code: 
  
  
  
  
  
  '''simple docstring'''
  
  import gc
  import unittest
  
  import numpy as np
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  
  from diffusers import (
      AutoencoderKL,
      DDIMScheduler,
      EulerAncestralDiscreteScheduler,
      PNDMScheduler,
      StableDiffusionModelEditingPipeline,
      UNetaDConditionModel,
  )
  from diffusers.utils import slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
  
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  @skip_mps
  class       a_  (       snake_case_   ,					snake_case_   ,					snake_case_   ,					unittest.TestCase ):
  
      '''simple docstring'''
      UpperCamelCase									=				StableDiffusionModelEditingPipeline
      UpperCamelCase									=				TEXT_TO_IMAGE_PARAMS
      UpperCamelCase									=				TEXT_TO_IMAGE_BATCH_PARAMS
      UpperCamelCase									=				TEXT_TO_IMAGE_IMAGE_PARAMS
      UpperCamelCase									=				TEXT_TO_IMAGE_IMAGE_PARAMS
  
  
      def     snake_case_(	self )	->     Optional[Any]:
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    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             =    DDIMScheduler()
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    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             =    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=1000				,	)
          _SCREAMING_SNAKE_CASE             =    CLIPTextModel(A )
          _SCREAMING_SNAKE_CASE             =    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
  
          _SCREAMING_SNAKE_CASE             =    {
              """unet""": unet,
              """scheduler""": scheduler,
              """vae""": vae,
              """text_encoder""": text_encoder,
              """tokenizer""": tokenizer,
              """safety_checker""": None,
              """feature_extractor""": None,
          }
          return components
  
  
      def     snake_case_(	self				,	A				,	A=0 )	->     Optional[Any]:
          _SCREAMING_SNAKE_CASE             =    torch.manual_seed(A )
          _SCREAMING_SNAKE_CASE             =    {
              """prompt""": """A field of roses""",
              """generator""": generator,
              # Setting height and width to None to prevent OOMs on CPU.
              """height""": None,
              """width""": None,
              """num_inference_steps""": 2,
              """guidance_scale""": 6.0,
              """output_type""": """numpy""",
          }
          return inputs
  
  
      def     snake_case_(	self )	->     List[Any]:
          _SCREAMING_SNAKE_CASE             =    """cpu"""  # ensure determinism for the device-dependent torch.Generator
          _SCREAMING_SNAKE_CASE             =    self.get_dummy_components()
          _SCREAMING_SNAKE_CASE             =    StableDiffusionModelEditingPipeline(**A )
          _SCREAMING_SNAKE_CASE             =    sd_pipe.to(A )
          sd_pipe.set_progress_bar_config(disable=A )
  
          _SCREAMING_SNAKE_CASE             =    self.get_dummy_inputs(A )
          _SCREAMING_SNAKE_CASE             =    sd_pipe(**A ).images
          _SCREAMING_SNAKE_CASE             =    image[0, -3:, -3:, -1]
          assert image.shape == (1, 64, 64, 3)
  
          _SCREAMING_SNAKE_CASE             =    np.array([0.4755, 0.5132, 0.4976, 0.3904, 0.3554, 0.4765, 0.5139, 0.5158, 0.4889] )
  
          assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
  
  
      def     snake_case_(	self )	->     str:
          _SCREAMING_SNAKE_CASE             =    """cpu"""  # ensure determinism for the device-dependent torch.Generator
          _SCREAMING_SNAKE_CASE             =    self.get_dummy_components()
          _SCREAMING_SNAKE_CASE             =    StableDiffusionModelEditingPipeline(**A )
          _SCREAMING_SNAKE_CASE             =    sd_pipe.to(A )
          sd_pipe.set_progress_bar_config(disable=A )
  
          _SCREAMING_SNAKE_CASE             =    self.get_dummy_inputs(A )
          _SCREAMING_SNAKE_CASE             =    """french fries"""
          _SCREAMING_SNAKE_CASE             =    sd_pipe(**A				,	negative_prompt=A )
          _SCREAMING_SNAKE_CASE             =    output.images
          _SCREAMING_SNAKE_CASE             =    image[0, -3:, -3:, -1]
  
          assert image.shape == (1, 64, 64, 3)
  
          _SCREAMING_SNAKE_CASE             =    np.array([0.4992, 0.5101, 0.5004, 0.3949, 0.3604, 0.4735, 0.5216, 0.5204, 0.4913] )
  
          assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
  
  
      def     snake_case_(	self )	->     List[str]:
          _SCREAMING_SNAKE_CASE             =    """cpu"""  # ensure determinism for the device-dependent torch.Generator
          _SCREAMING_SNAKE_CASE             =    self.get_dummy_components()
          _SCREAMING_SNAKE_CASE             =    EulerAncestralDiscreteScheduler(
              beta_start=0.0_0085				,	beta_end=0.012				,	beta_schedule="""scaled_linear""" )
          _SCREAMING_SNAKE_CASE             =    StableDiffusionModelEditingPipeline(**A )
          _SCREAMING_SNAKE_CASE             =    sd_pipe.to(A )
          sd_pipe.set_progress_bar_config(disable=A )
  
          _SCREAMING_SNAKE_CASE             =    self.get_dummy_inputs(A )
          _SCREAMING_SNAKE_CASE             =    sd_pipe(**A ).images
          _SCREAMING_SNAKE_CASE             =    image[0, -3:, -3:, -1]
  
          assert image.shape == (1, 64, 64, 3)
  
          _SCREAMING_SNAKE_CASE             =    np.array([0.4747, 0.5372, 0.4779, 0.4982, 0.5543, 0.4816, 0.5238, 0.4904, 0.5027] )
  
          assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
  
  
      def     snake_case_(	self )	->     Optional[Any]:
          _SCREAMING_SNAKE_CASE             =    """cpu"""  # ensure determinism for the device-dependent torch.Generator
          _SCREAMING_SNAKE_CASE             =    self.get_dummy_components()
          _SCREAMING_SNAKE_CASE             =    PNDMScheduler()
          _SCREAMING_SNAKE_CASE             =    StableDiffusionModelEditingPipeline(**A )
          _SCREAMING_SNAKE_CASE             =    sd_pipe.to(A )
          sd_pipe.set_progress_bar_config(disable=A )
  
          _SCREAMING_SNAKE_CASE             =    self.get_dummy_inputs(A )
          # the pipeline does not expect pndm so test if it raises error.
          with self.assertRaises(A ):
              _SCREAMING_SNAKE_CASE             =    sd_pipe(**A ).images
  
  
      def     snake_case_(	self )	->     Tuple:
          super().test_inference_batch_single_identical(expected_max_diff=5e-3 )
  
  
  
  
  
      def     snake_case_(	self )	->     str:
          super().test_attention_slicing_forward_pass(expected_max_diff=5e-3 )
  
  
  
  
  @slow
  @require_torch_gpu
  class       a_  (       unittest.TestCase ):
  
      '''simple docstring'''
  
  
      def     snake_case_(	self )	->     str:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
  
  
      def     snake_case_(	self				,	A=0 )	->     int:
          _SCREAMING_SNAKE_CASE             =    torch.manual_seed(A )
          _SCREAMING_SNAKE_CASE             =    {
              """prompt""": """A field of roses""",
              """generator""": generator,
              """num_inference_steps""": 3,
              """guidance_scale""": 7.5,
              """output_type""": """numpy""",
          }
          return inputs
  
  
      def     snake_case_(	self )	->     int:
          _SCREAMING_SNAKE_CASE             =    """CompVis/stable-diffusion-v1-4"""
          _SCREAMING_SNAKE_CASE             =    StableDiffusionModelEditingPipeline.from_pretrained(A				,	safety_checker=A )
          pipe.to(A )
          pipe.set_progress_bar_config(disable=A )
          pipe.enable_attention_slicing()
  
          _SCREAMING_SNAKE_CASE             =    self.get_inputs()
          _SCREAMING_SNAKE_CASE             =    pipe(**A ).images
          _SCREAMING_SNAKE_CASE             =    image[0, -3:, -3:, -1].flatten()
  
          assert image.shape == (1, 512, 512, 3)
  
          _SCREAMING_SNAKE_CASE             =    np.array(
              [0.674_9496, 0.638_6453, 0.5144_3267, 0.6609_4905, 0.6192_1215, 0.549_1332, 0.574_4417, 0.5807_5106, 0.517_4658] )
  
          assert np.abs(expected_slice - image_slice ).max() < 1e-2
  
          # make sure image changes after editing
          pipe.edit_model("""A pack of roses"""				,	"""A pack of blue roses""" )
  
          _SCREAMING_SNAKE_CASE             =    pipe(**A ).images
          _SCREAMING_SNAKE_CASE             =    image[0, -3:, -3:, -1].flatten()
  
          assert image.shape == (1, 512, 512, 3)
  
          assert np.abs(expected_slice - image_slice ).max() > 1e-1
  
  
  
  
  
      def     snake_case_(	self )	->     Optional[Any]:
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()
  
          _SCREAMING_SNAKE_CASE             =    """CompVis/stable-diffusion-v1-4"""
          _SCREAMING_SNAKE_CASE             =    DDIMScheduler.from_pretrained(A				,	subfolder="""scheduler""" )
          _SCREAMING_SNAKE_CASE             =    StableDiffusionModelEditingPipeline.from_pretrained(
              A				,	scheduler=A				,	safety_checker=A )
          _SCREAMING_SNAKE_CASE             =    pipe.to(A )
          pipe.set_progress_bar_config(disable=A )
          pipe.enable_attention_slicing(1 )
          pipe.enable_sequential_cpu_offload()
  
          _SCREAMING_SNAKE_CASE             =    self.get_inputs()
          _SCREAMING_SNAKE_CASE             =    pipe(**A )
  
          _SCREAMING_SNAKE_CASE             =    torch.cuda.max_memory_allocated()
          # make sure that less than 4.4 GB is allocated
          assert mem_bytes < 4.4 * 10**9
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  def lowerCamelCase		(  __lowerCamelCase				:					int = 10**6					)		->int:
  
      if n <= 0:
          raise ValueError("""Please enter an integer greater than 0"""					)
  
      _SCREAMING_SNAKE_CASE             =    list(range(n + 1					)					)
      for number in range(2      , n + 1					):
          if phi[number] == number:
              phi[number] -= 1
              for multiple in range(number * 2      , n + 1      , __lowerCamelCase					):
                  _SCREAMING_SNAKE_CASE             =    (phi[multiple] // number) * (number - 1)
  
      _SCREAMING_SNAKE_CASE             =    1
      for number in range(1      , n + 1					):
          if (answer / phi[answer]) < (number / phi[number]):
              _SCREAMING_SNAKE_CASE             =    number
  
      return answer
  
  
  if __name__ == "__main__":
        print(solution())
  
  
  
  