
from __future__ import annotations




def 				__magic_name__				(     __lowerCAmelCase			:		int	,						__lowerCAmelCase			:		int	,						__lowerCAmelCase			:		int							)    ->     tuple[float, float]:
				assert (
				    c % greatest_common_divisor(__lowerCAmelCase	,						__lowerCAmelCase							) == 0
				)  # greatest_common_divisor(a,b) function implemented below
				((__lowerCamelCase)      ,					(__lowerCamelCase)      ,					(__lowerCamelCase))					      =			extended_gcd(__lowerCAmelCase	,						__lowerCAmelCase							)  # extended_gcd(a,b) function implemented below
				__lowerCamelCase					      =			c / d
				return (r * x, r * y)




def 				__magic_name__				(     __lowerCAmelCase			:		int	,						__lowerCAmelCase			:		int	,						__lowerCAmelCase			:		int	,						__lowerCAmelCase			:		int = 2							)    ->     None:
				((__lowerCamelCase)      ,					(__lowerCamelCase))					      =			diophantine(__lowerCAmelCase	,						__lowerCAmelCase	,						__lowerCAmelCase							)  # Initial value
				__lowerCamelCase					      =			greatest_common_divisor(__lowerCAmelCase	,						__lowerCAmelCase							)
				__lowerCamelCase					      =			a // d
				__lowerCamelCase					      =			b // d

				for i in range(__lowerCAmelCase							):
								__lowerCamelCase					      =			xa + i * q
								__lowerCamelCase					      =			ya - i * p
								print(__lowerCAmelCase	,						__lowerCAmelCase							)




def 				__magic_name__				(     __lowerCAmelCase			:		int	,						__lowerCAmelCase			:		int							)    ->     int:
				if a < b:
								__lowerCamelCase      ,					__lowerCamelCase					      =			b, a

				while a % b != 0:
								__lowerCamelCase      ,					__lowerCamelCase					      =			b, a % b

				return b




def 				__magic_name__				(     __lowerCAmelCase			:		int	,						__lowerCAmelCase			:		int							)    ->     tuple[int, int, int]:
				assert a >= 0 and b >= 0

				if b == 0:
								__lowerCamelCase      ,					__lowerCamelCase      ,					__lowerCamelCase					      =			a, 1, 0
				else:
								((__lowerCamelCase)      ,					(__lowerCamelCase)      ,					(__lowerCamelCase))					      =			extended_gcd(__lowerCAmelCase	,						a % b							)
								__lowerCamelCase					      =			q
								__lowerCamelCase					      =			p - q * (a // b)

				assert a % d == 0 and b % d == 0
				assert d == a * x + b * y

				return (d, x, y)


if __name__ == "__main__":
					from doctest import testmod

					testmod(name="diophantine", verbose=True)
					testmod(name="diophantine_all_soln", verbose=True)
					testmod(name="extended_gcd", verbose=True)
					testmod(name="greatest_common_divisor", verbose=True)





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      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							str							,    SCREAMING_SNAKE_CASE__	:							Dict							,    SCREAMING_SNAKE_CASE__	:							Dict							,    SCREAMING_SNAKE_CASE__	:							Any=False							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=10_24							,    SCREAMING_SNAKE_CASE__	:							Dict=10_24							,    SCREAMING_SNAKE_CASE__	:							str=3.6							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=False							,    )  ->							Optional[int]:
						__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	:							Tuple						)  ->							Dict:
						__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(SCREAMING_SNAKE_CASE__						)[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(SCREAMING_SNAKE_CASE__							,    truncation=SCREAMING_SNAKE_CASE__						)['''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(SCREAMING_SNAKE_CASE__						)							,    self.seq_length						):
														__lowerCamelCase					      =			all_token_ids[i : i + self.seq_length]
														if len(SCREAMING_SNAKE_CASE__						) == self.seq_length:
																		self.current_size += 1
																		yield torch.tensor(SCREAMING_SNAKE_CASE__						)







		def 				__A (	self	:							List[str]							,    SCREAMING_SNAKE_CASE__	:							str=10_00						)  ->							Union[str, Any]:
						return ShufflerIterDataPipe(self							,    buffer_size=SCREAMING_SNAKE_CASE__						)





def 				__magic_name__				(     __lowerCAmelCase			:		Tuple							)    ->     Dict:
				__lowerCamelCase					      =			args.model_ckpt.split('''/'''							)[-1]
				__lowerCamelCase					      =			logging.getLogger(__name__							)
				__lowerCamelCase					      =			Path(args.save_dir							) / '''log/'''
				log_dir.mkdir(exist_ok=__lowerCAmelCase							)
				__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(__lowerCAmelCase	,						vars(__lowerCAmelCase							)							)
								__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 				__magic_name__				(     __lowerCAmelCase			:		Optional[Any]							)    ->     Tuple:
				__lowerCamelCase					      =			{'''streaming''': True}
				__lowerCamelCase					      =			load_dataset(args.dataset_name_train	,						split='''train'''	,						**__lowerCAmelCase							)
				__lowerCamelCase					      =			train_data.shuffle(buffer_size=args.shuffle_buffer	,						seed=args.seed							)
				__lowerCamelCase					      =			load_dataset(args.dataset_name_valid	,						split='''train'''	,						**__lowerCAmelCase							)
				__lowerCamelCase					      =			ConstantLengthDataset(
				    __lowerCAmelCase	,						__lowerCAmelCase	,						infinite=__lowerCAmelCase	,						seq_length=args.seq_length	,						tokenized=args.tokenized							)
				__lowerCamelCase					      =			ConstantLengthDataset(
				    __lowerCAmelCase	,						__lowerCAmelCase	,						infinite=__lowerCAmelCase	,						seq_length=args.seq_length	,						tokenized=args.tokenized							)
				__lowerCamelCase					      =			train_dataset.shuffle(buffer_size=args.shuffle_buffer							)
				__lowerCamelCase					      =			DataLoader(__lowerCAmelCase	,						batch_size=args.train_batch_size	,						shuffle=__lowerCAmelCase							)
				__lowerCamelCase					      =			DataLoader(__lowerCAmelCase	,						batch_size=args.valid_batch_size							)
				return train_dataloader, eval_dataloader




def 				__magic_name__				(     __lowerCAmelCase			:		int	,						__lowerCAmelCase			:		Optional[Any]	,						__lowerCAmelCase			:		Union[str, Any]=["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(__lowerCAmelCase							)
								else:
												params_with_wd.append(__lowerCAmelCase							)
				return [
				    {"params": params_with_wd, "weight_decay": args.weight_decay},
				    {"params": params_without_wd, "weight_decay": 0.0},
				]




def 				__magic_name__				(     __lowerCAmelCase			:		int	,						__lowerCAmelCase			:		Union[str, Any]							)    ->     str:
				logger.info(f'''Step {step}: {metrics}'''							)
				if accelerator.is_main_process:
								accelerator.log(__lowerCAmelCase	,						__lowerCAmelCase							)




def 				__magic_name__				(     __lowerCAmelCase			:		Any	,						__lowerCAmelCase			:		List[Any]	,						__lowerCAmelCase			:		Any							)    ->     Dict:
				# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
				__lowerCamelCase					      =			accelerator.unwrap_model(__lowerCAmelCase							).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 				__magic_name__				(     __lowerCAmelCase			:		Dict							)    ->     Optional[Any]:
				model.eval()
				__lowerCamelCase					      =			[]
				for step, batch in enumerate(__lowerCAmelCase							):
								with torch.no_grad():
												__lowerCamelCase					      =			model(__lowerCAmelCase	,						labels=__lowerCAmelCase							)
								__lowerCamelCase					      =			outputs.loss.repeat(args.valid_batch_size							)
								losses.append(accelerator.gather(__lowerCAmelCase							)							)
								if args.max_eval_steps > 0 and step >= args.max_eval_steps:
												break
				__lowerCamelCase					      =			torch.cat(__lowerCAmelCase							)
				__lowerCamelCase					      =			losses[: eval_dataloader.dataset.current_size].mean()
				try:
								__lowerCamelCase					      =			torch.exp(__lowerCAmelCase							)
				except OverflowError:
								__lowerCamelCase					      =			float('''inf'''							)
				return loss.item(), perplexity.item()


# Settings
SCREAMING_SNAKE_CASE__     :    str      							=					HfArgumentParser(TrainingArguments)
SCREAMING_SNAKE_CASE__     :    List[Any]      							=					parser.parse_args()

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

SCREAMING_SNAKE_CASE__     :    Tuple      							=					Namespace(**vars(args), **acc_state)
SCREAMING_SNAKE_CASE__     :    int      							=					accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
					SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__     :    List[str]      							=					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
SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
					model.gradient_checkpointing_enable()
SCREAMING_SNAKE_CASE__     :    Tuple      							=					AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__     :    List[str]      							=					create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					AdamW(get_grouped_params(model, args), lr=args.learning_rate)
SCREAMING_SNAKE_CASE__     :    Tuple      							=					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 				__magic_name__				(     )    ->     Any:
				return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__     :    Tuple      							=					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)
										SCREAMING_SNAKE_CASE__     :    Tuple      							=					os.path.basename(args.resume_from_checkpoint)
					else:
										# Get the most recent checkpoint
										SCREAMING_SNAKE_CASE__     :    str      							=					[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)
										SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
					# Extract the step of the checkpoint to continue from there
					SCREAMING_SNAKE_CASE__     :    str      							=					os.path.splitext(path)[0]
					SCREAMING_SNAKE_CASE__     :    Optional[Any]      							=					int(training_difference.replace("step_", ""))

# Train model
model.train()
SCREAMING_SNAKE_CASE__     :    Optional[Any]      							=					0
SCREAMING_SNAKE_CASE__     :    Any      							=					time.time()
SCREAMING_SNAKE_CASE__     :    int      							=					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
					SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					model(batch, labels=batch, use_cache=False).loss
					SCREAMING_SNAKE_CASE__     :    str      							=					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()})
					SCREAMING_SNAKE_CASE__     :    Optional[Any]      							=					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:
										SCREAMING_SNAKE_CASE__     :    Union[str, Any]      							=					get_lr()
										accelerator.backward(loss)
										accelerator.clip_grad_norm_(model.parameters(), 1.0)
										optimizer.step()
										lr_scheduler.step()
										optimizer.zero_grad()
										SCREAMING_SNAKE_CASE__     :    Tuple      							=					time.time() - t_start
										SCREAMING_SNAKE_CASE__     :    Any      							=					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,
										    },
										)
										SCREAMING_SNAKE_CASE__     :    Tuple      							=					time.time()
										SCREAMING_SNAKE_CASE__     :    str      							=					0
										completed_steps += 1
					if step % args.save_checkpoint_steps == 0:
										logger.info("Evaluating and saving model checkpoint")
										SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__     :    Union[str, Any]      							=					evaluate(args)
										log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
										accelerator.wait_for_everyone()
										SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					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")
SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__     :    List[Any]      							=					evaluate(args)
log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
accelerator.wait_for_everyone()
SCREAMING_SNAKE_CASE__     :    Optional[Any]      							=					accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
SCREAMING_SNAKE_CASE__     :    List[Any]      							=					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")



