"""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      : 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      : Dict       =[
    '''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				(  )    ->     int:







     '''simple docstring'''


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

     __A					:					Tuple								=		parser.parse_args()

     # Sanitize args
     if args.model_name in DEFAULT_MODELS:
          __A					:					List[Any]								=		DEFAULT_MODELS[args.model_name]
          __A					:					Tuple								=		defaults['model']
          if args.tokenizer_name is None:
               __A					:					Tuple								=		defaults.get('tokenizer'  ,       args.model_name						)
          if args.is_causal is None:
               __A					:					Optional[Any]								=		defaults['is_causal']
          if args.model_revision is None:
               __A					:					Optional[Any]								=		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					:					Optional[Any]								=		args.model_name
     if args.model_revision is None:
          __A					:					Union[str, Any]								=		'main'

     return args




def 						_lowercase				(  )    ->     Optional[int]:







     '''simple docstring'''


     transformers.utils.logging.set_verbosity_error()
     __A					:					Tuple								=		parse_args()

     if args.torch_dtype is None:
          __A					:					List[Any]								=		AutoConfig.from_pretrained(args.model_name						)
          __A					:					Dict								=		getattr(_SCREAMING_SNAKE_CASE  ,       'torch_dtype'  ,       torch.floataa						)
     else:
          __A					:					Dict								=		getattr(_SCREAMING_SNAKE_CASE  ,       args.torch_dtype						)
     __A					:					List[Any]								=		AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
     __A					:					Optional[int]								=		{
         'torch_dtype': torch_dtype,
         'revision': args.model_revision,
     }
     if args.disk_offload:
          __A					:					List[str]								=		'tmp_offload'
          __A					:					Dict								=		True

     __A					:					Any								=		start_measure()
     __A					:					int								=		model_cls.from_pretrained(args.model_name  ,       device_map='auto'  ,       **_SCREAMING_SNAKE_CASE						)
     __A					:					Optional[Any]								=		end_measure(_SCREAMING_SNAKE_CASE						)
     log_measures(_SCREAMING_SNAKE_CASE  ,       'Model loading'						)

     __A					:					str								=		compute_module_sizes(_SCREAMING_SNAKE_CASE						)
     __A					:					Optional[Any]								=		{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					:					Tuple								=		'\n'.join([F'- {device}: {size // 2**20}MiB' for device, size in device_size.items()]						)
     print(F'\nTheoretical use:\n{message}'						)

     __A					:					List[str]								=		AutoTokenizer.from_pretrained(args.tokenizer_name						)

     __A					:					str								=		start_measure()
     __A					:					Union[str, Any]								=		[]
     __A					:					Any								=		[]
     __A					:					Optional[int]								=		[]
     for prompt in PROMPTS:
          __A					:					Optional[Any]								=		tokenizer(_SCREAMING_SNAKE_CASE  ,       return_tensors='pt'						).to(0						)
          __A					:					Union[str, Any]								=		inputs['input_ids'][0].tolist()
          __A					:					Optional[Any]								=		time.time()
          __A					:					int								=		model.generate(inputs['input_ids']						)
          __A					:					List[str]								=		time.time()
          __A					:					str								=		outputs[0].tolist()
          __A					:					int								=		len(_SCREAMING_SNAKE_CASE						) if outputs[: len(_SCREAMING_SNAKE_CASE						)] != tokens else len(_SCREAMING_SNAKE_CASE						) - len(_SCREAMING_SNAKE_CASE						)
          __A					:					Optional[int]								=		after_generate - before_generate

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

     __A					:					int								=		end_measure(_SCREAMING_SNAKE_CASE						)
     log_measures(_SCREAMING_SNAKE_CASE  ,       'Model generation'						)

     __A					:					Union[str, Any]								=		[gen / tok for gen, tok in zip(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE						)]
     __A					:					List[Any]								=		sum(_SCREAMING_SNAKE_CASE						) / len(_SCREAMING_SNAKE_CASE						)
     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					:					List[Any]								=		sum(generation_times_per_token[1:]						) / (len(_SCREAMING_SNAKE_CASE						) - 1)
     print(F'Average time of generation per token (excluding the first): {avg_gen:.2f}s'						)


if __name__ == "__main__":
       main()




"""simple docstring"""







def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				list[int]						)    ->     tuple[int, int]:







     '''simple docstring'''


     if len(_SCREAMING_SNAKE_CASE						) == 1:
          return a[0], a[0]
     else:
          # split A into half.
          __A					:					List[str]								=		a[: len(_SCREAMING_SNAKE_CASE						) // 2]
          __A					:					Optional[Any]								=		a[len(_SCREAMING_SNAKE_CASE						) // 2 :]

          # 2 sub problems, 1/2 of original size.
          __A						,		__A					:					int								=		max_difference(_SCREAMING_SNAKE_CASE						)
          __A						,		__A					:					str								=		max_difference(_SCREAMING_SNAKE_CASE						)

          # get min of first and max of second
          # linear time
          __A					:					str								=		min(_SCREAMING_SNAKE_CASE						)
          __A					:					Optional[int]								=		max(_SCREAMING_SNAKE_CASE						)

          # 3 cases, either (small1, big1),
          # (min_first, max_second), (small2, big2)
          # constant comparisons
          if biga - smalla > max_second - min_first and biga - smalla > biga - smalla:
               return smalla, biga
          elif biga - smalla > max_second - min_first:
               return smalla, biga
          else:
               return min_first, max_second


if __name__ == "__main__":
       import doctest

       doctest.testmod()



