'''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__					(					_lowercase    :				Union[str, Any]     )							->	Optional[Any]:
  __UpperCAmelCase:					Optional[int]		= fname.split(os.path.sep     )[-1]
  return re.search(r"""^(.*)_\d+\.jpg$"""   ,    _lowercase     ).groups()[0]




class    a (       __lowerCAmelCase				):



  """simple docstring"""







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

    '''simple docstring'''







    __UpperCAmelCase:					int		= file_names
    __UpperCAmelCase:					Tuple		= image_transform
    __UpperCAmelCase:					str		= label_to_id







  def __len__(							self					):

    '''simple docstring'''







    return len(self.file_names					)







  def __getitem__(							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[Any]		= self.file_names[idx]
    __UpperCAmelCase:					List[Any]		= PIL.Image.open(snake_case_					)
    __UpperCAmelCase:					Any		= raw_image.convert("""RGB"""					)
    if self.image_transform is not None:
      __UpperCAmelCase:					Tuple		= self.image_transform(snake_case_					)
    __UpperCAmelCase:					Dict		= extract_label(snake_case_					)
    if self.label_to_id is not None:
      __UpperCAmelCase:					Optional[Any]		= self.label_to_id[label]
    return {"image": image, "label": label}






def  UpperCamelCase__					(					_lowercase    :				List[Any]   ,    _lowercase    :				Dict     )							->	Dict:
  # Initialize accelerator
  if args.with_tracking:
    __UpperCAmelCase:					Dict		= Accelerator(
        cpu=args.cpu   ,    mixed_precision=args.mixed_precision   ,    log_with="""all"""   ,    project_dir=args.project_dir     )
  else:
    __UpperCAmelCase:					Dict		= Accelerator(cpu=args.cpu   ,    mixed_precision=args.mixed_precision     )

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  __UpperCAmelCase:					List[Any]		= config["""lr"""]
  __UpperCAmelCase:					Optional[int]		= int(config["""num_epochs"""]     )
  __UpperCAmelCase:					Dict		= int(config["""seed"""]     )
  __UpperCAmelCase:					int		= int(config["""batch_size"""]     )
  __UpperCAmelCase:					Any		= config["""image_size"""]
  if not isinstance(_lowercase   ,    (list, tuple)     ):
    __UpperCAmelCase:					List[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:					Any		= args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      __UpperCAmelCase:					int		= 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:					str		= os.path.split(_lowercase     )[-1].split("""."""     )[0]
    accelerator.init_trackers(_lowercase   ,    _lowercase     )

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

  # Build the label correspondences
  __UpperCAmelCase:					str		= [extract_label(_lowercase     ) for fname in file_names]
  __UpperCAmelCase:					Union[str, Any]		= list(set(_lowercase     )     )
  id_to_label.sort()
  __UpperCAmelCase:					Union[str, Any]		= {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:					List[Any]		= np.random.permutation(len(_lowercase     )     )
  __UpperCAmelCase:					str		= int(0.8 * len(_lowercase     )     )
  __UpperCAmelCase:					List[str]		= random_perm[:cut]
  __UpperCAmelCase:					str		= random_perm[cut:]

  # For training we use a simple RandomResizedCrop
  __UpperCAmelCase:					Optional[Any]		= Compose([RandomResizedCrop(_lowercase   ,    scale=(0.5, 1.0)     ), ToTensor()]     )
  __UpperCAmelCase:					Tuple		= PetsDataset(
      [file_names[i] for i in train_split]   ,    image_transform=_lowercase   ,    label_to_id=_lowercase     )

  # For evaluation, we use a deterministic Resize
  __UpperCAmelCase:					Dict		= Compose([Resize(_lowercase     ), ToTensor()]     )
  __UpperCAmelCase:					Tuple		= PetsDataset([file_names[i] for i in eval_split]   ,    image_transform=_lowercase   ,    label_to_id=_lowercase     )

  # Instantiate dataloaders.
  __UpperCAmelCase:					List[str]		= DataLoader(_lowercase   ,    shuffle=_lowercase   ,    batch_size=_lowercase   ,    num_workers=4     )
  __UpperCAmelCase:					Union[str, Any]		= 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:					Dict		= 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:					List[Any]		= model.to(accelerator.device     )

  # Freezing the base model
  for param in model.parameters():
    __UpperCAmelCase:					str		= False
  for param in model.get_classifier().parameters():
    __UpperCAmelCase:					List[Any]		= 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:					Dict		= torch.tensor(model.default_cfg["""std"""]     )[None, :, None, None].to(accelerator.device     )

  # Instantiate optimizer
  __UpperCAmelCase:					int		= torch.optim.Adam(params=model.parameters()   ,    lr=lr / 2_5     )

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

    if "epoch" in training_difference:
      __UpperCAmelCase:					Tuple		= int(training_difference.replace("""epoch_"""   ,    """"""     )     ) + 1
      __UpperCAmelCase:					Optional[Any]		= None
    else:
      __UpperCAmelCase:					Optional[Any]		= int(training_difference.replace("""step_"""   ,    """"""     )     )
      __UpperCAmelCase:					Optional[int]		= 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:					str		= 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:					Union[str, Any]		= 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:					Tuple		= train_dataloader
    for batch in active_dataloader:
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      __UpperCAmelCase:					Any		= {k: v.to(accelerator.device     ) for k, v in batch.items()}
      __UpperCAmelCase:					Dict		= (batch["""image"""] - mean) / std
      __UpperCAmelCase:					Dict		= model(_lowercase     )
      __UpperCAmelCase:					Dict		= 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:					Optional[Any]		= F'''step_{overall_step}'''
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            __UpperCAmelCase:					Optional[int]		= os.path.join(args.output_dir   ,    _lowercase     )
          accelerator.save_state(_lowercase     )
    model.eval()
    __UpperCAmelCase:					List[Any]		= 0
    __UpperCAmelCase:					Tuple		= 0
    for step, batch in enumerate(_lowercase     ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      __UpperCAmelCase:					List[Any]		= {k: v.to(accelerator.device     ) for k, v in batch.items()}
      __UpperCAmelCase:					int		= (batch["""image"""] - mean) / std
      with torch.no_grad():
        __UpperCAmelCase:					Union[str, Any]		= model(_lowercase     )
      __UpperCAmelCase:					str		= outputs.argmax(dim=-1     )
      __UpperCAmelCase:					Dict		= accelerator.gather_for_metrics((predictions, batch["""label"""])     )
      __UpperCAmelCase:					List[Any]		= predictions == references
      num_elems += accurate_preds.shape[0]
      accurate += accurate_preds.long().sum()

    __UpperCAmelCase:					Dict		= accurate.item() / num_elems
    # Use accelerator.print to print only on the main process.
    accelerator.print(F'''epoch {epoch}: {1_0_0 * eval_metric:.2f}'''     )
    if args.with_tracking:
      accelerator.log(
          {
              """accuracy""": 1_0_0 * eval_metric,
              """train_loss""": total_loss.item() / len(_lowercase     ),
              """epoch""": epoch,
          }   ,    step=_lowercase   ,    )
    if checkpointing_steps == "epoch":
      __UpperCAmelCase:					int		= F'''epoch_{epoch}'''
      if args.output_dir is not None:
        __UpperCAmelCase:					Union[str, Any]		= os.path.join(args.output_dir   ,    _lowercase     )
      accelerator.save_state(_lowercase     )

  if args.with_tracking:
    accelerator.end_training()






def  UpperCamelCase__					(					)							->	Optional[int]:
  __UpperCAmelCase:					List[Any]		= 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:					Union[str, Any]		= parser.parse_args()
  __UpperCAmelCase:					List[Any]		= {"""lr""": 3E-2, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 6_4, """image_size""": 2_2_4}
  training_function(_lowercase   ,    _lowercase     )


if __name__ == "__main__":
    main()


'''simple docstring'''






from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
SCREAMING_SNAKE_CASE_	   =			numpy.array([0, 0])
SCREAMING_SNAKE_CASE_	   =			numpy.array([0.5, 0.866_0254])
SCREAMING_SNAKE_CASE_	   =			numpy.array([1, 0])
SCREAMING_SNAKE_CASE_	   =			[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]






def  UpperCamelCase__					(					_lowercase    :				list[numpy.ndarray]   ,    _lowercase    :				int     )							->	list[numpy.ndarray]:
  __UpperCAmelCase:					int		= initial_vectors
  for _ in range(_lowercase     ):
    __UpperCAmelCase:					Any		= iteration_step(_lowercase     )
  return vectors






def  UpperCamelCase__					(					_lowercase    :				list[numpy.ndarray]     )							->	list[numpy.ndarray]:
  __UpperCAmelCase:					Optional[int]		= []
  for i, start_vector in enumerate(vectors[:-1]     ):
    __UpperCAmelCase:					Union[str, Any]		= vectors[i + 1]
    new_vectors.append(_lowercase     )
    __UpperCAmelCase:					Optional[int]		= end_vector - start_vector
    new_vectors.append(start_vector + difference_vector / 3     )
    new_vectors.append(
        start_vector + difference_vector / 3 + rotate(difference_vector / 3   ,    6_0     )     )
    new_vectors.append(start_vector + difference_vector * 2 / 3     )
  new_vectors.append(vectors[-1]     )
  return new_vectors






def  UpperCamelCase__					(					_lowercase    :				numpy.ndarray   ,    _lowercase    :				float     )							->	numpy.ndarray:
  __UpperCAmelCase:					Tuple		= numpy.radians(_lowercase     )
  __UpperCAmelCase, __UpperCAmelCase:					Optional[Any]		= numpy.cos(_lowercase     ), numpy.sin(_lowercase     )
  __UpperCAmelCase:					Tuple		= numpy.array(((c, -s), (s, c))     )
  return numpy.dot(_lowercase   ,    _lowercase     )






def  UpperCamelCase__					(					_lowercase    :				list[numpy.ndarray]     )							->	None:
  __UpperCAmelCase:					Union[str, Any]		= plt.gca()
  axes.set_aspect("""equal"""     )

  # matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
  # y-coordinates as inputs, which are constructed from the vector-list using
  # zip()
  __UpperCAmelCase, __UpperCAmelCase:					Dict		= zip(*_lowercase     )
  plt.plot(_lowercase   ,    _lowercase     )
  plt.show()


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    SCREAMING_SNAKE_CASE_	   =			iterate(INITIAL_VECTORS, 5)
    plot(processed_vectors)