





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


UpperCamelCase__          ={
    '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'},
}

UpperCamelCase__          =[
    '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__	():
     _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							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"		)

     _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							parser.parse_args()

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

     return args






def 			lowerCamelCase__	():
     transformers.utils.logging.set_verbosity_error()
     _SCREAMING_SNAKE_CASE				:		int  			=							parse_args()

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

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

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

     _SCREAMING_SNAKE_CASE				:		Any  			=							AutoTokenizer.from_pretrained(args.tokenizer_name		)

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

          _SCREAMING_SNAKE_CASE				:		str  			=							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"""		)

     _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							end_measure(__lowerCamelCase		)
     log_measures(__lowerCamelCase,				"Model generation"		)

     _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							[gen / tok for gen, tok in zip(__lowerCamelCase,				__lowerCamelCase		)]
     _SCREAMING_SNAKE_CASE				:		Dict  			=							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"""		)
     _SCREAMING_SNAKE_CASE				:		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'''







from __future__ import annotations

_lowerCamelCase							:						Union[str, Any]								=  {1: 1}






def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







		if n in COLLATZ_SEQUENCE_LENGTHS:
				return COLLATZ_SEQUENCE_LENGTHS[n]
		_snake_case	       =       n // 2 if n % 2 == 0 else 3 * n + 1
		_snake_case	       =       collatz_sequence_length(lowercase_  ) + 1
		_snake_case	       =       sequence_length
		return sequence_length






def 		_lowercase		(	lowercase_ = 1000000  ):



		'''simple docstring'''







		_snake_case	       =       max((collatz_sequence_length(lowercase_  ), i) for i in range(1						,							lowercase_  )  )
		return result[1]


if __name__ == "__main__":
						print(solution(int(input().strip())))





