






def   lowerCamelCase      (		a_ = 4_000_000			)			->    int:

							if n <= 1:
														return 0
							lowerCAmelCase_       		=  0
							lowerCAmelCase_       		=  2
							lowerCAmelCase_       		=  0
							while 4 * b + a <= n:
														lowerCAmelCase_		,     lowerCAmelCase_       		=  b, 4 * b + a
														count += a
							return count + b


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











import logging
import os
import time
from argparse import Namespace
from pathlib import Path

import datasets
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import ProjectConfiguration
from arguments import TrainingArguments
from datasets import load_dataset
from huggingface_hub import Repository
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed




class 							a_				(		a_	):


							'''simple docstring'''






							def __init__(	self    ,    lowercase_    ,    lowercase_    ,    lowercase_=False    ,    lowercase_=1_0_2_4    ,    lowercase_=1_0_2_4    ,    lowercase_=3.6    ,    lowercase_=False    ,    )      ->      Any:
														'''simple docstring'''

														lowerCAmelCase_       		=  tokenizer
														lowerCAmelCase_       		=  tokenizer.bos_token_id
														lowerCAmelCase_       		=  dataset
														lowerCAmelCase_       		=  seq_length
														lowerCAmelCase_       		=  0
														lowerCAmelCase_       		=  infinite
														lowerCAmelCase_       		=  0
														lowerCAmelCase_       		=  tokenized

														if self.tokenized:
																					lowerCAmelCase_       		=  seq_length * num_of_sequences
																					lowerCAmelCase_       		=  'input_ids'
														else:
																					lowerCAmelCase_       		=  seq_length * chars_per_token * num_of_sequences
																					lowerCAmelCase_       		=  'content'

							def __iter__(	self     )      ->      Union[str, Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  iter(self.dataset     )
														lowerCAmelCase_       		=  True
														while more_examples:
																					lowerCAmelCase_		,     lowerCAmelCase_       		=  [], 0
																					while True:
																												if buffer_len >= self.max_buffer_size:
																																			break
																												try:
																																			buffer.append(next(lowercase_     )[self.content_field]     )
																																			buffer_len += len(buffer[-1]     )
																												except StopIteration:
																																			if self.infinite:
																																										lowerCAmelCase_       		=  iter(self.dataset     )
																																										self.epoch += 1
																																										logger.info(f'''Dataset epoch: {self.epoch}'''     )
																																			else:
																																										lowerCAmelCase_       		=  False
																																										break
																					if self.tokenized:
																												lowerCAmelCase_       		=  buffer
																					else:
																												lowerCAmelCase_       		=  self.tokenizer(lowercase_    ,    truncation=lowercase_     )['input_ids']
																					lowerCAmelCase_       		=  []
																					for tokenized_input in tokenized_inputs:
																												all_token_ids.extend(tokenized_input + [self.concat_token_id]     )
																					for i in range(0    ,    len(lowercase_     )    ,    self.seq_length     ):
																												lowerCAmelCase_       		=  all_token_ids[i : i + self.seq_length]
																												if len(lowercase_     ) == self.seq_length:
																																			self.current_size += 1
																																			yield torch.tensor(lowercase_     )






							def   _lowercase (	self    ,    lowercase_=1_0_0_0     )      ->      Any:
														'''simple docstring'''

														return ShufflerIterDataPipe(self    ,    buffer_size=lowercase_     )


def   lowerCamelCase      (		a_			)			->    Any:
							lowerCAmelCase_       		=  args.model_ckpt.split('/'			)[-1]
							lowerCAmelCase_       		=  logging.getLogger(__name__			)
							lowerCAmelCase_       		=  Path(args.save_dir			) / 'log/'
							log_dir.mkdir(exist_ok=a_			)
							lowerCAmelCase_       		=  F'''debug_{accelerator.process_index}.log'''
							logging.basicConfig(
							    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'				,		datefmt='%m/%d/%Y %H:%M:%S'				,		level=logging.INFO				,		handlers=[logging.FileHandler(log_dir / filename			), logging.StreamHandler()]				,		)
							if accelerator.is_main_process:  # we only want to setup logging once
														accelerator.init_trackers(a_				,		vars(a_			)			)
														lowerCAmelCase_       		=  accelerator.trackers[0].run.name
														logger.setLevel(logging.INFO			)
														datasets.utils.logging.set_verbosity_info()
														transformers.utils.logging.set_verbosity_info()
							else:
														lowerCAmelCase_       		=  ''
														logger.setLevel(logging.ERROR			)
														datasets.utils.logging.set_verbosity_error()
														transformers.utils.logging.set_verbosity_error()
							return logger, run_name
def   lowerCamelCase      (		a_			)			->    Union[str, Any]:
							lowerCAmelCase_       		=  {'streaming': True}
							lowerCAmelCase_       		=  load_dataset(args.dataset_name_train				,		split='train'				,		**a_			)
							lowerCAmelCase_       		=  train_data.shuffle(buffer_size=args.shuffle_buffer				,		seed=args.seed			)
							lowerCAmelCase_       		=  load_dataset(args.dataset_name_valid				,		split='train'				,		**a_			)
							lowerCAmelCase_       		=  ConstantLengthDataset(
							    a_				,		a_				,		infinite=a_				,		seq_length=args.seq_length				,		tokenized=args.tokenized			)
							lowerCAmelCase_       		=  ConstantLengthDataset(
							    a_				,		a_				,		infinite=a_				,		seq_length=args.seq_length				,		tokenized=args.tokenized			)
							lowerCAmelCase_       		=  train_dataset.shuffle(buffer_size=args.shuffle_buffer			)
							lowerCAmelCase_       		=  DataLoader(a_				,		batch_size=args.train_batch_size				,		shuffle=a_			)
							lowerCAmelCase_       		=  DataLoader(a_				,		batch_size=args.valid_batch_size			)
							return train_dataloader, eval_dataloader
def   lowerCamelCase      (		a_				,		a_				,		a_=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]			)			->    List[str]:
							lowerCAmelCase_		,     lowerCAmelCase_       		=  [], []
							for n, p in model.named_parameters():
														if any(nd in n for nd in no_decay			):
																					params_without_wd.append(a_			)
														else:
																					params_with_wd.append(a_			)
							return [
							    {"params": params_with_wd, "weight_decay": args.weight_decay},
							    {"params": params_without_wd, "weight_decay": 0.0},
							]
def   lowerCamelCase      (		a_				,		a_			)			->    List[str]:
							logger.info(F'''Step {step}: {metrics}'''			)
							if accelerator.is_main_process:
														accelerator.log(a_				,		a_			)
def   lowerCamelCase      (		a_				,		a_				,		a_			)			->    Tuple:
							# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
							lowerCAmelCase_       		=  accelerator.unwrap_model(a_			).config
							lowerCAmelCase_       		=  4 if args.gradient_checkpointing else 3
							lowerCAmelCase_       		=  args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
							lowerCAmelCase_       		=  24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
							lowerCAmelCase_       		=  factor * (
							    1.0
							    + (args.seq_length / (6.0 * config_model.n_embd))
							    + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
							)
							lowerCAmelCase_       		=  flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
							return tflops
def   lowerCamelCase      (		a_			)			->    List[Any]:
							model.eval()
							lowerCAmelCase_       		=  []
							for step, batch in enumerate(a_			):
														with torch.no_grad():
																					lowerCAmelCase_       		=  model(a_				,		labels=a_			)
														lowerCAmelCase_       		=  outputs.loss.repeat(args.valid_batch_size			)
														losses.append(accelerator.gather(a_			)			)
														if args.max_eval_steps > 0 and step >= args.max_eval_steps:
																					break
							lowerCAmelCase_       		=  torch.cat(a_			)
							lowerCAmelCase_       		=  losses[: eval_dataloader.dataset.current_size].mean()
							try:
														lowerCAmelCase_       		=  torch.exp(a_			)
							except OverflowError:
														lowerCAmelCase_       		=  float('inf'			)
							return loss.item(), perplexity.item()


# Settings
lowerCamelCase_		  =						HfArgumentParser(TrainingArguments)
lowerCamelCase_		  =						parser.parse_args()

# Accelerator
lowerCamelCase_		  =						ProjectConfiguration(project_dir=args.save_dir, logging_dir="""log""")
lowerCamelCase_		  =						Accelerator(log_with=["""wandb""", """tensorboard"""], project_config=config)
lowerCamelCase_		  =						{str(k): str(v) for k, v in accelerator.state.__dict__.items()}

lowerCamelCase_		  =						Namespace(**vars(args), **acc_state)
lowerCamelCase_		  =						accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
		lowerCamelCase_		  =						Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
lowerCamelCase_  ,  lowerCamelCase_		  =						setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
		hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
lowerCamelCase_		  =						AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
		model.gradient_checkpointing_enable()
lowerCamelCase_		  =						AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
lowerCamelCase_  ,  lowerCamelCase_		  =						create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
lowerCamelCase_		  =						AdamW(get_grouped_params(model, args), lr=args.learning_rate)
lowerCamelCase_		  =						get_scheduler(
    name=args.lr_scheduler_type,
    optimizer=optimizer,
    num_warmup_steps=args.num_warmup_steps,
    num_training_steps=args.max_train_steps,
)
accelerator.register_for_checkpointing(lr_scheduler)
def   lowerCamelCase      (		)			->    str:
							return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
lowerCamelCase_  ,  lowerCamelCase_  ,  lowerCamelCase_  ,  lowerCamelCase_		  =						accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

# load in the weights and states from a previous save
if args.resume_from_checkpoint:
		if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
				accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}''')
				accelerator.load_state(args.resume_from_checkpoint)
				lowerCamelCase_		  =						os.path.basename(args.resume_from_checkpoint)
		else:
				# Get the most recent checkpoint
				lowerCamelCase_		  =						[f.name for f in os.scandir(args.save_dir) if f.is_dir() and """step""" in str(f)]
				dirs.sort(key=os.path.getctime)
				lowerCamelCase_		  =						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract the step of the checkpoint to continue from there
		lowerCamelCase_		  =						os.path.splitext(path)[0]
		lowerCamelCase_		  =						int(training_difference.replace("""step_""", """"""))

# Train model
model.train()
lowerCamelCase_		  =						0
lowerCamelCase_		  =						time.time()
lowerCamelCase_		  =						0
for step, batch in enumerate(train_dataloader, start=1):
		if args.resume_from_checkpoint and step < resume_step:
				continue  # we need to skip steps until we reach the resumed step
		lowerCamelCase_		  =						model(batch, labels=batch, use_cache=False).loss
		lowerCamelCase_		  =						accelerator.gather(loss.repeat(args.train_batch_size)).mean()
		loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
		log_metrics(step, {"""samples""": step * samples_per_step, """loss_per_step/train""": loss.item()})
		lowerCamelCase_		  =						loss / args.gradient_accumulation_steps
		if step % args.gradient_accumulation_steps != 0:
				# Prevent backward from doing gradient all_reduce in every step
				if accelerator.distributed_type == DistributedType.MULTI_GPU:
						with model.no_sync():
								accelerator.backward(loss)
				else:
						accelerator.backward(loss)
		else:
				lowerCamelCase_		  =						get_lr()
				accelerator.backward(loss)
				accelerator.clip_grad_norm_(model.parameters(), 1.0)
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()
				lowerCamelCase_		  =						time.time() - t_start
				lowerCamelCase_		  =						compute_tflops(elapsed_time, accelerator, args)
				log_metrics(
				    step,
				    {
				        """steps""": completed_steps,
				        """loss/train""": loss_tracking,
				        """lr""": lr,
				        """tflops""": tflops,
				        """time_per_iteration""": elapsed_time,
				    },
				)
				lowerCamelCase_		  =						time.time()
				lowerCamelCase_		  =						0
				completed_steps += 1
		if step % args.save_checkpoint_steps == 0:
				logger.info("""Evaluating and saving model checkpoint""")
				lowerCamelCase_  ,  lowerCamelCase_		  =						evaluate(args)
				log_metrics(step, {"""loss/eval""": eval_loss, """perplexity""": perplexity})
				accelerator.wait_for_everyone()
				lowerCamelCase_		  =						os.path.join(args.save_dir, f'''step_{step}''')
				accelerator.save_state(save_dir)
				if accelerator.is_main_process:
						hf_repo.push_to_hub(commit_message=f'''step {step}''')
				model.train()
		if completed_steps >= args.max_train_steps:
				break

# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
lowerCamelCase_  ,  lowerCamelCase_		  =						evaluate(args)
log_metrics(step, {"""loss/eval""": eval_loss, """perplexity""": perplexity})
accelerator.wait_for_everyone()
lowerCamelCase_		  =						accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
lowerCamelCase_		  =						os.path.join(args.save_dir, f'''step_{step}''')
accelerator.save_state(save_dir)
if accelerator.is_main_process:
		hf_repo.push_to_hub(commit_message="""final model""")



