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    __A				(							lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase						,						unittest.TestCase ):
      lowerCAmelCase_							:		List[Any]						=							StableDiffusionImageVariationPipeline
      lowerCAmelCase_							:		int						=							IMAGE_VARIATION_PARAMS
      lowerCAmelCase_							:		Optional[int]						=							IMAGE_VARIATION_BATCH_PARAMS
      lowerCAmelCase_							:		Tuple						=							frozenset([] )
      # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
      lowerCAmelCase_							:		int						=							frozenset([] )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[Any]    ):
          torch.manual_seed(0    )
          lowerCAmelCase				:			Any    			=      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    ,				)
          lowerCAmelCase				:			Optional[Any]    			=      PNDMScheduler(skip_prk_steps=UpperCAmelCase_    )
          torch.manual_seed(0    )
          lowerCAmelCase				:			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    )
          lowerCAmelCase				:			List[Any]    			=      CLIPVisionConfig(
              hidden_size=32    ,				projection_dim=32    ,				intermediate_size=37    ,				layer_norm_eps=1E-05    ,				num_attention_heads=4    ,				num_hidden_layers=5    ,				image_size=32    ,				patch_size=4    ,				)
          lowerCAmelCase				:			Optional[Any]    			=      CLIPVisionModelWithProjection(UpperCAmelCase_    )
          lowerCAmelCase				:			List[Any]    			=      CLIPImageProcessor(crop_size=32    ,				size=32    )
  
          lowerCAmelCase				:			str    			=      {
              'unet': unet,
              'scheduler': scheduler,
              'vae': vae,
              'image_encoder': image_encoder,
              'feature_extractor': feature_extractor,
              'safety_checker': None,
          }
          return components
  
  
  
  
  
  
      def 	lowercase__ (	self			:    int    ,				UpperCAmelCase_			:    Dict    ,				UpperCAmelCase_			:    List[str]=0    ):
          lowerCAmelCase				:			Dict    			=      floats_tensor((1, 3, 32, 32)    ,				rng=random.Random(UpperCAmelCase_    )    )
          lowerCAmelCase				:			Optional[int]    			=      image.cpu().permute(0    ,				2    ,				3    ,				1    )[0]
          lowerCAmelCase				:			Dict    			=      Image.fromarray(np.uinta(UpperCAmelCase_    )    ).convert('RGB'    ).resize((32, 32)    )
          if str(UpperCAmelCase_    ).startswith('mps'    ):
              lowerCAmelCase				:			str    			=      torch.manual_seed(UpperCAmelCase_    )
          else:
              lowerCAmelCase				:			Optional[int]    			=      torch.Generator(device=UpperCAmelCase_    ).manual_seed(UpperCAmelCase_    )
          lowerCAmelCase				:			List[str]    			=      {
              'image': image,
              'generator': generator,
              'num_inference_steps': 2,
              'guidance_scale': 6.0,
              'output_type': 'numpy',
          }
          return inputs
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Any    ):
          lowerCAmelCase				:			List[str]    			=      'cpu'  # ensure determinism for the device-dependent torch.Generator
          lowerCAmelCase				:			List[str]    			=      self.get_dummy_components()
          lowerCAmelCase				:			List[Any]    			=      StableDiffusionImageVariationPipeline(**UpperCAmelCase_    )
          lowerCAmelCase				:			Optional[Any]    			=      sd_pipe.to(UpperCAmelCase_    )
          sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_    )
  
          lowerCAmelCase				:			Optional[int]    			=      self.get_dummy_inputs(UpperCAmelCase_    )
          lowerCAmelCase				:			List[Any]    			=      sd_pipe(**UpperCAmelCase_    ).images
          lowerCAmelCase				:			Any    			=      image[0, -3:, -3:, -1]
  
          assert image.shape == (1, 64, 64, 3)
          lowerCAmelCase				:			Any    			=      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			:    Dict    ):
          lowerCAmelCase				:			Dict    			=      'cpu'  # ensure determinism for the device-dependent torch.Generator
          lowerCAmelCase				:			Any    			=      self.get_dummy_components()
          lowerCAmelCase				:			Any    			=      StableDiffusionImageVariationPipeline(**UpperCAmelCase_    )
          lowerCAmelCase				:			int    			=      sd_pipe.to(UpperCAmelCase_    )
          sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_    )
  
          lowerCAmelCase				:			Tuple    			=      self.get_dummy_inputs(UpperCAmelCase_    )
          lowerCAmelCase				:			List[str]    			=      2 * [inputs['image']]
          lowerCAmelCase				:			Optional[int]    			=      sd_pipe(**UpperCAmelCase_    )
  
          lowerCAmelCase				:			List[str]    			=      output.images
  
          lowerCAmelCase				:			int    			=      image[-1, -3:, -3:, -1]
  
          assert image.shape == (2, 64, 64, 3)
          lowerCAmelCase				:			Optional[int]    			=      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			:    List[Any]    ):
          super().test_inference_batch_single_identical(expected_max_diff=3E-3    )
  
  
  
  
  
  
  @slow
  @require_torch_gpu
  class    __A				(							unittest.TestCase ):
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[Any]    ):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[Any]    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    Any="cpu"    ,				UpperCAmelCase_			:    int=torch.floataa    ,				UpperCAmelCase_			:    List[str]=0    ):
          lowerCAmelCase				:			List[Any]    			=      torch.Generator(device=UpperCAmelCase_    ).manual_seed(UpperCAmelCase_    )
          lowerCAmelCase				:			List[str]    			=      load_image(
              'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
              '/stable_diffusion_imgvar/input_image_vermeer.png'    )
          lowerCAmelCase				:			Any    			=      np.random.RandomState(UpperCAmelCase_    ).standard_normal((1, 4, 64, 64)    )
          lowerCAmelCase				:			Tuple    			=      torch.from_numpy(UpperCAmelCase_    ).to(device=UpperCAmelCase_    ,				dtype=UpperCAmelCase_    )
          lowerCAmelCase				:			str    			=      {
              'image': init_image,
              'latents': latents,
              'generator': generator,
              'num_inference_steps': 3,
              'guidance_scale': 7.5,
              'output_type': 'numpy',
          }
          return inputs
  
  
  
  
  
  
      def 	lowercase__ (	self			:    int    ):
          lowerCAmelCase				:			Dict    			=      StableDiffusionImageVariationPipeline.from_pretrained(
              'lambdalabs/sd-image-variations-diffusers'    ,				safety_checker=UpperCAmelCase_    )
          lowerCAmelCase				:			int    			=      sd_pipe.to(UpperCAmelCase_    )
          sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_    )
  
          lowerCAmelCase				:			Any    			=      self.get_inputs(UpperCAmelCase_    )
          lowerCAmelCase				:			List[str]    			=      sd_pipe(**UpperCAmelCase_    ).images
          lowerCAmelCase				:			int    			=      image[0, -3:, -3:, -1].flatten()
  
          assert image.shape == (1, 512, 512, 3)
          lowerCAmelCase				:			Optional[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			:    List[str]    ):
          lowerCAmelCase				:			str    			=      0
  
          def callback_fn(UpperCAmelCase_			:    int    ,				UpperCAmelCase_			:    int    ,				UpperCAmelCase_			:    torch.FloatTensor    ) -> None:
              lowerCAmelCase				:			Optional[int]    			=      True
              nonlocal number_of_steps
              number_of_steps += 1
              if step == 1:
                  lowerCAmelCase				:			List[str]    			=      latents.detach().cpu().numpy()
                  assert latents.shape == (1, 4, 64, 64)
                  lowerCAmelCase				:			Dict    			=      latents[0, -3:, -3:, -1]
                  lowerCAmelCase				:			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:
                  lowerCAmelCase				:			List[Any]    			=      latents.detach().cpu().numpy()
                  assert latents.shape == (1, 4, 64, 64)
                  lowerCAmelCase				:			Union[str, Any]    			=      latents[0, -3:, -3:, -1]
                  lowerCAmelCase				:			Optional[int]    			=      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
  
          lowerCAmelCase				:			Dict    			=      False
  
          lowerCAmelCase				:			List[Any]    			=      StableDiffusionImageVariationPipeline.from_pretrained(
              'fusing/sd-image-variations-diffusers'    ,				safety_checker=UpperCAmelCase_    ,				torch_dtype=torch.floataa    ,				)
          pipe.to(UpperCAmelCase_    )
          pipe.set_progress_bar_config(disable=UpperCAmelCase_    )
          pipe.enable_attention_slicing()
  
          lowerCAmelCase				:			Optional[Any]    			=      self.get_inputs(UpperCAmelCase_    ,				dtype=torch.floataa    )
          pipe(**UpperCAmelCase_    ,				callback=UpperCAmelCase_    ,				callback_steps=1    )
          assert callback_fn.has_been_called
          assert number_of_steps == inputs["num_inference_steps"]
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[str]    ):
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()
  
          lowerCAmelCase				:			Optional[int]    			=      'fusing/sd-image-variations-diffusers'
          lowerCAmelCase				:			int    			=      StableDiffusionImageVariationPipeline.from_pretrained(
              UpperCAmelCase_    ,				safety_checker=UpperCAmelCase_    ,				torch_dtype=torch.floataa    )
          lowerCAmelCase				:			int    			=      pipe.to(UpperCAmelCase_    )
          pipe.set_progress_bar_config(disable=UpperCAmelCase_    )
          pipe.enable_attention_slicing(1    )
          pipe.enable_sequential_cpu_offload()
  
          lowerCAmelCase				:			str    			=      self.get_inputs(UpperCAmelCase_    ,				dtype=torch.floataa    )
          lowerCAmelCase				:			List[str]    			=      pipe(**UpperCAmelCase_    )
  
          lowerCAmelCase				:			int    			=      torch.cuda.max_memory_allocated()
          # make sure that less than 2.6 GB is allocated
          assert mem_bytes < 2.6 * 10**9
  
  
  
  
  
  
  @nightly
  @require_torch_gpu
  class    __A				(							unittest.TestCase ):
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Dict    ):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[str]    ,				UpperCAmelCase_			:    int    ,				UpperCAmelCase_			:    Dict="cpu"    ,				UpperCAmelCase_			:    List[Any]=torch.floataa    ,				UpperCAmelCase_			:    Any=0    ):
          lowerCAmelCase				:			int    			=      torch.Generator(device=UpperCAmelCase_    ).manual_seed(UpperCAmelCase_    )
          lowerCAmelCase				:			Optional[Any]    			=      load_image(
              'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
              '/stable_diffusion_imgvar/input_image_vermeer.png'    )
          lowerCAmelCase				:			Tuple    			=      np.random.RandomState(UpperCAmelCase_    ).standard_normal((1, 4, 64, 64)    )
          lowerCAmelCase				:			int    			=      torch.from_numpy(UpperCAmelCase_    ).to(device=UpperCAmelCase_    ,				dtype=UpperCAmelCase_    )
          lowerCAmelCase				:			List[str]    			=      {
              'image': init_image,
              'latents': latents,
              'generator': generator,
              'num_inference_steps': 50,
              'guidance_scale': 7.5,
              'output_type': 'numpy',
          }
          return inputs
  
  
  
  
  
  
      def 	lowercase__ (	self			:    int    ):
          lowerCAmelCase				:			Union[str, Any]    			=      StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'    )
          sd_pipe.to(UpperCAmelCase_    )
          sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_    )
  
          lowerCAmelCase				:			Optional[Any]    			=      self.get_inputs(UpperCAmelCase_    )
          lowerCAmelCase				:			int    			=      sd_pipe(**UpperCAmelCase_    ).images[0]
  
          lowerCAmelCase				:			int    			=      load_numpy(
              'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
              '/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy'    )
          lowerCAmelCase				:			Optional[Any]    			=      np.abs(expected_image - image    ).max()
          assert max_diff < 1E-3
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[str]    ):
          lowerCAmelCase				:			Optional[Any]    			=      StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'    )
          lowerCAmelCase				:			str    			=      DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config    )
          sd_pipe.to(UpperCAmelCase_    )
          sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_    )
  
          lowerCAmelCase				:			Dict    			=      self.get_inputs(UpperCAmelCase_    )
          lowerCAmelCase				:			Optional[Any]    			=      25
          lowerCAmelCase				:			Optional[Any]    			=      sd_pipe(**UpperCAmelCase_    ).images[0]
  
          lowerCAmelCase				:			Tuple    			=      load_numpy(
              'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
              '/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy'    )
          lowerCAmelCase				:			str    			=      np.abs(expected_image - image    ).max()
          assert max_diff < 1E-3
  
  
  
  
  
  
style_context: 
  
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      )					-> tuple[list[list[int]], list[list[str]]]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			List[Any]    			=      list(_UpperCAmelCase				)
      lowerCAmelCase				:			Union[str, Any]    			=      list(_UpperCAmelCase				)
      lowerCAmelCase				:			Optional[int]    			=      len(_UpperCAmelCase				)
      lowerCAmelCase				:			List[Any]    			=      len(_UpperCAmelCase				)
  
      lowerCAmelCase				:			str    			=      [
          [0 for _ in range(len_destination_seq + 1				)] for _ in range(len_source_seq + 1				)
      ]
      lowerCAmelCase				:			str    			=      [
          ['0' for _ in range(len_destination_seq + 1				)] for _ in range(len_source_seq + 1				)
      ]
  
      for i in range(1,      len_source_seq + 1				):
          lowerCAmelCase				:			int    			=      i * delete_cost
          lowerCAmelCase				:			List[str]    			=      f"D{source_seq[i - 1]:c}"
  
      for i in range(1,      len_destination_seq + 1				):
          lowerCAmelCase				:			Tuple    			=      i * insert_cost
          lowerCAmelCase				:			Union[str, Any]    			=      f"I{destination_seq[i - 1]:c}"
  
      for i in range(1,      len_source_seq + 1				):
          for j in range(1,      len_destination_seq + 1				):
              if source_seq[i - 1] == destination_seq[j - 1]:
                  lowerCAmelCase				:			str    			=      costs[i - 1][j - 1] + copy_cost
                  lowerCAmelCase				:			Tuple    			=      f"C{source_seq[i - 1]:c}"
              else:
                  lowerCAmelCase				:			Optional[int]    			=      costs[i - 1][j - 1] + replace_cost
                  lowerCAmelCase				:			Optional[int]    			=      f"R{source_seq[i - 1]:c}" + str(destination_seq[j - 1]				)
  
              if costs[i - 1][j] + delete_cost < costs[i][j]:
                  lowerCAmelCase				:			List[str]    			=      costs[i - 1][j] + delete_cost
                  lowerCAmelCase				:			Optional[int]    			=      f"D{source_seq[i - 1]:c}"
  
              if costs[i][j - 1] + insert_cost < costs[i][j]:
                  lowerCAmelCase				:			Union[str, Any]    			=      costs[i][j - 1] + insert_cost
                  lowerCAmelCase				:			str    			=      f"I{destination_seq[j - 1]:c}"
  
      return costs, ops
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				)					-> list[str]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      if i == 0 and j == 0:
          return []
      else:
          if ops[i][j][0] == "C" or ops[i][j][0] == "R":
              lowerCAmelCase				:			List[str]    			=      assemble_transformation(_UpperCAmelCase,      i - 1,      j - 1				)
              seq.append(ops[i][j]				)
              return seq
          elif ops[i][j][0] == "D":
              lowerCAmelCase				:			int    			=      assemble_transformation(_UpperCAmelCase,      i - 1,      _UpperCAmelCase				)
              seq.append(ops[i][j]				)
              return seq
          else:
              lowerCAmelCase				:			Tuple    			=      assemble_transformation(_UpperCAmelCase,      _UpperCAmelCase,      j - 1				)
              seq.append(ops[i][j]				)
              return seq
  
  
  if __name__ == "__main__":
   __A     ,      __A   :     List[str]      =			compute_transform_tables('''Python''', '''Algorithms''', -1, 1, 2, 2)
  
   __A   :     Optional[int]      =			len(operations)
   __A   :     Optional[Any]      =			len(operations[0])
   __A   :     Any      =			assemble_transformation(operations, m - 1, n - 1)
  
   __A   :     int      =			list('''Python''')
   __A   :     Tuple      =			0
   __A   :     Tuple      =			0
  
   with open('''min_cost.txt''', '''w''') as file:
    for op in sequence:
     print(''''''.join(string))
  
     if op[0] == "C":
      file.write('''%-16s''' % '''Copy %c''' % op[1])
      file.write('''\t\t\t''' + ''''''.join(string))
      file.write('''\r\n''')
  
      cost -= 1
     elif op[0] == "R":
      __A   :     Any      =			op[2]
  
      file.write('''%-16s''' % ('''Replace %c''' % op[1] + ''' with ''' + str(op[2])))
      file.write('''\t\t''' + ''''''.join(string))
      file.write('''\r\n''')
  
      cost += 1
     elif op[0] == "D":
      string.pop(i)
  
      file.write('''%-16s''' % '''Delete %c''' % op[1])
      file.write('''\t\t\t''' + ''''''.join(string))
      file.write('''\r\n''')
  
      cost += 2
     else:
      string.insert(i, op[1])
  
      file.write('''%-16s''' % '''Insert %c''' % op[1])
      file.write('''\t\t\t''' + ''''''.join(string))
      file.write('''\r\n''')
  
      cost += 2
  
     i += 1
  
    print(''''''.join(string))
    print('''Cost: ''', cost)
  
    file.write('''\r\nMinimum cost: ''' + str(cost))
  
  
  
  
  
  