
'''simple docstring'''



import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin





@skip_mps
class      A							(    __UpperCAmelCase						,  __UpperCAmelCase						,  unittest.TestCase			):
			lowerCamelCase							:							List[str]    						=					IFSuperResolutionPipeline
			lowerCamelCase							:							Tuple    						=					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
			lowerCamelCase							:							Optional[Any]    						=					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			lowerCamelCase							:							Optional[int]    						=					PipelineTesterMixin.required_optional_params - {"""latents"""}



			def A__	(   self    )     ->						Any:




									'''simple docstring'''



									return self._get_superresolution_dummy_components()



			def A__	(   self    ,       lowerCamelCase__    ,       lowerCamelCase__=0    )     ->						List[str]:




									'''simple docstring'''



									if str(lowerCamelCase__    ).startswith("""mps"""    ):
															lowercase__								=    torch.manual_seed(lowerCamelCase__    )
									else:
															lowercase__								=    torch.Generator(device=lowerCamelCase__    ).manual_seed(lowerCamelCase__    )

									lowercase__								=    floats_tensor((1, 3, 32, 32)    ,       rng=random.Random(lowerCamelCase__    )    ).to(lowerCamelCase__    )

									lowercase__								=    {
									    """prompt""": """A painting of a squirrel eating a burger""",
									    """image""": image,
									    """generator""": generator,
									    """num_inference_steps""": 2,
									    """output_type""": """numpy""",
									}

									return inputs



			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()    ,       reason="""XFormers attention is only available with CUDA and `xformers` installed"""    ,       )
			def A__	(   self    )     ->						Tuple:




									'''simple docstring'''



									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )



			def A__	(   self    )     ->						List[Any]:




									'''simple docstring'''



									self._test_save_load_optional_components()



			@unittest.skipIf(torch_device != """cuda"""    ,       reason="""float16 requires CUDA"""    )
			def A__	(   self    )     ->						List[Any]:




									'''simple docstring'''



									super().test_save_load_floataa(expected_max_diff=1e-1    )



			def A__	(   self    )     ->						Union[str, Any]:




									'''simple docstring'''



									self._test_attention_slicing_forward_pass(expected_max_diff=1e-2    )



			def A__	(   self    )     ->						Dict:




									'''simple docstring'''



									self._test_save_load_local()



			def A__	(   self    )     ->						List[str]:




									'''simple docstring'''



									self._test_inference_batch_single_identical(
									    expected_max_diff=1e-2    ,       )








'''simple docstring'''



def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						if right <= left:
												return
						lowercase__								=    lowercase__								=    left
						lowercase__								=    right
						lowercase__								=    sorting[left]
						while i <= b:
												if sorting[i] < pivot:
																		lowercase__	,  lowercase__								=    sorting[i], sorting[a]
																		a += 1
																		i += 1
												elif sorting[i] > pivot:
																		lowercase__	,  lowercase__								=    sorting[i], sorting[b]
																		b -= 1
												else:
																		i += 1
						quick_sort_apartition(lowercase__						,						lowercase__						,						a - 1		)
						quick_sort_apartition(lowercase__						,						b + 1						,						lowercase__		)





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						if left < right:
												lowercase__								=    lomuto_partition(lowercase__						,						lowercase__						,						lowercase__		)
												quick_sort_lomuto_partition(lowercase__						,						lowercase__						,						pivot_index - 1		)
												quick_sort_lomuto_partition(lowercase__						,						pivot_index + 1						,						lowercase__		)





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						lowercase__								=    sorting[right]
						lowercase__								=    left
						for i in range(lowercase__						,						lowercase__		):
												if sorting[i] < pivot:
																		lowercase__	,  lowercase__								=    sorting[i], sorting[store_index]
																		store_index += 1
						lowercase__	,  lowercase__								=    sorting[store_index], sorting[right]
						return store_index





def 	_A						(							lowercase__		):
						if len(lowercase__		) <= 1:
												return sorting
						return (
						    three_way_radix_quicksort([i for i in sorting if i < sorting[0]]		)
						    + [i for i in sorting if i == sorting[0]]
						    + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]		)
						)


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)

				__A       =      input("Enter numbers separated by a comma:\n").strip()
				__A       =      [int(item) for item in user_input.split(",")]
				quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
				print(unsorted)






