


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_					(    _SCREAMING_SNAKE_CASE    ):





							def __init__(							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE=False			,       _SCREAMING_SNAKE_CASE=1024			,       _SCREAMING_SNAKE_CASE=1024			,       _SCREAMING_SNAKE_CASE=3.6			,       _SCREAMING_SNAKE_CASE=False			,       ):
								a_     			=   tokenizer
								a_     			=   tokenizer.bos_token_id
								a_     			=   dataset
								a_     			=   seq_length
								a_     			=   0
								a_     			=   infinite
								a_     			=   0
								a_     			=   tokenized

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





							def __iter__(							self    ):
								a_     			=   iter(self.dataset    )
								a_     			=   True
								while more_examples:
									a_			, a_     			=   [], 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:
												a_     			=   iter(self.dataset    )
												self.epoch += 1
												logger.info(f"""Dataset epoch: {self.epoch}"""    )
											else:
												a_     			=   False
												break
									if self.tokenized:
										a_     			=   buffer
									else:
										a_     			=   self.tokenizer(_SCREAMING_SNAKE_CASE			,       truncation=_SCREAMING_SNAKE_CASE    )["""input_ids"""]
									a_     			=   []
									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    ):
										a_     			=   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   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE=1000    ):
								return ShufflerIterDataPipe(self			,       buffer_size=_SCREAMING_SNAKE_CASE    )







def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Optional[int]	)     ->	Dict:



	"""simple docstring"""






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

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Any	)     ->	Optional[Any]:



	"""simple docstring"""






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

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      int     ,					UpperCamelCase		:      Union[str, Any]     ,					UpperCamelCase		:      str=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]	)     ->	Tuple:



	"""simple docstring"""






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

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Any     ,					UpperCamelCase		:      List[Any]	)     ->	int:



	"""simple docstring"""






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

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Dict     ,					UpperCamelCase		:      Dict     ,					UpperCamelCase		:      str	)     ->	Optional[int]:



	"""simple docstring"""






	a_     			=   accelerator.unwrap_model(UpperCamelCase	).config
	a_     			=   4 if args.gradient_checkpointing else 3
	a_     			=   args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
	a_     			=   24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
	a_     			=   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))
	)
	a_     			=   flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
	return tflops

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Optional[Any]	)     ->	Optional[Any]:



	"""simple docstring"""






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


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

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

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

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

# Logging
_A     ,				_A							     =       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							     =       AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
			model.gradient_checkpointing_enable()
_A							     =       AutoTokenizer.from_pretrained(args.save_dir)

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

# Prepare the optimizer and learning rate scheduler
_A							     =       AdamW(get_grouped_params(model, args), lr=args.learning_rate)
_A							     =       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 			__SCREAMING_SNAKE_CASE		(		)     ->	str:



	"""simple docstring"""






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


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

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



import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


_A							     =       set()
_A							     =       get_logger(__name__)

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Optional[str] = None	)     ->	Optional[int]:



	"""simple docstring"""






	def decorator(UpperCamelCase		:      Callable	):
		global _emitted_deprecation_warnings

		if inspect.isclass(UpperCamelCase	):
			a_     			=   deprecated_class_or_function.__init__
			a_     			=   deprecated_class_or_function.__name__
		else:
			a_     			=   deprecated_class_or_function
			a_     			=   deprecated_function.__name__
			# Support deprecating __init__ class method: class name instead
			a_     			=   name if name != """__init__""" else deprecated_function.__qualname__.split("""."""	)[-2]

		a_     			=   (
		    F"""{name} is deprecated and will be removed in the next major version of datasets.""" + F""" {help_message}"""
		    if help_message
		    else """"""
		)

		@wraps(UpperCamelCase	)
		def wrapper(*UpperCamelCase		:      List[str]     ,					**UpperCamelCase		:      Union[str, Any]	):
			a_     			=   hash(UpperCamelCase	)
			if func_hash not in _emitted_deprecation_warnings:
				warnings.warn(UpperCamelCase     ,					category=UpperCamelCase     ,					stacklevel=2	)
				_emitted_deprecation_warnings.add(UpperCamelCase	)
			return deprecated_function(*UpperCamelCase     ,					**UpperCamelCase	)

		a_     			=   """deprecated"""

		if inspect.isclass(UpperCamelCase	):
			a_     			=   wrapper
			return deprecated_class_or_function
		else:
			return wrapper

	return decorator

class 			lowerCamelCase_					(    enum.EnumMeta    ):





							def __getattribute__(							cls			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   super().__getattribute__(_SCREAMING_SNAKE_CASE    )
								if isinstance(_SCREAMING_SNAKE_CASE			,       enum.Enum    ) and obj._on_access:
									obj._on_access()
								return obj





							def __getitem__(							cls			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   super().__getitem__(_SCREAMING_SNAKE_CASE    )
								if member._on_access:
									member._on_access()
								return member





							def __call__(							cls			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE=None			,       *, _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=1    ):
								a_     			=   super().__call__(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       module=_SCREAMING_SNAKE_CASE			,       qualname=_SCREAMING_SNAKE_CASE			,       type=_SCREAMING_SNAKE_CASE			,       start=_SCREAMING_SNAKE_CASE    )
								if isinstance(_SCREAMING_SNAKE_CASE			,       enum.Enum    ) and obj._on_access:
									obj._on_access()
								return obj







class 			lowerCamelCase_					(    enum.Enum   ,		metaclass=_SCREAMING_SNAKE_CASE    ):





							def __new__(							cls			,       _SCREAMING_SNAKE_CASE    ):
								a_     			=   object.__new__(cls    )
								a_     			=   value
								a_     			=   member.deprecate
								return member





							@property
							def   __magic_name__      (							self    ):
								return ""





							def   __magic_name__      (							self    ):
								a_     			=   f""" {self.help_message}""" if self.help_message else """"""
								warnings.warn(
								    f"""'{self.__objclass__.__name__}' is deprecated and will be removed in the next major version of datasets."""
								    + help_message			,       _SCREAMING_SNAKE_CASE			,       stacklevel=3			,       )