCAmelCase    (						self :						str							,    lowerCAmelCase_ :						ImageInput							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						Dict[str, int] = None							,    lowerCAmelCase_ :						PILImageResampling = None							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						Dict[str, int] = None							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						float = None							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						bool = None							,    lowerCAmelCase_ :						Optional[Union[float, List[float]]] = None							,    lowerCAmelCase_ :						Optional[Union[float, List[float]]] = None							,    lowerCAmelCase_ :						Optional[Union[str, TensorType]] = None							,    lowerCAmelCase_ :						ChannelDimension = ChannelDimension.FIRST							,    **lowerCAmelCase_ :						Any							,    )    ->							PIL.Image.Image:


														"""simple docstring"""


														_a			  =       do_resize if do_resize is not None else self.do_resize
														_a			  =       resample if resample is not None else self.resample
														_a			  =       do_center_crop if do_center_crop is not None else self.do_center_crop
														_a			  =       do_rescale if do_rescale is not None else self.do_rescale
														_a			  =       rescale_factor if rescale_factor is not None else self.rescale_factor
														_a			  =       offset if offset is not None else self.offset
														_a			  =       do_normalize if do_normalize is not None else self.do_normalize
														_a			  =       image_mean if image_mean is not None else self.image_mean
														_a			  =       image_std if image_std is not None else self.image_std

														_a			  =       size if size is not None else self.size
														_a			  =       get_size_dict(lowerCAmelCase_							,    default_to_square=lowerCAmelCase_      )
														_a			  =       crop_size if crop_size is not None else self.crop_size
														_a			  =       get_size_dict(lowerCAmelCase_							,    param_name='''crop_size'''      )

														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.'''      )

														_a			  =       make_batched(lowerCAmelCase_      )

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

														_a			  =       {'''pixel_values''': videos}
														return BatchFeature(data=lowerCAmelCase_							,    tensor_type=lowerCAmelCase_      )

