

"""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	:       List[str]        =						{
    '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	:       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   __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					A     =   argparse.ArgumentParser(description="Run and time generations on a big model using Accelerate." )
					parser.add_argument("model_name"     ,				type=lowercase__     ,				default=lowercase__     ,				help="The name of the model to try." )
					parser.add_argument(
					    "--tokenizer_name"     ,				type=lowercase__     ,				default=lowercase__     ,				help="The name of the tokenizer (if different from the model." )
					parser.add_argument("--is_causal"     ,				type=lowercase__     ,				default=lowercase__     ,				help="Whether or not the model is causal." )
					parser.add_argument(
					    "--model_revision"     ,				type=lowercase__     ,				default=lowercase__     ,				help="The revision to use for the model checkpoint." )
					parser.add_argument("--torch_dtype"     ,				type=lowercase__     ,				default=lowercase__     ,				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   __SCREAMING_SNAKE_CASE		(		):
					"""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(lowercase__     ,				"torch_dtype"     ,				torch.floataa )
					else:
										A     =   getattr(lowercase__     ,				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"     ,				**lowercase__ )
					A     =   end_measure(lowercase__ )
					log_measures(lowercase__     ,				"Model loading" )

					A     =   compute_module_sizes(lowercase__ )
					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(lowercase__     ,				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(lowercase__ ) if outputs[: len(lowercase__ )] != tokens else len(lowercase__ ) - len(lowercase__ )
										A     =   after_generate - before_generate

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

					A     =   end_measure(lowercase__ )
					log_measures(lowercase__     ,				"Model generation" )

					A     =   [gen / tok for gen, tok in zip(lowercase__     ,				lowercase__ )]
					A     =   sum(lowercase__ ) / len(lowercase__ )
					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(lowercase__ ) - 1)
					print(F"""Average time of generation per token (excluding the first): {avg_gen:.2f}s""" )


if __name__ == "__main__":
	main()








"""simple docstring"""




__A	:       dict[str, float]        =						{
    "kilogram": 1,
    "gram": pow(10, 3),
    "milligram": pow(10, 6),
    "metric-ton": pow(10, -3),
    "long-ton": 0.00_09_84_20_73,
    "short-ton": 0.00_11_02_31_22,
    "pound": 2.20_46_24_42_02,
    "stone": 0.15_74_73_17_28,
    "ounce": 35.2_73_99_07_23,
    "carrat": 50_00,
    "atomic-mass-unit": 6.0_2213_6652E26,
}

__A	:       dict[str, float]        =						{
    "kilogram": 1,
    "gram": pow(10, -3),
    "milligram": pow(10, -6),
    "metric-ton": pow(10, 3),
    "long-ton": 10_16.0_46_08,
    "short-ton": 9_07.1_84,
    "pound": 0.45_35_92,
    "stone": 6.3_50_29,
    "ounce": 0.0_28_34_95,
    "carrat": 0.00_02,
    "atomic-mass-unit": 1.6_6054_0199E-27,
}
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
										A     =   (
										    F"""Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
										    F"""Supported values are: {", ".join(lowercase__ )}"""
										)
										raise ValueError(lowercase__ )
					return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
	import doctest

	doctest.testmod()





