code: 
  
  
  
  
  
  
  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			:							Dict       ={
      '''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			:							Optional[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 	SCREAMING_SNAKE_CASE							(      )				->			Union[str, Any]:
       UpperCamelCase__    :     Optional[int]        =						argparse.ArgumentParser(description="Run and time generations on a big model using Accelerate."    )
       parser.add_argument("model_name"      ,					type=__lowerCAmelCase      ,					default=__lowerCAmelCase      ,					help="The name of the model to try."    )
       parser.add_argument(
           "--tokenizer_name"      ,					type=__lowerCAmelCase      ,					default=__lowerCAmelCase      ,					help="The name of the tokenizer (if different from the model."    )
       parser.add_argument("--is_causal"      ,					type=__lowerCAmelCase      ,					default=__lowerCAmelCase      ,					help="Whether or not the model is causal."    )
       parser.add_argument(
           "--model_revision"      ,					type=__lowerCAmelCase      ,					default=__lowerCAmelCase      ,					help="The revision to use for the model checkpoint."    )
       parser.add_argument("--torch_dtype"      ,					type=__lowerCAmelCase      ,					default=__lowerCAmelCase      ,					help="The dtype for the model."    )
       parser.add_argument("--disk_offload"      ,					action="store_true"    )
  
       UpperCamelCase__    :     str        =						parser.parse_args()
  
       # Sanitize args
       if args.model_name in DEFAULT_MODELS:
            UpperCamelCase__    :     str        =						DEFAULT_MODELS[args.model_name]
            UpperCamelCase__    :     Optional[Any]        =						defaults["model"]
            if args.tokenizer_name is None:
                 UpperCamelCase__    :     Optional[Any]        =						defaults.get("tokenizer"      ,					args.model_name    )
            if args.is_causal is None:
                 UpperCamelCase__    :     Union[str, Any]        =						defaults["is_causal"]
            if args.model_revision is None:
                 UpperCamelCase__    :     str        =						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:
            UpperCamelCase__    :     Tuple        =						args.model_name
       if args.model_revision is None:
            UpperCamelCase__    :     Optional[int]        =						"main"
  
       return args
  
  
  
  
  
  
  
  def 	SCREAMING_SNAKE_CASE							(      )				->			Any:
       transformers.utils.logging.set_verbosity_error()
       UpperCamelCase__    :     Dict        =						parse_args()
  
       if args.torch_dtype is None:
            UpperCamelCase__    :     Union[str, Any]        =						AutoConfig.from_pretrained(args.model_name    )
            UpperCamelCase__    :     Optional[Any]        =						getattr(__lowerCAmelCase      ,					"torch_dtype"      ,					torch.floataa    )
       else:
            UpperCamelCase__    :     Optional[int]        =						getattr(__lowerCAmelCase      ,					args.torch_dtype    )
       UpperCamelCase__    :     List[Any]        =						AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
       UpperCamelCase__    :     int        =						{
           "torch_dtype": torch_dtype,
           "revision": args.model_revision,
       }
       if args.disk_offload:
            UpperCamelCase__    :     Dict        =						"tmp_offload"
            UpperCamelCase__    :     Tuple        =						True
  
       UpperCamelCase__    :     List[str]        =						start_measure()
       UpperCamelCase__    :     Union[str, Any]        =						model_cls.from_pretrained(args.model_name      ,					device_map="auto"      ,					**__lowerCAmelCase    )
       UpperCamelCase__    :     Any        =						end_measure(__lowerCAmelCase    )
       log_measures(__lowerCAmelCase      ,					"Model loading"    )
  
       UpperCamelCase__    :     Dict        =						compute_module_sizes(__lowerCAmelCase    )
       UpperCamelCase__    :     Optional[int]        =						{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]
       UpperCamelCase__    :     Optional[Any]        =						"\n".join([f'- {device}: {size // 2**20}MiB' for device, size in device_size.items()]    )
       print(f'\nTheoretical use:\n{message}'    )
  
       UpperCamelCase__    :     Optional[int]        =						AutoTokenizer.from_pretrained(args.tokenizer_name    )
  
       UpperCamelCase__    :     Any        =						start_measure()
       UpperCamelCase__    :     Any        =						[]
       UpperCamelCase__    :     Optional[int]        =						[]
       UpperCamelCase__    :     Optional[int]        =						[]
       for prompt in PROMPTS:
            UpperCamelCase__    :     int        =						tokenizer(__lowerCAmelCase      ,					return_tensors="pt"    ).to(0    )
            UpperCamelCase__    :     Union[str, Any]        =						inputs["input_ids"][0].tolist()
            UpperCamelCase__    :     List[Any]        =						time.time()
            UpperCamelCase__    :     List[Any]        =						model.generate(inputs["input_ids"]    )
            UpperCamelCase__    :     Any        =						time.time()
            UpperCamelCase__    :     Tuple        =						outputs[0].tolist()
            UpperCamelCase__    :     Optional[int]        =						len(__lowerCAmelCase    ) if outputs[: len(__lowerCAmelCase    )] != tokens else len(__lowerCAmelCase    ) - len(__lowerCAmelCase    )
            UpperCamelCase__    :     Any        =						after_generate - before_generate
  
            UpperCamelCase__    :     Optional[Any]        =						tokenizer.decode(__lowerCAmelCase      ,					skip_special_tokens=__lowerCAmelCase    )
            texts_outs.append(__lowerCAmelCase    )
            generation_times.append(__lowerCAmelCase    )
            gen_tokens.append(__lowerCAmelCase    )
            print(f'Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n'    )
  
       UpperCamelCase__    :     str        =						end_measure(__lowerCAmelCase    )
       log_measures(__lowerCAmelCase      ,					"Model generation"    )
  
       UpperCamelCase__    :     List[Any]        =						[gen / tok for gen, tok in zip(__lowerCAmelCase      ,					__lowerCAmelCase    )]
       UpperCamelCase__    :     List[Any]        =						sum(__lowerCAmelCase    ) / len(__lowerCAmelCase    )
       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'    )
       UpperCamelCase__    :     Dict        =						sum(generation_times_per_token[1:]    ) / (len(__lowerCAmelCase    ) - 1)
       print(f'Average time of generation per token (excluding the first): {avg_gen:.2f}s'    )
  
  
  if __name__ == "__main__":
   main()
style_context: 
  
  
  
  
  
  
  from __future__ import annotations
  
  from collections.abc import Iterator
  from typing import Any
  
  
  
  
  
  class     __a						:
  
  
  
  
  
  
  
   def __init__(	self      :     str							,     SCREAMING_SNAKE_CASE      :     Any		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Any        =						data
        UpperCamelCase__    :     Node | None        =						None
  
  
  
  
  
  
  
   def __str__(	self      :     Union[str, Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        return F'{self.data}'
  
  
  
  
  
  
  
  class     __a						:
  
  
  
  
  
  
  
   def __init__(	self      :     Optional[int]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Node | None        =						None
        UpperCamelCase__    :     Node | None        =						None
  
  
  
  
  
  
  
   def __iter__(	self      :     str		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     int        =						self.front
        while node:
             yield node.data
             UpperCamelCase__    :     Any        =						node.next
  
  
  
  
  
  
  
   def __len__(	self      :     Tuple		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        return len(tuple(iter(self		)		)		)
  
  
  
  
  
  
  
   def __str__(	self      :     Union[str, Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        return " <- ".join(str(SCREAMING_SNAKE_CASE		) for item in self		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     List[str]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        return len(self		) == 0
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Union[str, Any]							,     SCREAMING_SNAKE_CASE      :     Any		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     int        =						Node(SCREAMING_SNAKE_CASE		)
        if self.is_empty():
             UpperCamelCase__    :     int        =						node
        else:
             assert isinstance(self.rear							,     SCREAMING_SNAKE_CASE		)
             UpperCamelCase__    :     Dict        =						node
             UpperCamelCase__    :     str        =						node
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Dict		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        if self.is_empty():
             raise IndexError("dequeue from empty queue"		)
        assert isinstance(self.front							,     SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     Optional[int]        =						self.front
        UpperCamelCase__    :     List[str]        =						self.front.next
        if self.front is None:
             UpperCamelCase__    :     List[Any]        =						None
        return node.data
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     str		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Optional[int]        =						None
  
  
  if __name__ == "__main__":
   from doctest import testmod
  
   testmod()