




"""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 							snake_case_ (				lowerCamelCase_ ):







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_=False					,				lowerCamelCase_=1_0_2_4					,				lowerCamelCase_=1_0_2_4					,				lowerCamelCase_=3.6					,				lowerCamelCase_=False					,				)							->      Optional[Any]:
       UpperCamelCase										=  tokenizer
       UpperCamelCase										=  tokenizer.bos_token_id
       UpperCamelCase										=  dataset
       UpperCamelCase										=  seq_length
       UpperCamelCase										=  0
       UpperCamelCase										=  infinite
       UpperCamelCase										=  0
       UpperCamelCase										=  tokenized

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







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







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_=1_0_0_0)							->      Union[str, Any]:
       return ShufflerIterDataPipe(self					,				buffer_size=lowerCamelCase_)





def     __snake_case   (							_lowercase						):


    """simple docstring"""







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





def     __snake_case   (							_lowercase						):


    """simple docstring"""







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





def     __snake_case   (							_lowercase ,_lowercase ,_lowercase=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]						):


    """simple docstring"""







    UpperCamelCase					,  UpperCamelCase										=  [], []
    for n, p in model.named_parameters():
        if any(nd in n for nd in no_decay						):
            params_without_wd.append(_lowercase						)
        else:
            params_with_wd.append(_lowercase						)
    return [
        {"params": params_with_wd, "weight_decay": args.weight_decay},
        {"params": params_without_wd, "weight_decay": 0.0},
    ]





def     __snake_case   (							_lowercase ,_lowercase						):


    """simple docstring"""







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





def     __snake_case   (							_lowercase ,_lowercase ,_lowercase						):


    """simple docstring"""







    UpperCamelCase										=  accelerator.unwrap_model(_lowercase						).config
    UpperCamelCase										=  4 if args.gradient_checkpointing else 3
    UpperCamelCase										=  args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
    UpperCamelCase										=  24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
    UpperCamelCase										=  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										=  flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
    return tflops





def     __snake_case   (							_lowercase						):


    """simple docstring"""







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


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

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

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

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

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

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

# Prepare the optimizer and learning rate scheduler
SCREAMING_SNAKE_CASE_      =					AdamW(get_grouped_params(model, args), lr=args.learning_rate)
SCREAMING_SNAKE_CASE_      =					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     __snake_case   (							):


    """simple docstring"""







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


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

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

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import VideoMAEImageProcessor

class 							snake_case_ (				unittest.TestCase ):







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_=7					,				lowerCamelCase_=3					,				lowerCamelCase_=1_0					,				lowerCamelCase_=1_8					,				lowerCamelCase_=3_0					,				lowerCamelCase_=4_0_0					,				lowerCamelCase_=True					,				lowerCamelCase_=None					,				lowerCamelCase_=True					,				lowerCamelCase_=[0.5, 0.5, 0.5]					,				lowerCamelCase_=[0.5, 0.5, 0.5]					,				lowerCamelCase_=None					,				)							->      Optional[Any]:
       UpperCamelCase										=  size if size is not None else {'''shortest_edge''': 1_8}
       UpperCamelCase										=  crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}

       UpperCamelCase										=  parent
       UpperCamelCase										=  batch_size
       UpperCamelCase										=  num_channels
       UpperCamelCase										=  num_frames
       UpperCamelCase										=  image_size
       UpperCamelCase										=  min_resolution
       UpperCamelCase										=  max_resolution
       UpperCamelCase										=  do_resize
       UpperCamelCase										=  size
       UpperCamelCase										=  do_normalize
       UpperCamelCase										=  image_mean
       UpperCamelCase										=  image_std
       UpperCamelCase										=  crop_size







   def      UpperCAmelCase__ (		self)							->      Tuple:
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "crop_size": self.crop_size,
       }

@require_torch
@require_vision
class 							snake_case_ (				lowerCamelCase_  ,   unittest.TestCase ):







   """simple docstring"""




   A_										=					VideoMAEImageProcessor if is_vision_available() else None







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       UpperCamelCase										=  VideoMAEImageProcessingTester(self)







   @property
   def      UpperCAmelCase__ (		self)							->      Optional[int]:
       return self.image_processor_tester.prepare_image_processor_dict()







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
       self.assertTrue(hasattr(lowerCamelCase_					,				'''image_mean'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''image_std'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''do_normalize'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''do_resize'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''do_center_crop'''))
       self.assertTrue(hasattr(lowerCamelCase_					,				'''size'''))







   def      UpperCAmelCase__ (		self)							->      Dict:
       UpperCamelCase										=  self.image_processing_class.from_dict(self.image_processor_dict)
       self.assertEqual(image_processor.size					,				{'''shortest_edge''': 1_8})
       self.assertEqual(image_processor.crop_size					,				{'''height''': 1_8, '''width''': 1_8})

       UpperCamelCase										=  self.image_processing_class.from_dict(self.image_processor_dict					,				size=4_2					,				crop_size=8_4)
       self.assertEqual(image_processor.size					,				{'''shortest_edge''': 4_2})
       self.assertEqual(image_processor.crop_size					,				{'''height''': 8_4, '''width''': 8_4})







   def      UpperCAmelCase__ (		self)							->      List[str]:
       pass







   def      UpperCAmelCase__ (		self)							->      Dict:
       # Initialize image_processing
       UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
       # create random PIL videos
       UpperCamelCase										=  prepare_video_inputs(self.image_processor_tester					,				equal_resolution=lowerCamelCase_)
       for video in video_inputs:
           self.assertIsInstance(lowerCamelCase_					,				lowerCamelCase_)
           self.assertIsInstance(video[0]					,				Image.Image)

       # Test not batched input
       UpperCamelCase										=  image_processing(video_inputs[0]					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)

       # Test batched
       UpperCamelCase										=  image_processing(lowerCamelCase_					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)







   def      UpperCAmelCase__ (		self)							->      Dict:
       # Initialize image_processing
       UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
       # create random numpy tensors
       UpperCamelCase										=  prepare_video_inputs(self.image_processor_tester					,				equal_resolution=lowerCamelCase_					,				numpify=lowerCamelCase_)
       for video in video_inputs:
           self.assertIsInstance(lowerCamelCase_					,				lowerCamelCase_)
           self.assertIsInstance(video[0]					,				np.ndarray)

       # Test not batched input
       UpperCamelCase										=  image_processing(video_inputs[0]					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)

       # Test batched
       UpperCamelCase										=  image_processing(lowerCamelCase_					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)







   def      UpperCAmelCase__ (		self)							->      Optional[int]:
       # Initialize image_processing
       UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
       # create random PyTorch tensors
       UpperCamelCase										=  prepare_video_inputs(self.image_processor_tester					,				equal_resolution=lowerCamelCase_					,				torchify=lowerCamelCase_)
       for video in video_inputs:
           self.assertIsInstance(lowerCamelCase_					,				lowerCamelCase_)
           self.assertIsInstance(video[0]					,				torch.Tensor)

       # Test not batched input
       UpperCamelCase										=  image_processing(video_inputs[0]					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)

       # Test batched
       UpperCamelCase										=  image_processing(lowerCamelCase_					,				return_tensors='''pt''').pixel_values
       self.assertEqual(
           encoded_videos.shape					,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )					,				)