
"""simple docstring"""







import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator
def 			__UpperCAmelCase		(		a_			:		Tuple				)     ->       Optional[Any]:
   _UpperCAmelCase   	=					fname.split(os.path.sep				)[-1]
   return re.search(r"^(.*)_\d+\.jpg$"    ,  a_				).groups()[0]






class     __a			(					lowerCAmelCase_			):







       def __init__(					self							,lowerCamelCase_							,lowerCamelCase_=None							,lowerCamelCase_=None		):



          '''simple docstring'''

          _UpperCAmelCase   	=					file_names
          _UpperCAmelCase   	=					image_transform
          _UpperCAmelCase   	=					label_to_id







       def __len__(					self		):



          '''simple docstring'''

          return len(self.file_names		)







       def __getitem__(					self							,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase   	=					self.file_names[idx]
          _UpperCAmelCase   	=					PIL.Image.open(lowerCamelCase_		)
          _UpperCAmelCase   	=					raw_image.convert("RGB"		)
          if self.image_transform is not None:
             _UpperCAmelCase   	=					self.image_transform(lowerCamelCase_		)
          _UpperCAmelCase   	=					extract_label(lowerCamelCase_		)
          if self.label_to_id is not None:
             _UpperCAmelCase   	=					self.label_to_id[label]
          return {"image": image, "label": label}



def 			__UpperCAmelCase		(		a_			:		Optional[int]    ,  a_			:		Union[str, Any]				)     ->       Dict:
   # Initialize accelerator
   _UpperCAmelCase   	=					Accelerator(cpu=args.cpu    ,  mixed_precision=args.mixed_precision				)

   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   _UpperCAmelCase   	=					config["lr"]
   _UpperCAmelCase   	=					int(config["num_epochs"]				)
   _UpperCAmelCase   	=					int(config["seed"]				)
   _UpperCAmelCase   	=					int(config["batch_size"]				)
   _UpperCAmelCase   	=					config["image_size"]
   if not isinstance(a_    ,  (list, tuple)				):
      _UpperCAmelCase   	=					(image_size, image_size)

   # Grab all the image filenames
   _UpperCAmelCase   	=					[os.path.join(args.data_dir    ,  a_				) for fname in os.listdir(args.data_dir				) if fname.endswith(".jpg"				)]

   # Build the label correspondences
   _UpperCAmelCase   	=					[extract_label(a_				) for fname in file_names]
   _UpperCAmelCase   	=					list(set(a_				)				)
   id_to_label.sort()
   _UpperCAmelCase   	=					{lbl: i for i, lbl in enumerate(a_				)}

   # Set the seed before splitting the data.
   np.random.seed(a_				)
   torch.manual_seed(a_				)
   torch.cuda.manual_seed_all(a_				)

   # Split our filenames between train and validation
   _UpperCAmelCase   	=					np.random.permutation(len(a_				)				)
   _UpperCAmelCase   	=					int(0.8 * len(a_				)				)
   _UpperCAmelCase   	=					random_perm[:cut]
   _UpperCAmelCase   	=					random_perm[cut:]

   # For training we use a simple RandomResizedCrop
   _UpperCAmelCase   	=					Compose([RandomResizedCrop(a_    ,  scale=(0.5, 1.0)				), ToTensor()]				)
   _UpperCAmelCase   	=					PetsDataset(
       [file_names[i] for i in train_split]    ,  image_transform=a_    ,  label_to_id=a_				)

   # For evaluation, we use a deterministic Resize
   _UpperCAmelCase   	=					Compose([Resize(a_				), ToTensor()]				)
   _UpperCAmelCase   	=					PetsDataset([file_names[i] for i in eval_split]    ,  image_transform=a_    ,  label_to_id=a_				)

   # Instantiate dataloaders.
   _UpperCAmelCase   	=					DataLoader(a_    ,  shuffle=a_    ,  batch_size=a_    ,  num_workers=4				)
   _UpperCAmelCase   	=					DataLoader(a_    ,  shuffle=a_    ,  batch_size=a_    ,  num_workers=4				)

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   _UpperCAmelCase   	=					create_model("resnet50d"    ,  pretrained=a_    ,  num_classes=len(a_				)				)

   # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
   # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
   # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
   _UpperCAmelCase   	=					model.to(accelerator.device				)

   # Freezing the base model
   for param in model.parameters():
      _UpperCAmelCase   	=					False
   for param in model.get_classifier().parameters():
      _UpperCAmelCase   	=					True

   # We normalize the batches of images to be a bit faster.
   _UpperCAmelCase   	=					torch.tensor(model.default_cfg["mean"]				)[None, :, None, None].to(accelerator.device				)
   _UpperCAmelCase   	=					torch.tensor(model.default_cfg["std"]				)[None, :, None, None].to(accelerator.device				)

   # Instantiate optimizer
   _UpperCAmelCase   	=					torch.optim.Adam(params=model.parameters()    ,  lr=lr / 25				)

   # Instantiate learning rate scheduler
   _UpperCAmelCase   	=					OneCycleLR(optimizer=a_    ,  max_lr=a_    ,  epochs=a_    ,  steps_per_epoch=len(a_				)				)

   # Prepare everything
   # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
   # prepare method.
   _UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase   	=					accelerator.prepare(
       a_    ,  a_    ,  a_    ,  a_    ,  a_				)

   # Now we train the model
   for epoch in range(a_				):
      model.train()
      for step, batch in enumerate(a_				):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         _UpperCAmelCase   	=					{k: v.to(accelerator.device				) for k, v in batch.items()}
         _UpperCAmelCase   	=					(batch["image"] - mean) / std
         _UpperCAmelCase   	=					model(a_				)
         _UpperCAmelCase   	=					torch.nn.functional.cross_entropy(a_    ,  batch["label"]				)
         accelerator.backward(a_				)
         optimizer.step()
         lr_scheduler.step()
         optimizer.zero_grad()

      model.eval()
      _UpperCAmelCase   	=					0
      _UpperCAmelCase   	=					0
      for _, batch in enumerate(a_				):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         _UpperCAmelCase   	=					{k: v.to(accelerator.device				) for k, v in batch.items()}
         _UpperCAmelCase   	=					(batch["image"] - mean) / std
         with torch.no_grad():
            _UpperCAmelCase   	=					model(a_				)
         _UpperCAmelCase   	=					outputs.argmax(dim=-1				)
         _UpperCAmelCase	,							_UpperCAmelCase   	=					accelerator.gather_for_metrics((predictions, batch["label"])				)
         _UpperCAmelCase   	=					predictions == references
         num_elems += accurate_preds.shape[0]
         accurate += accurate_preds.long().sum()

      _UpperCAmelCase   	=					accurate.item() / num_elems
      # Use accelerator.print to print only on the main process.
      accelerator.print(F'''epoch {epoch}: {1_00 * eval_metric:.2f}'''				)
def 			__UpperCAmelCase		(		)     ->       Any:
   _UpperCAmelCase   	=					argparse.ArgumentParser(description="Simple example of training script."				)
   parser.add_argument("--data_dir"    ,  required=a_    ,  help="The data folder on disk."				)
   parser.add_argument(
       "--mixed_precision"    ,  type=a_    ,  default=a_    ,  choices=["no", "fp16", "bf16", "fp8"]    ,  help="Whether to use mixed precision. Choose"
       "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
       "and an Nvidia Ampere GPU."    ,  )
   parser.add_argument(
       "--checkpointing_steps"    ,  type=a_    ,  default=a_    ,  help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."    ,  )
   parser.add_argument("--cpu"    ,  action="store_true"    ,  help="If passed, will train on the CPU."				)
   _UpperCAmelCase   	=					parser.parse_args()
   _UpperCAmelCase   	=					{"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 2_24}
   training_function(a_    ,  a_				)


if __name__ == "__main__":
  main()



"""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     __a			(					lowerCAmelCase_			):







       def __init__(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_=False							,lowerCamelCase_=1024							,lowerCamelCase_=1024							,lowerCamelCase_=3.6							,lowerCamelCase_=False							,):



          '''simple docstring'''

          _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		):



          '''simple docstring'''

          _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    _snake_case			(					self							,lowerCamelCase_=1000		):



          '''simple docstring'''

          return ShufflerIterDataPipe(self							,buffer_size=lowerCamelCase_		)



def 			__UpperCAmelCase		(		a_			:		Dict				)     ->       Dict:
   _UpperCAmelCase   	=					args.model_ckpt.split("/"				)[-1]
   _UpperCAmelCase   	=					logging.getLogger(__name__				)
   _UpperCAmelCase   	=					Path(args.save_dir				) / "log/"
   log_dir.mkdir(exist_ok=a_				)
   _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(a_    ,  vars(a_				)				)
      _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 			__UpperCAmelCase		(		a_			:		Tuple				)     ->       Dict:
   _UpperCAmelCase   	=					{"streaming": True}
   _UpperCAmelCase   	=					load_dataset(args.dataset_name_train    ,  split="train"    ,  **a_				)
   _UpperCAmelCase   	=					train_data.shuffle(buffer_size=args.shuffle_buffer    ,  seed=args.seed				)
   _UpperCAmelCase   	=					load_dataset(args.dataset_name_valid    ,  split="train"    ,  **a_				)
   _UpperCAmelCase   	=					ConstantLengthDataset(
       a_    ,  a_    ,  infinite=a_    ,  seq_length=args.seq_length    ,  tokenized=args.tokenized				)
   _UpperCAmelCase   	=					ConstantLengthDataset(
       a_    ,  a_    ,  infinite=a_    ,  seq_length=args.seq_length    ,  tokenized=args.tokenized				)
   _UpperCAmelCase   	=					train_dataset.shuffle(buffer_size=args.shuffle_buffer				)
   _UpperCAmelCase   	=					DataLoader(a_    ,  batch_size=args.train_batch_size    ,  shuffle=a_				)
   _UpperCAmelCase   	=					DataLoader(a_    ,  batch_size=args.valid_batch_size				)
   return train_dataloader, eval_dataloader
def 			__UpperCAmelCase		(		a_			:		List[Any]    ,  a_			:		List[Any]    ,  a_			:		Optional[int]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]				)     ->       Dict:
   _UpperCAmelCase	,							_UpperCAmelCase   	=					[], []
   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_			:		List[str]				)     ->       Optional[Any]:
   logger.info(F'''Step {step}: {metrics}'''				)
   if accelerator.is_main_process:
      accelerator.log(a_    ,  a_				)
def 			__UpperCAmelCase		(		a_			:		Union[str, Any]    ,  a_			:		List[Any]    ,  a_			:		List[str]				)     ->       int:
   # TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
   _UpperCAmelCase   	=					accelerator.unwrap_model(a_				).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 			__UpperCAmelCase		(		a_			:		List[Any]				)     ->       Dict:
   model.eval()
   _UpperCAmelCase   	=					[]
   for step, batch in enumerate(a_				):
      with torch.no_grad():
         _UpperCAmelCase   	=					model(a_    ,  labels=a_				)
      _UpperCAmelCase   	=					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
   _UpperCAmelCase   	=					torch.cat(a_				)
   _UpperCAmelCase   	=					losses[: eval_dataloader.dataset.current_size].mean()
   try:
      _UpperCAmelCase   	=					torch.exp(a_				)
   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 			__UpperCAmelCase		(		)     ->       int:
   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')

