


'''simple docstring'''




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					(					a				):



	def __init__(       self			,       snake_case__			,       snake_case__			,       snake_case__=False			,       snake_case__=1024			,       snake_case__=1024			,       snake_case__=3.6			,       snake_case__=False			,       ):
		lowerCAmelCase       :     int   				=			tokenizer
		lowerCAmelCase       :     Any   				=			tokenizer.bos_token_id
		lowerCAmelCase       :     Tuple   				=			dataset
		lowerCAmelCase       :     List[str]   				=			seq_length
		lowerCAmelCase       :     int   				=			0
		lowerCAmelCase       :     Optional[int]   				=			infinite
		lowerCAmelCase       :     Tuple   				=			0
		lowerCAmelCase       :     Union[str, Any]   				=			tokenized

		if self.tokenized:
			lowerCAmelCase       :     List[Any]   				=			seq_length * num_of_sequences
			lowerCAmelCase       :     Union[str, Any]   				=			'input_ids'
		else:
			lowerCAmelCase       :     Dict   				=			seq_length * chars_per_token * num_of_sequences
			lowerCAmelCase       :     str   				=			'content'



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



	def 					lowercase  (       self			,       snake_case__=1000     ):
		return ShufflerIterDataPipe(self			,       buffer_size=snake_case__     )


def       __UpperCamelCase     (     _A     :					List[str]					)     ->   str:




	"""simple docstring"""





	lowerCAmelCase       :     Optional[Any]   				=			args.model_ckpt.split('/'					)[-1]
	lowerCAmelCase       :     List[Any]   				=			logging.getLogger(__name__					)
	lowerCAmelCase       :     str   				=			Path(args.save_dir					) / 'log/'
	log_dir.mkdir(exist_ok=_A					)
	lowerCAmelCase       :     Optional[Any]   				=			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       :     Optional[Any]   				=			accelerator.trackers[0].run.name
		logger.setLevel(logging.INFO					)
		datasets.utils.logging.set_verbosity_info()
		transformers.utils.logging.set_verbosity_info()
	else:
		lowerCAmelCase       :     Optional[int]   				=			''
		logger.setLevel(logging.ERROR					)
		datasets.utils.logging.set_verbosity_error()
		transformers.utils.logging.set_verbosity_error()
	return logger, run_name

def       __UpperCamelCase     (     _A     :					int					)     ->   Union[str, Any]:




	"""simple docstring"""





	lowerCAmelCase       :     List[str]   				=			{'streaming': True}
	lowerCAmelCase       :     List[Any]   				=			load_dataset(args.dataset_name_train       , split='train'       , **_A					)
	lowerCAmelCase       :     List[str]   				=			train_data.shuffle(buffer_size=args.shuffle_buffer       , seed=args.seed					)
	lowerCAmelCase       :     List[str]   				=			load_dataset(args.dataset_name_valid       , split='train'       , **_A					)
	lowerCAmelCase       :     List[str]   				=			ConstantLengthDataset(
	    _A       , _A       , infinite=_A       , seq_length=args.seq_length       , tokenized=args.tokenized					)
	lowerCAmelCase       :     List[str]   				=			ConstantLengthDataset(
	    _A       , _A       , infinite=_A       , seq_length=args.seq_length       , tokenized=args.tokenized					)
	lowerCAmelCase       :     Tuple   				=			train_dataset.shuffle(buffer_size=args.shuffle_buffer					)
	lowerCAmelCase       :     Union[str, Any]   				=			DataLoader(_A       , batch_size=args.train_batch_size       , shuffle=_A					)
	lowerCAmelCase       :     Union[str, Any]   				=			DataLoader(_A       , batch_size=args.valid_batch_size					)
	return train_dataloader, eval_dataloader

def       __UpperCamelCase     (     _A     :					Optional[int]       , _A     :					Tuple       , _A     :					Optional[int]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]					)     ->   Tuple:




	"""simple docstring"""





	lowerCAmelCase				, lowerCAmelCase       :     Dict   				=			[], []
	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       __UpperCamelCase     (     _A     :					List[str]       , _A     :					Optional[Any]					)     ->   int:




	"""simple docstring"""





	logger.info(F"Step {step}: {metrics}"					)
	if accelerator.is_main_process:
		accelerator.log(_A       , _A					)

def       __UpperCamelCase     (     _A     :					Dict       , _A     :					int       , _A     :					Any					)     ->   Optional[int]:




	"""simple docstring"""





	lowerCAmelCase       :     Union[str, Any]   				=			accelerator.unwrap_model(_A					).config
	lowerCAmelCase       :     List[str]   				=			4 if args.gradient_checkpointing else 3
	lowerCAmelCase       :     int   				=			args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
	lowerCAmelCase       :     int   				=			24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
	lowerCAmelCase       :     Optional[int]   				=			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       :     List[str]   				=			flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
	return tflops

def       __UpperCamelCase     (     _A     :					Optional[Any]					)     ->   Optional[Any]:




	"""simple docstring"""





	model.eval()
	lowerCAmelCase       :     Dict   				=			[]
	for step, batch in enumerate(_A					):
		with torch.no_grad():
			lowerCAmelCase       :     Optional[int]   				=			model(_A       , labels=_A					)
		lowerCAmelCase       :     List[Any]   				=			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       :     List[Any]   				=			torch.cat(_A					)
	lowerCAmelCase       :     str   				=			losses[: eval_dataloader.dataset.current_size].mean()
	try:
		lowerCAmelCase       :     Optional[int]   				=			torch.exp(_A					)
	except OverflowError:
		lowerCAmelCase       :     List[str]   				=			float('inf'					)
	return loss.item(), perplexity.item()


# Settings
_lowerCAmelCase	:		str		  =						HfArgumentParser(TrainingArguments)
_lowerCAmelCase	:		Optional[int]		  =						parser.parse_args()

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

_lowerCAmelCase	:		Dict		  =						Namespace(**vars(args), **acc_state)
_lowerCAmelCase	:		Optional[int]		  =						accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

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

# Logging
_lowerCAmelCase  ,   _lowerCAmelCase	:		Tuple		  =						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	:		Union[str, Any]		  =						AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
				model.gradient_checkpointing_enable()
_lowerCAmelCase	:		Optional[Any]		  =						AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
_lowerCAmelCase  ,   _lowerCAmelCase	:		Optional[Any]		  =						create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
_lowerCAmelCase	:		Optional[int]		  =						AdamW(get_grouped_params(model, args), lr=args.learning_rate)
_lowerCAmelCase	:		Any		  =						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       __UpperCamelCase     (     )     ->   List[str]:




	"""simple docstring"""





	return optimizer.param_groups[0]["lr"]


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

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




'''simple docstring'''




from itertools import combinations, product

def       __UpperCamelCase     (     _A     :					int       , _A     :					int       , _A     :					int       , _A     :					int					)     ->   bool:




	"""simple docstring"""





	if xa == ya == 0 or xa == ya == 0:
		return False
	lowerCAmelCase       :     Optional[Any]   				=			xa * xa + ya * ya
	lowerCAmelCase       :     Tuple   				=			xa * xa + ya * ya
	lowerCAmelCase       :     List[str]   				=			(xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
	return (
	    a_square + b_square == c_square
	    or a_square + c_square == b_square
	    or b_square + c_square == a_square
	)

def       __UpperCamelCase     (     _A     :					int = 50					)     ->   int:




	"""simple docstring"""





	return sum(
	    1
	    for pta, pta in combinations(product(range(limit + 1					)       , repeat=2					)       , 2					)
	    if is_right(*_A       , *_A					)					)


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