'''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 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  def __init__(					self ,						snake_case_ ,						snake_case_ ,						snake_case_=False ,						snake_case_=1_0_2_4 ,						snake_case_=1_0_2_4 ,						snake_case_=3.6 ,						snake_case_=False ,						):




       '''simple docstring'''

       UpperCAmelCase_				:  List[str]  = tokenizer
       UpperCAmelCase_				:  Any  = tokenizer.bos_token_id
       UpperCAmelCase_				:  int  = dataset
       UpperCAmelCase_				:  Optional[Any]  = seq_length
       UpperCAmelCase_				:  Any  = 0
       UpperCAmelCase_				:  Any  = infinite
       UpperCAmelCase_				:  Optional[Any]  = 0
       UpperCAmelCase_				:  List[Any]  = tokenized

       if self.tokenized:
            UpperCAmelCase_				:  Optional[Any]  = seq_length * num_of_sequences
            UpperCAmelCase_				:  List[Any]  = 'input_ids'
       else:
            UpperCAmelCase_				:  Optional[Any]  = seq_length * chars_per_token * num_of_sequences
            UpperCAmelCase_				:  Optional[Any]  = 'content'


  def __iter__(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Union[str, Any]  = iter(self.dataset		)
       UpperCAmelCase_				:  Optional[Any]  = True
       while more_examples:
            UpperCAmelCase_  ,     UpperCAmelCase_				:  Dict  = [], 0
            while True:
                 if buffer_len >= self.max_buffer_size:
                      break
                 try:
                      buffer.append(next(snake_case_		)[self.content_field]		)
                      buffer_len += len(buffer[-1]		)
                 except StopIteration:
                      if self.infinite:
                           UpperCAmelCase_				:  int  = iter(self.dataset		)
                           self.epoch += 1
                           logger.info(F'''Dataset epoch: {self.epoch}'''		)
                      else:
                           UpperCAmelCase_				:  Tuple  = False
                           break
            if self.tokenized:
                 UpperCAmelCase_				:  Union[str, Any]  = buffer
            else:
                 UpperCAmelCase_				:  Dict  = self.tokenizer(snake_case_ ,						truncation=snake_case_		)['input_ids']
            UpperCAmelCase_				:  str  = []
            for tokenized_input in tokenized_inputs:
                 all_token_ids.extend(tokenized_input + [self.concat_token_id]		)
            for i in range(0 ,						len(snake_case_		) ,						self.seq_length		):
                 UpperCAmelCase_				:  Tuple  = all_token_ids[i : i + self.seq_length]
                 if len(snake_case_		) == self.seq_length:
                      self.current_size += 1
                      yield torch.tensor(snake_case_		)






  def _UpperCamelCase	(					self ,						snake_case_=1_0_0_0		):




       '''simple docstring'''

       return ShufflerIterDataPipe(self ,						buffer_size=snake_case_		)







def    _lowerCamelCase       ( lowerCamelCase_   :				Union[str, Any]  ):



     """simple docstring"""





     UpperCAmelCase_				:  Optional[int]  = args.model_ckpt.split('/'  )[-1]
     UpperCAmelCase_				:  Dict  = logging.getLogger(__name__  )
     UpperCAmelCase_				:  List[Any]  = Path(args.save_dir  ) / 'log/'
     log_dir.mkdir(exist_ok=lowerCamelCase_  )
     UpperCAmelCase_				:  List[Any]  = 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_  )  )
          UpperCAmelCase_				:  int  = accelerator.trackers[0].run.name
          logger.setLevel(logging.INFO  )
          datasets.utils.logging.set_verbosity_info()
          transformers.utils.logging.set_verbosity_info()
     else:
          UpperCAmelCase_				:  int  = ''
          logger.setLevel(logging.ERROR  )
          datasets.utils.logging.set_verbosity_error()
          transformers.utils.logging.set_verbosity_error()
     return logger, run_name


def    _lowerCamelCase       ( lowerCamelCase_   :				str  ):



     """simple docstring"""





     UpperCAmelCase_				:  Optional[int]  = {'streaming': True}
     UpperCAmelCase_				:  Optional[int]  = load_dataset(args.dataset_name_train      ,	split='train'      ,	**lowerCamelCase_  )
     UpperCAmelCase_				:  int  = train_data.shuffle(buffer_size=args.shuffle_buffer      ,	seed=args.seed  )
     UpperCAmelCase_				:  Tuple  = load_dataset(args.dataset_name_valid      ,	split='train'      ,	**lowerCamelCase_  )
     UpperCAmelCase_				:  Tuple  = ConstantLengthDataset(
         lowerCamelCase_      ,	lowerCamelCase_      ,	infinite=lowerCamelCase_      ,	seq_length=args.seq_length      ,	tokenized=args.tokenized  )
     UpperCAmelCase_				:  Any  = ConstantLengthDataset(
         lowerCamelCase_      ,	lowerCamelCase_      ,	infinite=lowerCamelCase_      ,	seq_length=args.seq_length      ,	tokenized=args.tokenized  )
     UpperCAmelCase_				:  Optional[int]  = train_dataset.shuffle(buffer_size=args.shuffle_buffer  )
     UpperCAmelCase_				:  Optional[int]  = DataLoader(lowerCamelCase_      ,	batch_size=args.train_batch_size      ,	shuffle=lowerCamelCase_  )
     UpperCAmelCase_				:  Dict  = DataLoader(lowerCamelCase_      ,	batch_size=args.valid_batch_size  )
     return train_dataloader, eval_dataloader


def    _lowerCamelCase       ( lowerCamelCase_   :				List[Any]      ,	lowerCamelCase_   :				Tuple      ,	lowerCamelCase_   :				Dict=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]  ):



     """simple docstring"""





     UpperCAmelCase_  ,     UpperCAmelCase_				:  Dict  = [], []
     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    _lowerCamelCase       ( lowerCamelCase_   :				Union[str, Any]      ,	lowerCamelCase_   :				Union[str, Any]  ):



     """simple docstring"""





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


def    _lowerCamelCase       ( lowerCamelCase_   :				int      ,	lowerCamelCase_   :				Dict      ,	lowerCamelCase_   :				List[str]  ):



     """simple docstring"""





     UpperCAmelCase_				:  Optional[int]  = accelerator.unwrap_model(lowerCamelCase_  ).config
     UpperCAmelCase_				:  Union[str, Any]  = 4 if args.gradient_checkpointing else 3
     UpperCAmelCase_				:  Dict  = args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
     UpperCAmelCase_				:  List[str]  = 24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
     UpperCAmelCase_				:  int  = 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_				:  Tuple  = flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
     return tflops


def    _lowerCamelCase       ( lowerCamelCase_   :				Dict  ):



     """simple docstring"""





     model.eval()
     UpperCAmelCase_				:  Dict  = []
     for step, batch in enumerate(lowerCamelCase_  ):
          with torch.no_grad():
               UpperCAmelCase_				:  str  = model(lowerCamelCase_      ,	labels=lowerCamelCase_  )
          UpperCAmelCase_				:  Any  = 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
     UpperCAmelCase_				:  Union[str, Any]  = torch.cat(lowerCamelCase_  )
     UpperCAmelCase_				:  Optional[int]  = losses[: eval_dataloader.dataset.current_size].mean()
     try:
          UpperCAmelCase_				:  int  = torch.exp(lowerCamelCase_  )
     except OverflowError:
          UpperCAmelCase_				:  Any  = float('inf'  )
     return loss.item(), perplexity.item()


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

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

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

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

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

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

# Prepare the optimizer and learning rate scheduler
snake_case__	:     str      =    AdamW(get_grouped_params(model, args), lr=args.learning_rate)
snake_case__	:     Union[str, 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    _lowerCamelCase       ( ):



     """simple docstring"""





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


# Prepare everything with our `accelerator`.
snake_case__     , snake_case__     , snake_case__     , snake_case__	:     Optional[Any]      =    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__	:     List[str]      =    os.path.basename(args.resume_from_checkpoint)
 else:
  # Get the most recent checkpoint
  snake_case__	:     Any      =    [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__	:     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__	:     List[Any]      =    os.path.splitext(path)[0]
 snake_case__	:     List[Any]      =    int(training_difference.replace('''step_''', ''''''))

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







'''simple docstring'''




import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


__snake_case  : Optional[int]		       =    logging.getLogger(__name__)




class       __UpperCAmelCase							:





       '''simple docstring'''



       def __init__( self					)						->	List[str]:
           A_      =  False

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)						->	str:
           if not self.initialized:
               A_      =  RagRetriever(
                   _SCREAMING_SNAKE_CASE       ,	question_encoder_tokenizer=_SCREAMING_SNAKE_CASE       ,	generator_tokenizer=_SCREAMING_SNAKE_CASE       ,	index=_SCREAMING_SNAKE_CASE       ,	init_retrieval=_SCREAMING_SNAKE_CASE       ,	)
               A_      =  True

       def    __A			( self					)						->	List[Any]:
           self.retriever.index.init_index()

       def    __A			( self					)						->	Dict:
           # delete the old self.retriever object before assigning the new index
           del self.retriever
           A_      =  False

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)						->	Optional[int]:
           A_  ,A_      =  self.retriever._main_retrieve(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
           A_      =  self.retriever.index.get_doc_dicts(_SCREAMING_SNAKE_CASE					)
           return doc_ids, retrieved_doc_embeds, doc_dicts




class       __UpperCAmelCase							(							_UpperCamelCase	):





       '''simple docstring'''



       def __init__( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=None					)						->	List[Any]:
           if index is not None and index.is_initialized() and len(_SCREAMING_SNAKE_CASE					) > 0:
               raise ValueError(
                   '''When using Ray for distributed fine-tuning, '''
                   '''you\'ll need to provide the paths instead, '''
                   '''as the dataset and the index are loaded '''
                   '''separately. More info in examples/rag/use_own_knowledge_dataset.py '''					)

           super().__init__(
               _SCREAMING_SNAKE_CASE       ,	question_encoder_tokenizer=_SCREAMING_SNAKE_CASE       ,	generator_tokenizer=_SCREAMING_SNAKE_CASE       ,	index=_SCREAMING_SNAKE_CASE       ,	init_retrieval=_SCREAMING_SNAKE_CASE       ,	)

           A_      =  retrieval_workers
           A_      =  question_encoder_tokenizer
           A_      =  generator_tokenizer
           if len(self.retrieval_workers					) > 0:
               ray.get(
                   [
                       worker.create_rag_retriever.remote(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
                       for worker in self.retrieval_workers
                   ]					)

       def    __A			( self					)						->	List[Any]:
           logger.info('''initializing retrieval'''					)

           if len(self.retrieval_workers					) > 0:
               ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]					)
           else:
               # Non-distributed training. Load index into this same process.
               self.index.init_index()

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)						->	Optional[int]:
           if len(self.retrieval_workers					) > 0:
               # Select a random retrieval actor.
               A_      =  self.retrieval_workers[random.randint(0       ,	len(self.retrieval_workers					) - 1					)]
               A_  ,A_  ,A_      =  ray.get(
                   random_worker.retrieve.remote(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)					)
           else:
               A_  ,A_      =  self._main_retrieve(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
               A_      =  self.index.get_doc_dicts(_SCREAMING_SNAKE_CASE					)
           return retrieved_doc_embeds, doc_ids, doc_dicts

       @classmethod
       def    __A			( cls       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=None       ,	**_SCREAMING_SNAKE_CASE					)						->	List[Any]:
           return super(_SCREAMING_SNAKE_CASE       ,	cls					).get_tokenizers(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)

       @classmethod
       def    __A			( cls       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=None       ,	**_SCREAMING_SNAKE_CASE					)						->	int:
           A_      =  kwargs.pop('''config'''       ,	_SCREAMING_SNAKE_CASE					) or RagConfig.from_pretrained(_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)
           A_      =  RagTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE       ,	config=_SCREAMING_SNAKE_CASE					)
           A_      =  rag_tokenizer.question_encoder
           A_      =  rag_tokenizer.generator

           if indexed_dataset is not None:
               A_      =  '''custom'''
               A_      =  CustomHFIndex(config.retrieval_vector_size       ,	_SCREAMING_SNAKE_CASE					)
           else:
               A_      =  cls._build_index(_SCREAMING_SNAKE_CASE					)

           return cls(
               _SCREAMING_SNAKE_CASE       ,	question_encoder_tokenizer=_SCREAMING_SNAKE_CASE       ,	generator_tokenizer=_SCREAMING_SNAKE_CASE       ,	retrieval_workers=_SCREAMING_SNAKE_CASE       ,	index=_SCREAMING_SNAKE_CASE       ,	)

       def    __A			( self					)						->	Optional[int]:
           logger.info('''re-loading the new dataset with embeddings'''					)
           # access from the training loop

           ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]					)

           # build the index object again
           A_      =  self._build_index(self.config					)

           ray.get(
               [
                   worker.create_rag_retriever.remote(
                       self.config       ,	self.question_encoder_tokenizer       ,	self.generator_tokenizer       ,	_SCREAMING_SNAKE_CASE					)
                   for worker in self.retrieval_workers
               ]					)






