

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  __snake_case					(      lowerCAmelCase__    ):

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

      if self.tokenized:
          SCREAMING_SNAKE_CASE_													=  seq_length * num_of_sequences
          SCREAMING_SNAKE_CASE_													=  'input_ids'
      else:
          SCREAMING_SNAKE_CASE_													=  seq_length * chars_per_token * num_of_sequences
          SCREAMING_SNAKE_CASE_													=  'content'

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


  def   lowerCAmelCase__       (   self  ,						_A=1000):
      return ShufflerIterDataPipe(self  ,						buffer_size=_A)






def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Tuple    ):





    """simple docstring"""


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


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Optional[Any]    ):





    """simple docstring"""


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


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						int  ,				_SCREAMING_SNAKE_CASE   :						Union[str, Any]  ,				_SCREAMING_SNAKE_CASE   :						str=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]    ):





    """simple docstring"""


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


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Optional[Any]  ,				_SCREAMING_SNAKE_CASE   :						Tuple    ):





    """simple docstring"""


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


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						List[str]  ,				_SCREAMING_SNAKE_CASE   :						Tuple  ,				_SCREAMING_SNAKE_CASE   :						int    ):





    """simple docstring"""


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


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Dict    ):





    """simple docstring"""


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


# Settings
UpperCamelCase__					: List[Any]								=      HfArgumentParser(TrainingArguments)
UpperCamelCase__					: Tuple								=      parser.parse_args()

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

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

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

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

# Load dataset and dataloader
UpperCamelCase__					: Optional[Any]								=      create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
UpperCamelCase__					: Any								=      AdamW(get_grouped_params(model, args), lr=args.learning_rate)
UpperCamelCase__					: 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    _UpperCAmelCase    (					):





    """simple docstring"""


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


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

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





def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int    ):





    """simple docstring"""


    if power < 0:
        return -1
    base %= mod
    SCREAMING_SNAKE_CASE_													=  1

    while power > 0:
        if power & 1:
            SCREAMING_SNAKE_CASE_													=  (result * base) % mod
        SCREAMING_SNAKE_CASE_													=  power >> 1
        SCREAMING_SNAKE_CASE_													=  (base * base) % mod

    return result


def    _UpperCAmelCase    (					):





    """simple docstring"""


    print(modular_exponential(3	,				200	,				13    )    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       main()


