


"""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__			(				SCREAMING_SNAKE_CASE    :    Optional[Any]				):


  '''simple docstring'''







  lowerCAmelCase     :     Optional[Any]								=							fname.split(os.path.sep				)[-1]
  return re.search(r"^(.*)_\d+\.jpg$"				,      SCREAMING_SNAKE_CASE				).groups()[0]


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


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






         """simple docstring"""





         lowerCAmelCase     :     Optional[Any]								=							file_names
         lowerCAmelCase     :     List[Any]								=							image_transform
         lowerCAmelCase     :     int								=							label_to_id

       def __len__(						self						):






         """simple docstring"""





         return len(self.file_names						)







       def __getitem__(						self		,     snake_case__						):






         """simple docstring"""





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

def 					a__			(				SCREAMING_SNAKE_CASE    :    List[Any]				,      SCREAMING_SNAKE_CASE    :    str				):


  '''simple docstring'''







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

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

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

  # Grab all the image filenames
  lowerCAmelCase     :     Optional[int]								=							[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
  lowerCAmelCase     :     List[str]								=							[extract_label(SCREAMING_SNAKE_CASE				) for fname in file_names]
  lowerCAmelCase     :     List[str]								=							list(set(SCREAMING_SNAKE_CASE				)				)
  id_to_label.sort()
  lowerCAmelCase     :     Optional[Any]								=							{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
  lowerCAmelCase     :     Any								=							np.random.permutation(len(SCREAMING_SNAKE_CASE				)				)
  lowerCAmelCase     :     Dict								=							int(0.8 * len(SCREAMING_SNAKE_CASE				)				)
  lowerCAmelCase     :     Any								=							random_perm[:cut]
  lowerCAmelCase     :     Union[str, Any]								=							random_perm[cut:]

  # For training we use a simple RandomResizedCrop
  lowerCAmelCase     :     List[str]								=							Compose([RandomResizedCrop(SCREAMING_SNAKE_CASE				,      scale=(0.5, 1.0)				), ToTensor()]				)
  lowerCAmelCase     :     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
  lowerCAmelCase     :     str								=							Compose([Resize(SCREAMING_SNAKE_CASE				), ToTensor()]				)
  lowerCAmelCase     :     Any								=							PetsDataset([file_names[i] for i in eval_split]				,      image_transform=SCREAMING_SNAKE_CASE				,      label_to_id=SCREAMING_SNAKE_CASE				)

  # Instantiate dataloaders.
  lowerCAmelCase     :     Optional[Any]								=							DataLoader(SCREAMING_SNAKE_CASE				,      shuffle=SCREAMING_SNAKE_CASE				,      batch_size=SCREAMING_SNAKE_CASE				,      num_workers=4				)
  lowerCAmelCase     :     int								=							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)
  lowerCAmelCase     :     int								=							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).
  lowerCAmelCase     :     Any								=							model.to(accelerator.device				)

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

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

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

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

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

    lowerCAmelCase     :     Optional[Any]								=							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(SCREAMING_SNAKE_CASE				),
              "epoch": epoch,
          }				,      step=SCREAMING_SNAKE_CASE				,      )
    if checkpointing_steps == "epoch":
      lowerCAmelCase     :     List[str]								=							f"""epoch_{epoch}"""
      if args.output_dir is not None:
        lowerCAmelCase     :     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'''







  lowerCAmelCase     :     List[str]								=							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"				,      )
  lowerCAmelCase     :     List[str]								=							parser.parse_args()
  lowerCAmelCase     :     Optional[int]								=							{"lr": 3E-2, "num_epochs": 3, "seed": 4_2, "batch_size": 6_4, "image_size": 2_2_4}
  training_function(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
     main()









"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   {}


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				str   ="llama"
       a  :				List[str]   =["past_key_values"]

       def __init__(						self		,     snake_case__=32_000		,     snake_case__=4_096		,     snake_case__=11_008		,     snake_case__=32		,     snake_case__=32		,     snake_case__=None		,     snake_case__="silu"		,     snake_case__=2_048		,     snake_case__=0.02		,     snake_case__=1e-6		,     snake_case__=True		,     snake_case__=0		,     snake_case__=1		,     snake_case__=2		,     snake_case__=1		,     snake_case__=False		,     snake_case__=None		,     **snake_case__		,     ):






         """simple docstring"""





         lowerCAmelCase     :     Optional[Any]								=							vocab_size
         lowerCAmelCase     :     str								=							max_position_embeddings
         lowerCAmelCase     :     str								=							hidden_size
         lowerCAmelCase     :     Optional[int]								=							intermediate_size
         lowerCAmelCase     :     Any								=							num_hidden_layers
         lowerCAmelCase     :     List[str]								=							num_attention_heads

         # for backward compatibility
         if num_key_value_heads is None:
           lowerCAmelCase     :     Tuple								=							num_attention_heads

         lowerCAmelCase     :     Dict								=							num_key_value_heads
         lowerCAmelCase     :     Optional[Any]								=							hidden_act
         lowerCAmelCase     :     Optional[Any]								=							initializer_range
         lowerCAmelCase     :     Any								=							rms_norm_eps
         lowerCAmelCase     :     List[Any]								=							pretraining_tp
         lowerCAmelCase     :     int								=							use_cache
         lowerCAmelCase     :     List[str]								=							rope_scaling
         self._rope_scaling_validation()

         super().__init__(
             pad_token_id=snake_case__		,     bos_token_id=snake_case__		,     eos_token_id=snake_case__		,     tie_word_embeddings=snake_case__		,     **snake_case__		,     )







       def        lowercase__							(						self						):






         """simple docstring"""





         if self.rope_scaling is None:
           return

         if not isinstance(self.rope_scaling		,     snake_case__						) or len(self.rope_scaling						) != 2:
           raise ValueError(
               "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
               f"""got {self.rope_scaling}"""						)
         lowerCAmelCase     :     Optional[Any]								=							self.rope_scaling.get("type"		,     snake_case__						)
         lowerCAmelCase     :     int								=							self.rope_scaling.get("factor"		,     snake_case__						)
         if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
           raise ValueError(
               f"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""						)
         if rope_scaling_factor is None or not isinstance(snake_case__		,     snake_case__						) or rope_scaling_factor <= 1.0:
           raise ValueError(f"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""						)





