"""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_	(				lowercase__   ):
    def __init__(	self			:       Optional[Any]   ,						_lowercase			:       Tuple   ,						_lowercase			:       Tuple   ,						_lowercase			:       str=False   ,						_lowercase			:       List[str]=1_0_2_4   ,						_lowercase			:       List[Any]=1_0_2_4   ,						_lowercase			:       Optional[int]=3.6   ,						_lowercase			:       str=False   ,						)						->    List[str]:
       _lowercase							     =       tokenizer
       _lowercase							     =       tokenizer.bos_token_id
       _lowercase							     =       dataset
       _lowercase							     =       seq_length
       _lowercase							     =       0
       _lowercase							     =       infinite
       _lowercase							     =       0
       _lowercase							     =       tokenized

       if self.tokenized:
          _lowercase							     =       seq_length * num_of_sequences
          _lowercase							     =       "input_ids"
       else:
          _lowercase							     =       seq_length * chars_per_token * num_of_sequences
          _lowercase							     =       "content"
    def __iter__(	self			:       List[str]		)						->    Optional[int]:
       _lowercase							     =       iter(self.dataset		)
       _lowercase							     =       True
       while more_examples:
          _lowercase     ,  _lowercase							     =       [], 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:
                   _lowercase							     =       iter(self.dataset		)
                   self.epoch += 1
                   logger.info(f"""Dataset epoch: {self.epoch}"""		)
                else:
                   _lowercase							     =       False
                   break
          if self.tokenized:
             _lowercase							     =       buffer
          else:
             _lowercase							     =       self.tokenizer(_lowercase   ,						truncation=_lowercase		)["input_ids"]
          _lowercase							     =       []
          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		):
             _lowercase							     =       all_token_ids[i : i + self.seq_length]
             if len(_lowercase		) == self.seq_length:
                self.current_size += 1
                yield torch.tensor(_lowercase		)




    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       List[str]=1_0_0_0		)						->    str:
       return ShufflerIterDataPipe(self   ,						buffer_size=_lowercase		)


def   __UpperCAmelCase     (	_snake_case		:       List[Any]       ):
   _lowercase							     =       args.model_ckpt.split("/"       )[-1]
   _lowercase							     =       logging.getLogger(__name__       )
   _lowercase							     =       Path(args.save_dir       ) / "log/"
   log_dir.mkdir(exist_ok=_snake_case       )
   _lowercase							     =       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(_snake_case,				vars(_snake_case       )       )
      _lowercase							     =       accelerator.trackers[0].run.name
      logger.setLevel(logging.INFO       )
      datasets.utils.logging.set_verbosity_info()
      transformers.utils.logging.set_verbosity_info()
   else:
      _lowercase							     =       ""
      logger.setLevel(logging.ERROR       )
      datasets.utils.logging.set_verbosity_error()
      transformers.utils.logging.set_verbosity_error()
   return logger, run_name


def   __UpperCAmelCase     (	_snake_case		:       List[Any]       ):
   _lowercase							     =       {"streaming": True}
   _lowercase							     =       load_dataset(args.dataset_name_train,				split="train",				**_snake_case       )
   _lowercase							     =       train_data.shuffle(buffer_size=args.shuffle_buffer,				seed=args.seed       )
   _lowercase							     =       load_dataset(args.dataset_name_valid,				split="train",				**_snake_case       )
   _lowercase							     =       ConstantLengthDataset(
       _snake_case,				_snake_case,				infinite=_snake_case,				seq_length=args.seq_length,				tokenized=args.tokenized       )
   _lowercase							     =       ConstantLengthDataset(
       _snake_case,				_snake_case,				infinite=_snake_case,				seq_length=args.seq_length,				tokenized=args.tokenized       )
   _lowercase							     =       train_dataset.shuffle(buffer_size=args.shuffle_buffer       )
   _lowercase							     =       DataLoader(_snake_case,				batch_size=args.train_batch_size,				shuffle=_snake_case       )
   _lowercase							     =       DataLoader(_snake_case,				batch_size=args.valid_batch_size       )
   return train_dataloader, eval_dataloader


def   __UpperCAmelCase     (	_snake_case		:       Optional[int],				_snake_case		:       Any,				_snake_case		:       List[str]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]       ):
   _lowercase     ,  _lowercase							     =       [], []
   for n, p in model.named_parameters():
      if any(nd in n for nd in no_decay       ):
         params_without_wd.append(_snake_case       )
      else:
         params_with_wd.append(_snake_case       )
   return [
       {"params": params_with_wd, "weight_decay": args.weight_decay},
       {"params": params_without_wd, "weight_decay": 0.0},
   ]


def   __UpperCAmelCase     (	_snake_case		:       Dict,				_snake_case		:       Optional[Any]       ):
   logger.info(f"""Step {step}: {metrics}"""       )
   if accelerator.is_main_process:
      accelerator.log(_snake_case,				_snake_case       )


def   __UpperCAmelCase     (	_snake_case		:       Dict,				_snake_case		:       int,				_snake_case		:       Optional[Any]       ):
   # TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
   _lowercase							     =       accelerator.unwrap_model(_snake_case       ).config
   _lowercase							     =       4 if args.gradient_checkpointing else 3
   _lowercase							     =       args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
   _lowercase							     =       2_4 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
   _lowercase							     =       factor * (
       1.0
       + (args.seq_length / (6.0 * config_model.n_embd))
       + (tokenizer.vocab_size / (1_6.0 * config_model.n_layer * config_model.n_embd))
   )
   _lowercase							     =       flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (1_0**1_2))
   return tflops


def   __UpperCAmelCase     (	_snake_case		:       Union[str, Any]       ):
   model.eval()
   _lowercase							     =       []
   for step, batch in enumerate(_snake_case       ):
      with torch.no_grad():
         _lowercase							     =       model(_snake_case,				labels=_snake_case       )
      _lowercase							     =       outputs.loss.repeat(args.valid_batch_size       )
      losses.append(accelerator.gather(_snake_case       )       )
      if args.max_eval_steps > 0 and step >= args.max_eval_steps:
         break
   _lowercase							     =       torch.cat(_snake_case       )
   _lowercase							     =       losses[: eval_dataloader.dataset.current_size].mean()
   try:
      _lowercase							     =       torch.exp(_snake_case       )
   except OverflowError:
      _lowercase							     =       float("inf"       )
   return loss.item(), perplexity.item()


# Settings
__UpperCamelCase       :    str         =		HfArgumentParser(TrainingArguments)
__UpperCamelCase       :    Dict         =		parser.parse_args()

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

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

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

# Logging
__UpperCamelCase       ,				__UpperCamelCase       :    List[Any]         =		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
__UpperCamelCase       :    List[str]         =		AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
  model.gradient_checkpointing_enable()
__UpperCamelCase       :    Tuple         =		AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
__UpperCamelCase       ,				__UpperCamelCase       :    List[Any]         =		create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
__UpperCamelCase       :    List[str]         =		AdamW(get_grouped_params(model, args), lr=args.learning_rate)
__UpperCamelCase       :    Optional[int]         =		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     (	):
   return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
__UpperCamelCase       ,				__UpperCamelCase       ,				__UpperCamelCase       ,				__UpperCamelCase       :    Dict         =		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)
    __UpperCamelCase       :    Tuple         =		os.path.basename(args.resume_from_checkpoint)
  else:
    # Get the most recent checkpoint
    __UpperCamelCase       :    Optional[int]         =		[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)
    __UpperCamelCase       :    Optional[int]         =		dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  # Extract the step of the checkpoint to continue from there
  __UpperCamelCase       :    Optional[Any]         =		os.path.splitext(path)[0]
  __UpperCamelCase       :    Any         =		int(training_difference.replace("step_", ""))

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


__UpperCamelCase       :    List[Any]         =		"\n# Diffusers installation\n! pip install diffusers transformers datasets accelerate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/diffusers.git\n"

__UpperCamelCase       :    Any         =		[{"type": "code", "content": INSTALL_CONTENT}]