





'''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 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		def __init__(self    :   int   ,   _lowerCAmelCase    :   int   ,   _lowerCAmelCase    :   Tuple   ,   _lowerCAmelCase    :   List[str]=False   ,   _lowerCAmelCase    :   Any=1024   ,   _lowerCAmelCase    :   Optional[Any]=1024   ,   _lowerCAmelCase    :   Optional[int]=3.6   ,   _lowerCAmelCase    :   Tuple=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    :   Any   ):
			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(_lowerCAmelCase   )[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(_lowerCAmelCase   ,   truncation=_lowerCAmelCase   )["""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(_lowerCAmelCase   )   ,   self.seq_length   ):
					A					=   all_token_ids[i : i + self.seq_length]
					if len(_lowerCAmelCase   ) == self.seq_length:
						self.current_size += 1
						yield torch.tensor(_lowerCAmelCase   )







		def 							A  (self    :   List[str]   ,   _lowerCAmelCase    :   Union[str, Any]=1000   ):
			return ShufflerIterDataPipe(self   ,   buffer_size=_lowerCAmelCase   )






def  __a			(  UpperCAmelCase     )   ->Optional[Any]:



	"""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  __a			(  UpperCAmelCase     )   ->List[str]:



	"""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  __a			(  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]     )   ->List[Any]:



	"""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  __a			(  UpperCAmelCase							,      UpperCAmelCase     )   ->str:



	"""simple docstring"""







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




def  __a			(  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase     )   ->Tuple:



	"""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  __a			(  UpperCAmelCase     )   ->List[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
_lowerCamelCase					:						Any					     =							HfArgumentParser(TrainingArguments)
_lowerCamelCase					:						int					     =							parser.parse_args()

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

_lowerCamelCase					:						str					     =							Namespace(**vars(args), **acc_state)
_lowerCamelCase					:						List[str]					     =							accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

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

# Logging
_lowerCamelCase     ,	_lowerCamelCase					:						Optional[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
_lowerCamelCase					:						Any					     =							AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
				model.gradient_checkpointing_enable()
_lowerCamelCase					:						List[Any]					     =							AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
_lowerCamelCase     ,	_lowerCamelCase					:						Optional[int]					     =							create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
_lowerCamelCase					:						List[Any]					     =							AdamW(get_grouped_params(model, args), lr=args.learning_rate)
_lowerCamelCase					:						str					     =							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  __a			(  )   ->Union[str, Any]:



	"""simple docstring"""







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


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

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








'''simple docstring'''


from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


_lowerCamelCase					:						str					     =							{
    'configuration_ernie_m': ['ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieMConfig'],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCamelCase					:						List[str]					     =							['ErnieMTokenizer']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCamelCase					:						Union[str, Any]					     =							[
				    'ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'ErnieMForMultipleChoice',
				    'ErnieMForQuestionAnswering',
				    'ErnieMForSequenceClassification',
				    'ErnieMForTokenClassification',
				    'ErnieMModel',
				    'ErnieMPreTrainedModel',
				    'ErnieMForInformationExtraction',
				]


if TYPE_CHECKING:
				from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ernie_m import ErnieMTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ernie_m import (
								    ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ErnieMForInformationExtraction,
								    ErnieMForMultipleChoice,
								    ErnieMForQuestionAnswering,
								    ErnieMForSequenceClassification,
								    ErnieMForTokenClassification,
								    ErnieMModel,
								    ErnieMPreTrainedModel,
								)


else:
				import sys

				_lowerCamelCase					:						int					     =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

