code: 
  
  
  
  
  
  import gc
  import random
  import unittest
  
  import numpy as np
  import torch
  from PIL import Image
  from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection
  
  from diffusers import (
      AutoencoderKL,
      DPMSolverMultistepScheduler,
      PNDMScheduler,
      StableDiffusionImageVariationPipeline,
      UNetaDConditionModel,
  )
  from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
  
  from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
  from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					UpperCamelCase__		,       UpperCamelCase__		,       UpperCamelCase__		,       unittest.TestCase):
        _lowercase       :      Optional[Any]        =    StableDiffusionImageVariationPipeline
        _lowercase       :      Optional[int]        =    IMAGE_VARIATION_PARAMS
        _lowercase       :      Optional[int]        =    IMAGE_VARIATION_BATCH_PARAMS
        _lowercase       :      Optional[int]        =    frozenset([])
        # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
        _lowercase       :      str        =    frozenset([])
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		str:
  
  
  
          '''simple docstring'''
  
  
  
  
          torch.manual_seed(0							)
          a__			:      List[Any]            =UNetaDConditionModel(
              block_out_channels=(3_2, 6_4)	,   layers_per_block=2	,   sample_size=3_2	,   in_channels=4	,   out_channels=4	,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,   up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,   cross_attention_dim=3_2	,   )
          a__			:      Union[str, Any]            =PNDMScheduler(skip_prk_steps=lowerCAmelCase__							)
          torch.manual_seed(0							)
          a__			:      int            =AutoencoderKL(
              block_out_channels=[3_2, 6_4]	,   in_channels=3	,   out_channels=3	,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]	,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]	,   latent_channels=4	,   )
          torch.manual_seed(0							)
          a__			:      List[str]            =CLIPVisionConfig(
              hidden_size=3_2	,   projection_dim=3_2	,   intermediate_size=3_7	,   layer_norm_eps=1E-05	,   num_attention_heads=4	,   num_hidden_layers=5	,   image_size=3_2	,   patch_size=4	,   )
          a__			:      Tuple            =CLIPVisionModelWithProjection(lowerCAmelCase__							)
          a__			:      int            =CLIPImageProcessor(crop_size=3_2	,   size=3_2							)
  
          a__			:      Optional[int]            ={
              "unet": unet,
              "scheduler": scheduler,
              "vae": vae,
              "image_encoder": image_encoder,
              "feature_extractor": feature_extractor,
              "safety_checker": None,
          }
          return components
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=0							)     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Optional[Any]            =floats_tensor((1, 3, 3_2, 3_2)	,   rng=random.Random(lowerCAmelCase__							)							)
          a__			:      Optional[Any]            =image.cpu().permute(0	,   2	,   3	,   1							)[0]
          a__			:      int            =Image.fromarray(np.uinta(lowerCAmelCase__							)							).convert("RGB"							).resize((3_2, 3_2)							)
          if str(lowerCAmelCase__							).startswith("mps"							):
            a__			:      str            =torch.manual_seed(lowerCAmelCase__							)
          else:
            a__			:      str            =torch.Generator(device=lowerCAmelCase__							).manual_seed(lowerCAmelCase__							)
          a__			:      str            ={
              "image": image,
              "generator": generator,
              "num_inference_steps": 2,
              "guidance_scale": 6.0,
              "output_type": "numpy",
          }
          return inputs
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Tuple            ="cpu"  # ensure determinism for the device-dependent torch.Generator
          a__			:      Optional[Any]            =self.get_dummy_components()
          a__			:      List[str]            =StableDiffusionImageVariationPipeline(**lowerCAmelCase__							)
          a__			:      Optional[Any]            =sd_pipe.to(lowerCAmelCase__							)
          sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)
  
          a__			:      Any            =self.get_dummy_inputs(lowerCAmelCase__							)
          a__			:      Optional[Any]            =sd_pipe(**lowerCAmelCase__							).images
          a__			:      int            =image[0, -3:, -3:, -1]
  
          assert image.shape == (1, 6_4, 6_4, 3)
          a__			:      int            =np.array([0.52_39, 0.57_23, 0.47_96, 0.50_49, 0.55_50, 0.46_85, 0.53_29, 0.48_91, 0.49_21]							)
  
          assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		List[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      int            ="cpu"  # ensure determinism for the device-dependent torch.Generator
          a__			:      Any            =self.get_dummy_components()
          a__			:      Tuple            =StableDiffusionImageVariationPipeline(**lowerCAmelCase__							)
          a__			:      Dict            =sd_pipe.to(lowerCAmelCase__							)
          sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)
  
          a__			:      Union[str, Any]            =self.get_dummy_inputs(lowerCAmelCase__							)
          a__			:      Optional[int]            =2 * [inputs["image"]]
          a__			:      Optional[Any]            =sd_pipe(**lowerCAmelCase__							)
  
          a__			:      Union[str, Any]            =output.images
  
          a__			:      Any            =image[-1, -3:, -3:, -1]
  
          assert image.shape == (2, 6_4, 6_4, 3)
          a__			:      List[str]            =np.array([0.68_92, 0.56_37, 0.58_36, 0.57_71, 0.62_54, 0.64_09, 0.55_80, 0.55_69, 0.52_89]							)
  
          assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Union[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          super().test_inference_batch_single_identical(expected_max_diff=3E-3							)
  
  
  
  
  
  
  
  @slow
  @require_torch_gpu
  class        __lowerCAmelCase (					unittest.TestCase):
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Union[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__="cpu"	,   lowerCAmelCase__=torch.floataa	,   lowerCAmelCase__=0							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      int            =torch.Generator(device=lowerCAmelCase__							).manual_seed(lowerCAmelCase__							)
          a__			:      List[Any]            =load_image(
              "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
              "/stable_diffusion_imgvar/input_image_vermeer.png"							)
          a__			:      int            =np.random.RandomState(lowerCAmelCase__							).standard_normal((1, 4, 6_4, 6_4)							)
          a__			:      Any            =torch.from_numpy(lowerCAmelCase__							).to(device=lowerCAmelCase__	,   dtype=lowerCAmelCase__							)
          a__			:      str            ={
              "image": init_image,
              "latents": latents,
              "generator": generator,
              "num_inference_steps": 3,
              "guidance_scale": 7.5,
              "output_type": "numpy",
          }
          return inputs
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		List[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      int            =StableDiffusionImageVariationPipeline.from_pretrained(
              "lambdalabs/sd-image-variations-diffusers"	,   safety_checker=lowerCAmelCase__							)
          a__			:      Union[str, Any]            =sd_pipe.to(lowerCAmelCase__							)
          sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)
  
          a__			:      Dict            =self.get_inputs(lowerCAmelCase__							)
          a__			:      Any            =sd_pipe(**lowerCAmelCase__							).images
          a__			:      List[Any]            =image[0, -3:, -3:, -1].flatten()
  
          assert image.shape == (1, 5_1_2, 5_1_2, 3)
          a__			:      Any            =np.array([0.8_44_91, 0.9_07_89, 0.7_57_08, 0.7_87_34, 0.8_34_85, 0.7_00_99, 0.6_69_38, 0.6_87_27, 0.6_13_79]							)
          assert np.abs(image_slice - expected_slice							).max() < 6E-3
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Union[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Tuple            =0
  
          def callback_fn(lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							) -> None:
            a__			:      int            =True
            nonlocal number_of_steps
            number_of_steps += 1
            if step == 1:
              a__			:      Union[str, Any]            =latents.detach().cpu().numpy()
              assert latents.shape == (1, 4, 6_4, 6_4)
              a__			:      Any            =latents[0, -3:, -3:, -1]
              a__			:      Any            =np.array(
                  [-0.16_21, 0.28_37, -0.79_79, -0.12_21, -1.30_57, 0.76_81, -2.11_91, 0.04_64, 1.63_09]							)
  
              assert np.abs(latents_slice.flatten() - expected_slice							).max() < 5E-2
            elif step == 2:
              a__			:      List[Any]            =latents.detach().cpu().numpy()
              assert latents.shape == (1, 4, 6_4, 6_4)
              a__			:      Union[str, Any]            =latents[0, -3:, -3:, -1]
              a__			:      Dict            =np.array([0.62_99, 1.75_00, 1.19_92, -2.15_82, -1.89_94, 0.73_34, -0.70_90, 1.01_37, 1.52_73]							)
  
              assert np.abs(latents_slice.flatten() - expected_slice							).max() < 5E-2
  
          a__			:      str            =False
  
          a__			:      List[Any]            =StableDiffusionImageVariationPipeline.from_pretrained(
              "fusing/sd-image-variations-diffusers"	,   safety_checker=lowerCAmelCase__	,   torch_dtype=torch.floataa	,   )
          pipe.to(lowerCAmelCase__							)
          pipe.set_progress_bar_config(disable=lowerCAmelCase__							)
          pipe.enable_attention_slicing()
  
          a__			:      Optional[int]            =self.get_inputs(lowerCAmelCase__	,   dtype=torch.floataa							)
          pipe(**lowerCAmelCase__	,   callback=lowerCAmelCase__	,   callback_steps=1							)
          assert callback_fn.has_been_called
          assert number_of_steps == inputs["num_inference_steps"]
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()
  
          a__			:      Dict            ="fusing/sd-image-variations-diffusers"
          a__			:      Optional[int]            =StableDiffusionImageVariationPipeline.from_pretrained(
              lowerCAmelCase__	,   safety_checker=lowerCAmelCase__	,   torch_dtype=torch.floataa							)
          a__			:      Optional[Any]            =pipe.to(lowerCAmelCase__							)
          pipe.set_progress_bar_config(disable=lowerCAmelCase__							)
          pipe.enable_attention_slicing(1							)
          pipe.enable_sequential_cpu_offload()
  
          a__			:      Any            =self.get_inputs(lowerCAmelCase__	,   dtype=torch.floataa							)
          a__			:      str            =pipe(**lowerCAmelCase__							)
  
          a__			:      Tuple            =torch.cuda.max_memory_allocated()
          # make sure that less than 2.6 GB is allocated
          assert mem_bytes < 2.6 * 1_0**9
  
  
  
  
  
  
  
  @nightly
  @require_torch_gpu
  class        __lowerCAmelCase (					unittest.TestCase):
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Dict:
  
  
  
          '''simple docstring'''
  
  
  
  
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__="cpu"	,   lowerCAmelCase__=torch.floataa	,   lowerCAmelCase__=0							)     ->		Dict:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Tuple            =torch.Generator(device=lowerCAmelCase__							).manual_seed(lowerCAmelCase__							)
          a__			:      List[str]            =load_image(
              "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
              "/stable_diffusion_imgvar/input_image_vermeer.png"							)
          a__			:      Tuple            =np.random.RandomState(lowerCAmelCase__							).standard_normal((1, 4, 6_4, 6_4)							)
          a__			:      Any            =torch.from_numpy(lowerCAmelCase__							).to(device=lowerCAmelCase__	,   dtype=lowerCAmelCase__							)
          a__			:      str            ={
              "image": init_image,
              "latents": latents,
              "generator": generator,
              "num_inference_steps": 5_0,
              "guidance_scale": 7.5,
              "output_type": "numpy",
          }
          return inputs
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Any:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Optional[int]            =StableDiffusionImageVariationPipeline.from_pretrained("fusing/sd-image-variations-diffusers"							)
          sd_pipe.to(lowerCAmelCase__							)
          sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)
  
          a__			:      Union[str, Any]            =self.get_inputs(lowerCAmelCase__							)
          a__			:      Dict            =sd_pipe(**lowerCAmelCase__							).images[0]
  
          a__			:      Any            =load_numpy(
              "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
              "/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy"							)
          a__			:      Tuple            =np.abs(expected_image - image							).max()
          assert max_diff < 1E-3
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[Any]            =StableDiffusionImageVariationPipeline.from_pretrained("fusing/sd-image-variations-diffusers"							)
          a__			:      Union[str, Any]            =DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config							)
          sd_pipe.to(lowerCAmelCase__							)
          sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__							)
  
          a__			:      Any            =self.get_inputs(lowerCAmelCase__							)
          a__			:      Union[str, Any]            =2_5
          a__			:      List[str]            =sd_pipe(**lowerCAmelCase__							).images[0]
  
          a__			:      int            =load_numpy(
              "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
              "/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy"							)
          a__			:      Any            =np.abs(expected_image - image							).max()
          assert max_diff < 1E-3
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  def   _A (	SCREAMING_SNAKE_CASE  :     int		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    barcode //= 10  # exclude the last digit
    a__			:      int            =False
    a__			:      List[str]            =0
  
    # extract and check each digit
    while barcode != 0:
      a__			:      Optional[Any]            =1 if checker else 3
      s += mult * (barcode % 10)
      barcode //= 10
      a__			:      Union[str, Any]            =not checker
  
    return (10 - (s % 10)) % 10
  def   _A (	SCREAMING_SNAKE_CASE  :     int		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    return len(str(SCREAMING_SNAKE_CASE		)		) == 13 and get_check_digit(SCREAMING_SNAKE_CASE		) == barcode % 10
  def   _A (	SCREAMING_SNAKE_CASE  :     str		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    if str(SCREAMING_SNAKE_CASE		).isalpha():
      a__			:      Tuple            =f'''Barcode \'{barcode}\' has alphabetic characters.'''
      raise ValueError(SCREAMING_SNAKE_CASE		)
    elif int(SCREAMING_SNAKE_CASE		) < 0:
      raise ValueError("The entered barcode has a negative value. Try again."		)
    else:
      return int(SCREAMING_SNAKE_CASE		)
  
  
  if __name__ == "__main__":
       import doctest
  
       doctest.testmod()
       UpperCAmelCase			:			List[str]	  = get_barcode(input("""Barcode: """).strip())
  
       if is_valid(barcode):
            print(F"""'{barcode}' is a valid barcode.""")
       else:
            print(F"""'{barcode}' is NOT a valid barcode.""")
  
  
  
  
  
  
  