code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  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 							__snake_case    (				__magic_name__					,					__magic_name__					,					__magic_name__					,					unittest.TestCase       ):
  	__lowerCAmelCase						=     StableDiffusionImageVariationPipeline
  	__lowerCAmelCase						=     IMAGE_VARIATION_PARAMS
  	__lowerCAmelCase						=     IMAGE_VARIATION_BATCH_PARAMS
  	__lowerCAmelCase						=     frozenset([]       )
  	# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  	__lowerCAmelCase						=     frozenset([]       )
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Union[str, Any]:
  		torch.manual_seed(0		)
  		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			,					)
  		snake_case__  					=      PNDMScheduler(skip_prk_steps=UpperCamelCase_		)
  		torch.manual_seed(0		)
  		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		)
  		snake_case__  					=      CLIPVisionConfig(
  		    hidden_size=32			,					projection_dim=32			,					intermediate_size=37			,					layer_norm_eps=1E-0_5			,					num_attention_heads=4			,					num_hidden_layers=5			,					image_size=32			,					patch_size=4			,					)
  		snake_case__  					=      CLIPVisionModelWithProjection(UpperCamelCase_		)
  		snake_case__  					=      CLIPImageProcessor(crop_size=32			,					size=32		)
  
  		snake_case__  					=      {
  		    'unet': unet,
  		    'scheduler': scheduler,
  		    'vae': vae,
  		    'image_encoder': image_encoder,
  		    'feature_extractor': feature_extractor,
  		    'safety_checker': None,
  		}
  		return components
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_			,					UpperCamelCase_=0		)			->		int:
  		snake_case__  					=      floats_tensor((1, 3, 32, 32)			,					rng=random.Random(UpperCamelCase_		)		)
  		snake_case__  					=      image.cpu().permute(0			,					2			,					3			,					1		)[0]
  		snake_case__  					=      Image.fromarray(np.uinta(UpperCamelCase_		)		).convert('RGB'		).resize((32, 32)		)
  		if str(UpperCamelCase_		).startswith('mps'		):
  			snake_case__  					=      torch.manual_seed(UpperCamelCase_		)
  		else:
  			snake_case__  					=      torch.Generator(device=UpperCamelCase_		).manual_seed(UpperCamelCase_		)
  		snake_case__  					=      {
  		    'image': image,
  		    'generator': generator,
  		    'num_inference_steps': 2,
  		    'guidance_scale': 6.0,
  		    'output_type': 'numpy',
  		}
  		return inputs
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Optional[int]:
  		snake_case__  					=      'cpu'  # ensure determinism for the device-dependent torch.Generator
  		snake_case__  					=      self.get_dummy_components()
  		snake_case__  					=      StableDiffusionImageVariationPipeline(**UpperCamelCase_		)
  		snake_case__  					=      sd_pipe.to(UpperCamelCase_		)
  		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)
  
  		snake_case__  					=      self.get_dummy_inputs(UpperCamelCase_		)
  		snake_case__  					=      sd_pipe(**UpperCamelCase_		).images
  		snake_case__  					=      image[0, -3:, -3:, -1]
  
  		assert image.shape == (1, 64, 64, 3)
  		snake_case__  					=      np.array([0.5_2_3_9, 0.5_7_2_3, 0.4_7_9_6, 0.5_0_4_9, 0.5_5_5_0, 0.4_6_8_5, 0.5_3_2_9, 0.4_8_9_1, 0.4_9_2_1]		)
  
  		assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[Any]:
  		snake_case__  					=      'cpu'  # ensure determinism for the device-dependent torch.Generator
  		snake_case__  					=      self.get_dummy_components()
  		snake_case__  					=      StableDiffusionImageVariationPipeline(**UpperCamelCase_		)
  		snake_case__  					=      sd_pipe.to(UpperCamelCase_		)
  		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)
  
  		snake_case__  					=      self.get_dummy_inputs(UpperCamelCase_		)
  		snake_case__  					=      2 * [inputs['image']]
  		snake_case__  					=      sd_pipe(**UpperCamelCase_		)
  
  		snake_case__  					=      output.images
  
  		snake_case__  					=      image[-1, -3:, -3:, -1]
  
  		assert image.shape == (2, 64, 64, 3)
  		snake_case__  					=      np.array([0.6_8_9_2, 0.5_6_3_7, 0.5_8_3_6, 0.5_7_7_1, 0.6_2_5_4, 0.6_4_0_9, 0.5_5_8_0, 0.5_5_6_9, 0.5_2_8_9]		)
  
  		assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3
  
  
  
  
  
  	def      _snake_case      (		self		)			->		str:
  		super().test_inference_batch_single_identical(expected_max_diff=3E-3		)
  
  
  
  
  
  
  @slow
  @require_torch_gpu
  class 							__snake_case    (				unittest.TestCase       ):
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Any:
  		super().tearDown()
  		gc.collect()
  		torch.cuda.empty_cache()
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_			,					UpperCamelCase_="cpu"			,					UpperCamelCase_=torch.floataa			,					UpperCamelCase_=0		)			->		Union[str, Any]:
  		snake_case__  					=      torch.Generator(device=UpperCamelCase_		).manual_seed(UpperCamelCase_		)
  		snake_case__  					=      load_image(
  		    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
  		    '/stable_diffusion_imgvar/input_image_vermeer.png'		)
  		snake_case__  					=      np.random.RandomState(UpperCamelCase_		).standard_normal((1, 4, 64, 64)		)
  		snake_case__  					=      torch.from_numpy(UpperCamelCase_		).to(device=UpperCamelCase_			,					dtype=UpperCamelCase_		)
  		snake_case__  					=      {
  		    'image': init_image,
  		    'latents': latents,
  		    'generator': generator,
  		    'num_inference_steps': 3,
  		    'guidance_scale': 7.5,
  		    'output_type': 'numpy',
  		}
  		return inputs
  
  
  
  
  
  	def      _snake_case      (		self		)			->		str:
  		snake_case__  					=      StableDiffusionImageVariationPipeline.from_pretrained(
  		    'lambdalabs/sd-image-variations-diffusers'			,					safety_checker=UpperCamelCase_		)
  		snake_case__  					=      sd_pipe.to(UpperCamelCase_		)
  		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)
  
  		snake_case__  					=      self.get_inputs(UpperCamelCase_		)
  		snake_case__  					=      sd_pipe(**UpperCamelCase_		).images
  		snake_case__  					=      image[0, -3:, -3:, -1].flatten()
  
  		assert image.shape == (1, 512, 512, 3)
  		snake_case__  					=      np.array([0.8_4_4_9_1, 0.9_0_7_8_9, 0.7_5_7_0_8, 0.7_8_7_3_4, 0.8_3_4_8_5, 0.7_0_0_9_9, 0.6_6_9_3_8, 0.6_8_7_2_7, 0.6_1_3_7_9]		)
  		assert np.abs(image_slice - expected_slice		).max() < 6E-3
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Optional[Any]:
  		snake_case__  					=      0
  
  		def callback_fn(UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_		) -> None:
  			snake_case__  					=      True
  			nonlocal number_of_steps
  			number_of_steps += 1
  			if step == 1:
  				snake_case__  					=      latents.detach().cpu().numpy()
  				assert latents.shape == (1, 4, 64, 64)
  				snake_case__  					=      latents[0, -3:, -3:, -1]
  				snake_case__  					=      np.array(
  				    [-0.1_6_2_1, 0.2_8_3_7, -0.7_9_7_9, -0.1_2_2_1, -1.3_0_5_7, 0.7_6_8_1, -2.1_1_9_1, 0.0_4_6_4, 1.6_3_0_9]		)
  
  				assert np.abs(latents_slice.flatten() - expected_slice		).max() < 5E-2
  			elif step == 2:
  				snake_case__  					=      latents.detach().cpu().numpy()
  				assert latents.shape == (1, 4, 64, 64)
  				snake_case__  					=      latents[0, -3:, -3:, -1]
  				snake_case__  					=      np.array([0.6_2_9_9, 1.7_5_0_0, 1.1_9_9_2, -2.1_5_8_2, -1.8_9_9_4, 0.7_3_3_4, -0.7_0_9_0, 1.0_1_3_7, 1.5_2_7_3]		)
  
  				assert np.abs(latents_slice.flatten() - expected_slice		).max() < 5E-2
  
  		snake_case__  					=      False
  
  		snake_case__  					=      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()
  
  		snake_case__  					=      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      _snake_case      (		self		)			->		Optional[Any]:
  		torch.cuda.empty_cache()
  		torch.cuda.reset_max_memory_allocated()
  		torch.cuda.reset_peak_memory_stats()
  
  		snake_case__  					=      'fusing/sd-image-variations-diffusers'
  		snake_case__  					=      StableDiffusionImageVariationPipeline.from_pretrained(
  		    UpperCamelCase_			,					safety_checker=UpperCamelCase_			,					torch_dtype=torch.floataa		)
  		snake_case__  					=      pipe.to(UpperCamelCase_		)
  		pipe.set_progress_bar_config(disable=UpperCamelCase_		)
  		pipe.enable_attention_slicing(1		)
  		pipe.enable_sequential_cpu_offload()
  
  		snake_case__  					=      self.get_inputs(UpperCamelCase_			,					dtype=torch.floataa		)
  		snake_case__  					=      pipe(**UpperCamelCase_		)
  
  		snake_case__  					=      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 							__snake_case    (				unittest.TestCase       ):
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[str]:
  		super().tearDown()
  		gc.collect()
  		torch.cuda.empty_cache()
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_			,					UpperCamelCase_="cpu"			,					UpperCamelCase_=torch.floataa			,					UpperCamelCase_=0		)			->		Any:
  		snake_case__  					=      torch.Generator(device=UpperCamelCase_		).manual_seed(UpperCamelCase_		)
  		snake_case__  					=      load_image(
  		    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
  		    '/stable_diffusion_imgvar/input_image_vermeer.png'		)
  		snake_case__  					=      np.random.RandomState(UpperCamelCase_		).standard_normal((1, 4, 64, 64)		)
  		snake_case__  					=      torch.from_numpy(UpperCamelCase_		).to(device=UpperCamelCase_			,					dtype=UpperCamelCase_		)
  		snake_case__  					=      {
  		    'image': init_image,
  		    'latents': latents,
  		    'generator': generator,
  		    'num_inference_steps': 50,
  		    'guidance_scale': 7.5,
  		    'output_type': 'numpy',
  		}
  		return inputs
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Optional[int]:
  		snake_case__  					=      StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'		)
  		sd_pipe.to(UpperCamelCase_		)
  		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)
  
  		snake_case__  					=      self.get_inputs(UpperCamelCase_		)
  		snake_case__  					=      sd_pipe(**UpperCamelCase_		).images[0]
  
  		snake_case__  					=      load_numpy(
  		    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
  		    '/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy'		)
  		snake_case__  					=      np.abs(expected_image - image		).max()
  		assert max_diff < 1E-3
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Union[str, Any]:
  		snake_case__  					=      StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'		)
  		snake_case__  					=      DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config		)
  		sd_pipe.to(UpperCamelCase_		)
  		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)
  
  		snake_case__  					=      self.get_inputs(UpperCamelCase_		)
  		snake_case__  					=      25
  		snake_case__  					=      sd_pipe(**UpperCamelCase_		).images[0]
  
  		snake_case__  					=      load_numpy(
  		    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
  		    '/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy'		)
  		snake_case__  					=      np.abs(expected_image - image		).max()
  		assert max_diff < 1E-3
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  import sys
  
  from transformers import AutoTokenizer
  
  
  a__				: Optional[int]									=    sys.argv[1]
  a__				: Any									=    sys.argv[2]
  a__				: str									=    int(sys.argv[3])
  
  a__				: str									=    0
  
  a__				: int									=    AutoTokenizer.from_pretrained(model_name_or_path)
  max_len -= tokenizer.num_special_tokens_to_add()
  
  with open(dataset, '''rt''') as f_p:
  			for line in f_p:
  						a__				: Tuple									=    line.rstrip()
  
  						if not line:
  									print(line)
  									a__				: List[str]									=    0
  									continue
  
  						a__				: Optional[int]									=    line.split()[0]
  
  						a__				: Tuple									=    len(tokenizer.tokenize(token))
  
  						# Token contains strange control characters like \x96 or \x95
  						# Just filter out the complete line
  						if current_subwords_len == 0:
  									continue
  
  						if (subword_len_counter + current_subwords_len) > max_len:
  									print('''''')
  									print(line)
  									a__				: List[str]									=    current_subwords_len
  									continue
  
  						subword_len_counter += current_subwords_len
  
  						print(line)
  
  
  
  