





"""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 		lowercase ( _UpperCAmelCase ):





      def __init__(	self				,						lowercase				,						lowercase				,						lowercase=False				,						lowercase=1_024				,						lowercase=1_024				,						lowercase=3.6				,						lowercase=False				,						)		->					Union[str, Any]:
            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			)		->					Tuple:
            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(lowercase			)[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(lowercase				,						truncation=lowercase			)["""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(lowercase			)				,						self.seq_length			):
                        lowerCAmelCase  						=	all_token_ids[i : i + self.seq_length]
                        if len(lowercase			) == self.seq_length:
                              self.current_size += 1
                              yield torch.tensor(lowercase			)





      def    _snake_case      (	self				,						lowercase=1_000			)		->					List[str]:
            return ShufflerIterDataPipe(self				,						buffer_size=lowercase			)







def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						List[Any]      ):







      '''simple docstring'''




      lowerCAmelCase  						=	args.model_ckpt.split("""/"""      )[-1]
      lowerCAmelCase  						=	logging.getLogger(__name__      )
      lowerCAmelCase  						=	Path(args.save_dir      ) / """log/"""
      log_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE      )
      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(SCREAMING_SNAKE_CASE							,						vars(SCREAMING_SNAKE_CASE      )      )
            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       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						List[Any]      ):







      '''simple docstring'''




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


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Optional[int]							,						SCREAMING_SNAKE_CASE					:						Optional[Any]							,						SCREAMING_SNAKE_CASE					:						Tuple=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]      ):







      '''simple docstring'''




      lowerCAmelCase						,		lowerCAmelCase  						=	[], []
      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					:						List[str]							,						SCREAMING_SNAKE_CASE					:						int      ):







      '''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					:						Any							,						SCREAMING_SNAKE_CASE					:						List[Any]							,						SCREAMING_SNAKE_CASE					:						List[Any]      ):







      '''simple docstring'''




      lowerCAmelCase  						=	accelerator.unwrap_model(SCREAMING_SNAKE_CASE      ).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       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Optional[int]      ):







      '''simple docstring'''




      model.eval()
      lowerCAmelCase  						=	[]
      for step, batch in enumerate(SCREAMING_SNAKE_CASE      ):
            with torch.no_grad():
                  lowerCAmelCase  						=	model(SCREAMING_SNAKE_CASE							,						labels=SCREAMING_SNAKE_CASE      )
            lowerCAmelCase  						=	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
      lowerCAmelCase  						=	torch.cat(SCREAMING_SNAKE_CASE      )
      lowerCAmelCase  						=	losses[: eval_dataloader.dataset.current_size].mean()
      try:
            lowerCAmelCase  						=	torch.exp(SCREAMING_SNAKE_CASE      )
      except OverflowError:
            lowerCAmelCase  						=	float("""inf"""      )
      return loss.item(), perplexity.item()


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

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

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

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

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

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

# Prepare the optimizer and learning rate scheduler
SCREAMING_SNAKE_CASE__ =       AdamW(get_grouped_params(model, args), lr=args.learning_rate)
SCREAMING_SNAKE_CASE__ =       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`.
SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__ =       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)
              SCREAMING_SNAKE_CASE__ =       os.path.basename(args.resume_from_checkpoint)
       else:
              # Get the most recent checkpoint
              SCREAMING_SNAKE_CASE__ =       [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)
              SCREAMING_SNAKE_CASE__ =       dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
       # Extract the step of the checkpoint to continue from there
       SCREAMING_SNAKE_CASE__ =       os.path.splitext(path)[0]
       SCREAMING_SNAKE_CASE__ =       int(training_difference.replace("step_", ""))

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





def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						int							,						SCREAMING_SNAKE_CASE					:						int      ):







      '''simple docstring'''




      lowerCAmelCase  						=	1.0
      for i in range(1							,						r + 1      ):
            ret *= (n + 1 - i) / i
      return round(SCREAMING_SNAKE_CASE      )


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						int      ):







      '''simple docstring'''




      return choose(8 + n							,						SCREAMING_SNAKE_CASE      ) + choose(9 + n							,						SCREAMING_SNAKE_CASE      ) - 10


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						int      ):







      '''simple docstring'''




      return sum(non_bouncy_exact(SCREAMING_SNAKE_CASE      ) for i in range(1							,						n + 1      )      )


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						int = 1_00      ):







      '''simple docstring'''




      return non_bouncy_upto(SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
       print(f'{solution() = }')






