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 							__snake_case    (				lowerCAmelCase_					,					lowerCAmelCase_					,					lowerCAmelCase_					,					unittest.TestCase       ):
  	__lowerCAmelCase						=     StableDiffusionModelEditingPipeline
  	__lowerCAmelCase						=     TEXT_TO_IMAGE_PARAMS
  	__lowerCAmelCase						=     TEXT_TO_IMAGE_BATCH_PARAMS
  	__lowerCAmelCase						=     TEXT_TO_IMAGE_IMAGE_PARAMS
  	__lowerCAmelCase						=     TEXT_TO_IMAGE_IMAGE_PARAMS
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Optional[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__  					=      DDIMScheduler()
  		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__  					=      CLIPTextConfig(
  		    bos_token_id=0			,					eos_token_id=2			,					hidden_size=32			,					intermediate_size=37			,					layer_norm_eps=1E-0_5			,					num_attention_heads=4			,					num_hidden_layers=5			,					pad_token_id=1			,					vocab_size=1000			,					)
  		snake_case__  					=      CLIPTextModel(lowerCamelCase_		)
  		snake_case__  					=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)
  
  		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			,					UpperCamelCase_			,					UpperCamelCase_=0		)			->		str:
  		snake_case__  					=      torch.manual_seed(lowerCamelCase_		)
  		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		)			->		Tuple:
  		snake_case__  					=      'cpu'  # ensure determinism for the device-dependent torch.Generator
  		snake_case__  					=      self.get_dummy_components()
  		snake_case__  					=      StableDiffusionModelEditingPipeline(**lowerCamelCase_		)
  		snake_case__  					=      sd_pipe.to(lowerCamelCase_		)
  		sd_pipe.set_progress_bar_config(disable=lowerCamelCase_		)
  
  		snake_case__  					=      self.get_dummy_inputs(lowerCamelCase_		)
  		snake_case__  					=      sd_pipe(**lowerCamelCase_		).images
  		snake_case__  					=      image[0, -3:, -3:, -1]
  		assert image.shape == (1, 64, 64, 3)
  
  		snake_case__  					=      np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9]		)
  
  		assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Dict:
  		snake_case__  					=      'cpu'  # ensure determinism for the device-dependent torch.Generator
  		snake_case__  					=      self.get_dummy_components()
  		snake_case__  					=      StableDiffusionModelEditingPipeline(**lowerCamelCase_		)
  		snake_case__  					=      sd_pipe.to(lowerCamelCase_		)
  		sd_pipe.set_progress_bar_config(disable=lowerCamelCase_		)
  
  		snake_case__  					=      self.get_dummy_inputs(lowerCamelCase_		)
  		snake_case__  					=      'french fries'
  		snake_case__  					=      sd_pipe(**lowerCamelCase_			,					negative_prompt=lowerCamelCase_		)
  		snake_case__  					=      output.images
  		snake_case__  					=      image[0, -3:, -3:, -1]
  
  		assert image.shape == (1, 64, 64, 3)
  
  		snake_case__  					=      np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3]		)
  
  		assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[str]:
  		snake_case__  					=      'cpu'  # ensure determinism for the device-dependent torch.Generator
  		snake_case__  					=      self.get_dummy_components()
  		snake_case__  					=      EulerAncestralDiscreteScheduler(
  		    beta_start=0.0_0_0_8_5			,					beta_end=0.0_1_2			,					beta_schedule='scaled_linear'		)
  		snake_case__  					=      StableDiffusionModelEditingPipeline(**lowerCamelCase_		)
  		snake_case__  					=      sd_pipe.to(lowerCamelCase_		)
  		sd_pipe.set_progress_bar_config(disable=lowerCamelCase_		)
  
  		snake_case__  					=      self.get_dummy_inputs(lowerCamelCase_		)
  		snake_case__  					=      sd_pipe(**lowerCamelCase_		).images
  		snake_case__  					=      image[0, -3:, -3:, -1]
  
  		assert image.shape == (1, 64, 64, 3)
  
  		snake_case__  					=      np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7]		)
  
  		assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Tuple:
  		snake_case__  					=      'cpu'  # ensure determinism for the device-dependent torch.Generator
  		snake_case__  					=      self.get_dummy_components()
  		snake_case__  					=      PNDMScheduler()
  		snake_case__  					=      StableDiffusionModelEditingPipeline(**lowerCamelCase_		)
  		snake_case__  					=      sd_pipe.to(lowerCamelCase_		)
  		sd_pipe.set_progress_bar_config(disable=lowerCamelCase_		)
  
  		snake_case__  					=      self.get_dummy_inputs(lowerCamelCase_		)
  		# the pipeline does not expect pndm so test if it raises error.
  		with self.assertRaises(lowerCamelCase_		):
  			snake_case__  					=      sd_pipe(**lowerCamelCase_		).images
  
  
  
  
  
  	def      _snake_case      (		self		)			->		str:
  		super().test_inference_batch_single_identical(expected_max_diff=5E-3		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Dict:
  		super().test_attention_slicing_forward_pass(expected_max_diff=5E-3		)
  
  
  
  
  
  
  @slow
  @require_torch_gpu
  class 							__snake_case    (				unittest.TestCase       ):
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Optional[int]:
  		super().tearDown()
  		gc.collect()
  		torch.cuda.empty_cache()
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_=0		)			->		Any:
  		snake_case__  					=      torch.manual_seed(lowerCamelCase_		)
  		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		)			->		Union[str, Any]:
  		snake_case__  					=      'CompVis/stable-diffusion-v1-4'
  		snake_case__  					=      StableDiffusionModelEditingPipeline.from_pretrained(lowerCamelCase_			,					safety_checker=lowerCamelCase_		)
  		pipe.to(lowerCamelCase_		)
  		pipe.set_progress_bar_config(disable=lowerCamelCase_		)
  		pipe.enable_attention_slicing()
  
  		snake_case__  					=      self.get_inputs()
  		snake_case__  					=      pipe(**lowerCamelCase_		).images
  		snake_case__  					=      image[0, -3:, -3:, -1].flatten()
  
  		assert image.shape == (1, 512, 512, 3)
  
  		snake_case__  					=      np.array(
  		    [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8]		)
  
  		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'		)
  
  		snake_case__  					=      pipe(**lowerCamelCase_		).images
  		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		)			->		Dict:
  		torch.cuda.empty_cache()
  		torch.cuda.reset_max_memory_allocated()
  		torch.cuda.reset_peak_memory_stats()
  
  		snake_case__  					=      'CompVis/stable-diffusion-v1-4'
  		snake_case__  					=      DDIMScheduler.from_pretrained(lowerCamelCase_			,					subfolder='scheduler'		)
  		snake_case__  					=      StableDiffusionModelEditingPipeline.from_pretrained(
  		    lowerCamelCase_			,					scheduler=lowerCamelCase_			,					safety_checker=lowerCamelCase_		)
  		snake_case__  					=      pipe.to(lowerCamelCase_		)
  		pipe.set_progress_bar_config(disable=lowerCamelCase_		)
  		pipe.enable_attention_slicing(1		)
  		pipe.enable_sequential_cpu_offload()
  
  		snake_case__  					=      self.get_inputs()
  		snake_case__  					=      pipe(**lowerCamelCase_		)
  
  		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'''
  
  
  
  
  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)
  
  
  
  