'''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        UpperCAmelCase__   (				)    ->		Union[str, Any]:



						"""simple docstring"""

						snake_case :       str				=    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")

						snake_case :       Any				=    parser.parse_args()

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

						return args


def        UpperCAmelCase__   (				)    ->		List[Any]:



						"""simple docstring"""

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

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

						snake_case :       int				=    start_measure()
						snake_case :       Any				=    model_cls.from_pretrained(args.model_name,      device_map="auto",      **_lowerCAmelCase)
						snake_case :       Tuple				=    end_measure(_lowerCAmelCase)
						log_measures(_lowerCAmelCase,      "Model loading")

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

						snake_case :       str				=    AutoTokenizer.from_pretrained(args.tokenizer_name)

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

												snake_case :       Tuple				=    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""")

						snake_case :       str				=    end_measure(_lowerCAmelCase)
						log_measures(_lowerCAmelCase,      "Model generation")

						snake_case :       Any				=    [gen / tok for gen, tok in zip(_lowerCAmelCase,      _lowerCAmelCase)]
						snake_case :       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""")
						snake_case :       List[Any]				=    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()






'''simple docstring'''

def        UpperCAmelCase__   (				_lowerCAmelCase    :			int = 400_0000)    ->		int:



						"""simple docstring"""

						snake_case :       List[Any]				=    1
						snake_case :       int				=    2
						snake_case :       List[str]				=    0
						while j <= n:
												if j % 2 == 0:
																		total += j
												snake_case				,					snake_case :       Any				=    j, i + j

						return total


if __name__ == "__main__":
						print(f"{solution() = }")





