code: 
  
  
  
  """simple docstring"""
  
  
  
  
  # limitations under the License.
  
  
  from typing import Optional, Tuple, Union
  
  import torch
  
  from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  
  
  
  
  
  
  class 			UpperCAmelCase_						(       _UpperCamelCase		):
  
  
  
        def __init__(  self  :			List[Any]  ,			A  :			Any  ,			A  :			Any		):
             super().__init__()
             self.register_modules(unet=A  ,			scheduler=A		)
  
  
  
        @torch.no_grad()
        def __call__(  self  :			str  ,			A  :			int = 1  ,			A  :			Optional[torch.Generator] = None  ,			A  :			int = 5_0  ,			A  :			Optional[str] = "pil"  ,			A  :			bool = True  ,			**A  :			Dict  ,			):
             _UpperCAmelCase    :			Any              =					torch.randn(
                 (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)  ,			generator=A  ,			)
             _UpperCAmelCase    :			str              =					image.to(self.device		)
  
             # set step values
             self.scheduler.set_timesteps(A		)
  
             for t in self.progress_bar(self.scheduler.timesteps		):
                  # 1. predict noise model_output
                  _UpperCAmelCase    :			Dict              =					self.unet(A  ,			A		).sample
  
                  # 2. predict previous mean of image x_t-1 and add variance depending on eta
                  # eta corresponds to η in paper and should be between [0, 1]
                  # do x_t -> x_t-1
                  _UpperCAmelCase    :			Any              =					self.scheduler.step(A  ,			A  ,			A		).prev_sample
  
             _UpperCAmelCase    :			Optional[int]              =					(image / 2 + 0.5).clamp(0  ,			1		)
             _UpperCAmelCase    :			Tuple              =					image.cpu().permute(0  ,			2  ,			3  ,			1		).numpy()
             if output_type == "pil":
                  _UpperCAmelCase    :			Union[str, Any]              =					self.numpy_to_pil(A		)
  
             if not return_dict:
                  return (image,), "This is a local test"
  
             return ImagePipelineOutput(images=A		), "This is a local test"
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  from arguments import TokenizerTrainingArguments
  from datasets import load_dataset
  from tqdm import tqdm
  
  from transformers import AutoTokenizer, HfArgumentParser
  from transformers.models.gpta.tokenization_gpta import bytes_to_unicode
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		str=10       )						->     List[Any]:
  
  
  
  
  
       '''simple docstring'''
  
       for _ in tqdm(range(0		,						args.n_examples		,						SCREAMING_SNAKE_CASE__       )       ):
            yield [next(SCREAMING_SNAKE_CASE__       )[args.text_column] for _ in range(SCREAMING_SNAKE_CASE__       )]
  
  
  # Configuration
  _lowerCAmelCase       :      Optional[Any]							=							HfArgumentParser(TokenizerTrainingArguments)
  _lowerCAmelCase       :      Dict							=							parser.parse_args()
  
  # Base tokenizer
  _lowerCAmelCase       :      int							=							AutoTokenizer.from_pretrained(args.base_tokenizer)
  _lowerCAmelCase       :      List[str]							=							list(bytes_to_unicode().values())
  
  # Load dataset
  _lowerCAmelCase       :      Dict							=							load_dataset(args.dataset_name, split="train", streaming=True)
  _lowerCAmelCase       :      List[Any]							=							iter(dataset)
  
  
  # Training and saving
  _lowerCAmelCase       :      List[str]							=							tokenizer.train_new_from_iterator(
      batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
  )
  new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
  