






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 						a_      ()							->	int:



					"""simple docstring"""
					__a				:	Any			=				argparse.ArgumentParser(description="Run and time generations on a big model using Accelerate."			)
					parser.add_argument("model_name"							,					type=__A							,					default=__A							,					help="The name of the model to try."			)
					parser.add_argument(
					    "--tokenizer_name"							,					type=__A							,					default=__A							,					help="The name of the tokenizer (if different from the model."			)
					parser.add_argument("--is_causal"							,					type=__A							,					default=__A							,					help="Whether or not the model is causal."			)
					parser.add_argument(
					    "--model_revision"							,					type=__A							,					default=__A							,					help="The revision to use for the model checkpoint."			)
					parser.add_argument("--torch_dtype"							,					type=__A							,					default=__A							,					help="The dtype for the model."			)
					parser.add_argument("--disk_offload"							,					action="store_true"			)

					__a				:	str			=				parser.parse_args()

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

					return args







def 						a_      ()							->	Tuple:



					"""simple docstring"""
					transformers.utils.logging.set_verbosity_error()
					__a				:	Dict			=				parse_args()

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

					__a				:	str			=				start_measure()
					__a				:	Tuple			=				model_cls.from_pretrained(args.model_name							,					device_map="auto"							,					**__A			)
					__a				:	int			=				end_measure(__A			)
					log_measures(__A							,					"Model loading"			)

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

					__a				:	Optional[Any]			=				AutoTokenizer.from_pretrained(args.tokenizer_name			)

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

										__a				:	str			=				tokenizer.decode(__A							,					skip_special_tokens=__A			)
										texts_outs.append(__A			)
										generation_times.append(__A			)
										gen_tokens.append(__A			)
										print(f'Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n'			)

					__a				:	Dict			=				end_measure(__A			)
					log_measures(__A							,					"Model generation"			)

					__a				:	int			=				[gen / tok for gen, tok in zip(__A							,					__A			)]
					__a				:	Tuple			=				sum(__A			) / len(__A			)
					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				:	Optional[int]			=				sum(generation_times_per_token[1:]			) / (len(__A			) - 1)
					print(f'Average time of generation per token (excluding the first): {avg_gen:.2f}s'			)


if __name__ == "__main__":
			main()













def 						a_      (__A							,					__A			)							->	list[tuple[int, int, int]]:



					"""simple docstring"""
					__a				:	str			=				sorted(__A							,					key=lambda __A			: edge[2]			)

					__a				:	int			=				list(range(__A			)			)

					def find_parent(__A			):
										if i != parent[i]:
															__a				:	Optional[Any]			=				find_parent(parent[i]			)
										return parent[i]

					__a				:	Any			=				0
					__a				:	Optional[int]			=				[]

					for edge in edges:
										__a				:	Any			=				find_parent(edge[0]			)
										__a				:	Optional[Any]			=				find_parent(edge[1]			)
										if parent_a != parent_b:
															minimum_spanning_tree_cost += edge[2]
															minimum_spanning_tree.append(__A			)
															__a				:	List[str]			=				parent_b

					return minimum_spanning_tree


if __name__ == "__main__":  # pragma: no cover
			UpperCAmelCase__ ,       UpperCAmelCase__			 =	list(map(int, input().strip().split()))
			UpperCAmelCase__			 =	[]

			for _ in range(num_edges):
						UpperCAmelCase__ ,       UpperCAmelCase__ ,       UpperCAmelCase__			 =	(int(x) for x in input().strip().split())
						edges.append((nodea, nodea, cost))

			kruskal(num_nodes, edges)





