

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    (					_SCREAMING_SNAKE_CASE   :						Optional[Any]    ):





    """simple docstring"""


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



class  __snake_case					(      lowercase_    ):

  def __init__(   self  ,						_A  ,						_A=None  ,						_A=None):
      SCREAMING_SNAKE_CASE_													=  file_names
      SCREAMING_SNAKE_CASE_													=  image_transform
      SCREAMING_SNAKE_CASE_													=  label_to_id

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


  def __getitem__(   self  ,						_A):
      SCREAMING_SNAKE_CASE_													=  self.file_names[idx]
      SCREAMING_SNAKE_CASE_													=  PIL.Image.open(UpperCamelCase_)
      SCREAMING_SNAKE_CASE_													=  raw_image.convert('RGB')
      if self.image_transform is not None:
          SCREAMING_SNAKE_CASE_													=  self.image_transform(UpperCamelCase_)
      SCREAMING_SNAKE_CASE_													=  extract_label(UpperCamelCase_)
      if self.label_to_id is not None:
          SCREAMING_SNAKE_CASE_													=  self.label_to_id[label]
      return {"image": image, "label": label}






def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Any	,				_SCREAMING_SNAKE_CASE   :						int    ):





    """simple docstring"""


    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(__lowercase	,				(list, tuple)    ):
        SCREAMING_SNAKE_CASE_													=  (image_size, image_size)

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

    # Build the label correspondences
    SCREAMING_SNAKE_CASE_													=  [extract_label(__lowercase    ) for fname in file_names]
    SCREAMING_SNAKE_CASE_													=  list(set(__lowercase    )    )
    id_to_label.sort()
    SCREAMING_SNAKE_CASE_													=  {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
    SCREAMING_SNAKE_CASE_													=  np.random.permutation(len(__lowercase    )    )
    SCREAMING_SNAKE_CASE_													=  int(0.8 * len(__lowercase    )    )
    SCREAMING_SNAKE_CASE_													=  random_perm[:cut]
    SCREAMING_SNAKE_CASE_													=  random_perm[cut:]

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

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

    # Instantiate dataloaders.
    SCREAMING_SNAKE_CASE_													=  DataLoader(__lowercase	,				shuffle=__lowercase	,				batch_size=__lowercase	,				num_workers=4    )
    SCREAMING_SNAKE_CASE_													=  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)
    SCREAMING_SNAKE_CASE_													=  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).
    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=__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.
    SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  accelerator.prepare(
        __lowercase	,				__lowercase	,				__lowercase	,				__lowercase	,				__lowercase    )

    # Now we train the model
    for epoch in range(__lowercase    ):
        model.train()
        for step, batch in enumerate(__lowercase    ):
            # 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(__lowercase    )
            SCREAMING_SNAKE_CASE_													=  torch.nn.functional.cross_entropy(__lowercase	,				batch['label']    )
            accelerator.backward(__lowercase    )
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

        model.eval()
        SCREAMING_SNAKE_CASE_													=  0
        SCREAMING_SNAKE_CASE_													=  0
        for _, batch in enumerate(__lowercase    ):
            # 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(__lowercase    )
            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    _UpperCAmelCase    (					):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  argparse.ArgumentParser(description='Simple example of training script.'    )
    parser.add_argument('--data_dir'	,				required=__lowercase	,				help='The data folder on disk.'    )
    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(
        '--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('--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(__lowercase	,				__lowercase    )


if __name__ == "__main__":
       main()





import cva
import numpy as np


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						np.ndarray	,				_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int    ):





    """simple docstring"""


    try:
        return int(image[x_coordinate][y_coordinate] >= center    )
    except (IndexError, TypeError):
        return 0


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						np.ndarray	,				_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						int    ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  image[x_coordinate][y_coordinate]
    SCREAMING_SNAKE_CASE_													=  [1, 2, 4, 8, 16, 32, 64, 128]

    # skip get_neighbors_pixel if center is null
    if center is None:
        return 0

    # Starting from the top right, assigning value to pixels clockwise
    SCREAMING_SNAKE_CASE_													=  [
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate - 1	,				y_coordinate + 1	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				y_coordinate + 1	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate - 1	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate + 1	,				y_coordinate + 1	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate + 1	,				_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate + 1	,				y_coordinate - 1	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				y_coordinate - 1	,				_SCREAMING_SNAKE_CASE    ),
        get_neighbors_pixel(_SCREAMING_SNAKE_CASE	,				x_coordinate - 1	,				y_coordinate - 1	,				_SCREAMING_SNAKE_CASE    ),
    ]

    # Converting the binary value to decimal.
    return sum(
        binary_value * power for binary_value, power in zip(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE    )    )


if __name__ == "__main__":
       # Reading the image and converting it to grayscale.
       UpperCamelCase__					: Optional[int]								=      cva.imread(
           "digital_image_processing/image_data/lena.jpg", cva.IMREAD_GRAYSCALE
       )

       # Create a numpy array as the same height and width of read image
       UpperCamelCase__					: Dict								=      np.zeros((image.shape[0], image.shape[1]))

       # Iterating through the image and calculating the
       # local binary pattern value for each pixel.
       for i in range(0, image.shape[0]):
              for j in range(0, image.shape[1]):
                     UpperCamelCase__					: Any								=      local_binary_value(image, i, j)

       cva.imshow("local binary pattern", lbp_image)
       cva.waitKey(0)
       cva.destroyAllWindows()


