


from __future__ import annotations



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> None:
				if (direction == 1 and array[indexa] > array[indexa]) or (
				    direction == 0 and array[indexa] < array[indexa]
				):
								lowerCAmelCase_				,  lowerCAmelCase_ :      List[Any]    				=				array[indexa], array[indexa]



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> None:
				if length > 1:
								lowerCAmelCase_ :      Tuple    				=				int(length / 2 )
								for i in range(lowerCAmelCase_ ,   low + middle ):
												comp_and_swap(lowerCAmelCase_ ,   lowerCAmelCase_ ,   i + middle ,   lowerCAmelCase_ )
								bitonic_merge(lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )
								bitonic_merge(lowerCAmelCase_ ,   low + middle ,   lowerCAmelCase_ ,   lowerCAmelCase_ )



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> None:
				if length > 1:
								lowerCAmelCase_ :      List[Any]    				=				int(length / 2 )
								bitonic_sort(lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   1 )
								bitonic_sort(lowerCAmelCase_ ,   low + middle ,   lowerCAmelCase_ ,   0 )
								bitonic_merge(lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )


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

						bitonic_sort(unsorted, 0, len(unsorted), 1)
						print("""\nSorted array in ascending order is: """, end="""""")
						print(*unsorted, sep=""", """)

						bitonic_merge(unsorted, 0, len(unsorted), 0)
						print("""Sorted array in descending order is: """, end="""""")
						print(*unsorted, sep=""", """)



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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


_UpperCAmelCase			:  Any     =logging.get_logger(__name__)







class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  Dict					=							["""pixel_values"""]




						def __init__(				self   ,						__lowercase = True   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = PILImageResampling.BILINEAR   ,						__lowercase = True   ,						__lowercase = 1 / 2_5_5   ,						__lowercase = True   ,						__lowercase = None   ,						__lowercase = None   ,						**__lowercase   ,						)					->    None:
										super().__init__(**__lowercase							)
										lowerCAmelCase_ :      Dict    				=				size if size is not None else {'''shortest_edge''': 3_8_4}
										lowerCAmelCase_ :      Optional[Any]    				=				get_size_dict(__lowercase   ,						default_to_square=__lowercase							)

										lowerCAmelCase_ :      List[Any]    				=				do_resize
										lowerCAmelCase_ :      Optional[int]    				=				size
										# Default value set here for backwards compatibility where the value in config is None
										lowerCAmelCase_ :      str    				=				crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
										lowerCAmelCase_ :      Tuple    				=				resample
										lowerCAmelCase_ :      Optional[int]    				=				do_rescale
										lowerCAmelCase_ :      Any    				=				rescale_factor
										lowerCAmelCase_ :      List[str]    				=				do_normalize
										lowerCAmelCase_ :      str    				=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										lowerCAmelCase_ :      Dict    				=				image_std if image_std is not None else IMAGENET_STANDARD_STD




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase = PILImageResampling.BICUBIC   ,						__lowercase = None   ,						**__lowercase   ,						)					->    np.ndarray:
										lowerCAmelCase_ :      Optional[Any]    				=				get_size_dict(__lowercase   ,						default_to_square=__lowercase							)
										if "shortest_edge" not in size:
														raise ValueError(f"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""							)
										lowerCAmelCase_ :      Optional[int]    				=				size['''shortest_edge''']

										if shortest_edge < 3_8_4:
														# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
														lowerCAmelCase_ :      Optional[Any]    				=				int(shortest_edge / crop_pct							)
														lowerCAmelCase_ :      List[str]    				=				get_resize_output_image_size(__lowercase   ,						size=__lowercase   ,						default_to_square=__lowercase							)
														lowerCAmelCase_ :      List[Any]    				=				resize(image=__lowercase   ,						size=__lowercase   ,						resample=__lowercase   ,						data_format=__lowercase   ,						**__lowercase							)
														# then crop to (shortest_edge, shortest_edge)
														return center_crop(image=__lowercase   ,						size=(shortest_edge, shortest_edge)   ,						data_format=__lowercase   ,						**__lowercase							)
										else:
														# warping (no cropping) when evaluated at 384 or larger
														return resize(
														    __lowercase   ,						size=(shortest_edge, shortest_edge)   ,						resample=__lowercase   ,						data_format=__lowercase   ,						**__lowercase							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase = None   ,						**__lowercase   ,						)					->    Any:
										return rescale(__lowercase   ,						scale=__lowercase   ,						data_format=__lowercase   ,						**__lowercase							)




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




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = ChannelDimension.FIRST   ,						**__lowercase   ,						)					->    PIL.Image.Image:
										lowerCAmelCase_ :      Optional[int]    				=				do_resize if do_resize is not None else self.do_resize
										lowerCAmelCase_ :      Any    				=				crop_pct if crop_pct is not None else self.crop_pct
										lowerCAmelCase_ :      str    				=				resample if resample is not None else self.resample
										lowerCAmelCase_ :      Union[str, Any]    				=				do_rescale if do_rescale is not None else self.do_rescale
										lowerCAmelCase_ :      str    				=				rescale_factor if rescale_factor is not None else self.rescale_factor
										lowerCAmelCase_ :      Any    				=				do_normalize if do_normalize is not None else self.do_normalize
										lowerCAmelCase_ :      str    				=				image_mean if image_mean is not None else self.image_mean
										lowerCAmelCase_ :      int    				=				image_std if image_std is not None else self.image_std

										lowerCAmelCase_ :      int    				=				size if size is not None else self.size
										lowerCAmelCase_ :      List[str]    				=				get_size_dict(__lowercase   ,						default_to_square=__lowercase							)

										lowerCAmelCase_ :      Tuple    				=				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 or resample is None:
														raise ValueError('''Size and resample must be specified if do_resize is True.'''							)

										if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
														raise ValueError('''crop_pct must be specified if size < 384.'''							)

										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_ :      Optional[Any]    				=				[to_numpy_array(__lowercase							) for image in images]

										if do_resize:
														lowerCAmelCase_ :      Union[str, Any]    				=				[self.resize(image=__lowercase   ,						size=__lowercase   ,						crop_pct=__lowercase   ,						resample=__lowercase							) for image in images]

										if do_rescale:
														lowerCAmelCase_ :      Any    				=				[self.rescale(image=__lowercase   ,						scale=__lowercase							) for image in images]

										if do_normalize:
														lowerCAmelCase_ :      List[Any]    				=				[self.normalize(image=__lowercase   ,						mean=__lowercase   ,						std=__lowercase							) for image in images]

										lowerCAmelCase_ :      Optional[Any]    				=				[to_channel_dimension_format(__lowercase   ,						__lowercase							) for image in images]

										lowerCAmelCase_ :      Dict    				=				{'''pixel_values''': images}
										return BatchFeature(data=__lowercase   ,						tensor_type=__lowercase							)