




"""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, 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


__A 		=			logging.get_logger(__name__)
class 			lowerCamelCase__    (  lowerCamelCase_			):
			a__ :      Dict								=     ["""pixel_values"""]


			def __init__(     self				, SCREAMING_SNAKE_CASE = True				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = PIL.Image.NEAREST				, SCREAMING_SNAKE_CASE = False				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = 1 / 255				, SCREAMING_SNAKE_CASE = False				, SCREAMING_SNAKE_CASE = True				, SCREAMING_SNAKE_CASE = True				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = True				, **SCREAMING_SNAKE_CASE				, ):



					"""simple docstring"""







					super().__init__(**SCREAMING_SNAKE_CASE							)
					snake_case		:						Optional[Any]								=				size if size is not None else {"height": 346, "width": 346}
					snake_case		:						List[str]								=				get_size_dict(SCREAMING_SNAKE_CASE							)
					snake_case		:						Optional[int]								=				crop_size if crop_size is not None else {"height": 289, "width": 289}
					snake_case		:						str								=				get_size_dict(SCREAMING_SNAKE_CASE				, param_name="crop_size"							)

					snake_case		:						List[str]								=				do_resize
					snake_case		:						List[Any]								=				size
					snake_case		:						Tuple								=				resample
					snake_case		:						str								=				do_center_crop
					snake_case		:						Tuple								=				crop_size
					snake_case		:						Union[str, Any]								=				do_rescale
					snake_case		:						List[Any]								=				rescale_factor
					snake_case		:						Optional[int]								=				rescale_offset
					snake_case		:						List[Any]								=				do_normalize
					snake_case		:						Optional[Any]								=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					snake_case		:						str								=				image_std if image_std is not None else IMAGENET_STANDARD_STD
					snake_case		:						List[str]								=				include_top


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE = PIL.Image.NEAREST				, SCREAMING_SNAKE_CASE = None				, **SCREAMING_SNAKE_CASE				, ):



					"""simple docstring"""







					snake_case		:						Optional[int]								=				get_size_dict(SCREAMING_SNAKE_CASE							)
					if "height" not in size or "width" not in size:
							raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''							)
					return resize(
					    SCREAMING_SNAKE_CASE				, size=(size["height"], size["width"])				, resample=SCREAMING_SNAKE_CASE				, data_format=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE = None				, **SCREAMING_SNAKE_CASE				, ):



					"""simple docstring"""







					snake_case		:						int								=				get_size_dict(SCREAMING_SNAKE_CASE							)
					if "height" not in size or "width" not in size:
							raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''							)
					return center_crop(SCREAMING_SNAKE_CASE				, size=(size["height"], size["width"])				, data_format=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE = True				, SCREAMING_SNAKE_CASE = None				, **SCREAMING_SNAKE_CASE				, ):



					"""simple docstring"""







					if offset:
							snake_case		:						str								=				(image - 1_27.5) * scale
							if data_format is not None:
									snake_case		:						List[str]								=				to_channel_dimension_format(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
							snake_case		:						Tuple								=				rescaled_image.astype(np.floataa							)
					else:
							snake_case		:						Any								=				rescale(SCREAMING_SNAKE_CASE				, scale=SCREAMING_SNAKE_CASE				, data_format=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
					return rescaled_image


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE = None				, **SCREAMING_SNAKE_CASE				, ):



					"""simple docstring"""







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



			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = ChannelDimension.FIRST				, **SCREAMING_SNAKE_CASE				, ):



					"""simple docstring"""







					snake_case		:						int								=				do_resize if do_resize is not None else self.do_resize
					snake_case		:						List[Any]								=				resample if resample is not None else self.resample
					snake_case		:						Any								=				do_center_crop if do_center_crop is not None else self.do_center_crop
					snake_case		:						List[str]								=				do_rescale if do_rescale is not None else self.do_rescale
					snake_case		:						List[str]								=				rescale_factor if rescale_factor is not None else self.rescale_factor
					snake_case		:						Union[str, Any]								=				rescale_offset if rescale_offset is not None else self.rescale_offset
					snake_case		:						Dict								=				do_normalize if do_normalize is not None else self.do_normalize
					snake_case		:						Union[str, Any]								=				image_mean if image_mean is not None else self.image_mean
					snake_case		:						Dict								=				image_std if image_std is not None else self.image_std
					snake_case		:						List[str]								=				include_top if include_top is not None else self.include_top

					snake_case		:						Tuple								=				size if size is not None else self.size
					snake_case		:						Union[str, Any]								=				get_size_dict(SCREAMING_SNAKE_CASE							)
					snake_case		:						Tuple								=				crop_size if crop_size is not None else self.crop_size
					snake_case		:						Tuple								=				get_size_dict(SCREAMING_SNAKE_CASE				, param_name="crop_size"							)

					snake_case		:						Union[str, Any]								=				make_list_of_images(SCREAMING_SNAKE_CASE							)

					if not valid_images(SCREAMING_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_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.
					snake_case		:						Optional[Any]								=				[to_numpy_array(SCREAMING_SNAKE_CASE							) for image in images]

					if do_resize:
							snake_case		:						List[Any]								=				[self.resize(image=SCREAMING_SNAKE_CASE				, size=SCREAMING_SNAKE_CASE				, resample=SCREAMING_SNAKE_CASE							) for image in images]

					if do_center_crop:
							snake_case		:						List[Any]								=				[self.center_crop(image=SCREAMING_SNAKE_CASE				, size=SCREAMING_SNAKE_CASE							) for image in images]

					if do_rescale:
							snake_case		:						Tuple								=				[self.rescale(image=SCREAMING_SNAKE_CASE				, scale=SCREAMING_SNAKE_CASE				, offset=SCREAMING_SNAKE_CASE							) for image in images]

					if do_normalize:
							snake_case		:						List[str]								=				[self.normalize(image=SCREAMING_SNAKE_CASE				, mean=SCREAMING_SNAKE_CASE				, std=SCREAMING_SNAKE_CASE							) for image in images]

					if include_top:
							snake_case		:						List[str]								=				[self.normalize(image=SCREAMING_SNAKE_CASE				, mean=[0, 0, 0]				, std=SCREAMING_SNAKE_CASE							) for image in images]

					snake_case		:						Union[str, Any]								=				[to_channel_dimension_format(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							) for image in images]

					snake_case		:						Union[str, Any]								=				{"pixel_values": images}
					return BatchFeature(data=SCREAMING_SNAKE_CASE				, tensor_type=SCREAMING_SNAKE_CASE							)







"""simple docstring"""

import random



def   UpperCamelCase__     (		lowercase__  :     Union[str, Any]   ):

		def is_sorted(lowercase__  :     int   ):
				for i in range(len(lowercase__   ) - 1   ):
						if collection[i] > collection[i + 1]:
								return False
				return True

		while not is_sorted(lowercase__   ):
				random.shuffle(lowercase__   )
		return collection


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

