





'''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 				lowercase__   (   __lowercase						:  List[str]     ) ->      Optional[int]:



       """simple docstring"""






       __UpperCamelCase       =  fname.split(os.path.sep     )[-1]
       return re.search(R'^(.*)_\d+\.jpg$'				,  __lowercase     ).groups()[0]

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 def __init__(       self       :							List[Any]							,	__A       :							Dict							,	__A       :							Dict=None							,	__A       :							List[str]=None	):
        __UpperCamelCase       =  file_names
        __UpperCamelCase       =  image_transform
        __UpperCamelCase       =  label_to_id






 def __len__(       self       :							int	):
        return len(self.file_names	)






 def __getitem__(       self       :							Tuple							,	__A       :							List[str]	):
        __UpperCamelCase       =  self.file_names[idx]
        __UpperCamelCase       =  PIL.Image.open(__A	)
        __UpperCamelCase       =  raw_image.convert('RGB'	)
        if self.image_transform is not None:
               __UpperCamelCase       =  self.image_transform(__A	)
        __UpperCamelCase       =  extract_label(__A	)
        if self.label_to_id is not None:
               __UpperCamelCase       =  self.label_to_id[label]
        return {"image": image, "label": label}






def 				lowercase__   (   __lowercase						:  Tuple				,  __lowercase						:  Tuple     ) ->      List[str]:



       """simple docstring"""






       if args.with_tracking:
              __UpperCamelCase       =  Accelerator(
                  cpu=args.cpu				,  mixed_precision=args.mixed_precision				,  log_with='all'				,  project_dir=args.project_dir     )
       else:
              __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(__lowercase				,  (list, tuple)     ):
              __UpperCamelCase       =  (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       =  args.checkpointing_steps
              elif args.checkpointing_steps.isdigit():
                     __UpperCamelCase       =  int(args.checkpointing_steps     )
              else:
                     raise ValueError(
                         F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''     )
       else:
              __UpperCamelCase       =  None

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

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

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

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

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

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

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

       # Instantiate dataloaders.
       __UpperCamelCase       =  DataLoader(__lowercase				,  shuffle=__lowercase				,  batch_size=__lowercase				,  num_workers=4     )
       __UpperCamelCase       =  DataLoader(__lowercase				,  shuffle=__lowercase				,  batch_size=__lowercase				,  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=__lowercase				,  num_classes=len(__lowercase     )     )

       # 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=__lowercase				,  max_lr=__lowercase				,  epochs=__lowercase				,  steps_per_epoch=len(__lowercase     )     )

       # 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(
           __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase     )
       # We need to keep track of how many total steps we have iterated over
       __UpperCamelCase       =  0
       # We also need to keep track of the starting epoch so files are named properly
       __UpperCamelCase       =  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       =  os.path.basename(args.resume_from_checkpoint     )
              else:
                     # Get the most recent checkpoint
                     __UpperCamelCase       =  [f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
                     dirs.sort(key=os.path.getctime     )
                     __UpperCamelCase       =  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
              # Extract `epoch_{i}` or `step_{i}`
              __UpperCamelCase       =  os.path.splitext(__lowercase     )[0]

              if "epoch" in training_difference:
                     __UpperCamelCase       =  int(training_difference.replace('epoch_'				,  ''     )     ) + 1
                     __UpperCamelCase       =  None
              else:
                     __UpperCamelCase       =  int(training_difference.replace('step_'				,  ''     )     )
                     __UpperCamelCase       =  resume_step // len(__lowercase     )
                     resume_step -= starting_epoch * len(__lowercase     )

    # Now we train the model
       for epoch in range(__lowercase				,  __lowercase     ):
              model.train()
              if args.with_tracking:
                     __UpperCamelCase       =  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       =  accelerator.skip_first_batches(__lowercase				,  __lowercase     )
                     overall_step += resume_step
              else:
                     # After the first iteration though, we need to go back to the original dataloader
                     __UpperCamelCase       =  train_dataloader
              for batch in active_dataloader:
                     # 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(__lowercase     )
                     __UpperCamelCase       =  torch.nn.functional.cross_entropy(__lowercase				,  batch['label']     )
                     # We keep track of the loss at each epoch
                     if args.with_tracking:
                            total_loss += loss.detach().float()
                     accelerator.backward(__lowercase     )
                     optimizer.step()
                     lr_scheduler.step()
                     optimizer.zero_grad()
                     overall_step += 1
                     if isinstance(__lowercase				,  __lowercase     ):
                            __UpperCamelCase       =  F'''step_{overall_step}'''
                            if overall_step % checkpointing_steps == 0:
                                   if args.output_dir is not None:
                                          __UpperCamelCase       =  os.path.join(args.output_dir				,  __lowercase     )
                                   accelerator.save_state(__lowercase     )
              model.eval()
              __UpperCamelCase       =  0
              __UpperCamelCase       =  0
              for step, batch in enumerate(__lowercase     ):
                     # 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(__lowercase     )
                     __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}: {100 * eval_metric:.2f}'''     )
              if args.with_tracking:
                     accelerator.log(
                         {
                             'accuracy': 100 * eval_metric,
                             'train_loss': total_loss.item() / len(__lowercase     ),
                             'epoch': epoch,
                         }				,  step=__lowercase				,  )
              if checkpointing_steps == "epoch":
                     __UpperCamelCase       =  F'''epoch_{epoch}'''
                     if args.output_dir is not None:
                            __UpperCamelCase       =  os.path.join(args.output_dir				,  __lowercase     )
                     accelerator.save_state(__lowercase     )

       if args.with_tracking:
              accelerator.end_training()
def 				lowercase__   (   ) ->      int:



       """simple docstring"""






       __UpperCamelCase       =  argparse.ArgumentParser(description='Simple example of training script.'     )
       parser.add_argument('--data_dir'				,  required=__lowercase				,  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=__lowercase				,  default=__lowercase				,  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=__lowercase				,  default=__lowercase				,  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=__lowercase				,  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=__lowercase				,  default=__lowercase				,  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=__lowercase				,  default='logs'				,  help='Location on where to store experiment tracking logs` and relevent project information'				,  )
       __UpperCamelCase       =  parser.parse_args()
       __UpperCamelCase       =  {'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
       training_function(__lowercase				,  __lowercase     )


if __name__ == "__main__":
    main()







'''simple docstring'''
from __future__ import annotations

from typing import Any

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 pass






class      snake_case     :




 """simple docstring"""



 def __init__(       self       :							List[Any]							,	__A       :							Any	):
        __UpperCamelCase       =  data
        __UpperCamelCase       =  None






 def __iter__(       self       :							Optional[Any]	):
        __UpperCamelCase       =  self
        __UpperCamelCase       =  []
        while node:
               if node in visited:
                      raise ContainsLoopError
               visited.append(__A	)
               yield node.data
               __UpperCamelCase       =  node.next_node






 @property
 def        _lowerCamelCase					(       self       :							List[str]	):
        try:
               list(self	)
               return False
        except ContainsLoopError:
               return True


if __name__ == "__main__":
    a__							:							Dict =Node(1)
    a__							:							Optional[int] =Node(2)
    a__							:							List[str] =Node(3)
    a__							:							Optional[int] =Node(4)
    print(root_node.has_loop)  # False
    a__							:							str =root_node.next_node
    print(root_node.has_loop)  # True

    a__							:							Optional[int] =Node(5)
    a__							:							List[Any] =Node(6)
    a__							:							int =Node(5)
    a__							:							Tuple =Node(6)
    print(root_node.has_loop)  # False

    a__							:							str =Node(1)
    print(root_node.has_loop)  # False
