





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__			:    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'},
}

a__			:    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     UpperCAmelCase_						(						)  ->							Optional[int]:
     '''simple docstring'''


     A_					      =					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'''							)

     A_					      =					parser.parse_args()

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

     return args

def     UpperCAmelCase_						(						)  ->							List[str]:
     '''simple docstring'''


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

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

     A_					      =					start_measure()
     A_					      =					model_cls.from_pretrained(args.model_name     ,	device_map='''auto'''     ,	**_UpperCAmelCase							)
     A_					      =					end_measure(_UpperCAmelCase							)
     log_measures(_UpperCAmelCase     ,	'''Model loading'''							)

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

     A_					      =					AutoTokenizer.from_pretrained(args.tokenizer_name							)

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

          A_					      =					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'							)

     A_					      =					end_measure(_UpperCAmelCase							)
     log_measures(_UpperCAmelCase     ,	'''Model generation'''							)

     A_					      =					[gen / tok for gen, tok in zip(_UpperCAmelCase     ,	_UpperCAmelCase							)]
     A_					      =					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'							)
     A_					      =					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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a__			:    Tuple			      =		{'configuration_yoso': ['YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YosoConfig']}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__			:    Optional[int]			      =		[
          'YOSO_PRETRAINED_MODEL_ARCHIVE_LIST',
          'YosoForMaskedLM',
          'YosoForMultipleChoice',
          'YosoForQuestionAnswering',
          'YosoForSequenceClassification',
          'YosoForTokenClassification',
          'YosoLayer',
          'YosoModel',
          'YosoPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_yoso import (
                YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
                YosoForMaskedLM,
                YosoForMultipleChoice,
                YosoForQuestionAnswering,
                YosoForSequenceClassification,
                YosoForTokenClassification,
                YosoLayer,
                YosoModel,
                YosoPreTrainedModel,
            )


else:
      import sys

      a__			:    Union[str, Any]			      =		_LazyModule(__name__, globals()['__file__'], _import_structure)



