


import unittest

import numpy as np

from transformers.testing_utils import 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 GLPNImageProcessor
class 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     def __init__(					self:Union[str, Any]     ,		_a:Optional[int]     ,		_a:Tuple=7     ,		_a:Dict=3     ,		_a:Optional[Any]=18     ,		_a:Optional[Any]=30     ,		_a:Union[str, Any]=4_00     ,		_a:str=True     ,		_a:Optional[Any]=32     ,		_a:Tuple=True     ,		):
       snake_case__						= parent
       snake_case__						= batch_size
       snake_case__						= num_channels
       snake_case__						= image_size
       snake_case__						= min_resolution
       snake_case__						= max_resolution
       snake_case__						= do_resize
       snake_case__						= size_divisor
       snake_case__						= do_rescale







     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       return {
           "do_resize": self.do_resize,
           "size_divisor": self.size_divisor,
           "do_rescale": self.do_rescale,
       }




@require_torch
@require_vision
class 		__magic_name__						(snake_case_   ,unittest.TestCase				):





     '''simple docstring'''



     __lowercase				:							Optional[Any]	   =       GLPNImageProcessor if is_vision_available() else None

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= GLPNImageProcessingTester(self   )

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       return self.image_processor_tester.prepare_image_processor_dict()

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       snake_case__						= self.image_processing_class(**self.image_processor_dict   )
       self.assertTrue(hasattr(_a     ,		'''do_resize'''   )   )
       self.assertTrue(hasattr(_a     ,		'''size_divisor'''   )   )
       self.assertTrue(hasattr(_a     ,		'''resample'''   )   )
       self.assertTrue(hasattr(_a     ,		'''do_rescale'''   )   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       pass

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       # Initialize image_processing
       snake_case__						= self.image_processing_class(**self.image_processor_dict   )
       # create random PIL images
       snake_case__						= prepare_image_inputs(self.image_processor_tester     ,		equal_resolution=_a   )
       for image in image_inputs:
         self.assertIsInstance(_a     ,		Image.Image   )

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       snake_case__						= image_processing(image_inputs[0]     ,		return_tensors='''pt'''   ).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       # Initialize image_processing
       snake_case__						= self.image_processing_class(**self.image_processor_dict   )
       # create random numpy tensors
       snake_case__						= 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 (GLPNImageProcessor doesn't support batching)
       snake_case__						= image_processing(image_inputs[0]     ,		return_tensors='''pt'''   ).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )







     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       # Initialize image_processing
       snake_case__						= self.image_processing_class(**self.image_processor_dict   )
       # create random PyTorch tensors
       snake_case__						= 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 (GLPNImageProcessor doesn't support batching)
       snake_case__						= image_processing(image_inputs[0]     ,		return_tensors='''pt'''   ).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )






UpperCAmelCase_		 =					0  # The first color of the flag.
UpperCAmelCase_		 =					1  # The second color of the flag.
UpperCAmelCase_		 =					2  # The third color of the flag.
UpperCAmelCase_		 =					(red, white, blue)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:list				)  ->     list:
   if not sequence:
      return []
   if len(_snake_case				) == 1:
      return list(_snake_case				)
   _A      =       0
   _A      =       len(_snake_case				) - 1
   _A      =       0
   while mid <= high:
      if sequence[mid] == colors[0]:
         _A  ,    _A      =       sequence[mid], sequence[low]
         low += 1
         mid += 1
      elif sequence[mid] == colors[1]:
         mid += 1
      elif sequence[mid] == colors[2]:
         _A  ,    _A      =       sequence[high], sequence[mid]
         high -= 1
      else:
         _A      =       F'''The elements inside the sequence must contains only {colors} values'''
         raise ValueError(_snake_case				)
   return sequence


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   UpperCAmelCase_		 =					input("""Enter numbers separated by commas:\n""").strip()
   UpperCAmelCase_		 =					[int(item.strip()) for item in user_input.split(""",""")]
   print(f'{dutch_national_flag_sort(unsorted)}')



