
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      lowerCAmelCase__  (			__lowercase    ):






  def __init__(	self	:							str							,    SCREAMING_SNAKE_CASE__	:							Dict							,    SCREAMING_SNAKE_CASE__	:							Dict							,    SCREAMING_SNAKE_CASE__	:							Any=False							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=10_24							,    SCREAMING_SNAKE_CASE__	:							Dict=10_24							,    SCREAMING_SNAKE_CASE__	:							str=3.6							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=False							,    )  ->							Optional[int]:
      __lowerCamelCase					      =			tokenizer
      __lowerCamelCase					      =			tokenizer.bos_token_id
      __lowerCamelCase					      =			dataset
      __lowerCamelCase					      =			seq_length
      __lowerCamelCase					      =			0
      __lowerCamelCase					      =			infinite
      __lowerCamelCase					      =			0
      __lowerCamelCase					      =			tokenized

      if self.tokenized:
          __lowerCamelCase					      =			seq_length * num_of_sequences
          __lowerCamelCase					      =			'''input_ids'''
      else:
          __lowerCamelCase					      =			seq_length * chars_per_token * num_of_sequences
          __lowerCamelCase					      =			'''content'''






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







  def 				__A (	self	:							List[str]							,    SCREAMING_SNAKE_CASE__	:							str=10_00						)  ->							Union[str, Any]:
      return ShufflerIterDataPipe(self							,    buffer_size=SCREAMING_SNAKE_CASE__						)





def 				__magic_name__				(     __lowerCAmelCase			:		Tuple							)    ->     Dict:
    __lowerCamelCase					      =			args.model_ckpt.split('''/'''							)[-1]
    __lowerCamelCase					      =			logging.getLogger(__name__							)
    __lowerCamelCase					      =			Path(args.save_dir							) / '''log/'''
    log_dir.mkdir(exist_ok=__lowerCAmelCase							)
    __lowerCamelCase					      =			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							)							)
        __lowerCamelCase					      =			accelerator.trackers[0].run.name
        logger.setLevel(logging.INFO							)
        datasets.utils.logging.set_verbosity_info()
        transformers.utils.logging.set_verbosity_info()
    else:
        __lowerCamelCase					      =			''''''
        logger.setLevel(logging.ERROR							)
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()
    return logger, run_name




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




def 				__magic_name__				(     __lowerCAmelCase			:		int	,						__lowerCAmelCase			:		Optional[Any]	,						__lowerCAmelCase			:		Union[str, Any]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]							)    ->     List[str]:
    __lowerCamelCase      ,					__lowerCamelCase					      =			[], []
    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 				__magic_name__				(     __lowerCAmelCase			:		int	,						__lowerCAmelCase			:		Union[str, Any]							)    ->     str:
    logger.info(f'''Step {step}: {metrics}'''							)
    if accelerator.is_main_process:
        accelerator.log(__lowerCAmelCase	,						__lowerCAmelCase							)




def 				__magic_name__				(     __lowerCAmelCase			:		Any	,						__lowerCAmelCase			:		List[Any]	,						__lowerCAmelCase			:		Any							)    ->     Dict:
    # TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
    __lowerCamelCase					      =			accelerator.unwrap_model(__lowerCAmelCase							).config
    __lowerCamelCase					      =			4 if args.gradient_checkpointing else 3
    __lowerCamelCase					      =			args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
    __lowerCamelCase					      =			24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
    __lowerCamelCase					      =			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))
    )
    __lowerCamelCase					      =			flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
    return tflops




def 				__magic_name__				(     __lowerCAmelCase			:		Dict							)    ->     Optional[Any]:
    model.eval()
    __lowerCamelCase					      =			[]
    for step, batch in enumerate(__lowerCAmelCase							):
        with torch.no_grad():
            __lowerCamelCase					      =			model(__lowerCAmelCase	,						labels=__lowerCAmelCase							)
        __lowerCamelCase					      =			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
    __lowerCamelCase					      =			torch.cat(__lowerCAmelCase							)
    __lowerCamelCase					      =			losses[: eval_dataloader.dataset.current_size].mean()
    try:
        __lowerCamelCase					      =			torch.exp(__lowerCAmelCase							)
    except OverflowError:
        __lowerCamelCase					      =			float('''inf'''							)
    return loss.item(), perplexity.item()


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

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

SCREAMING_SNAKE_CASE__     :    Tuple      							=					Namespace(**vars(args), **acc_state)
SCREAMING_SNAKE_CASE__     :    int      							=					accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
     SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
SCREAMING_SNAKE_CASE__     :    List[str]      							=					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__     :    Optional[int]      							=					AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
     model.gradient_checkpointing_enable()
SCREAMING_SNAKE_CASE__     :    Tuple      							=					AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
SCREAMING_SNAKE_CASE__     :    List[str]      							=					create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					AdamW(get_grouped_params(model, args), lr=args.learning_rate)
SCREAMING_SNAKE_CASE__     :    Tuple      							=					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 				__magic_name__				(     )    ->     Any:
    return optimizer.param_groups[0]["lr"]


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

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





from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    "cross_attention",
    "0.20.0",
    "Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.",
    standard_warn=False,
)


SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					AttentionProcessor

class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							Tuple							,    *SCREAMING_SNAKE_CASE__	:							Dict							,    **SCREAMING_SNAKE_CASE__	:							int						)  ->							List[Any]:
						__lowerCamelCase					      =			f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'						)						)} instead.'''
						deprecate('''cross_attention'''							,    '''0.20.0'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							str							,    *SCREAMING_SNAKE_CASE__	:							Optional[Any]							,    **SCREAMING_SNAKE_CASE__	:							Optional[int]						)  ->							Optional[Any]:
						__lowerCamelCase					      =			f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'						)						)} instead.'''
						deprecate('''cross_attention'''							,    '''0.20.0'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							str							,    *SCREAMING_SNAKE_CASE__	:							List[Any]							,    **SCREAMING_SNAKE_CASE__	:							List[str]						)  ->							List[Any]:
						__lowerCamelCase					      =			f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'						)						)} instead.'''
						deprecate('''cross_attention'''							,    '''0.20.0'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							Optional[int]							,    *SCREAMING_SNAKE_CASE__	:							Optional[int]							,    **SCREAMING_SNAKE_CASE__	:							Tuple						)  ->							Optional[Any]:
						__lowerCamelCase					      =			f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'						)						)} instead.'''
						deprecate('''cross_attention'''							,    '''0.20.0'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							Optional[Any]							,    *SCREAMING_SNAKE_CASE__	:							int							,    **SCREAMING_SNAKE_CASE__	:							Optional[Any]						)  ->							List[str]:
						__lowerCamelCase					      =			f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'						)						)} instead.'''
						deprecate('''cross_attention'''							,    '''0.20.0'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							Tuple							,    *SCREAMING_SNAKE_CASE__	:							Tuple							,    **SCREAMING_SNAKE_CASE__	:							Any						)  ->							Union[str, Any]:
						__lowerCamelCase					      =			f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'						)						)} instead.'''
						deprecate('''cross_attention'''							,    '''0.20.0'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							str							,    *SCREAMING_SNAKE_CASE__	:							Union[str, Any]							,    **SCREAMING_SNAKE_CASE__	:							Tuple						)  ->							int:
						__lowerCamelCase					      =			f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'						)						)} instead.'''
						deprecate('''cross_attention'''							,    '''0.20.0'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)





class      lowerCAmelCase__  (			__lowercase    ):






		def __init__(	self	:							Optional[Any]							,    *SCREAMING_SNAKE_CASE__	:							Any							,    **SCREAMING_SNAKE_CASE__	:							str						)  ->							Dict:
						__lowerCamelCase					      =			f'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'						)						)} instead.'''
						deprecate('''cross_attention'''							,    '''0.20.0'''							,    SCREAMING_SNAKE_CASE__							,    standard_warn=SCREAMING_SNAKE_CASE__						)
						super().__init__(*SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)



