



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      (  snake_case__			:       int      )      ->	Union[str, Any]:
   UpperCamelCase       :    str		      =      fname.split(os.path.sep      )[-1]
   return re.search(R'^(.*)_\d+\.jpg$'			,	_lowerCamelCase      ).groups()[0]



class 							lowerCAmelCase_						(    UpperCamelCase__				):
       def __init__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=None,     SCREAMING_SNAKE_CASE_=None			)	->						str:
          UpperCamelCase       :    Dict		      =      file_names
          UpperCamelCase       :    Tuple		      =      image_transform
          UpperCamelCase       :    Optional[int]		      =      label_to_id
       def __len__(     self			)	->						List[Any]:
          return len(self.file_names			)






       def __getitem__(     self,     SCREAMING_SNAKE_CASE_			)	->						Any:
          UpperCamelCase       :    Optional[int]		      =      self.file_names[idx]
          UpperCamelCase       :    int		      =      PIL.Image.open(lowercase_			)
          UpperCamelCase       :    Union[str, Any]		      =      raw_image.convert('RGB'			)
          if self.image_transform is not None:
             UpperCamelCase       :    List[Any]		      =      self.image_transform(lowercase_			)
          UpperCamelCase       :    Optional[Any]		      =      extract_label(lowercase_			)
          if self.label_to_id is not None:
             UpperCamelCase       :    Optional[int]		      =      self.label_to_id[label]
          return {"image": image, "label": label}






def 						UpperCamelCase      (  snake_case__			:       Optional[Any]			,	snake_case__			:       Optional[int]      )      ->	str:
   # Initialize accelerator
   if args.with_tracking:
      UpperCamelCase       :    Optional[Any]		      =      Accelerator(
          cpu=args.cpu			,	mixed_precision=args.mixed_precision			,	log_with='all'			,	project_dir=args.project_dir      )
   else:
      UpperCamelCase       :    Optional[Any]		      =      Accelerator(cpu=args.cpu			,	mixed_precision=args.mixed_precision      )

   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   UpperCamelCase       :    Union[str, Any]		      =      config['lr']
   UpperCamelCase       :    List[Any]		      =      int(config['num_epochs']      )
   UpperCamelCase       :    Dict		      =      int(config['seed']      )
   UpperCamelCase       :    Optional[Any]		      =      int(config['batch_size']      )
   UpperCamelCase       :    Union[str, Any]		      =      config['image_size']
   if not isinstance(_lowerCamelCase			,	(list, tuple)      ):
      UpperCamelCase       :    str		      =      (image_size, image_size)

   # Parse out whether we are saving every epoch or after a certain number of batches
   if hasattr(args.checkpointing_steps			,	'isdigit'      ):
      if args.checkpointing_steps == "epoch":
         UpperCamelCase       :    List[Any]		      =      args.checkpointing_steps
      elif args.checkpointing_steps.isdigit():
         UpperCamelCase       :    Dict		      =      int(args.checkpointing_steps      )
      else:
         raise ValueError(
             F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""      )
   else:
      UpperCamelCase       :    Union[str, Any]		      =      None

   # We need to initialize the trackers we use, and also store our configuration
   if args.with_tracking:
      UpperCamelCase       :    List[str]		      =      os.path.split(_lowerCamelCase      )[-1].split('.'      )[0]
      accelerator.init_trackers(_lowerCamelCase			,	_lowerCamelCase      )

   # Grab all the image filenames
   UpperCamelCase       :    List[Any]		      =      [os.path.join(args.data_dir			,	_lowerCamelCase      ) for fname in os.listdir(args.data_dir      ) if fname.endswith('.jpg'      )]

   # Build the label correspondences
   UpperCamelCase       :    Tuple		      =      [extract_label(_lowerCamelCase      ) for fname in file_names]
   UpperCamelCase       :    int		      =      list(set(_lowerCamelCase      )      )
   id_to_label.sort()
   UpperCamelCase       :    Optional[int]		      =      {lbl: i for i, lbl in enumerate(_lowerCamelCase      )}

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

   # Split our filenames between train and validation
   UpperCamelCase       :    Union[str, Any]		      =      np.random.permutation(len(_lowerCamelCase      )      )
   UpperCamelCase       :    Dict		      =      int(0.8 * len(_lowerCamelCase      )      )
   UpperCamelCase       :    Optional[Any]		      =      random_perm[:cut]
   UpperCamelCase       :    Any		      =      random_perm[cut:]

   # For training we use a simple RandomResizedCrop
   UpperCamelCase       :    int		      =      Compose([RandomResizedCrop(_lowerCamelCase			,	scale=(0.5, 1.0)      ), ToTensor()]      )
   UpperCamelCase       :    Dict		      =      PetsDataset(
       [file_names[i] for i in train_split]			,	image_transform=_lowerCamelCase			,	label_to_id=_lowerCamelCase      )

   # For evaluation, we use a deterministic Resize
   UpperCamelCase       :    str		      =      Compose([Resize(_lowerCamelCase      ), ToTensor()]      )
   UpperCamelCase       :    Any		      =      PetsDataset([file_names[i] for i in eval_split]			,	image_transform=_lowerCamelCase			,	label_to_id=_lowerCamelCase      )

   # Instantiate dataloaders.
   UpperCamelCase       :    Optional[Any]		      =      DataLoader(_lowerCamelCase			,	shuffle=_lowerCamelCase			,	batch_size=_lowerCamelCase			,	num_workers=4      )
   UpperCamelCase       :    Optional[int]		      =      DataLoader(_lowerCamelCase			,	shuffle=_lowerCamelCase			,	batch_size=_lowerCamelCase			,	num_workers=4      )

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   UpperCamelCase       :    Tuple		      =      create_model('resnet50d'			,	pretrained=_lowerCamelCase			,	num_classes=len(_lowerCamelCase      )      )

   # 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       :    Dict		      =      model.to(accelerator.device      )

   # Freezing the base model
   for param in model.parameters():
      UpperCamelCase       :    Tuple		      =      False
   for param in model.get_classifier().parameters():
      UpperCamelCase       :    Optional[int]		      =      True

   # We normalize the batches of images to be a bit faster.
   UpperCamelCase       :    Optional[int]		      =      torch.tensor(model.default_cfg['mean']      )[None, :, None, None].to(accelerator.device      )
   UpperCamelCase       :    Union[str, Any]		      =      torch.tensor(model.default_cfg['std']      )[None, :, None, None].to(accelerator.device      )

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

   # Instantiate learning rate scheduler
   UpperCamelCase       :    Union[str, Any]		      =      OneCycleLR(optimizer=_lowerCamelCase			,	max_lr=_lowerCamelCase			,	epochs=_lowerCamelCase			,	steps_per_epoch=len(_lowerCamelCase      )      )

   # 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       :    Tuple		      =      accelerator.prepare(
       _lowerCamelCase			,	_lowerCamelCase			,	_lowerCamelCase			,	_lowerCamelCase			,	_lowerCamelCase      )
   # We need to keep track of how many total steps we have iterated over
   UpperCamelCase       :    List[Any]		      =      0
   # We also need to keep track of the starting epoch so files are named properly
   UpperCamelCase       :    Tuple		      =      0

   # Potentially 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       :    Tuple		      =      [f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
         dirs.sort(key=os.path.getctime      )
         UpperCamelCase       :    List[Any]		      =      dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
      # Extract `epoch_{i}` or `step_{i}`
      UpperCamelCase       :    Union[str, Any]		      =      os.path.splitext(_lowerCamelCase      )[0]

      if "epoch" in training_difference:
         UpperCamelCase       :    Optional[int]		      =      int(training_difference.replace('epoch_'			,	''      )      ) + 1
         UpperCamelCase       :    Union[str, Any]		      =      None
      else:
         UpperCamelCase       :    int		      =      int(training_difference.replace('step_'			,	''      )      )
         UpperCamelCase       :    int		      =      resume_step // len(_lowerCamelCase      )
         resume_step -= starting_epoch * len(_lowerCamelCase      )

    # Now we train the model
   for epoch in range(_lowerCamelCase			,	_lowerCamelCase      ):
      model.train()
      if args.with_tracking:
         UpperCamelCase       :    Optional[Any]		      =      0
      if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
         # We need to skip steps until we reach the resumed step
         UpperCamelCase       :    Optional[Any]		      =      accelerator.skip_first_batches(_lowerCamelCase			,	_lowerCamelCase      )
         overall_step += resume_step
      else:
         # After the first iteration though, we need to go back to the original dataloader
         UpperCamelCase       :    Tuple		      =      train_dataloader
      for batch in active_dataloader:
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         UpperCamelCase       :    Optional[int]		      =      {k: v.to(accelerator.device      ) for k, v in batch.items()}
         UpperCamelCase       :    Union[str, Any]		      =      (batch['image'] - mean) / std
         UpperCamelCase       :    List[str]		      =      model(_lowerCamelCase      )
         UpperCamelCase       :    Dict		      =      torch.nn.functional.cross_entropy(_lowerCamelCase			,	batch['label']      )
         # We keep track of the loss at each epoch
         if args.with_tracking:
            total_loss += loss.detach().float()
         accelerator.backward(_lowerCamelCase      )
         optimizer.step()
         lr_scheduler.step()
         optimizer.zero_grad()
         overall_step += 1
         if isinstance(_lowerCamelCase			,	_lowerCamelCase      ):
            UpperCamelCase       :    int		      =      F"""step_{overall_step}"""
            if overall_step % checkpointing_steps == 0:
               if args.output_dir is not None:
                  UpperCamelCase       :    Union[str, Any]		      =      os.path.join(args.output_dir			,	_lowerCamelCase      )
               accelerator.save_state(_lowerCamelCase      )
      model.eval()
      UpperCamelCase       :    int		      =      0
      UpperCamelCase       :    List[Any]		      =      0
      for step, batch in enumerate(_lowerCamelCase      ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         UpperCamelCase       :    int		      =      {k: v.to(accelerator.device      ) for k, v in batch.items()}
         UpperCamelCase       :    Optional[int]		      =      (batch['image'] - mean) / std
         with torch.no_grad():
            UpperCamelCase       :    int		      =      model(_lowerCamelCase      )
         UpperCamelCase       :    Optional[int]		      =      outputs.argmax(dim=-1      )
         UpperCamelCase      ,	UpperCamelCase       :    int		      =      accelerator.gather_for_metrics((predictions, batch['label'])      )
         UpperCamelCase       :    str		      =      predictions == references
         num_elems += accurate_preds.shape[0]
         accurate += accurate_preds.long().sum()

      UpperCamelCase       :    Union[str, Any]		      =      accurate.item() / num_elems
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"""epoch {epoch}: {100 * eval_metric:.2f}"""      )
      if args.with_tracking:
         accelerator.log(
             {
                 'accuracy': 100 * eval_metric,
                 'train_loss': total_loss.item() / len(_lowerCamelCase      ),
                 'epoch': epoch,
             }			,	step=_lowerCamelCase			,	)
      if checkpointing_steps == "epoch":
         UpperCamelCase       :    Union[str, Any]		      =      F"""epoch_{epoch}"""
         if args.output_dir is not None:
            UpperCamelCase       :    List[Any]		      =      os.path.join(args.output_dir			,	_lowerCamelCase      )
         accelerator.save_state(_lowerCamelCase      )

   if args.with_tracking:
      accelerator.end_training()
def 						UpperCamelCase      (  )      ->	Dict:
   UpperCamelCase       :    int		      =      argparse.ArgumentParser(description='Simple example of training script.'      )
   parser.add_argument('--data_dir'			,	required=_lowerCamelCase			,	help='The data folder on disk.'      )
   parser.add_argument('--fp16'			,	action='store_true'			,	help='If passed, will use FP16 training.'      )
   parser.add_argument(
       '--mixed_precision'			,	type=_lowerCamelCase			,	default=_lowerCamelCase			,	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('--cpu'			,	action='store_true'			,	help='If passed, will train on the CPU.'      )
   parser.add_argument(
       '--checkpointing_steps'			,	type=_lowerCamelCase			,	default=_lowerCamelCase			,	help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'			,	)
   parser.add_argument(
       '--output_dir'			,	type=_lowerCamelCase			,	default='.'			,	help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'			,	)
   parser.add_argument(
       '--resume_from_checkpoint'			,	type=_lowerCamelCase			,	default=_lowerCamelCase			,	help='If the training should continue from a checkpoint folder.'			,	)
   parser.add_argument(
       '--with_tracking'			,	action='store_true'			,	help='Whether to load in all available experiment trackers from the environment and use them for logging.'			,	)
   parser.add_argument(
       '--project_dir'			,	type=_lowerCamelCase			,	default='logs'			,	help='Location on where to store experiment tracking logs` and relevent project information'			,	)
   UpperCamelCase       :    Optional[int]		      =      parser.parse_args()
   UpperCamelCase       :    Optional[Any]		      =      {'lr': 3E-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
   training_function(_lowerCamelCase			,	_lowerCamelCase      )


if __name__ == "__main__":
    main()










'''simple docstring'''

import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__lowerCAmelCase				:      int						      =5_0000
__lowerCAmelCase				:      Union[str, Any]						      =5000

__lowerCAmelCase      ,	__lowerCAmelCase				:      Tuple						      =os.path.split(__file__)
__lowerCAmelCase				:      int						      =os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))





@get_duration
def 		UpperCamelCase							(				_lowerCamelCase   :   datasets.Dataset		,			_lowerCamelCase   :   int			):
    for i in range(_lowerCamelCase			):
        A__							=     dataset[i]





@get_duration
def 		UpperCamelCase							(				_lowerCamelCase   :   datasets.Dataset		,			_lowerCamelCase   :   Optional[int]		,			_lowerCamelCase   :   Any			):
    for i in range(0		,			len(_lowerCamelCase			)		,			_lowerCamelCase			):
        A__							=     dataset[i : i + batch_size]





@get_duration
def 		UpperCamelCase							(				_lowerCamelCase   :   datasets.Dataset		,			_lowerCamelCase   :   Optional[Any]		,			_lowerCamelCase   :   List[str]			):
    with dataset.formatted_as(type=_lowerCamelCase			):
        for i in range(_lowerCamelCase			):
            A__							=     dataset[i]





@get_duration
def 		UpperCamelCase							(				_lowerCamelCase   :   datasets.Dataset		,			_lowerCamelCase   :   Optional[int]		,			_lowerCamelCase   :   int		,			_lowerCamelCase   :   Dict			):
    with dataset.formatted_as(type=_lowerCamelCase			):
        for i in range(0		,			_lowerCamelCase		,			_lowerCamelCase			):
            A__							=     dataset[i : i + batch_size]





def 		UpperCamelCase							(				):
    A__							=     {"num examples": SPEED_TEST_N_EXAMPLES}
    A__							=     [
        (read, {"length": SMALL_TEST}),
        (read, {"length": SPEED_TEST_N_EXAMPLES}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_00}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10_00}),
        (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
        (read_formatted, {"type": "pandas", "length": SMALL_TEST}),
        (read_formatted, {"type": "torch", "length": SMALL_TEST}),
        (read_formatted, {"type": "tensorflow", "length": SMALL_TEST}),
        (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
        (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10_00}),
    ]

    A__							=     [
        (read, {"length": SMALL_TEST}),
        (read, {"length": SPEED_TEST_N_EXAMPLES}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_00}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10_00}),
        (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
        (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
        (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10_00}),
    ]
    with tempfile.TemporaryDirectory() as tmp_dir:
        print("generating dataset"			)
        A__							=     datasets.Features(
            {"list": datasets.Sequence(datasets.Value("float32"			)			), "numbers": datasets.Value("float32"			)}			)
        A__							=     generate_example_dataset(
            os.path.join(_lowerCamelCase		,			"dataset.arrow"			)		,			_lowerCamelCase		,			num_examples=_lowerCamelCase		,			seq_shapes={"list": (1_00,)}		,			)
        print("first set of iterations"			)
        for func, kwargs in functions:
            print(func.__name__		,			str(_lowerCamelCase			)			)
            A__							=     func(_lowerCamelCase		,			**_lowerCamelCase			)

        print("shuffling dataset"			)
        A__							=     dataset.shuffle()
        print("Second set of iterations (after shuffling"			)
        for func, kwargs in functions_shuffled:
            print("shuffled "		,			func.__name__		,			str(_lowerCamelCase			)			)
            A__							=     func(
                _lowerCamelCase		,			**_lowerCamelCase			)

    with open(_lowerCamelCase		,			"wb"			) as f:
        f.write(json.dumps(_lowerCamelCase			).encode("utf-8"			)			)


if __name__ == "__main__":  # useful to run the profiler
  benchmark_iterating()



