




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     lowerCamelCase__(					unittest.TestCase):
      def __init__(				self: Dict    ,       UpperCamelCase_: Dict    ,       UpperCamelCase_: Any=7    ,       UpperCamelCase_: Union[str, Any]=3    ,       UpperCamelCase_: Optional[int]=18    ,       UpperCamelCase_: Optional[Any]=30    ,       UpperCamelCase_: Any=4_00    ,       UpperCamelCase_: str=True    ,       UpperCamelCase_: List[str]=None    ,       UpperCamelCase_: int=True    ,       UpperCamelCase_: str=False    ,       UpperCamelCase_: int=True    ,       UpperCamelCase_: Any=True    ,       UpperCamelCase_: Optional[int]=[0.5, 0.5, 0.5]    ,       UpperCamelCase_: Optional[Any]=[0.5, 0.5, 0.5]    ,       ):
             __lowerCamelCase   =						parent
             __lowerCamelCase   =						batch_size
             __lowerCamelCase   =						num_channels
             __lowerCamelCase   =						image_size
             __lowerCamelCase   =						min_resolution
             __lowerCamelCase   =						max_resolution
             __lowerCamelCase   =						do_resize
             __lowerCamelCase   =						size if size is not None else {"""height""": 18, """width""": 20}
             __lowerCamelCase   =						do_thumbnail
             __lowerCamelCase   =						do_align_axis
             __lowerCamelCase   =						do_pad
             __lowerCamelCase   =						do_normalize
             __lowerCamelCase   =						image_mean
             __lowerCamelCase   =						image_std




      def 			lowerCAmelCase__						(				self: Optional[Any]   ):
             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     lowerCamelCase__(					UpperCAmelCase__ ,     unittest.TestCase):
      UpperCAmelCase__      :	Tuple    		=   DonutImageProcessor if is_vision_available() else None
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             __lowerCamelCase   =						DonutImageProcessingTester(self   )
      @property
      def 			lowerCAmelCase__						(				self: Optional[Any]   ):
             return self.image_processor_tester.prepare_image_processor_dict()
      def 			lowerCAmelCase__						(				self: int   ):
             __lowerCamelCase   =						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 			lowerCAmelCase__						(				self: str   ):
             __lowerCamelCase   =						self.image_processing_class.from_dict(self.image_processor_dict   )
             self.assertEqual(image_processor.size    ,       {"""height""": 18, """width""": 20}   )

             __lowerCamelCase   =						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
             __lowerCamelCase   =						self.image_processing_class.from_dict(self.image_processor_dict    ,       size=(42, 84)   )
             self.assertEqual(image_processor.size    ,       {"""height""": 84, """width""": 42}   )
      def 			lowerCAmelCase__						(				self: int   ):
             pass
      @is_flaky()
      def 			lowerCAmelCase__						(				self: int   ):
             __lowerCamelCase   =						self.image_processing_class(**self.image_processor_dict   )
             # create random PIL images
             __lowerCamelCase   =						prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=_a   )
             for image in image_inputs:
                    self.assertIsInstance(_a    ,       Image.Image   )

             # Test not batched input
             __lowerCamelCase   =						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
             __lowerCamelCase   =						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 			lowerCAmelCase__						(				self: List[Any]   ):
             __lowerCamelCase   =						self.image_processing_class(**self.image_processor_dict   )
             # create random numpy tensors
             __lowerCamelCase   =						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
             __lowerCamelCase   =						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
             __lowerCamelCase   =						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 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             __lowerCamelCase   =						self.image_processing_class(**self.image_processor_dict   )
             # create random PyTorch tensors
             __lowerCamelCase   =						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
             __lowerCamelCase   =						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
             __lowerCamelCase   =						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"""],
                 )    ,       )







"""simple docstring"""




def     a__ (				snake_case__				)						->					list:

			if len(snake_case__				) < 2:
						return collection

			def circle_sort_util(snake_case__       , snake_case__       , snake_case__				) -> bool:
						lowerCamelCase			      =					False

						if low == high:
									return swapped

						lowerCamelCase			      =					low
						lowerCamelCase			      =					high

						while left < right:
									if collection[left] > collection[right]:
												lowerCamelCase      ,   lowerCamelCase			      =					(
												    collection[right],
												    collection[left],
												)
												lowerCamelCase			      =					True

									left += 1
									right -= 1

						if left == right and collection[left] > collection[right + 1]:
									lowerCamelCase      ,   lowerCamelCase			      =					(
									    collection[right + 1],
									    collection[left],
									)

									lowerCamelCase			      =					True

						lowerCamelCase			      =					low + int((high - low) / 2				)
						lowerCamelCase			      =					circle_sort_util(snake_case__       , snake_case__       , snake_case__				)
						lowerCamelCase			      =					circle_sort_util(snake_case__       , mid + 1       , snake_case__				)

						return swapped or left_swap or right_swap

			lowerCamelCase			      =					True

			while is_not_sorted is True:
						lowerCamelCase			      =					circle_sort_util(snake_case__       , 0       , len(snake_case__				) - 1				)

			return collection


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



