

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 		__magic_name__    (							lowerCamelCase__							):

   '''simple docstring'''




   def __init__(       self,					lowercase_,					lowercase_,					lowercase_=False,					lowercase_=1024,					lowercase_=1024,					lowercase_=3.6,					lowercase_=False,					)							->					Any:






     """simple docstring"""






     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 )							->					List[str]:






     """simple docstring"""






     a__						=iter(self.dataset )
     a__						=True
     while more_examples:
       a__,   a__						=[], 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:
             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(lowercase_,					truncation=lowercase_ )['''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(lowercase_ ),					self.seq_length ):
         a__						=all_token_ids[i : i + self.seq_length]
         if len(lowercase_ ) == self.seq_length:
           self.current_size += 1
           yield torch.tensor(lowercase_ )



   def 				_UpperCAmelCase				(       self,					lowercase_=1000 )							->					Any:






     """simple docstring"""






     return ShufflerIterDataPipe(self,					buffer_size=lowercase_ )





def     UpperCAmelCase__       (    _A   :				List[Any]							):





  '''simple docstring'''







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





  '''simple docstring'''







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




def     UpperCAmelCase__       (    _A   :				Dict			,   _A   :				List[Any]			,   _A   :				str=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]							):





  '''simple docstring'''







  a__,   a__						=[], []
  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   :				Optional[Any]							):





  '''simple docstring'''







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




def     UpperCAmelCase__       (    _A   :				Optional[Any]			,   _A   :				str			,   _A   :				List[Any]							):





  '''simple docstring'''







  a__						=accelerator.unwrap_model(_A							).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     UpperCAmelCase__       (    _A   :				int							):





  '''simple docstring'''







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


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

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

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

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

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

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

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

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



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_xglm import XGLMTokenizer
else:
       lowerCamelCase     =      None


lowerCamelCase     =      logging.get_logger(__name__)

lowerCamelCase     =      {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase     =      {
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json''',
    },
}

lowerCamelCase     =      {
    '''facebook/xglm-564M''': 2048,
}







class 		__magic_name__    (							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__				:			int												=     VOCAB_FILES_NAMES
   lowerCamelCase__				:			List[str]												=     PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__				:			Dict												=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__				:			Any												=     ['input_ids', 'attention_mask']
   lowerCamelCase__				:			Any												=     XGLMTokenizer



   def __init__(       self,					lowercase_=None,					lowercase_=None,					lowercase_="<s>",					lowercase_="</s>",					lowercase_="</s>",					lowercase_="<s>",					lowercase_="<unk>",					lowercase_="<pad>",					**lowercase_,					)							->					Any:






     """simple docstring"""






     a__						=7
     a__						=[F"""<madeupword{i}>""" for i in range(self.num_madeup_words )]

     a__						=kwargs.get('''additional_special_tokens''',					[] )
     kwargs["additional_special_tokens"] += [
         word for word in madeup_words if word not in kwargs["additional_special_tokens"]
     ]

     super().__init__(
         lowercase_,					tokenizer_file=lowercase_,					bos_token=lowercase_,					eos_token=lowercase_,					sep_token=lowercase_,					cls_token=lowercase_,					unk_token=lowercase_,					pad_token=lowercase_,					**lowercase_,					)

     a__						=vocab_file
     a__						=False if not self.vocab_file else True



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					List[int]:






     """simple docstring"""






     if token_ids_a is None:
       return [self.sep_token_id] + token_ids_a
     a__						=[self.sep_token_id]
     return sep + token_ids_a + sep + sep + token_ids_a



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					List[int]:






     """simple docstring"""






     a__						=[self.sep_token_id]

     if token_ids_a is None:
       return len(sep + token_ids_a ) * [0]
     return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]



   def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					Tuple[str]:






     """simple docstring"""






     if not self.can_save_slow_tokenizer:
       raise ValueError(
           '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
           '''tokenizer.''' )

     if not os.path.isdir(lowercase_ ):
       logger.error(F"""Vocabulary path ({save_directory}) should be a directory.""" )
       return
     a__						=os.path.join(
         lowercase_,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

     if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ):
       copyfile(self.vocab_file,					lowercase_ )

     return (out_vocab_file,)
