



from timeit import timeit






def  UpperCAmelCase__    (    lowercase__   ) ->      int:
						if number < 0:
												raise ValueError("""the value of input must not be negative"""   )
						__lowercase					=				0
						while number:
												number &= number - 1
												result += 1
						return result






def  UpperCAmelCase__    (    lowercase__   ) ->      int:
						if number < 0:
												raise ValueError("""the value of input must not be negative"""   )
						__lowercase					=				0
						while number:
												if number % 2 == 1:
																		result += 1
												number >>= 1
						return result






def  UpperCAmelCase__    (    ) ->      None:

						def do_benchmark(lowercase__   ) -> None:
												__lowercase					=				"""import __main__ as z"""
												print(F"Benchmark when {number = }:"   )
												print(F"{get_set_bits_count_using_modulo_operator(lowercase__   ) = }"   )
												__lowercase					=				timeit("""z.get_set_bits_count_using_modulo_operator(25)"""						,       setup=lowercase__   )
												print(F"timeit() runs in {timing} seconds"   )
												print(F"{get_set_bits_count_using_brian_kernighans_algorithm(lowercase__   ) = }"   )
												__lowercase					=				timeit(
												    """z.get_set_bits_count_using_brian_kernighans_algorithm(25)"""						,       setup=lowercase__						,       )
												print(F"timeit() runs in {timing} seconds"   )

						for number in (25, 37, 58, 0):
												do_benchmark(lowercase__   )
												print()


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			benchmark()










import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
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 			_lowerCAmelCase   ( _UpperCAmelCase    ,					_UpperCAmelCase    ,					unittest.TestCase			):






			"""simple docstring"""





			lowercase__      :  int      =			IFImgaImgSuperResolutionPipeline
			lowercase__      :  Optional[int]      =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
			lowercase__      :  Optional[int]      =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""original_image"""}			)
			lowercase__      :  Tuple      =			PipelineTesterMixin.required_optional_params - {"""latents"""}





			def 		snake_case__			(				self	:       Tuple   )						->					Any:
									"""simple docstring"""







									return self._get_superresolution_dummy_components()





			def 		snake_case__			(				self	:       List[str]			,     lowercase	:       Optional[int]			,     lowercase	:       Optional[Any]=0   )						->					Union[str, Any]:
									"""simple docstring"""







									if str(lowercase   ).startswith("""mps"""   ):
															__lowercase					=				torch.manual_seed(lowercase   )
									else:
															__lowercase					=				torch.Generator(device=lowercase   ).manual_seed(lowercase   )

									__lowercase					=				floats_tensor((1, 3, 32, 32)			,     rng=random.Random(lowercase   )   ).to(lowercase   )
									__lowercase					=				floats_tensor((1, 3, 16, 16)			,     rng=random.Random(lowercase   )   ).to(lowercase   )

									__lowercase					=				{
									    """prompt""": """A painting of a squirrel eating a burger""",
									    """image""": image,
									    """original_image""": original_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 		snake_case__			(				self	:       Optional[Any]   )						->					Tuple:
									"""simple docstring"""







									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )





			def 		snake_case__			(				self	:       List[Any]   )						->					Union[str, Any]:
									"""simple docstring"""







									self._test_save_load_optional_components()





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







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





			def 		snake_case__			(				self	:       Dict   )						->					int:
									"""simple docstring"""







									self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )





			def 		snake_case__			(				self	:       Optional[Any]   )						->					int:
									"""simple docstring"""







									self._test_save_load_local()







			def 		snake_case__			(				self	:       Optional[int]   )						->					Optional[int]:
									"""simple docstring"""







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





