

'''simple docstring'''





from typing import Any

import numpy as np





def 							__a       (					__A	):
    assert np.shape(__A	)[0] == np.shape(__A	)[1]

    # If matrix not symmetric, exit right away.
    if np.allclose(__A ,       matrix.T	) is False:
        return False

    # Get eigenvalues and eignevectors for a symmetric matrix.
    SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						np.linalg.eigh(__A	)

    # Check sign of all eigenvalues.
    # np.all returns a value of type np.bool_
    return bool(np.all(eigen_values > 0	)	)





def 							__a       (					__A	):
    SCREAMING_SNAKE_CASE__					     =						np.random.randn(__A ,       __A	)
    SCREAMING_SNAKE_CASE__					     =						np.dot(__A ,       random_matrix.T	)
    assert _is_matrix_spd(__A	)
    return spd_matrix





def 							__a       (					__A ,       __A ,       __A = 1000 ,       __A = 1E-8 ,       ):
    assert np.shape(__A	)[0] == np.shape(__A	)[1]
    assert np.shape(__A	)[0] == np.shape(__A	)[0]
    assert _is_matrix_spd(__A	)

    # Initialize solution guess, residual, search direction.
    SCREAMING_SNAKE_CASE__					     =						np.zeros((np.shape(__A	)[0], 1)	)
    SCREAMING_SNAKE_CASE__					     =						np.copy(__A	)
    SCREAMING_SNAKE_CASE__					     =						np.copy(__A	)

    # Set initial errors in solution guess and residual.
    SCREAMING_SNAKE_CASE__					     =						1E9
    SCREAMING_SNAKE_CASE__					     =						1E9
    SCREAMING_SNAKE_CASE__					     =						1E9

    # Set iteration counter to threshold number of iterations.
    SCREAMING_SNAKE_CASE__					     =						0

    while error > tol:
        # Save this value so we only calculate the matrix-vector product once.
        SCREAMING_SNAKE_CASE__					     =						np.dot(__A ,       __A	)

        # The main algorithm.

        # Update search direction magnitude.
        SCREAMING_SNAKE_CASE__					     =						np.dot(ra.T ,       __A	) / np.dot(pa.T ,       __A	)
        # Update solution guess.
        SCREAMING_SNAKE_CASE__					     =						xa + alpha * pa
        # Calculate new residual.
        SCREAMING_SNAKE_CASE__					     =						ra - alpha * w
        # Calculate new Krylov subspace scale.
        SCREAMING_SNAKE_CASE__					     =						np.dot(r.T ,       __A	) / np.dot(ra.T ,       __A	)
        # Calculate new A conjuage search direction.
        SCREAMING_SNAKE_CASE__					     =						r + beta * pa

        # Calculate errors.
        SCREAMING_SNAKE_CASE__					     =						np.linalg.norm(r - ra	)
        SCREAMING_SNAKE_CASE__					     =						np.linalg.norm(x - xa	)
        SCREAMING_SNAKE_CASE__					     =						np.maximum(__A ,       __A	)

        # Update variables.
        SCREAMING_SNAKE_CASE__					     =						np.copy(__A	)
        SCREAMING_SNAKE_CASE__					     =						np.copy(__A	)
        SCREAMING_SNAKE_CASE__					     =						np.copy(__A	)

        # Update number of iterations.
        iterations += 1
        if iterations > max_iterations:
            break

    return x





def 							__a       (					):
    SCREAMING_SNAKE_CASE__					     =						3
    SCREAMING_SNAKE_CASE__					     =						_create_spd_matrix(__A	)
    SCREAMING_SNAKE_CASE__					     =						np.random.randn(__A ,       1	)
    SCREAMING_SNAKE_CASE__					     =						np.dot(__A ,       __A	)

    # Numpy solution.
    SCREAMING_SNAKE_CASE__					     =						np.linalg.solve(__A ,       __A	)

    # Our implementation.
    SCREAMING_SNAKE_CASE__					     =						conjugate_gradient(__A ,       __A	)

    # Ensure both solutions are close to x_true (and therefore one another).
    assert np.linalg.norm(x_numpy - x_true	) <= 1E-6
    assert np.linalg.norm(x_conjugate_gradient - x_true	) <= 1E-6


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  test_conjugate_gradient()





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







class    UpperCamelCase_			(  _snake_case			):


  """simple docstring"""



  def __init__(	self    :				List[Any]     ,  A_    :				List[str]     ,  A_    :				Optional[int]=None     ,  A_    :				Union[str, Any]=None	)					-> List[Any]:
      SCREAMING_SNAKE_CASE__					     =						file_names
      SCREAMING_SNAKE_CASE__					     =						image_transform
      SCREAMING_SNAKE_CASE__					     =						label_to_id



  def __len__(	self    :				Dict	)					-> Tuple:
      return len(self.file_names	)







  def __getitem__(	self    :				str     ,  A_    :				Dict	)					-> Any:
      SCREAMING_SNAKE_CASE__					     =						self.file_names[idx]
      SCREAMING_SNAKE_CASE__					     =						PIL.Image.open(A_	)
      SCREAMING_SNAKE_CASE__					     =						raw_image.convert("RGB"	)
      if self.image_transform is not None:
          SCREAMING_SNAKE_CASE__					     =						self.image_transform(A_	)
      SCREAMING_SNAKE_CASE__					     =						extract_label(A_	)
      if self.label_to_id is not None:
          SCREAMING_SNAKE_CASE__					     =						self.label_to_id[label]
      return {"image": image, "label": label}





def 							__a       (					__A ,       __A	):
    # Initialize accelerator
    SCREAMING_SNAKE_CASE__					     =						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__					     =						config["lr"]
    SCREAMING_SNAKE_CASE__					     =						int(config["num_epochs"]	)
    SCREAMING_SNAKE_CASE__					     =						int(config["seed"]	)
    SCREAMING_SNAKE_CASE__					     =						int(config["batch_size"]	)
    SCREAMING_SNAKE_CASE__					     =						config["image_size"]
    if not isinstance(__A ,       (list, tuple)	):
        SCREAMING_SNAKE_CASE__					     =						(image_size, image_size)

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

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

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

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

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

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

    # Instantiate dataloaders.
    SCREAMING_SNAKE_CASE__					     =						DataLoader(__A ,       shuffle=__A ,       batch_size=__A ,       num_workers=4	)
    SCREAMING_SNAKE_CASE__					     =						DataLoader(__A ,       shuffle=__A ,       batch_size=__A ,       num_workers=4	)

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

    # 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__					     =						model.to(accelerator.device	)

    # Freezing the base model
    for param in model.parameters():
        SCREAMING_SNAKE_CASE__					     =						False
    for param in model.get_classifier().parameters():
        SCREAMING_SNAKE_CASE__					     =						True

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

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

    # Instantiate learning rate scheduler
    SCREAMING_SNAKE_CASE__					     =						OneCycleLR(optimizer=__A ,       max_lr=__A ,       epochs=__A ,       steps_per_epoch=len(__A	)	)

    # 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__					     =						accelerator.prepare(
        __A ,       __A ,       __A ,       __A ,       __A	)

    # Now we train the model
    for epoch in range(__A	):
        model.train()
        for step, batch in enumerate(__A	):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            SCREAMING_SNAKE_CASE__					     =						{k: v.to(accelerator.device	) for k, v in batch.items()}
            SCREAMING_SNAKE_CASE__					     =						(batch["image"] - mean) / std
            SCREAMING_SNAKE_CASE__					     =						model(__A	)
            SCREAMING_SNAKE_CASE__					     =						torch.nn.functional.cross_entropy(__A ,       batch["label"]	)
            accelerator.backward(__A	)
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

        model.eval()
        SCREAMING_SNAKE_CASE__					     =						0
        SCREAMING_SNAKE_CASE__					     =						0
        for _, batch in enumerate(__A	):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            SCREAMING_SNAKE_CASE__					     =						{k: v.to(accelerator.device	) for k, v in batch.items()}
            SCREAMING_SNAKE_CASE__					     =						(batch["image"] - mean) / std
            with torch.no_grad():
                SCREAMING_SNAKE_CASE__					     =						model(__A	)
            SCREAMING_SNAKE_CASE__					     =						outputs.argmax(dim=-1	)
            SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						accelerator.gather_for_metrics((predictions, batch["label"])	)
            SCREAMING_SNAKE_CASE__					     =						predictions == references
            num_elems += accurate_preds.shape[0]
            accurate += accurate_preds.long().sum()

        SCREAMING_SNAKE_CASE__					     =						accurate.item() / num_elems
        # Use accelerator.print to print only on the main process.
        accelerator.print(f"epoch {epoch}: {100 * eval_metric:.2f}"	)





def 							__a       (					):
    SCREAMING_SNAKE_CASE__					     =						argparse.ArgumentParser(description="Simple example of training script."	)
    parser.add_argument("--data_dir" ,       required=__A ,       help="The data folder on disk."	)
    parser.add_argument(
        "--mixed_precision" ,       type=__A ,       default=__A ,       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(
        "--checkpointing_steps" ,       type=__A ,       default=__A ,       help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch." ,       )
    parser.add_argument("--cpu" ,       action="store_true" ,       help="If passed, will train on the CPU."	)
    SCREAMING_SNAKE_CASE__					     =						parser.parse_args()
    SCREAMING_SNAKE_CASE__					     =						{"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
    training_function(__A ,       __A	)


if __name__ == "__main__":
  main()


