
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 							A			(  _UpperCAmelCase       :     List[str]					)       ->				List[List[ImageInput]]:







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

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

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

			raise ValueError(F"Could not make batched video from {videos}"					)







class     __lowerCAmelCase					(    A		):
			UpperCamelCase           =  ["""pixel_values"""]



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



						"""simple docstring"""






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

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



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



						"""simple docstring"""






						_UpperCAmelCase	 =     get_size_dict(A				,	default_to_square=A)
						if "shortest_edge" in size:
									_UpperCAmelCase	 =     get_resize_output_image_size(A				,	size['shortest_edge']				,	default_to_square=A)
						elif "height" in size and "width" in size:
									_UpperCAmelCase	 =     (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 					_lowerCamelCase (		self     :						Tuple				,	A     :						np.ndarray				,	A     :						Dict[str, int]				,	A     :						Optional[Union[str, ChannelDimension]] = None				,	**A     :						Any				,	)     ->					np.ndarray:



						"""simple docstring"""






						_UpperCAmelCase	 =     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 					_lowerCamelCase (		self     :						Dict				,	A     :						np.ndarray				,	A     :						Union[int, float]				,	A     :						Optional[Union[str, ChannelDimension]] = None				,	**A     :						Dict				,	)     ->					Union[str, Any]:



						"""simple docstring"""






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



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



						"""simple docstring"""






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



			def 					_lowerCamelCase (		self     :						Optional[int]				,	A     :						ImageInput				,	A     :						bool = None				,	A     :						Dict[str, int] = None				,	A     :						PILImageResampling = None				,	A     :						bool = None				,	A     :						Dict[str, int] = None				,	A     :						bool = None				,	A     :						float = None				,	A     :						bool = None				,	A     :						Optional[Union[float, List[float]]] = None				,	A     :						Optional[Union[float, List[float]]] = None				,	A     :						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.')

						# All transformations expect numpy arrays.
						_UpperCAmelCase	 =     to_numpy_array(A)

						if do_resize:
									_UpperCAmelCase	 =     self.resize(image=A				,	size=A				,	resample=A)

						if do_center_crop:
									_UpperCAmelCase	 =     self.center_crop(A				,	size=A)

						if do_rescale:
									_UpperCAmelCase	 =     self.rescale(image=A				,	scale=A)

						if do_normalize:
									_UpperCAmelCase	 =     self.normalize(image=A				,	mean=A				,	std=A)

						_UpperCAmelCase	 =     to_channel_dimension_format(A				,	A)
						return image



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



						"""simple docstring"""






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

						_UpperCAmelCase	 =     size if size is not None else self.size
						_UpperCAmelCase	 =     get_size_dict(A				,	default_to_square=A)
						_UpperCAmelCase	 =     crop_size if crop_size is not None else self.crop_size
						_UpperCAmelCase	 =     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.')

						_UpperCAmelCase	 =     make_batched(A)

						_UpperCAmelCase	 =     [
						    [
						        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
						]

						_UpperCAmelCase	 =     {'pixel_values': videos}
						return BatchFeature(data=A				,	tensor_type=A)





def 							A			(  _UpperCAmelCase       :     list					)       ->				list:







			'''simple docstring'''
			if len(_UpperCAmelCase					) <= 1:
						return lst

			_UpperCAmelCase	 =     1

			while i < len(_UpperCAmelCase					):
						if lst[i - 1] <= lst[i]:
									i += 1
						else:
									_UpperCAmelCase ,  _UpperCAmelCase	 =     lst[i], lst[i - 1]
									i -= 1
									if i == 0:
												_UpperCAmelCase	 =     1

			return lst


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