

import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
       print("#################### AVAILABLE DEVICES ####################")
       print(jax.devices())
       print("###########################################################")

       # setup for wandb sweep
       UpperCamelCase__					: Tuple								=      Args()
       UpperCamelCase__					: int								=      wandb.init(project="bigbird-natural-questions", config=args.__dict__)
       UpperCamelCase__					: Optional[int]								=      dict(logger.config)
       del wandb_args["batch_size"]
       UpperCamelCase__					: Any								=      replace(args, **wandb_args)
       UpperCamelCase__					: Optional[int]								=      args.base_dir + "-" + wandb.run.id
       UpperCamelCase__					: Tuple								=      replace(args, base_dir=base_dir)
       print(args)

       UpperCamelCase__					: List[Any]								=      load_dataset("json", data_files=args.tr_data_path)["train"]
       UpperCamelCase__					: Any								=      load_dataset("json", data_files=args.val_data_path)["train"]

       # drop extra batch for now
       UpperCamelCase__					: Optional[Any]								=      range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
       UpperCamelCase__					: List[Any]								=      tr_dataset.shuffle().select(indices)
       UpperCamelCase__					: List[str]								=      range(len(val_dataset) - len(val_dataset) % args.batch_size)
       UpperCamelCase__					: Tuple								=      val_dataset.shuffle().select(indices)

       if os.environ.get("TRAIN_ON_SMALL", "false") == "true":
              UpperCamelCase__					: Union[str, Any]								=      tr_dataset.shuffle().select(range(80_000))
              UpperCamelCase__					: List[str]								=      val_dataset.shuffle().select(range(8_000))

       print(tr_dataset)
       print(val_dataset)

       UpperCamelCase__					: Optional[int]								=      FlaxBigBirdForNaturalQuestions.from_pretrained(
           args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
       )
       UpperCamelCase__					: Dict								=      BigBirdTokenizerFast.from_pretrained(args.model_id)
       UpperCamelCase__					: List[str]								=      DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_096)

       UpperCamelCase__					: Dict								=      {
           "lr": args.lr,
           "init_lr": args.init_lr,
           "warmup_steps": args.warmup_steps,
           "num_train_steps": args.max_epochs * (len(tr_dataset) // args.batch_size),
           "weight_decay": args.weight_decay,
       }
       UpperCamelCase__		,	UpperCamelCase__					: Optional[int]								=      build_tx(**tx_args)

       UpperCamelCase__					: Union[str, Any]								=      Trainer(
           args=args,
           data_collator=data_collator,
           model_save_fn=model.save_pretrained,
           train_step_fn=train_step,
           val_step_fn=val_step,
           logger=logger,
           scheduler_fn=lr,
       )

       UpperCamelCase__					: Optional[int]								=      None
       UpperCamelCase__					: List[Any]								=      trainer.create_state(model, tx, num_train_steps=tx_args["num_train_steps"], ckpt_dir=ckpt_dir)
       try:
              trainer.train(state, tr_dataset, val_dataset)
       except KeyboardInterrupt:
              print("Oooops; TRAINING STOPPED UNFORTUNATELY")

       print("SAVING WEIGHTS IN `final-weights`")
       UpperCamelCase__					: Tuple								=      jax_utils.unreplicate(state.params)
       model.save_pretrained(os.path.join(args.base_dir, "final-weights"), params=params)





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()


