



from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any



class 							A					:
					'''simple docstring'''






					A__    =				("""_front""", """_back""", """_len""")



					@dataclass
					class 							A					:
										'''simple docstring'''






										A__    =				None
										A__    =				None
										A__    =				None






					class 							A					:
										'''simple docstring'''






										A__    =				("""_cur""",)

										def __init__(self					:		Dict   ,      _UpperCAmelCase					:		Deque._Node | None	)		->     Tuple:




												"""simple docstring"""


												lowercase__      =	cur

										def __iter__(self					:		List[Any]	)		->     Tuple:




												"""simple docstring"""


												return self


										def 						lowerCamelCase__  (self					:		str	)		->     List[str]:




												"""simple docstring"""


												if self._cur is None:
														# finished iterating
														raise StopIteration
												lowercase__      =	self._cur.val
												lowercase__      =	self._cur.next_node

												return val






					def __init__(self					:		str   ,      _UpperCAmelCase					:		Iterable[Any] | None = None	)		->     int:




							"""simple docstring"""


							lowercase__      =	None
							lowercase__      =	None
							lowercase__      =	0

							if iterable is not None:
									# append every value to the deque
									for val in iterable:
											self.append(_UpperCAmelCase	)

					def 						lowerCamelCase__  (self					:		Tuple   ,      _UpperCAmelCase					:		Any	)		->     List[Any]:




							"""simple docstring"""


							lowercase__      =	self._Node(_UpperCAmelCase   ,      _UpperCAmelCase   ,      _UpperCAmelCase	)
							if self.is_empty():
									# front = back
									lowercase__      =	lowercase__      =	node
									lowercase__      =	1
							else:
									# connect nodes
									lowercase__      =	node
									lowercase__      =	self._back
									lowercase__      =	node  # assign new back to the new node

									self._len += 1

									# make sure there were no errors
									assert not self.is_empty(), "Error on appending value."

					def 						lowerCamelCase__  (self					:		List[Any]   ,      _UpperCAmelCase					:		Any	)		->     int:




							"""simple docstring"""


							lowercase__      =	self._Node(_UpperCAmelCase   ,      _UpperCAmelCase   ,      _UpperCAmelCase	)
							if self.is_empty():
									# front = back
									lowercase__      =	lowercase__      =	node
									lowercase__      =	1
							else:
									# connect nodes
									lowercase__      =	self._front
									lowercase__      =	node
									lowercase__      =	node  # assign new front to the new node

									self._len += 1

									# make sure there were no errors
									assert not self.is_empty(), "Error on appending value."

					def 						lowerCamelCase__  (self					:		Tuple   ,      _UpperCAmelCase					:		Iterable[Any]	)		->     Optional[int]:




							"""simple docstring"""


							for val in iterable:
									self.append(_UpperCAmelCase	)

					def 						lowerCamelCase__  (self					:		Any   ,      _UpperCAmelCase					:		Iterable[Any]	)		->     Dict:




							"""simple docstring"""


							for val in iterable:
									self.appendleft(_UpperCAmelCase	)

					def 						lowerCamelCase__  (self					:		Optional[int]	)		->     Dict:




							"""simple docstring"""


							assert not self.is_empty(), "Deque is empty."

							lowercase__      =	self._back
							lowercase__      =	self._back.prev_node  # set new back
							# drop the last node - python will deallocate memory automatically
							lowercase__      =	None

							self._len -= 1

							return topop.val

					def 						lowerCamelCase__  (self					:		Optional[Any]	)		->     Optional[Any]:




							"""simple docstring"""


							assert not self.is_empty(), "Deque is empty."

							lowercase__      =	self._front
							lowercase__      =	self._front.next_node  # set new front and drop the first node
							lowercase__      =	None

							self._len -= 1

							return topop.val

					def 						lowerCamelCase__  (self					:		Optional[int]	)		->     str:




							"""simple docstring"""


							return self._front is None

					def __len__(self					:		Optional[int]	)		->     int:




							"""simple docstring"""


							return self._len

					def __eq__(self					:		Optional[int]   ,      _UpperCAmelCase					:		object	)		->     List[Any]:




							"""simple docstring"""


							if not isinstance(_UpperCAmelCase   ,      _UpperCAmelCase	):
									return NotImplemented

							lowercase__      =	self._front
							lowercase__      =	other._front

							# if the length of the dequeues are not the same, they are not equal
							if len(self	) != len(_UpperCAmelCase	):
									return False

							while me is not None and oth is not None:
									# compare every value
									if me.val != oth.val:
											return False
									lowercase__      =	me.next_node
									lowercase__      =	oth.next_node

							return True

					def __iter__(self					:		Optional[Any]	)		->     List[Any]:




							"""simple docstring"""


							return Deque._Iterator(self._front	)


					def __repr__(self					:		List[str]	)		->     int:




							"""simple docstring"""


							lowercase__      =	[]
							lowercase__      =	self._front
							while aux is not None:
									# append the values in a list to display
									values_list.append(aux.val	)
									lowercase__      =	aux.next_node

							return f'''[{", ".join(repr(_UpperCAmelCase	) for val in values_list	)}]'''


if __name__ == "__main__":
	import doctest

	doctest.testmod()





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					( UpperCAmelCase__							):
					'''simple docstring'''






					def __init__(self					:		Optional[int]   ,      _UpperCAmelCase					:		Optional[int]   ,      _UpperCAmelCase					:		int   ,      _UpperCAmelCase					:		int=False   ,      _UpperCAmelCase					:		List[str]=1024   ,      _UpperCAmelCase					:		Dict=1024   ,      _UpperCAmelCase					:		List[str]=3.6   ,      _UpperCAmelCase					:		Optional[int]=False   ,      )		->     List[str]:




							"""simple docstring"""


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

							if self.tokenized:
									lowercase__      =	seq_length * num_of_sequences
									lowercase__      =	"""input_ids"""
							else:
									lowercase__      =	seq_length * chars_per_token * num_of_sequences
									lowercase__      =	"""content"""

					def __iter__(self					:		Dict	)		->     Any:




							"""simple docstring"""


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


					def 						lowerCamelCase__  (self					:		Optional[Any]   ,      _UpperCAmelCase					:		Optional[Any]=1000	)		->     Optional[int]:




							"""simple docstring"""


							return ShufflerIterDataPipe(self   ,      buffer_size=_UpperCAmelCase	)






def 				UpperCamelCase      (			__magic_name__  :							Optional[int]  )     ->						Tuple:
		"""simple docstring"""




		lowercase__      =	args.model_ckpt.split("""/"""  )[-1]
		lowercase__      =	logging.getLogger(__name__  )
		lowercase__      =	Path(args.save_dir  ) / """log/"""
		log_dir.mkdir(exist_ok=__magic_name__  )
		lowercase__      =	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(__magic_name__					,					vars(__magic_name__  )  )
				lowercase__      =	accelerator.trackers[0].run.name
				logger.setLevel(logging.INFO  )
				datasets.utils.logging.set_verbosity_info()
				transformers.utils.logging.set_verbosity_info()
		else:
				lowercase__      =	""""""
				logger.setLevel(logging.ERROR  )
				datasets.utils.logging.set_verbosity_error()
				transformers.utils.logging.set_verbosity_error()
		return logger, run_name






def 				UpperCamelCase      (			__magic_name__  :							Optional[Any]  )     ->						List[str]:
		"""simple docstring"""




		lowercase__      =	{"""streaming""": True}
		lowercase__      =	load_dataset(args.dataset_name_train					,					split="""train"""					,					**__magic_name__  )
		lowercase__      =	train_data.shuffle(buffer_size=args.shuffle_buffer					,					seed=args.seed  )
		lowercase__      =	load_dataset(args.dataset_name_valid					,					split="""train"""					,					**__magic_name__  )
		lowercase__      =	ConstantLengthDataset(
		    __magic_name__					,					__magic_name__					,					infinite=__magic_name__					,					seq_length=args.seq_length					,					tokenized=args.tokenized  )
		lowercase__      =	ConstantLengthDataset(
		    __magic_name__					,					__magic_name__					,					infinite=__magic_name__					,					seq_length=args.seq_length					,					tokenized=args.tokenized  )
		lowercase__      =	train_dataset.shuffle(buffer_size=args.shuffle_buffer  )
		lowercase__      =	DataLoader(__magic_name__					,					batch_size=args.train_batch_size					,					shuffle=__magic_name__  )
		lowercase__      =	DataLoader(__magic_name__					,					batch_size=args.valid_batch_size  )
		return train_dataloader, eval_dataloader






def 				UpperCamelCase      (			__magic_name__  :							int					,					__magic_name__  :							List[Any]					,					__magic_name__  :							int=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]  )     ->						List[str]:
		"""simple docstring"""




		lowercase__    ,			lowercase__      =	[], []
		for n, p in model.named_parameters():
				if any(nd in n for nd in no_decay  ):
						params_without_wd.append(__magic_name__  )
				else:
						params_with_wd.append(__magic_name__  )
		return [
		    {"params": params_with_wd, "weight_decay": args.weight_decay},
		    {"params": params_without_wd, "weight_decay": 0.0},
		]






def 				UpperCamelCase      (			__magic_name__  :							Tuple					,					__magic_name__  :							Dict  )     ->						Optional[int]:
		"""simple docstring"""




		logger.info(f'''Step {step}: {metrics}'''  )
		if accelerator.is_main_process:
				accelerator.log(__magic_name__					,					__magic_name__  )






def 				UpperCamelCase      (			__magic_name__  :							str					,					__magic_name__  :							Union[str, Any]					,					__magic_name__  :							List[str]  )     ->						str:
		"""simple docstring"""




		lowercase__      =	accelerator.unwrap_model(__magic_name__  ).config
		lowercase__      =	4 if args.gradient_checkpointing else 3
		lowercase__      =	args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
		lowercase__      =	24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
		lowercase__      =	factor * (
		    1.0
		    + (args.seq_length / (6.0 * config_model.n_embd))
		    + (tokenizer.vocab_size / (1_6.0 * config_model.n_layer * config_model.n_embd))
		)
		lowercase__      =	flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
		return tflops






def 				UpperCamelCase      (			__magic_name__  :							str  )     ->						int:
		"""simple docstring"""




		model.eval()
		lowercase__      =	[]
		for step, batch in enumerate(__magic_name__  ):
				with torch.no_grad():
						lowercase__      =	model(__magic_name__					,					labels=__magic_name__  )
				lowercase__      =	outputs.loss.repeat(args.valid_batch_size  )
				losses.append(accelerator.gather(__magic_name__  )  )
				if args.max_eval_steps > 0 and step >= args.max_eval_steps:
						break
		lowercase__      =	torch.cat(__magic_name__  )
		lowercase__      =	losses[: eval_dataloader.dataset.current_size].mean()
		try:
				lowercase__      =	torch.exp(__magic_name__  )
		except OverflowError:
				lowercase__      =	float("""inf"""  )
		return loss.item(), perplexity.item()


# Settings
A :	Any												=     HfArgumentParser(TrainingArguments)
A :	int												=     parser.parse_args()

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

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

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

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

# Load dataset and dataloader
A						,   A :	List[Any]												=     create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
A :	int												=     AdamW(get_grouped_params(model, args), lr=args.learning_rate)
A :	int												=     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      (			)     ->						Any:
		"""simple docstring"""




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


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

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

