





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_     (     snake_case__       ):



     def __init__(		self    :    List[Any]					,			lowerCamelCase    :    List[str]					,			lowerCamelCase    :    Union[str, Any]					,			lowerCamelCase    :    Union[str, Any]=False					,			lowerCamelCase    :    Tuple=10_24					,			lowerCamelCase    :    int=10_24					,			lowerCamelCase    :    Any=3.6					,			lowerCamelCase    :    Dict=False					,			):
       lowerCamelCase_  :    str   	=			tokenizer
       lowerCamelCase_  :    str   	=			tokenizer.bos_token_id
       lowerCamelCase_  :    Dict   	=			dataset
       lowerCamelCase_  :    Dict   	=			seq_length
       lowerCamelCase_  :    Optional[int]   	=			0
       lowerCamelCase_  :    Union[str, Any]   	=			infinite
       lowerCamelCase_  :    Optional[int]   	=			0
       lowerCamelCase_  :    str   	=			tokenized

       if self.tokenized:
         lowerCamelCase_  :    int   	=			seq_length * num_of_sequences
         lowerCamelCase_  :    List[str]   	=			'input_ids'
       else:
         lowerCamelCase_  :    str   	=			seq_length * chars_per_token * num_of_sequences
         lowerCamelCase_  :    int   	=			'content'



     def __iter__(		self    :    Optional[Any]	):
       lowerCamelCase_  :    Tuple   	=			iter(self.dataset	)
       lowerCamelCase_  :    Any   	=			True
       while more_examples:
         lowerCamelCase_  :    str   	=			[], 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:
               lowerCamelCase_  :    Any   	=			iter(self.dataset	)
               self.epoch += 1
               logger.info(F"Dataset epoch: {self.epoch}"	)
             else:
               lowerCamelCase_  :    List[str]   	=			False
               break
         if self.tokenized:
           lowerCamelCase_  :    Optional[Any]   	=			buffer
         else:
           lowerCamelCase_  :    Union[str, Any]   	=			self.tokenizer(lowerCamelCase					,			truncation=lowerCamelCase	)['input_ids']
         lowerCamelCase_  :    Union[str, Any]   	=			[]
         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	):
           lowerCamelCase_  :    Optional[int]   	=			all_token_ids[i : i + self.seq_length]
           if len(lowerCamelCase	) == self.seq_length:
             self.current_size += 1
             yield torch.tensor(lowerCamelCase	)





     def      __a    (		self    :    Dict					,			lowerCamelCase    :    Optional[int]=10_00	):
       return ShufflerIterDataPipe(self					,			buffer_size=lowerCamelCase	)


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


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


def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]	):
  lowerCamelCase_  :    Union[str, Any]   	=			[], []
  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 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__	):
  logger.info(F"Step {step}: {metrics}"	)
  if accelerator.is_main_process:
    accelerator.log(lowerCAmelCase__       ,lowerCAmelCase__	)


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


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


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

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

_lowercase		:     str 					=Namespace(**vars(args), **acc_state)
_lowercase		:     Any 					=accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

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

# Logging
_lowercase		:     Optional[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
_lowercase		:     int 					=AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
  model.gradient_checkpointing_enable()
_lowercase		:     Tuple 					=AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
_lowercase		:     Dict 					=create_dataloaders(args)

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


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

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

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

_lowercase		:     Optional[Any] 					=[(1,)]


def 		_SCREAMING_SNAKE_CASE    (   ):
		lowerCamelCase_  :    Union[str, Any]   	=			bool(os.getenv('RUN_SLOW'       ,0	)	)
		if run_slow:
				assert True
		else:
				assert False, "should have been skipped"


def 		_SCREAMING_SNAKE_CASE    (   ):
		lowerCamelCase_  :    List[str]   	=			bool(os.getenv('RUN_SLOW'       ,0	)	)
		if run_slow and torch_device == "cuda":
				assert True
		else:
				assert False, "should have been skipped"






@require_torch
class 	UpperCamelCase_     (     unittest.TestCase       ):



					@slow
					@require_torch_gpu
					def      __a    (		self    :    Tuple	):
							check_slow_torch_cuda()



					@require_torch_gpu
					@slow
					def      __a    (		self    :    Optional[int]	):
							check_slow_torch_cuda()





					@parameterized.expand(lowerCamelCase	)
					@slow
					def      __a    (		self    :    List[str]					,			lowerCamelCase    :    Optional[Any]=None	):
							check_slow()


@slow
@require_torch_gpu
def 		_SCREAMING_SNAKE_CASE    (   ):
		check_slow_torch_cuda()


@require_torch_gpu
@slow
def 		_SCREAMING_SNAKE_CASE    (   ):
		check_slow_torch_cuda()


@slow
@pytest.mark.parametrize('param'       ,[1]	)
def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
		check_slow()





@pytest.mark.parametrize('param'       ,[1]	)
@slow
def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
		check_slow()
