code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import argparse
  import time
  
  import torch
  import transformers
  from measures_util import end_measure, log_measures, start_measure
  from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForSeqaSeqLM, AutoTokenizer
  
  from accelerate.utils import compute_module_sizes
  
  
  _lowerCamelCase							:		Union[str, Any]										=						{
      'gpt-j-6b': {'is_causal': True, 'model': 'sgugger/sharded-gpt-j-6B', 'tokenizer': 'EleutherAI/gpt-j-6B'},
      'gpt-neox': {'is_causal': True, 'model': 'EleutherAI/gpt-neox-20b'},
      'opt': {'is_causal': True, 'model': 'facebook/opt-30b'},
      'T0pp': {'is_causal': False, 'model': 'bigscience/T0pp', 'model_revision': 'sharded'},
  }
  
  _lowerCamelCase							:		Union[str, Any]										=						[
      'Hello, my name is',
      'Are unicorns real? Unicorns are',
      'For the first time in several years,',
      'My name is Julien and I am',
      'The goal of life is',
      'Whenever I\'m sad, I like to',
  ]
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = argparse.ArgumentParser(description="Run and time generations on a big model using Accelerate."						)
  		parser.add_argument("model_name"		,							type=lowercase_		,							default=lowercase_		,							help="The name of the model to try."						)
  		parser.add_argument(
  		    "--tokenizer_name"		,							type=lowercase_		,							default=lowercase_		,							help="The name of the tokenizer (if different from the model."						)
  		parser.add_argument("--is_causal"		,							type=lowercase_		,							default=lowercase_		,							help="Whether or not the model is causal."						)
  		parser.add_argument(
  		    "--model_revision"		,							type=lowercase_		,							default=lowercase_		,							help="The revision to use for the model checkpoint."						)
  		parser.add_argument("--torch_dtype"		,							type=lowercase_		,							default=lowercase_		,							help="The dtype for the model."						)
  		parser.add_argument("--disk_offload"		,							action="store_true"						)
  
  		_snake_case			 = parser.parse_args()
  
  		# Sanitize args
  		if args.model_name in DEFAULT_MODELS:
  				_snake_case			 = DEFAULT_MODELS[args.model_name]
  				_snake_case			 = defaults["model"]
  				if args.tokenizer_name is None:
  						_snake_case			 = defaults.get("tokenizer"		,							args.model_name						)
  				if args.is_causal is None:
  						_snake_case			 = defaults["is_causal"]
  				if args.model_revision is None:
  						_snake_case			 = defaults.get("model_revision"		,							"main"						)
  
  		if args.is_causal is None:
  				raise ValueError("Could not infer the default for `--is_causal`, pass either True or False for it."						)
  		if args.tokenizer_name is None:
  				_snake_case			 = args.model_name
  		if args.model_revision is None:
  				_snake_case			 = "main"
  
  		return args
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		transformers.utils.logging.set_verbosity_error()
  		_snake_case			 = parse_args()
  
  		if args.torch_dtype is None:
  				_snake_case			 = AutoConfig.from_pretrained(args.model_name						)
  				_snake_case			 = getattr(lowercase_		,							"torch_dtype"		,							torch.floataa						)
  		else:
  				_snake_case			 = getattr(lowercase_		,							args.torch_dtype						)
  		_snake_case			 = AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
  		_snake_case			 = {
  		    "torch_dtype": torch_dtype,
  		    "revision": args.model_revision,
  		}
  		if args.disk_offload:
  				_snake_case			 = "tmp_offload"
  				_snake_case			 = True
  
  		_snake_case			 = start_measure()
  		_snake_case			 = model_cls.from_pretrained(args.model_name		,							device_map="auto"		,							**lowercase_						)
  		_snake_case			 = end_measure(lowercase_						)
  		log_measures(lowercase_		,							"Model loading"						)
  
  		_snake_case			 = compute_module_sizes(lowercase_						)
  		_snake_case			 = {v: 0 for v in model.hf_device_map.values()}
  		for module, device in model.hf_device_map.items():
  				device_size[device] += module_sizes[module]
  		_snake_case			 = "\n".join([f'''- {device}: {size // 2**20}MiB''' for device, size in device_size.items()]						)
  		print(f'''\nTheoretical use:\n{message}'''						)
  
  		_snake_case			 = AutoTokenizer.from_pretrained(args.tokenizer_name						)
  
  		_snake_case			 = start_measure()
  		_snake_case			 = []
  		_snake_case			 = []
  		_snake_case			 = []
  		for prompt in PROMPTS:
  				_snake_case			 = tokenizer(lowercase_		,							return_tensors="pt"						).to(0						)
  				_snake_case			 = inputs["input_ids"][0].tolist()
  				_snake_case			 = time.time()
  				_snake_case			 = model.generate(inputs["input_ids"]						)
  				_snake_case			 = time.time()
  				_snake_case			 = outputs[0].tolist()
  				_snake_case			 = len(lowercase_						) if outputs[: len(lowercase_						)] != tokens else len(lowercase_						) - len(lowercase_						)
  				_snake_case			 = after_generate - before_generate
  
  				_snake_case			 = tokenizer.decode(lowercase_		,							skip_special_tokens=lowercase_						)
  				texts_outs.append(lowercase_						)
  				generation_times.append(lowercase_						)
  				gen_tokens.append(lowercase_						)
  				print(f'''Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n'''						)
  
  		_snake_case			 = end_measure(lowercase_						)
  		log_measures(lowercase_		,							"Model generation"						)
  
  		_snake_case			 = [gen / tok for gen, tok in zip(lowercase_		,							lowercase_						)]
  		_snake_case			 = sum(lowercase_						) / len(lowercase_						)
  		print(f'''Average time of generation per token: {avg_gen:.2f}s'''						)
  		print(f'''First generation (avg time per token): {generation_times_per_token[0]:.2f}s'''						)
  		_snake_case			 = sum(generation_times_per_token[1:]						) / (len(lowercase_						) - 1)
  		print(f'''Average time of generation per token (excluding the first): {avg_gen:.2f}s'''						)
  
  
  if __name__ == "__main__":
  						main()
  
  
  
  
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import warnings
  
  from ...utils import logging
  from .image_processing_vilt import ViltImageProcessor
  
  
  _lowerCamelCase							:		Optional[Any]										=						logging.get_logger(__name__)
  
  
  
  
  
  class   lowerCamelCase_							( UpperCAmelCase_							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def __init__(					self,						*__UpperCamelCase,						**__UpperCamelCase				):
  							warnings.warn(
  							    "The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
  							    " use ViltImageProcessor instead.",						__UpperCamelCase,						)
  							super().__init__(*__UpperCamelCase,						**__UpperCamelCase				)
  
  
  
  
  
  