






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()







from __future__ import annotations







def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			list[int]:
     if not list_of_ints:
          return []

     if min(__lowerCAmelCase    ) < 0:
          raise ValueError("All numbers must be positive"    )

     UpperCamelCase__    :     Dict        =						max(len(bin(__lowerCAmelCase    )[2:]    ) for x in list_of_ints    )
     return _msd_radix_sort(__lowerCAmelCase      ,					__lowerCAmelCase    )







def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase    )				->			list[int]:
     if bit_position == 0 or len(__lowerCAmelCase    ) in [0, 1]:
          return list_of_ints

     UpperCamelCase__    :     Union[str, Any]        =						[]
     UpperCamelCase__    :     Dict        =						[]
     # Split numbers based on bit at bit_position from the right
     for number in list_of_ints:
          if (number >> (bit_position - 1)) & 1:
               # number has a one at bit bit_position
               ones.append(__lowerCAmelCase    )
          else:
               # number has a zero at bit bit_position
               zeros.append(__lowerCAmelCase    )

    # recursively split both lists further
     UpperCamelCase__    :     str        =						_msd_radix_sort(__lowerCAmelCase      ,					bit_position - 1    )
     UpperCamelCase__    :     Optional[Any]        =						_msd_radix_sort(__lowerCAmelCase      ,					bit_position - 1    )

     # recombine lists
     UpperCamelCase__    :     Optional[int]        =						zeros
     res.extend(__lowerCAmelCase    )

     return res







def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			Dict:
     UpperCamelCase__    :     Optional[int]        =						len(__lowerCAmelCase    )
     if not list_of_ints or length == 1:
          return

     if min(__lowerCAmelCase    ) < 0:
          raise ValueError("All numbers must be positive"    )

     UpperCamelCase__    :     int        =						max(len(bin(__lowerCAmelCase    )[2:]    ) for x in list_of_ints    )
     _msd_radix_sort_inplace(__lowerCAmelCase      ,					__lowerCAmelCase      ,					0      ,					__lowerCAmelCase    )







def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase    )				->			List[str]:
     if bit_position == 0 or end_index - begin_index <= 1:
          return

     bit_position -= 1

     UpperCamelCase__    :     Any        =						begin_index
     UpperCamelCase__    :     List[str]        =						end_index - 1
     while i <= j:
          UpperCamelCase__    :     Tuple        =						False
          if not (list_of_ints[i] >> bit_position) & 1:
               # found zero at the beginning
               i += 1
               UpperCamelCase__    :     Any        =						True
          if (list_of_ints[j] >> bit_position) & 1:
               # found one at the end
               j -= 1
               UpperCamelCase__    :     Dict        =						True

          if changed:
               continue

          UpperCamelCase__       ,  UpperCamelCase__    :     Any        =						list_of_ints[j], list_of_ints[i]
          j -= 1
          if j != i:
               i += 1

     _msd_radix_sort_inplace(__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase    )
     _msd_radix_sort_inplace(__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase    )


if __name__ == "__main__":
 import doctest

 doctest.testmod()