
from __future__ import annotations


def    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						Optional[int]:
  '''simple docstring'''

  if (direction == 1 and array[indexa] > array[indexa]) or (
      direction == 0 and array[indexa] < array[indexa]
  ):
    UpperCamelCase						=  array[indexa], array[indexa]


def    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						Union[str, Any]:
  '''simple docstring'''

  if length > 1:
    UpperCamelCase						=  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    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						List[str]:
  '''simple docstring'''

  if length > 1:
    UpperCamelCase						=  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      :				Any          = 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=", ")


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        lowercase      (	unittest.TestCase     ):



		def __init__(				self       ,   A_       ,   A_=7       ,   A_=3       ,   A_=18       ,   A_=30       ,   A_=400       ,   A_=True       ,   A_=None       ,   A_=True       ,   A_=False       ,   A_=True       ,   A_=True       ,   A_=[0.5, 0.5, 0.5]       ,   A_=[0.5, 0.5, 0.5]       ,   )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  parent
				UpperCamelCase						=  batch_size
				UpperCamelCase						=  num_channels
				UpperCamelCase						=  image_size
				UpperCamelCase						=  min_resolution
				UpperCamelCase						=  max_resolution
				UpperCamelCase						=  do_resize
				UpperCamelCase						=  size if size is not None else {'height': 18, 'width': 20}
				UpperCamelCase						=  do_thumbnail
				UpperCamelCase						=  do_align_axis
				UpperCamelCase						=  do_pad
				UpperCamelCase						=  do_normalize
				UpperCamelCase						=  image_mean
				UpperCamelCase						=  image_std



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				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        lowercase      (	_SCREAMING_SNAKE_CASE						,     unittest.TestCase     ):
		__lowercase : Optional[int]			  =       DonutImageProcessor if is_vision_available() else None



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  DonutImageProcessingTester(self )



		@property
		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				return self.image_processor_tester.prepare_image_processor_dict()



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  self.image_processing_class(**self.image_processor_dict )
				self.assertTrue(hasattr(A_       ,   'do_resize' ) )
				self.assertTrue(hasattr(A_       ,   'size' ) )
				self.assertTrue(hasattr(A_       ,   'do_thumbnail' ) )
				self.assertTrue(hasattr(A_       ,   'do_align_long_axis' ) )
				self.assertTrue(hasattr(A_       ,   'do_pad' ) )
				self.assertTrue(hasattr(A_       ,   'do_normalize' ) )
				self.assertTrue(hasattr(A_       ,   'image_mean' ) )
				self.assertTrue(hasattr(A_       ,   'image_std' ) )



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				UpperCamelCase						=  self.image_processing_class.from_dict(self.image_processor_dict )
				self.assertEqual(image_processor.size       ,   {'height': 18, 'width': 20} )

				UpperCamelCase						=  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
				UpperCamelCase						=  self.image_processing_class.from_dict(self.image_processor_dict       ,   size=(42, 84) )
				self.assertEqual(image_processor.size       ,   {'height': 84, 'width': 42} )



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				pass



		@is_flaky()
		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				# Initialize image_processing
				UpperCamelCase						=  self.image_processing_class(**self.image_processor_dict )
				# create random PIL images
				UpperCamelCase						=  prepare_image_inputs(self.image_processor_tester       ,   equal_resolution=A_ )
				for image in image_inputs:
						self.assertIsInstance(A_       ,   Image.Image )

				# Test not batched input
				UpperCamelCase						=  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
				UpperCamelCase						=  image_processing(A_       ,   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 __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				# Initialize image_processing
				UpperCamelCase						=  self.image_processing_class(**self.image_processor_dict )
				# create random numpy tensors
				UpperCamelCase						=  prepare_image_inputs(self.image_processor_tester       ,   equal_resolution=A_       ,   numpify=A_ )
				for image in image_inputs:
						self.assertIsInstance(A_       ,   np.ndarray )

				# Test not batched input
				UpperCamelCase						=  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
				UpperCamelCase						=  image_processing(A_       ,   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 __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				# Initialize image_processing
				UpperCamelCase						=  self.image_processing_class(**self.image_processor_dict )
				# create random PyTorch tensors
				UpperCamelCase						=  prepare_image_inputs(self.image_processor_tester       ,   equal_resolution=A_       ,   torchify=A_ )
				for image in image_inputs:
						self.assertIsInstance(A_       ,   torch.Tensor )

				# Test not batched input
				UpperCamelCase						=  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
				UpperCamelCase						=  image_processing(A_       ,   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'],
				    )       ,   )
