"""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 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str     )	->				List[str]:
  _lowerCAmelCase						:	Optional[int]        =					fname.split(os.path.sep     )[-1]
  return re.search(r"""^(.*)_\d+\.jpg$"""      ,_UpperCAmelCase     ).groups()[0]
class 						__A							(			__UpperCamelCase		):






     def __init__(		self    ,					a__    ,					a__=None    ,					a__=None  ):
       _lowerCAmelCase						:	List[Any]        =					file_names
       _lowerCAmelCase						:	Optional[int]        =					image_transform
       _lowerCAmelCase						:	int        =					label_to_id






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






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







def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	int      ,_lowerCamelCase						:	Any     )	->				Tuple:
  # Initialize accelerator
  if args.with_tracking:
    _lowerCAmelCase						:	Tuple        =					Accelerator(
        cpu=args.cpu      ,mixed_precision=args.mixed_precision      ,log_with="""all"""      ,project_dir=args.project_dir     )
  else:
    _lowerCAmelCase						:	Tuple        =					Accelerator(cpu=args.cpu      ,mixed_precision=args.mixed_precision     )

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _lowerCAmelCase						:	Optional[int]        =					config['lr']
  _lowerCAmelCase						:	Union[str, Any]        =					int(config["""num_epochs"""]     )
  _lowerCAmelCase						:	Any        =					int(config["""seed"""]     )
  _lowerCAmelCase						:	Union[str, Any]        =					int(config["""batch_size"""]     )
  _lowerCAmelCase						:	Any        =					config['image_size']
  if not isinstance(_UpperCAmelCase      ,(list, tuple)     ):
    _lowerCAmelCase						:	Dict        =					(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":
      _lowerCAmelCase						:	Dict        =					args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      _lowerCAmelCase						:	Optional[int]        =					int(args.checkpointing_steps     )
    else:
      raise ValueError(
          f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."     )
  else:
    _lowerCAmelCase						:	Union[str, Any]        =					None

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

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

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

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

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

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

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

  # Instantiate dataloaders.
  _lowerCAmelCase						:	List[Any]        =					DataLoader(_UpperCAmelCase      ,shuffle=_UpperCAmelCase      ,batch_size=_UpperCAmelCase      ,num_workers=4     )
  _lowerCAmelCase						:	int        =					DataLoader(_UpperCAmelCase      ,shuffle=_UpperCAmelCase      ,batch_size=_UpperCAmelCase      ,num_workers=4     )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  _lowerCAmelCase						:	List[Any]        =					create_model("""resnet50d"""      ,pretrained=_UpperCAmelCase      ,num_classes=len(_UpperCAmelCase     )     )

  # 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).
  _lowerCAmelCase						:	str        =					model.to(accelerator.device     )

  # Freezing the base model
  for param in model.parameters():
    _lowerCAmelCase						:	Union[str, Any]        =					False
  for param in model.get_classifier().parameters():
    _lowerCAmelCase						:	Tuple        =					True

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

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

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

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

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

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

    _lowerCAmelCase						:	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(_UpperCAmelCase     ),
              """epoch""": epoch,
          }      ,step=_UpperCAmelCase      ,)
    if checkpointing_steps == "epoch":
      _lowerCAmelCase						:	Tuple        =					f"epoch_{epoch}"
      if args.output_dir is not None:
        _lowerCAmelCase						:	Optional[Any]        =					os.path.join(args.output_dir      ,_UpperCAmelCase     )
      accelerator.save_state(_UpperCAmelCase     )

  if args.with_tracking:
    accelerator.end_training()





def 							SCREAMING_SNAKE_CASE					(      )	->				int:
  _lowerCAmelCase						:	Optional[Any]        =					argparse.ArgumentParser(description="""Simple example of training script."""     )
  parser.add_argument("""--data_dir"""      ,required=_UpperCAmelCase      ,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=_UpperCAmelCase      ,default=_UpperCAmelCase      ,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=_UpperCAmelCase      ,default=_UpperCAmelCase      ,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=_UpperCAmelCase      ,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=_UpperCAmelCase      ,default=_UpperCAmelCase      ,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=_UpperCAmelCase      ,default="""logs"""      ,help="""Location on where to store experiment tracking logs` and relevent project information"""      ,)
  _lowerCAmelCase						:	Any        =					parser.parse_args()
  _lowerCAmelCase						:	Any        =					{'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
  training_function(_UpperCAmelCase      ,_UpperCAmelCase     )


if __name__ == "__main__":
     main()



"""simple docstring"""




import socket





def 							SCREAMING_SNAKE_CASE					(      )	->				str:
  _lowerCAmelCase						:	Optional[int]        =					socket.socket(socket.AF_INET      ,socket.SOCK_STREAM     )
  _lowerCAmelCase						:	Optional[int]        =					socket.gethostname()
  _lowerCAmelCase						:	Tuple        =					12312

  sock.connect((host, port)     )
  sock.send(b"""Hello server!"""     )

  with open("""Received_file"""      ,"""wb"""     ) as out_file:
    print("""File opened"""     )
    print("""Receiving data..."""     )
    while True:
      _lowerCAmelCase						:	List[Any]        =					sock.recv(1024     )
      if not data:
        break
      out_file.write(_lowerCamelCase     )

  print("""Successfully received the file"""     )
  sock.close()
  print("""Connection closed"""     )


if __name__ == "__main__":
     main()




