






'''simple docstring'''
from timeit import timeit





def 	_a(					UpperCamelCase__			:  int							):




      '''simple docstring'''






      if number < 0:
            raise ValueError('''the value of input must not be negative'''							)
      SCREAMING_SNAKE_CASE__       :			Any													=0
      while number:
            number &= number - 1
            result += 1
      return result





def 	_a(					UpperCamelCase__			:  int							):




      '''simple docstring'''






      if number < 0:
            raise ValueError('''the value of input must not be negative'''							)
      SCREAMING_SNAKE_CASE__       :			str													=0
      while number:
            if number % 2 == 1:
                  result += 1
            number >>= 1
      return result





def 	_a(					):




      '''simple docstring'''






      def do_benchmark(UpperCamelCase__			:  int							) -> None:
            SCREAMING_SNAKE_CASE__       :			int													='''import __main__ as z'''
            print(f"Benchmark when {number = }:"							)
            print(f"{get_set_bits_count_using_modulo_operator(__A							) = }"							)
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=timeit('''z.get_set_bits_count_using_modulo_operator(25)''',		setup=__A							)
            print(f"timeit() runs in {timing} seconds"							)
            print(f"{get_set_bits_count_using_brian_kernighans_algorithm(__A							) = }"							)
            SCREAMING_SNAKE_CASE__       :			Tuple													=timeit(
                '''z.get_set_bits_count_using_brian_kernighans_algorithm(25)''',		setup=__A,		)
            print(f"timeit() runs in {timing} seconds"							)

      for number in (2_5, 3_7, 5_8, 0):
            do_benchmark(__A							)
            print()


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  benchmark()







'''simple docstring'''
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase		,			lowerCamelCase		,			unittest.TestCase			):
 snake_case_												=							StableDiffusionSAGPipeline
 snake_case_												=							TEXT_TO_IMAGE_PARAMS
 snake_case_												=							TEXT_TO_IMAGE_BATCH_PARAMS
 snake_case_												=							TEXT_TO_IMAGE_IMAGE_PARAMS
 snake_case_												=							TEXT_TO_IMAGE_IMAGE_PARAMS
 snake_case_												=							False




 def        __magic_name__		( self		:							str )		->  Union[str, Any]:
       torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			Tuple													=UNetaDConditionModel(
           block_out_channels=(32, 64)   , layers_per_block=2   , sample_size=32   , in_channels=4   , out_channels=4   , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   , cross_attention_dim=32   , )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=DDIMScheduler(
           beta_start=0.00085   , beta_end=0.012   , beta_schedule='''scaled_linear'''   , clip_sample=__lowercase   , set_alpha_to_one=__lowercase   , )
       torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=AutoencoderKL(
           block_out_channels=[32, 64]   , in_channels=3   , out_channels=3   , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   , latent_channels=4   , )
       torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=CLIPTextConfig(
           bos_token_id=0   , eos_token_id=2   , hidden_size=32   , intermediate_size=37   , layer_norm_eps=1e-05   , num_attention_heads=4   , num_hidden_layers=5   , pad_token_id=1   , vocab_size=10_00   , )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=CLIPTextModel(__lowercase )
       SCREAMING_SNAKE_CASE__       :			List[str]													=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

       SCREAMING_SNAKE_CASE__       :			List[Any]													={
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components




 def        __magic_name__		( self		:							int   , __lowercase		:							Union[str, Any]   , __lowercase		:							Any=0 )		->  Optional[Any]:
       if str(__lowercase ).startswith('''mps''' ):
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=torch.manual_seed(__lowercase )
       else:
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=torch.Generator(device=__lowercase ).manual_seed(__lowercase )
       SCREAMING_SNAKE_CASE__       :			Dict													={
           '''prompt''': '''.''',
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 1.0,
           '''sag_scale''': 1.0,
           '''output_type''': '''numpy''',
       }
       return inputs




 def        __magic_name__		( self		:							int )		->  str:
       super().test_inference_batch_single_identical(expected_max_diff=3e-3 )






@slow
@require_torch_gpu
class        __SCREAMING_SNAKE_CASE (     unittest.TestCase			):




 def        __magic_name__		( self		:							int )		->  Optional[int]:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def        __magic_name__		( self		:							int )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			Any													=StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''' )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=sag_pipe.to(__lowercase )
       sag_pipe.set_progress_bar_config(disable=__lowercase )

       SCREAMING_SNAKE_CASE__       :			List[Any]													='''.'''
       SCREAMING_SNAKE_CASE__       :			Tuple													=torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=sag_pipe(
           [prompt]   , generator=__lowercase   , guidance_scale=7.5   , sag_scale=1.0   , num_inference_steps=20   , output_type='''np''' )

       SCREAMING_SNAKE_CASE__       :			int													=output.images

       SCREAMING_SNAKE_CASE__       :			int													=image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       SCREAMING_SNAKE_CASE__       :			str													=np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-2




 def        __magic_name__		( self		:							List[Any] )		->  Any:
       SCREAMING_SNAKE_CASE__       :			Tuple													=StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=sag_pipe.to(__lowercase )
       sag_pipe.set_progress_bar_config(disable=__lowercase )

       SCREAMING_SNAKE_CASE__       :			List[str]													='''.'''
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=sag_pipe(
           [prompt]   , generator=__lowercase   , guidance_scale=7.5   , sag_scale=1.0   , num_inference_steps=20   , output_type='''np''' )

       SCREAMING_SNAKE_CASE__       :			Tuple													=output.images

       SCREAMING_SNAKE_CASE__       :			Tuple													=image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       SCREAMING_SNAKE_CASE__       :			List[Any]													=np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-2




 def        __magic_name__		( self		:							str )		->  Any:
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=sag_pipe.to(__lowercase )
       sag_pipe.set_progress_bar_config(disable=__lowercase )

       SCREAMING_SNAKE_CASE__       :			List[Any]													='''.'''
       SCREAMING_SNAKE_CASE__       :			Dict													=torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			Tuple													=sag_pipe(
           [prompt]   , width=7_68   , height=5_12   , generator=__lowercase   , guidance_scale=7.5   , sag_scale=1.0   , num_inference_steps=20   , output_type='''np'''   , )

       SCREAMING_SNAKE_CASE__       :			Any													=output.images

       assert image.shape == (1, 5_12, 7_68, 3)