





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  ):




   def __init__(self      :Optional[int]       ,  _UpperCamelCase      :List[str]       ,  _UpperCamelCase      :Union[str, Any]       ,  _UpperCamelCase      :str=False       ,  _UpperCamelCase      :List[Any]=1024       ,  _UpperCamelCase      :Dict=1024       ,  _UpperCamelCase      :Union[str, Any]=3.6       ,  _UpperCamelCase      :int=False       ,  )->   Union[str, Any]:
         __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      :int					)->   List[str]:
         __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(_UpperCamelCase					)[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(_UpperCamelCase       ,  truncation=_UpperCamelCase					)['''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(_UpperCamelCase					)       ,  self.seq_length					):
                     __A			  =							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[int]       ,  _UpperCamelCase      :List[Any]=1000					)->   str:
         return ShufflerIterDataPipe(self       ,  buffer_size=_UpperCamelCase					)





def _a							(      lowerCamelCase:  str					)      ->		Dict:







      '''simple docstring'''






      __A			  =							args.model_ckpt.split('''/'''					)[-1]
      __A			  =							logging.getLogger(__name__					)
      __A			  =							Path(args.save_dir					) / '''log/'''
      log_dir.mkdir(exist_ok=lowerCamelCase					)
      __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(lowerCamelCase   ,							vars(lowerCamelCase					)					)
            __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							(      lowerCamelCase:  Dict					)      ->		Tuple:







      '''simple docstring'''






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



def _a							(      lowerCamelCase:  List[Any]   ,							lowerCamelCase:  Optional[int]   ,							lowerCamelCase:  Any=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]					)      ->		List[str]:







      '''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(lowerCamelCase					)
            else:
                  params_with_wd.append(lowerCamelCase					)
      return [
          {"params": params_with_wd, "weight_decay": args.weight_decay},
          {"params": params_without_wd, "weight_decay": 0.0},
      ]



def _a							(      lowerCamelCase:  Optional[Any]   ,							lowerCamelCase:  Optional[int]					)      ->		str:







      '''simple docstring'''






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



def _a							(      lowerCamelCase:  int   ,							lowerCamelCase:  Optional[Any]   ,							lowerCamelCase:  str					)      ->		Optional[Any]:







      '''simple docstring'''






      __A			  =							accelerator.unwrap_model(lowerCamelCase					).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							(      lowerCamelCase:  Optional[int]					)      ->		int:







      '''simple docstring'''






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


# Settings
snake_case__   :				Optional[Any]						=	HfArgumentParser(TrainingArguments)
snake_case__   :				Any						=	parser.parse_args()

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

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

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

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

# Load dataset and dataloader
snake_case__		,       snake_case__   :				Dict						=	create_dataloaders(args)

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







      '''simple docstring'''






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


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

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












def _a							(      lowerCamelCase:  list					)      ->		list:







      '''simple docstring'''






      __A			  =							len(lowerCamelCase					)
      for cycle_start in range(0   ,							array_len - 1					):
            __A			  =							array[cycle_start]

            __A			  =							cycle_start
            for i in range(cycle_start + 1   ,							lowerCamelCase					):
                  if array[i] < item:
                        pos += 1

            if pos == cycle_start:
                  continue

            while item == array[pos]:
                  pos += 1

            __A  ,		__A			  =							item, array[pos]
            while pos != cycle_start:
                  __A			  =							cycle_start
                  for i in range(cycle_start + 1   ,							lowerCamelCase					):
                        if array[i] < item:
                              pos += 1

                  while item == array[pos]:
                        pos += 1

                  __A  ,		__A			  =							item, array[pos]

      return array


if __name__ == "__main__":
       assert cycle_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
       assert cycle_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]





