





"""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 		lowercase					(					unittest.TestCase					):





      def __init__(       self      ,						lowercase      ,						lowercase=7      ,						lowercase=3      ,						lowercase=18      ,						lowercase=30      ,						lowercase=400      ,						lowercase=True      ,						lowercase=None      ,						lowercase=True      ,						lowercase=False      ,						lowercase=True      ,						lowercase=True      ,						lowercase=[0.5, 0.5, 0.5]      ,						lowercase=[0.5, 0.5, 0.5]      ,						)		->     Any:
            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     _snake_case			(       self )		->     List[str]:
            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					(					_UpperCAmelCase     ,  unittest.TestCase					):
      _SCREAMING_SNAKE_CASE					=      DonutImageProcessor if is_vision_available() else None





      def     _snake_case			(       self )		->     Union[str, Any]:
            lowerCAmelCase  						=       DonutImageProcessingTester(self )





      @property
      def     _snake_case			(       self )		->     List[Any]:
            return self.image_processor_tester.prepare_image_processor_dict()





      def     _snake_case			(       self )		->     List[Any]:
            lowerCAmelCase  						=       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 )		->     List[Any]:
            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     _snake_case			(       self )		->     Tuple:
            pass





      @is_flaky()
      def     _snake_case			(       self )		->     Any:
            # Initialize image_processing
            lowerCAmelCase  						=       self.image_processing_class(**self.image_processor_dict )
            # create random PIL images
            lowerCAmelCase  						=       prepare_image_inputs(self.image_processor_tester      ,						equal_resolution=lowercase )
            for image in image_inputs:
                  self.assertIsInstance(lowercase      ,						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(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 )		->     Optional[int]:
            # Initialize image_processing
            lowerCAmelCase  						=       self.image_processing_class(**self.image_processor_dict )
            # create random numpy tensors
            lowerCAmelCase  						=       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
            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(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 )		->     Any:
            # Initialize image_processing
            lowerCAmelCase  						=       self.image_processing_class(**self.image_processor_dict )
            # create random PyTorch tensors
            lowerCAmelCase  						=       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
            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(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"""],
                )      ,						)











"""simple docstring"""






from __future__ import annotations


def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case					,__snake_case	)						->   list:
			__lowerCAmelCase      :  Dict												=       []
			__lowerCAmelCase						,	__lowerCAmelCase      :  Any												=       input_list[low:mid], input_list[mid : high + 1]
			while left and right:
						result.append((left if left[0] <= right[0] else right).pop(0	)	)
			__lowerCAmelCase      :  int												=       result + left + right
			return input_list


def 			_lowercase				(  __snake_case	)						->   list:
			if len(__snake_case	) <= 1:
						return input_list
			__lowerCAmelCase      :  int												=       list(__snake_case	)

			# iteration for two-way merging
			__lowerCAmelCase      :  Optional[int]												=       2
			while p <= len(__snake_case	):
						# getting low, high and middle value for merge-sort of single list
						for i in range(0					,len(__snake_case	)					,__snake_case	):
									__lowerCAmelCase      :  Union[str, Any]												=       i
									__lowerCAmelCase      :  Tuple												=       i + p - 1
									__lowerCAmelCase      :  Optional[Any]												=       (low + high + 1) // 2
									__lowerCAmelCase      :  Any												=       merge(__snake_case					,__snake_case					,__snake_case					,__snake_case	)
						# final merge of last two parts
						if p * 2 >= len(__snake_case	):
									__lowerCAmelCase      :  Optional[Any]												=       i
									__lowerCAmelCase      :  Union[str, Any]												=       merge(__snake_case					,0					,__snake_case					,len(__snake_case	) - 1	)
									break
						p *= 2

			return input_list


if __name__ == "__main__":
	__snake_case							:   Union[str, Any]      =    input('Enter numbers separated by a comma:\n').strip()
	if user_input == "":
		__snake_case							:   Optional[int]      =    []
	else:
		__snake_case							:   int      =    [int(item.strip()) for item in user_input.split(',')]
	print(iter_merge_sort(unsorted))