


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__							)	-> Union[str, Any]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Tuple						     =		fname.split(os.path.sep							)[-1]
      return re.search(r'''^(.*)_\d+\.jpg$'''      ,    __snake_case							).groups()[0]

class 							lowercase  (						a_			):







     def __init__(						self :						Optional[Any]   ,   _lowercase :						Tuple   ,   _lowercase :						Union[str, Any]=None   ,   _lowercase :						Union[str, Any]=None      ):
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		file_names
           SCREAMING_SNAKE_CASE__       :  str						     =		image_transform
           SCREAMING_SNAKE_CASE__       :  int						     =		label_to_id







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







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




def    a   (							A__      ,    A__							)	-> Optional[Any]:




      '''simple docstring'''

      if args.with_tracking:
            SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		Accelerator(
                cpu=args.cpu      ,    mixed_precision=args.mixed_precision      ,    log_with='''all'''      ,    project_dir=args.project_dir							)
      else:
            SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		Accelerator(cpu=args.cpu      ,    mixed_precision=args.mixed_precision							)

      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		config['''lr''']
      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		int(config['''num_epochs''']							)
      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		int(config['''seed''']							)
      SCREAMING_SNAKE_CASE__       :  Tuple						     =		int(config['''batch_size''']							)
      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		config['''image_size''']
      if not isinstance(__snake_case      ,    (list, tuple)							):
            SCREAMING_SNAKE_CASE__       :  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":
                  SCREAMING_SNAKE_CASE__       :  Dict						     =		args.checkpointing_steps
            elif args.checkpointing_steps.isdigit():
                  SCREAMING_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:
            SCREAMING_SNAKE_CASE__       :  Dict						     =		None

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

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

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

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

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

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

      # For evaluation, we use a deterministic Resize
      SCREAMING_SNAKE_CASE__       :  Tuple						     =		Compose([Resize(__snake_case							), ToTensor()]							)
      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		PetsDataset([file_names[i] for i in eval_split]      ,    image_transform=__snake_case      ,    label_to_id=__snake_case							)

      # Instantiate dataloaders.
      SCREAMING_SNAKE_CASE__       :  str						     =		DataLoader(__snake_case      ,    shuffle=__snake_case      ,    batch_size=__snake_case      ,    num_workers=4							)
      SCREAMING_SNAKE_CASE__       :  Dict						     =		DataLoader(__snake_case      ,    shuffle=__snake_case      ,    batch_size=__snake_case      ,    num_workers=4							)

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      SCREAMING_SNAKE_CASE__       :  Any						     =		create_model('''resnet50d'''      ,    pretrained=__snake_case      ,    num_classes=len(__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).
      SCREAMING_SNAKE_CASE__       :  Dict						     =		model.to(accelerator.device							)

      # Freezing the base model
      for param in model.parameters():
            SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		False
      for param in model.get_classifier().parameters():
            SCREAMING_SNAKE_CASE__       :  Dict						     =		True

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

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

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

            if "epoch" in training_difference:
                  SCREAMING_SNAKE_CASE__       :  str						     =		int(training_difference.replace('''epoch_'''      ,    ''''''							)							) + 1
                  SCREAMING_SNAKE_CASE__       :  List[str]						     =		None
            else:
                  SCREAMING_SNAKE_CASE__       :  Tuple						     =		int(training_difference.replace('''step_'''      ,    ''''''							)							)
                  SCREAMING_SNAKE_CASE__       :  Dict						     =		resume_step // len(__snake_case							)
                  resume_step -= starting_epoch * len(__snake_case							)

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

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

      if args.with_tracking:
            accelerator.end_training()




def    a   (							)	-> int:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  List[str]						     =		argparse.ArgumentParser(description='''Simple example of training script.'''							)
      parser.add_argument('''--data_dir'''      ,    required=__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=__snake_case      ,    default=__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=__snake_case      ,    default=__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=__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=__snake_case      ,    default=__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=__snake_case      ,    default='''logs'''      ,    help='''Location on where to store experiment tracking logs` and relevent project information'''      ,    )
      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		parser.parse_args()
      SCREAMING_SNAKE_CASE__       :  Tuple						     =		{'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 6_4, '''image_size''': 2_2_4}
      training_function(__snake_case      ,    __snake_case							)


if __name__ == "__main__":
    main()














from sklearn.metrics import fa_score

import datasets


lowerCamelCase      :       Union[str, Any]    =						'''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''


lowerCamelCase      :       Optional[Any]    =						'''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.

        - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {\'f1\': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results[\'f1\'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(round(results[\'f1\'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")
        >>> print(round(results[\'f1\'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {\'f1\': array([0.8, 0. , 0. ])}
'''


lowerCamelCase      :       Optional[Any]    =						'''
@article{scikit-learn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
}
'''



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,		_KWARGS_DESCRIPTION      )
class 				_UpperCamelCase      (datasets.Metric      ):


      def 			__UpperCAmelCase		(						self						)->						Tuple:
           return datasets.MetricInfo(
               description=_DESCRIPTION       ,    citation=_CITATION       ,    inputs_description=_KWARGS_DESCRIPTION       ,    features=datasets.Features(
                   {
                       "predictions": datasets.Sequence(datasets.Value("int32"						)						),
                       "references": datasets.Sequence(datasets.Value("int32"						)						),
                   }
                   if self.config_name == "multilabel"
                   else {
                       "predictions": datasets.Value("int32"						),
                       "references": datasets.Value("int32"						),
                   }						)       ,    reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"]       ,    )


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase=None       ,    __UpperCamelCase=1       ,    __UpperCamelCase="binary"       ,    __UpperCamelCase=None						)->						Optional[int]:
           __lowerCAmelCase             =       fa_score(
               __UpperCamelCase       ,    __UpperCamelCase       ,    labels=__UpperCamelCase       ,    pos_label=__UpperCamelCase       ,    average=__UpperCamelCase       ,    sample_weight=__UpperCamelCase						)
           return {"f1": float(__UpperCamelCase						) if score.size == 1 else score}






