"""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 			lowerCamelCase_ (					)							->					int:




						'''simple docstring'''



						lowerCamelCase__					=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"				)

						lowerCamelCase__					=parser.parse_args()

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

						return args





def 			lowerCamelCase_ (					)							->					Union[str, Any]:




						'''simple docstring'''



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

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

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

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

						lowerCamelCase__					=AutoTokenizer.from_pretrained(args.tokenizer_name				)

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

												lowerCamelCase__					=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'''				)

						lowerCamelCase__					=end_measure(__lowerCAmelCase				)
						log_measures(__lowerCAmelCase   ,    "Model generation"				)

						lowerCamelCase__					=[gen / tok for gen, tok in zip(__lowerCAmelCase   ,    __lowerCAmelCase				)]
						lowerCamelCase__					=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'''				)
						lowerCamelCase__					=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"""


from __future__ import annotations





def 			lowerCamelCase_ (					__lowerCAmelCase				)							->					int | float:




						'''simple docstring'''



						if len(__lowerCAmelCase				) == 0:
												raise ValueError("find_max() arg is an empty sequence"				)
						lowerCamelCase__					=nums[0]
						for x in nums:
												if x > max_num:
																		lowerCamelCase__					=x
						return max_num


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)


