




'''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    :	Optional[Any]			,					_lowercase    :	int			,					_lowercase    :	Optional[int]			,					_lowercase    :	Optional[Any]=False			,					_lowercase    :	str=1_024			,					_lowercase    :	int=1_024			,					_lowercase    :	Optional[int]=3.6			,					_lowercase    :	Tuple=False			,					)						->       Optional[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    :	Optional[Any])						->       str:
        A_       		=   iter(self.dataset)
        A_       		=   True
        while more_examples:
           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 						__snake_case	(  self    :	Optional[Any]			,					_lowercase    :	Optional[int]=1_000)						->       Optional[Any]:
        return ShufflerIterDataPipe(self			,					buffer_size=lowerCamelCase__)





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





def  lowerCamelCase(       SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]    )		->		Dict:
   A_       		=   [], []
   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  lowerCamelCase(       SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )		->		Union[str, Any]:
   logger.info(F'Step {step}: {metrics}'    )
   if accelerator.is_main_process:
      accelerator.log(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )





def  lowerCamelCase(       SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )		->		Optional[int]:
   # TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
   A_       		=   accelerator.unwrap_model(SCREAMING_SNAKE_CASE_    ).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  lowerCamelCase(       SCREAMING_SNAKE_CASE_    )		->		Optional[int]:
   model.eval()
   A_       		=   []
   for step, batch in enumerate(SCREAMING_SNAKE_CASE_    ):
      with torch.no_grad():
         A_       		=   model(SCREAMING_SNAKE_CASE_					,labels=SCREAMING_SNAKE_CASE_    )
      A_       		=   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
   A_       		=   torch.cat(SCREAMING_SNAKE_CASE_    )
   A_       		=   losses[: eval_dataloader.dataset.current_size].mean()
   try:
      A_       		=   torch.exp(SCREAMING_SNAKE_CASE_    )
   except OverflowError:
      A_       		=   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 =	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 =	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  lowerCamelCase(       )		->		Union[str, Any]:
   return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
_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 =	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 =	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  lowerCamelCase(       SCREAMING_SNAKE_CASE_    )		->		int:

			if not isinstance(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    ) or number < 0:
						raise ValueError('Input must be a non-negative integer'    )

			A_       		=   number & ~(number - 1)
			A_       		=   0
			while intermediate:
						intermediate >>= 1
						index += 1
			return index - 1


if __name__ == "__main__":
							import doctest

							doctest.testmod(verbose=True)




