






'''simple docstring'''

from typing import Dict, Iterable, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__a:      Union[str, Any]  				=				logging.get_logger(__name__)






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      List[str]        =  ['pixel_values']
	def __init__(							self  ,      _lowercase = True  ,      _lowercase = None  ,      _lowercase = PILImageResampling.BICUBIC  ,      _lowercase = True  ,      _lowercase = None  ,      _lowercase = True  ,      _lowercase = 1 / 255  ,      _lowercase = True  ,      _lowercase = IMAGENET_DEFAULT_MEAN  ,      _lowercase = IMAGENET_DEFAULT_STD  ,      **_lowercase  ,      )						->  None:
							super().__init__(**_lowercase       )
							lowercase_      :   Any        =			size if size is not None else {'shortest_edge': 224}
							lowercase_      :   List[str]        =			get_size_dict(_lowercase  ,      default_to_square=_lowercase       )
							lowercase_      :   int        =			crop_size if crop_size is not None else {'height': 224, 'width': 224}
							lowercase_      :   str        =			get_size_dict(_lowercase  ,      param_name='crop_size'       )

							lowercase_      :   Any        =			do_resize
							lowercase_      :   List[Any]        =			size
							lowercase_      :   Tuple        =			resample
							lowercase_      :   Union[str, Any]        =			do_center_crop
							lowercase_      :   List[str]        =			crop_size
							lowercase_      :   Any        =			do_rescale
							lowercase_      :   Any        =			rescale_factor
							lowercase_      :   Any        =			do_normalize
							lowercase_      :   Dict        =			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							lowercase_      :   Optional[int]        =			image_std if image_std is not None else IMAGENET_DEFAULT_STD
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase = PILImageResampling.BICUBIC  ,      _lowercase = None  ,      **_lowercase  ,      )						->  np.ndarray:
							lowercase_      :   Optional[Any]        =			get_size_dict(_lowercase  ,      default_to_square=_lowercase       )
							# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
							if "shortest_edge" in size:
													lowercase_      :   Any        =			int((256 / 224) * size['shortest_edge']       )
													lowercase_      :   Dict        =			get_resize_output_image_size(_lowercase  ,      size=_lowercase  ,      default_to_square=_lowercase       )
													lowercase_      :   str        =			{'height': output_size[0], 'width': output_size[1]}
							if "height" not in size_dict or "width" not in size_dict:
													raise ValueError(
													    f"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"       )
							return resize(
							    _lowercase  ,      size=(size_dict['height'], size_dict['width'])  ,      resample=_lowercase  ,      data_format=_lowercase  ,      **_lowercase       )
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase = None  ,      **_lowercase  ,      )						->  np.ndarray:
							lowercase_      :   Optional[Any]        =			get_size_dict(_lowercase       )
							if "height" not in size or "width" not in size:
													raise ValueError(f"Size dict must have keys 'height' and 'width'. Got {size.keys()}"       )
							return center_crop(_lowercase  ,      size=(size['height'], size['width'])  ,      data_format=_lowercase  ,      **_lowercase       )
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase = None  ,      **_lowercase  ,      )						->  np.ndarray:
							return rescale(_lowercase  ,      scale=_lowercase  ,      data_format=_lowercase  ,      **_lowercase       )
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase = None  ,      **_lowercase  ,      )						->  np.ndarray:
							return normalize(_lowercase  ,      mean=_lowercase  ,      std=_lowercase  ,      data_format=_lowercase  ,      **_lowercase       )







	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = ChannelDimension.FIRST  ,      **_lowercase  ,      )						->  BatchFeature:
							lowercase_      :   int        =			do_resize if do_resize is not None else self.do_resize
							lowercase_      :   Optional[int]        =			resample if resample is not None else self.resample
							lowercase_      :   Optional[int]        =			do_center_crop if do_center_crop is not None else self.do_center_crop
							lowercase_      :   int        =			do_rescale if do_rescale is not None else self.do_rescale
							lowercase_      :   List[Any]        =			rescale_factor if rescale_factor is not None else self.rescale_factor
							lowercase_      :   str        =			do_normalize if do_normalize is not None else self.do_normalize
							lowercase_      :   Dict        =			image_mean if image_mean is not None else self.image_mean
							lowercase_      :   str        =			image_std if image_std is not None else self.image_std

							lowercase_      :   List[str]        =			size if size is not None else self.size
							lowercase_      :   str        =			get_size_dict(_lowercase  ,      default_to_square=_lowercase       )
							lowercase_      :   Optional[Any]        =			crop_size if crop_size is not None else self.crop_size
							lowercase_      :   Optional[Any]        =			get_size_dict(_lowercase  ,      param_name='crop_size'       )

							lowercase_      :   Union[str, Any]        =			make_list_of_images(_lowercase       )

							if not valid_images(_lowercase       ):
													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:
													raise ValueError('Size 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.
							lowercase_      :   Union[str, Any]        =			[to_numpy_array(_lowercase       ) for image in images]

							if do_resize:
													lowercase_      :   Dict        =			[self.resize(_lowercase  ,      _lowercase  ,      _lowercase       ) for image in images]

							if do_center_crop:
													lowercase_      :   Optional[int]        =			[self.center_crop(_lowercase  ,      _lowercase       ) for image in images]

							if do_rescale:
													lowercase_      :   Optional[Any]        =			[self.rescale(_lowercase  ,      _lowercase       ) for image in images]

							if do_normalize:
													lowercase_      :   Dict        =			[self.normalize(_lowercase  ,      _lowercase  ,      _lowercase       ) for image in images]

							lowercase_      :   Tuple        =			[to_channel_dimension_format(_lowercase  ,      _lowercase       ) for image in images]

							lowercase_      :   Optional[Any]        =			{'pixel_values': images}
							return BatchFeature(data=_lowercase  ,      tensor_type=_lowercase       )








'''simple docstring'''

def 	_UpperCAmelCase    (  a		:	list							)			->			list:





						"""simple docstring"""
						for i in range(len(a							) - 1						,			0						,			-1							):
												lowercase_      :   Any        =			False

												for j in range(a						,			0						,			-1							):
																		if unsorted[j] < unsorted[j - 1]:
																								lowercase_  ,      lowercase_      :   Any        =			unsorted[j - 1], unsorted[j]
																								lowercase_      :   int        =			True

												for j in range(a							):
																		if unsorted[j] > unsorted[j + 1]:
																								lowercase_  ,      lowercase_      :   Union[str, Any]        =			unsorted[j + 1], unsorted[j]
																								lowercase_      :   Optional[Any]        =			True

												if not swapped:
																		break
						return unsorted


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						A:      Union[str, Any]  				=				input("Enter numbers separated by a comma:\n").strip()
						A:      Tuple  				=				[int(item) for item in user_input.split(",")]
						print(f"""{cocktail_shaker_sort(unsorted) = }""")
