'''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     a__( lowerCAmelCase__     ):


			'''simple docstring'''






			def __init__(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase=False	,   __lowerCAmelCase=1024	,   __lowerCAmelCase=1024	,   __lowerCAmelCase=3.6	,   __lowerCAmelCase=False	,   ):




					"""simple docstring"""


					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):




					"""simple docstring"""


					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(__lowerCAmelCase)[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(__lowerCAmelCase	,   truncation=__lowerCAmelCase)["""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(__lowerCAmelCase)	,   self.seq_length):
									lowerCAmelCase        =  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	,   __lowerCAmelCase=1000):




					"""simple docstring"""


					return ShufflerIterDataPipe(self	,   buffer_size=__lowerCAmelCase)







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






def     snake_case__							(      _A: Any				,       _A: Optional[Any]				,       _A: List[Any]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]			)   ->     str:
		lowerCAmelCase ,					lowerCAmelCase        =  [], []
		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     snake_case__							(      _A: Tuple				,       _A: Optional[Any]			)   ->     int:
		logger.info(f"Step {step}: {metrics}"			)
		if accelerator.is_main_process:
				accelerator.log(_A				,       _A			)






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


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

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

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

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

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

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

# Prepare the optimizer and learning rate scheduler
__lowercase				   =  AdamW(get_grouped_params(model, args), lr=args.learning_rate)
__lowercase				   =  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     snake_case__							(      )   ->     List[str]:
		return optimizer.param_groups[0]["lr"]


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

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






class     a__( metaclass=lowerCAmelCase__     ):


			'''simple docstring'''






			UpperCAmelCase_      :					Optional[int]								=	['''torch''', '''transformers''', '''k_diffusion''']



			def __init__(       self	,   *__lowerCAmelCase	,   **__lowerCAmelCase):




					"""simple docstring"""


					requires_backends(self	,   ["""torch""", """transformers""", """k_diffusion"""])



			@classmethod
			def 							a_	(       cls	,   *__lowerCAmelCase	,   **__lowerCAmelCase):




					"""simple docstring"""


					requires_backends(cls	,   ["""torch""", """transformers""", """k_diffusion"""])







			@classmethod
			def 							a_	(       cls	,   *__lowerCAmelCase	,   **__lowerCAmelCase):




					"""simple docstring"""


					requires_backends(cls	,   ["""torch""", """transformers""", """k_diffusion"""])




