
"""simple docstring"""







from __future__ import annotations
def 			__UpperCAmelCase		(		a_			:		list[int]				)     ->       list[int]:
   if not list_of_ints:
      return []

   if min(a_				) < 0:
      raise ValueError("All numbers must be positive"				)

   _UpperCAmelCase   	=					max(len(bin(a_				)[2:]				) for x in list_of_ints				)
   return _msd_radix_sort(a_    ,  a_				)
def 			__UpperCAmelCase		(		a_			:		list[int]    ,  a_			:		int				)     ->       list[int]:
   if bit_position == 0 or len(a_				) in [0, 1]:
      return list_of_ints

   _UpperCAmelCase   	=					[]
   _UpperCAmelCase   	=					[]
   # Split numbers based on bit at bit_position from the right
   for number in list_of_ints:
      if (number >> (bit_position - 1)) & 1:
         # number has a one at bit bit_position
         ones.append(a_				)
      else:
         # number has a zero at bit bit_position
         zeros.append(a_				)

      # recursively split both lists further
   _UpperCAmelCase   	=					_msd_radix_sort(a_    ,  bit_position - 1				)
   _UpperCAmelCase   	=					_msd_radix_sort(a_    ,  bit_position - 1				)

   # recombine lists
   _UpperCAmelCase   	=					zeros
   res.extend(a_				)

   return res
def 			__UpperCAmelCase		(		a_			:		list[int]				)     ->       int:
   _UpperCAmelCase   	=					len(a_				)
   if not list_of_ints or length == 1:
      return

   if min(a_				) < 0:
      raise ValueError("All numbers must be positive"				)

   _UpperCAmelCase   	=					max(len(bin(a_				)[2:]				) for x in list_of_ints				)
   _msd_radix_sort_inplace(a_    ,  a_    ,  0    ,  a_				)
def 			__UpperCAmelCase		(		a_			:		list[int]    ,  a_			:		int    ,  a_			:		int    ,  a_			:		int				)     ->       Union[str, Any]:
   if bit_position == 0 or end_index - begin_index <= 1:
      return

   bit_position -= 1

   _UpperCAmelCase   	=					begin_index
   _UpperCAmelCase   	=					end_index - 1
   while i <= j:
      _UpperCAmelCase   	=					False
      if not (list_of_ints[i] >> bit_position) & 1:
         # found zero at the beginning
         i += 1
         _UpperCAmelCase   	=					True
      if (list_of_ints[j] >> bit_position) & 1:
         # found one at the end
         j -= 1
         _UpperCAmelCase   	=					True

      if changed:
         continue

      _UpperCAmelCase   	=					list_of_ints[j], list_of_ints[i]
      j -= 1
      if j != i:
         i += 1

   _msd_radix_sort_inplace(a_    ,  a_    ,  a_    ,  a_				)
   _msd_radix_sort_inplace(a_    ,  a_    ,  a_    ,  a_				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()



"""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_			):







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



          '''simple docstring'''

          _UpperCAmelCase   	=					tokenizer
          _UpperCAmelCase   	=					tokenizer.bos_token_id
          _UpperCAmelCase   	=					dataset
          _UpperCAmelCase   	=					seq_length
          _UpperCAmelCase   	=					0
          _UpperCAmelCase   	=					infinite
          _UpperCAmelCase   	=					0
          _UpperCAmelCase   	=					tokenized

          if self.tokenized:
             _UpperCAmelCase   	=					seq_length * num_of_sequences
             _UpperCAmelCase   	=					"input_ids"
          else:
             _UpperCAmelCase   	=					seq_length * chars_per_token * num_of_sequences
             _UpperCAmelCase   	=					"content"







       def __iter__(					self		):



          '''simple docstring'''

          _UpperCAmelCase   	=					iter(self.dataset		)
          _UpperCAmelCase   	=					True
          while more_examples:
             _UpperCAmelCase	,							_UpperCAmelCase   	=					[], 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:
                      _UpperCAmelCase   	=					iter(self.dataset		)
                      self.epoch += 1
                      logger.info(f'''Dataset epoch: {self.epoch}'''		)
                   else:
                      _UpperCAmelCase   	=					False
                      break
             if self.tokenized:
                _UpperCAmelCase   	=					buffer
             else:
                _UpperCAmelCase   	=					self.tokenizer(lowerCamelCase_							,truncation=lowerCamelCase_		)["input_ids"]
             _UpperCAmelCase   	=					[]
             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		):
                _UpperCAmelCase   	=					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							,lowerCamelCase_=1000		):



          '''simple docstring'''

          return ShufflerIterDataPipe(self							,buffer_size=lowerCamelCase_		)



def 			__UpperCAmelCase		(		a_			:		Dict				)     ->       Dict:
   _UpperCAmelCase   	=					args.model_ckpt.split("/"				)[-1]
   _UpperCAmelCase   	=					logging.getLogger(__name__				)
   _UpperCAmelCase   	=					Path(args.save_dir				) / "log/"
   log_dir.mkdir(exist_ok=a_				)
   _UpperCAmelCase   	=					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_				)				)
      _UpperCAmelCase   	=					accelerator.trackers[0].run.name
      logger.setLevel(logging.INFO				)
      datasets.utils.logging.set_verbosity_info()
      transformers.utils.logging.set_verbosity_info()
   else:
      _UpperCAmelCase   	=					""
      logger.setLevel(logging.ERROR				)
      datasets.utils.logging.set_verbosity_error()
      transformers.utils.logging.set_verbosity_error()
   return logger, run_name
def 			__UpperCAmelCase		(		a_			:		Tuple				)     ->       Dict:
   _UpperCAmelCase   	=					{"streaming": True}
   _UpperCAmelCase   	=					load_dataset(args.dataset_name_train    ,  split="train"    ,  **a_				)
   _UpperCAmelCase   	=					train_data.shuffle(buffer_size=args.shuffle_buffer    ,  seed=args.seed				)
   _UpperCAmelCase   	=					load_dataset(args.dataset_name_valid    ,  split="train"    ,  **a_				)
   _UpperCAmelCase   	=					ConstantLengthDataset(
       a_    ,  a_    ,  infinite=a_    ,  seq_length=args.seq_length    ,  tokenized=args.tokenized				)
   _UpperCAmelCase   	=					ConstantLengthDataset(
       a_    ,  a_    ,  infinite=a_    ,  seq_length=args.seq_length    ,  tokenized=args.tokenized				)
   _UpperCAmelCase   	=					train_dataset.shuffle(buffer_size=args.shuffle_buffer				)
   _UpperCAmelCase   	=					DataLoader(a_    ,  batch_size=args.train_batch_size    ,  shuffle=a_				)
   _UpperCAmelCase   	=					DataLoader(a_    ,  batch_size=args.valid_batch_size				)
   return train_dataloader, eval_dataloader
def 			__UpperCAmelCase		(		a_			:		List[Any]    ,  a_			:		List[Any]    ,  a_			:		Optional[int]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]				)     ->       Dict:
   _UpperCAmelCase	,							_UpperCAmelCase   	=					[], []
   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 			__UpperCAmelCase		(		a_			:		Union[str, Any]    ,  a_			:		List[str]				)     ->       Optional[Any]:
   logger.info(F'''Step {step}: {metrics}'''				)
   if accelerator.is_main_process:
      accelerator.log(a_    ,  a_				)
def 			__UpperCAmelCase		(		a_			:		Union[str, Any]    ,  a_			:		List[Any]    ,  a_			:		List[str]				)     ->       int:
   # TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
   _UpperCAmelCase   	=					accelerator.unwrap_model(a_				).config
   _UpperCAmelCase   	=					4 if args.gradient_checkpointing else 3
   _UpperCAmelCase   	=					args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
   _UpperCAmelCase   	=					24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
   _UpperCAmelCase   	=					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))
   )
   _UpperCAmelCase   	=					flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
   return tflops
def 			__UpperCAmelCase		(		a_			:		List[Any]				)     ->       Dict:
   model.eval()
   _UpperCAmelCase   	=					[]
   for step, batch in enumerate(a_				):
      with torch.no_grad():
         _UpperCAmelCase   	=					model(a_    ,  labels=a_				)
      _UpperCAmelCase   	=					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
   _UpperCAmelCase   	=					torch.cat(a_				)
   _UpperCAmelCase   	=					losses[: eval_dataloader.dataset.current_size].mean()
   try:
      _UpperCAmelCase   	=					torch.exp(a_				)
   except OverflowError:
      _UpperCAmelCase   	=					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		(		)     ->       int:
   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')

