

"""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


_a				   =							{
    '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'},
}

_a				   =							[
    '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 						_A						(						)			->		Tuple:
   '''simple docstring'''


   __lowercase			=		argparse.ArgumentParser(description="Run and time generations on a big model using Accelerate.")
   parser.add_argument("model_name",    type=UpperCamelCase_,    default=UpperCamelCase_,    help="The name of the model to try.")
   parser.add_argument(
       "--tokenizer_name",    type=UpperCamelCase_,    default=UpperCamelCase_,    help="The name of the tokenizer (if different from the model.")
   parser.add_argument("--is_causal",    type=UpperCamelCase_,    default=UpperCamelCase_,    help="Whether or not the model is causal.")
   parser.add_argument(
       "--model_revision",    type=UpperCamelCase_,    default=UpperCamelCase_,    help="The revision to use for the model checkpoint.")
   parser.add_argument("--torch_dtype",    type=UpperCamelCase_,    default=UpperCamelCase_,    help="The dtype for the model.")
   parser.add_argument("--disk_offload",    action="store_true")

   __lowercase			=		parser.parse_args()

   # Sanitize args
   if args.model_name in DEFAULT_MODELS:
      __lowercase			=		DEFAULT_MODELS[args.model_name]
      __lowercase			=		defaults["model"]
      if args.tokenizer_name is None:
         __lowercase			=		defaults.get("tokenizer",    args.model_name)
      if args.is_causal is None:
         __lowercase			=		defaults["is_causal"]
      if args.model_revision is None:
         __lowercase			=		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:
      __lowercase			=		args.model_name
   if args.model_revision is None:
      __lowercase			=		"main"

   return args




def 						_A						(						)			->		Tuple:
   '''simple docstring'''


   transformers.utils.logging.set_verbosity_error()
   __lowercase			=		parse_args()

   if args.torch_dtype is None:
      __lowercase			=		AutoConfig.from_pretrained(args.model_name)
      __lowercase			=		getattr(UpperCamelCase_,    "torch_dtype",    torch.floataa)
   else:
      __lowercase			=		getattr(UpperCamelCase_,    args.torch_dtype)
   __lowercase			=		AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
   __lowercase			=		{
       "torch_dtype": torch_dtype,
       "revision": args.model_revision,
   }
   if args.disk_offload:
      __lowercase			=		"tmp_offload"
      __lowercase			=		True

   __lowercase			=		start_measure()
   __lowercase			=		model_cls.from_pretrained(args.model_name,    device_map="auto",    **UpperCamelCase_)
   __lowercase			=		end_measure(UpperCamelCase_)
   log_measures(UpperCamelCase_,    "Model loading")

   __lowercase			=		compute_module_sizes(UpperCamelCase_)
   __lowercase			=		{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]
   __lowercase			=		"\n".join([F"""- {device}: {size // 2**20}MiB""" for device, size in device_size.items()])
   print(F"""\nTheoretical use:\n{message}""")

   __lowercase			=		AutoTokenizer.from_pretrained(args.tokenizer_name)

   __lowercase			=		start_measure()
   __lowercase			=		[]
   __lowercase			=		[]
   __lowercase			=		[]
   for prompt in PROMPTS:
      __lowercase			=		tokenizer(UpperCamelCase_,    return_tensors="pt").to(0)
      __lowercase			=		inputs["input_ids"][0].tolist()
      __lowercase			=		time.time()
      __lowercase			=		model.generate(inputs["input_ids"])
      __lowercase			=		time.time()
      __lowercase			=		outputs[0].tolist()
      __lowercase			=		len(UpperCamelCase_) if outputs[: len(UpperCamelCase_)] != tokens else len(UpperCamelCase_) - len(UpperCamelCase_)
      __lowercase			=		after_generate - before_generate

      __lowercase			=		tokenizer.decode(UpperCamelCase_,    skip_special_tokens=UpperCamelCase_)
      texts_outs.append(UpperCamelCase_)
      generation_times.append(UpperCamelCase_)
      gen_tokens.append(UpperCamelCase_)
      print(F"""Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n""")

   __lowercase			=		end_measure(UpperCamelCase_)
   log_measures(UpperCamelCase_,    "Model generation")

   __lowercase			=		[gen / tok for gen, tok in zip(UpperCamelCase_,    UpperCamelCase_)]
   __lowercase			=		sum(UpperCamelCase_) / len(UpperCamelCase_)
   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""")
   __lowercase			=		sum(generation_times_per_token[1:]) / (len(UpperCamelCase_) - 1)
   print(F"""Average time of generation per token (excluding the first): {avg_gen:.2f}s""")


if __name__ == "__main__":
   main()







"""simple docstring"""





def 						_A						(						UpperCamelCase_  : list,    UpperCamelCase_  : list,    UpperCamelCase_  : list,    UpperCamelCase_  : int,    UpperCamelCase_  : int)			->		None:
   '''simple docstring'''


   if target == 0:
      answer.append(path.copy())
   else:
      for index in range(UpperCamelCase_,    len(UpperCamelCase_)):
         if target >= candidates[index]:
            path.append(candidates[index])
            backtrack(UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_,    target - candidates[index],    UpperCamelCase_)
            path.pop(len(UpperCamelCase_) - 1)




def 						_A						(						UpperCamelCase_  : list,    UpperCamelCase_  : int)			->		list:
   '''simple docstring'''


   __lowercase			=		[]  # type: list[int]
   __lowercase			=		[]  # type: list[int]
   backtrack(UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_,    0)
   return answer




def 						_A						(						)			->		None:
   '''simple docstring'''


   print(combination_sum([-8, 2.3, 0],    1))


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()




