


from __future__ import annotations







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				None:
  if (direction == 1 and array[indexa] > array[indexa]) or (
      direction == 0 and array[indexa] < array[indexa]
  ):
    snake_case__				,				snake_case__						= array[indexa], array[indexa]







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				None:
  if length > 1:
    snake_case__						= 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 SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				None:
  if length > 1:
    snake_case__						= 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__":
   lowerCamelCase__							:						List[str]							     =   input("""Enter numbers separated by a comma:\n""").strip()
   lowerCamelCase__							:						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=""", """)







'''simple docstring'''
import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import DonutImageProcessor
class   __UpperCAmelCase				(     unittest.TestCase				):



					'''simple docstring'''





					def __init__(	self     :						int      ,       _lowercase     :						Any      ,       _lowercase     :						Tuple=7      ,       _lowercase     :						Tuple=3      ,       _lowercase     :						str=18      ,       _lowercase     :						Union[str, Any]=30      ,       _lowercase     :						Dict=400      ,       _lowercase     :						int=True      ,       _lowercase     :						List[Any]=None      ,       _lowercase     :						int=True      ,       _lowercase     :						Optional[int]=False      ,       _lowercase     :						str=True      ,       _lowercase     :						Union[str, Any]=True      ,       _lowercase     :						Any=[0.5, 0.5, 0.5]      ,       _lowercase     :						str=[0.5, 0.5, 0.5]      ,       )    ->							Optional[Any]:
								A_									= parent
								A_									= batch_size
								A_									= num_channels
								A_									= image_size
								A_									= min_resolution
								A_									= max_resolution
								A_									= do_resize
								A_									= size if size is not None else {'height': 18, 'width': 20}
								A_									= do_thumbnail
								A_									= do_align_axis
								A_									= do_pad
								A_									= do_normalize
								A_									= image_mean
								A_									= image_std



					def     __snake_case						(	self     :						int)    ->							int:
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_thumbnail": self.do_thumbnail,
								    "do_align_long_axis": self.do_align_axis,
								    "do_pad": self.do_pad,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								}





@require_torch
@require_vision
class   __UpperCAmelCase				(     lowerCAmelCase			,unittest.TestCase				):



					'''simple docstring'''





					_UpperCamelCase							=		DonutImageProcessor if is_vision_available() else None



					def     __snake_case						(	self     :						Optional[int])    ->							Union[str, Any]:
								A_									= DonutImageProcessingTester(self)



					@property
					def     __snake_case						(	self     :						int)    ->							int:
								return self.image_processor_tester.prepare_image_processor_dict()



					def     __snake_case						(	self     :						Any)    ->							Tuple:
								A_									= self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(_lowercase      ,       'do_resize'))
								self.assertTrue(hasattr(_lowercase      ,       'size'))
								self.assertTrue(hasattr(_lowercase      ,       'do_thumbnail'))
								self.assertTrue(hasattr(_lowercase      ,       'do_align_long_axis'))
								self.assertTrue(hasattr(_lowercase      ,       'do_pad'))
								self.assertTrue(hasattr(_lowercase      ,       'do_normalize'))
								self.assertTrue(hasattr(_lowercase      ,       'image_mean'))
								self.assertTrue(hasattr(_lowercase      ,       'image_std'))



					def     __snake_case						(	self     :						int)    ->							Optional[int]:
								A_									= self.image_processing_class.from_dict(self.image_processor_dict)
								self.assertEqual(image_processor.size      ,       {'height': 18, 'width': 20})

								A_									= self.image_processing_class.from_dict(self.image_processor_dict      ,       size=42)
								self.assertEqual(image_processor.size      ,       {'height': 42, 'width': 42})

								# Previous config had dimensions in (width, height) order
								A_									= self.image_processing_class.from_dict(self.image_processor_dict      ,       size=(42, 84))
								self.assertEqual(image_processor.size      ,       {'height': 84, 'width': 42})



					def     __snake_case						(	self     :						Optional[Any])    ->							Optional[Any]:
								pass



					@is_flaky()
					def     __snake_case						(	self     :						List[str])    ->							int:
								# Initialize image_processing
								A_									= self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								A_									= prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase      ,       Image.Image)

								# Test not batched input
								A_									= image_processing(image_inputs[0]      ,       return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape      ,       (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['height'],
								        self.image_processor_tester.size['width'],
								    )      ,       )

								# Test batched
								A_									= image_processing(_lowercase      ,       return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape      ,       (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['height'],
								        self.image_processor_tester.size['width'],
								    )      ,       )



					@is_flaky()
					def     __snake_case						(	self     :						List[Any])    ->							List[Any]:
								# Initialize image_processing
								A_									= self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								A_									= prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=_lowercase      ,       numpify=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase      ,       np.ndarray)

								# Test not batched input
								A_									= image_processing(image_inputs[0]      ,       return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape      ,       (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['height'],
								        self.image_processor_tester.size['width'],
								    )      ,       )

								# Test batched
								A_									= image_processing(_lowercase      ,       return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape      ,       (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['height'],
								        self.image_processor_tester.size['width'],
								    )      ,       )



					@is_flaky()
					def     __snake_case						(	self     :						Dict)    ->							int:
								# Initialize image_processing
								A_									= self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								A_									= prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=_lowercase      ,       torchify=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase      ,       torch.Tensor)

								# Test not batched input
								A_									= image_processing(image_inputs[0]      ,       return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape      ,       (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['height'],
								        self.image_processor_tester.size['width'],
								    )      ,       )

								# Test batched
								A_									= image_processing(_lowercase      ,       return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape      ,       (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['height'],
								        self.image_processor_tester.size['width'],
								    )      ,       )




