
"""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 				a__   (   a   :				int		):

  """simple docstring"""







  _snake_case     :						Optional[int]						=   fname.split(os.path.sep		)[-1]
  return re.search(R"^(.*)_\d+\.jpg$"       ,     SCREAMING_SNAKE_CASE_		).groups()[0]






class      _UpperCAmelCase				(					_snake_case):
    def __init__(    self						,    snake_case_						,    snake_case_=None						,    snake_case_=None     ):
      _snake_case     :						Optional[int]						=   file_names
      _snake_case     :						str						=   image_transform
      _snake_case     :						Tuple						=   label_to_id
    def __len__(    self     ):
      return len(self.file_names     )


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


def 				a__   (   a   :				Dict       ,     a   :				str		):

  """simple docstring"""







  if args.with_tracking:
    _snake_case     :						List[str]						=   Accelerator(
        cpu=args.cpu       ,     mixed_precision=args.mixed_precision       ,     log_with="all"       ,     project_dir=args.project_dir		)
  else:
    _snake_case     :						List[str]						=   Accelerator(cpu=args.cpu       ,     mixed_precision=args.mixed_precision		)

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

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

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

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

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

  # Split our filenames between train and validation
  _snake_case     :						Any						=   np.random.permutation(len(SCREAMING_SNAKE_CASE_		)		)
  _snake_case     :						List[str]						=   int(0.8 * len(SCREAMING_SNAKE_CASE_		)		)
  _snake_case     :						str						=   random_perm[:cut]
  _snake_case     :						Tuple						=   random_perm[cut:]

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

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

  # Instantiate dataloaders.
  _snake_case     :						Dict						=   DataLoader(SCREAMING_SNAKE_CASE_       ,     shuffle=SCREAMING_SNAKE_CASE_       ,     batch_size=SCREAMING_SNAKE_CASE_       ,     num_workers=4		)
  _snake_case     :						Union[str, Any]						=   DataLoader(SCREAMING_SNAKE_CASE_       ,     shuffle=SCREAMING_SNAKE_CASE_       ,     batch_size=SCREAMING_SNAKE_CASE_       ,     num_workers=4		)

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

  # 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).
  _snake_case     :						List[Any]						=   model.to(accelerator.device		)

  # Freezing the base model
  for param in model.parameters():
    _snake_case     :						Any						=   False
  for param in model.get_classifier().parameters():
    _snake_case     :						List[Any]						=   True

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

  # Instantiate optimizer
  _snake_case     :						Optional[int]						=   torch.optim.Adam(params=model.parameters()       ,     lr=lr / 25		)

  # Instantiate learning rate scheduler
  _snake_case     :						str						=   OneCycleLR(optimizer=SCREAMING_SNAKE_CASE_       ,     max_lr=SCREAMING_SNAKE_CASE_       ,     epochs=SCREAMING_SNAKE_CASE_       ,     steps_per_epoch=len(SCREAMING_SNAKE_CASE_		)		)

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

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

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

    _snake_case     :						List[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(SCREAMING_SNAKE_CASE_		),
              "epoch": epoch,
          }       ,     step=SCREAMING_SNAKE_CASE_       ,     )
    if checkpointing_steps == "epoch":
      _snake_case     :						List[str]						=   f'epoch_{epoch}'
      if args.output_dir is not None:
        _snake_case     :						int						=   os.path.join(args.output_dir       ,     SCREAMING_SNAKE_CASE_		)
      accelerator.save_state(SCREAMING_SNAKE_CASE_		)

  if args.with_tracking:
    accelerator.end_training()
def 				a__   (   ):

  """simple docstring"""







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


if __name__ == "__main__":
    main()





"""simple docstring"""
from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy






class      _UpperCAmelCase				(					_snake_case):
    __lowercase							: int  =					"""EncodecFeatureExtractor"""
    __lowercase							: str  =					("""T5Tokenizer""", """T5TokenizerFast""")
    def __init__(    self						,    snake_case_						,    snake_case_     ):
      super().__init__(snake_case_						,    snake_case_     )
      _snake_case     :						Dict						=   self.feature_extractor
      _snake_case     :						Any						=   False
    def 					lowerCamelCase__	(    self						,    snake_case_=None						,    snake_case_=None						,    snake_case_=True     ):
      return self.tokenizer.get_decoder_prompt_ids(task=snake_case_						,    language=snake_case_						,    no_timestamps=snake_case_     )
    def __call__(    self						,    *snake_case_						,    **snake_case_     ):
      # For backward compatibility
      if self._in_target_context_manager:
        return self.current_processor(*snake_case_						,    **snake_case_     )

      _snake_case     :						str						=   kwargs.pop("audio"						,    snake_case_     )
      _snake_case     :						Optional[int]						=   kwargs.pop("sampling_rate"						,    snake_case_     )
      _snake_case     :						Optional[Any]						=   kwargs.pop("text"						,    snake_case_     )
      if len(snake_case_     ) > 0:
        _snake_case     :						Any						=   args[0]
        _snake_case     :						Union[str, Any]						=   args[1:]

      if audio is None and text is None:
        raise ValueError("You need to specify either an `audio` or `text` input to process."     )

      if text is not None:
        _snake_case     :						Any						=   self.tokenizer(snake_case_						,    **snake_case_     )

      if audio is not None:
        _snake_case     :						Any						=   self.feature_extractor(snake_case_						,    *snake_case_						,    sampling_rate=snake_case_						,    **snake_case_     )

      if audio is None:
        return inputs

      elif text is None:
        return audio_inputs

      else:
        _snake_case     :						str						=   audio_inputs["input_values"]
        if "padding_mask" in audio_inputs:
          _snake_case     :						List[str]						=   audio_inputs["padding_mask"]
        return inputs
    def 					lowerCamelCase__	(    self						,    *snake_case_						,    **snake_case_     ):
      _snake_case     :						Tuple						=   kwargs.pop("audio"						,    snake_case_     )
      _snake_case     :						List[str]						=   kwargs.pop("padding_mask"						,    snake_case_     )

      if len(snake_case_     ) > 0:
        _snake_case     :						Tuple						=   args[0]
        _snake_case     :						Dict						=   args[1:]

      if audio_values is not None:
        return self._decode_audio(snake_case_						,    padding_mask=snake_case_     )
      else:
        return self.tokenizer.batch_decode(*snake_case_						,    **snake_case_     )
    def 					lowerCamelCase__	(    self						,    *snake_case_						,    **snake_case_     ):
      return self.tokenizer.decode(*snake_case_						,    **snake_case_     )


    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_ = None     ):
      _snake_case     :						Optional[int]						=   to_numpy(snake_case_     )
      _snake_case			,					_snake_case			,					_snake_case     :						Tuple						=   audio_values.shape

      if padding_mask is None:
        return list(snake_case_     )

      _snake_case     :						Optional[int]						=   to_numpy(snake_case_     )

      # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
      # token (so that the generated audio values are **not** treated as padded tokens)
      _snake_case     :						Any						=   seq_len - padding_mask.shape[-1]
      _snake_case     :						Optional[Any]						=   1 - self.feature_extractor.padding_value
      _snake_case     :						Optional[int]						=   np.pad(snake_case_						,    ((0, 0), (0, difference))						,    "constant"						,    constant_values=snake_case_     )

      _snake_case     :						Any						=   audio_values.tolist()
      for i in range(snake_case_     ):
        _snake_case     :						Tuple						=   np.asarray(audio_values[i]     )[
            padding_mask[i][None, :] != self.feature_extractor.padding_value
        ]
        _snake_case     :						Tuple						=   sliced_audio.reshape(snake_case_						,    -1     )

      return audio_values

