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     :					List[str]				  =						{
    '''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     :					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    (   )				->   Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= 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"""      )

  SCREAMING_SNAKE_CASE__     				= parser.parse_args()

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

  return args



def       __SCREAMING_SNAKE_CASE    (   )				->   List[Any]:







  """simple docstring"""





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

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

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

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

  SCREAMING_SNAKE_CASE__     				= AutoTokenizer.from_pretrained(args.tokenizer_name      )

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

    SCREAMING_SNAKE_CASE__     				= 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"""      )

  SCREAMING_SNAKE_CASE__     				= end_measure(__UpperCamelCase      )
  log_measures(__UpperCamelCase					,	"""Model generation"""      )

  SCREAMING_SNAKE_CASE__     				= [gen / tok for gen, tok in zip(__UpperCamelCase					,	__UpperCamelCase      )]
  SCREAMING_SNAKE_CASE__     				= 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"""      )
  SCREAMING_SNAKE_CASE__     				= 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()

from hashlib import mda, shaaaa

__lowerCamelCase     :					Dict				  =						(shaaaa, mda)







class __snake_case							:




   def __init__(       self     :							str			,      _lowercase     :							int = 8 ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= 0B0
     SCREAMING_SNAKE_CASE__     				= size




   def       __a		(       self     :							List[str]			,      _lowercase     :							str ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.hash_(_lowercase )
     self.bitarray |= h




   def       __a		(       self     :							int			,      _lowercase     :							str ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.hash_(_lowercase )
     return (h & self.bitarray) == h




   def __contains__(       self     :							Union[str, Any]			,      _lowercase     :							str ):



     """simple docstring"""






     return self.exists(_lowercase )




   def       __a		(       self     :							Dict			,      _lowercase     :							int ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= bin(_lowercase )[2:]
     return res.zfill(self.size )




   @property
   def       __a		(       self     :							Tuple ):



     """simple docstring"""






     return self.format_bin(self.bitarray )




   def       __a		(       self     :							Optional[int]			,      _lowercase     :							str ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= 0B0
     for func in HASH_FUNCTIONS:
       SCREAMING_SNAKE_CASE__     				= (
           int.from_bytes(func(value.encode() ).digest()			,      """little""" ) % self.size
       )
       res |= 2**position
     return res




   def       __a		(       self     :							str			,      _lowercase     :							str ):



     """simple docstring"""






     return self.format_bin(self.hash_(_lowercase ) )




   @property
   def       __a		(       self     :							Union[str, Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= bin(self.bitarray ).count("""1""" )
     return (n_ones / self.size) ** len(_lowercase )
