"""simple docstring"""
import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

lowercase__   			=      180







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->Union[str, Any]:
  a__:	List[str]     				=      k_size // 2
  a__				,       a__:	Union[str, Any]     				=      np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
  a__:	Tuple     				=      (
      1
      / (2 * np.pi * sigma)
      * np.exp(-(np.square(_SCREAMING_SNAKE_CASE  ) + np.square(_SCREAMING_SNAKE_CASE  )) / (2 * np.square(_SCREAMING_SNAKE_CASE  ))  )
  )
  return g







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->Optional[int]:
  a__:	Union[str, Any]     				=      np.zeros(_SCREAMING_SNAKE_CASE  )

  for row in range(1						,	image_shape[0] - 1  ):
    for col in range(1						,	image_shape[1] - 1  ):
      a__:	int     				=      gradient_direction[row, col]

      if (
          0 <= direction < PI / 8
          or 15 * PI / 8 <= direction <= 2 * PI
          or 7 * PI / 8 <= direction <= 9 * PI / 8
      ):
        a__:	List[Any]     				=      sobel_grad[row, col - 1]
        a__:	Any     				=      sobel_grad[row, col + 1]
        if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
          a__:	Optional[Any]     				=      sobel_grad[row, col]

      elif (
          PI / 8 <= direction < 3 * PI / 8
          or 9 * PI / 8 <= direction < 11 * PI / 8
      ):
        a__:	Optional[int]     				=      sobel_grad[row + 1, col - 1]
        a__:	int     				=      sobel_grad[row - 1, col + 1]
        if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
          a__:	int     				=      sobel_grad[row, col]

      elif (
          3 * PI / 8 <= direction < 5 * PI / 8
          or 11 * PI / 8 <= direction < 13 * PI / 8
      ):
        a__:	List[Any]     				=      sobel_grad[row - 1, col]
        a__:	List[str]     				=      sobel_grad[row + 1, col]
        if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
          a__:	List[str]     				=      sobel_grad[row, col]

      elif (
          5 * PI / 8 <= direction < 7 * PI / 8
          or 13 * PI / 8 <= direction < 15 * PI / 8
      ):
        a__:	Tuple     				=      sobel_grad[row - 1, col - 1]
        a__:	str     				=      sobel_grad[row + 1, col + 1]
        if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
          a__:	Optional[Any]     				=      sobel_grad[row, col]

  return destination







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->Any:
  for row in range(1						,	image_shape[0] - 1  ):
    for col in range(1						,	image_shape[1] - 1  ):
      if destination[row, col] >= threshold_high:
        a__:	Union[str, Any]     				=      strong
      elif destination[row, col] <= threshold_low:
        a__:	Dict     				=      0
      else:
        a__:	int     				=      weak







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->int:
  for row in range(1						,	image_shape[0]  ):
    for col in range(1						,	image_shape[1]  ):
      if destination[row, col] == weak:
        if 255 in (
            destination[row, col + 1],
            destination[row, col - 1],
            destination[row - 1, col],
            destination[row + 1, col],
            destination[row - 1, col - 1],
            destination[row + 1, col - 1],
            destination[row - 1, col + 1],
            destination[row + 1, col + 1],
        ):
          a__:	Optional[int]     				=      strong
        else:
          a__:	List[str]     				=      0







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE=15						,	_SCREAMING_SNAKE_CASE=30						,	_SCREAMING_SNAKE_CASE=128						,	_SCREAMING_SNAKE_CASE=255  )      ->Tuple:
  # gaussian_filter
  a__:	Union[str, Any]     				=      img_convolve(_SCREAMING_SNAKE_CASE						,	gen_gaussian_kernel(9						,	sigma=1.4  )  )
  # get the gradient and degree by sobel_filter
  a__				,       a__:	Optional[int]     				=      sobel_filter(_SCREAMING_SNAKE_CASE  )
  a__:	Optional[Any]     				=      PI + np.radadeg(_SCREAMING_SNAKE_CASE  )

  a__:	Tuple     				=      suppress_non_maximum(image.shape						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )

  detect_high_low_threshold(
      image.shape						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )

  track_edge(image.shape						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )

  return destination


if __name__ == "__main__":
 # read original image in gray mode
 lowercase__   			=      cva.imread(r'../image_data/lena.jpg', 0)
 # canny edge detection
 lowercase__   			=      canny(lena)
 cva.imshow('canny', canny_destination)
 cva.waitKey(0)









"""simple docstring"""






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
    lowerCAmelCase_      =				Args()
    lowerCAmelCase_      =				wandb.init(project='''bigbird-natural-questions''', config=args.__dict__)
    lowerCAmelCase_      =				dict(logger.config)
    del wandb_args["batch_size"]
    lowerCAmelCase_      =				replace(args, **wandb_args)
    lowerCAmelCase_      =				args.base_dir + '''-''' + wandb.run.id
    lowerCAmelCase_      =				replace(args, base_dir=base_dir)
    print(args)

    lowerCAmelCase_      =				load_dataset('''json''', data_files=args.tr_data_path)['''train''']
    lowerCAmelCase_      =				load_dataset('''json''', data_files=args.val_data_path)['''train''']

    # drop extra batch for now
    lowerCAmelCase_      =				range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
    lowerCAmelCase_      =				tr_dataset.shuffle().select(indices)
    lowerCAmelCase_      =				range(len(val_dataset) - len(val_dataset) % args.batch_size)
    lowerCAmelCase_      =				val_dataset.shuffle().select(indices)

    if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
        lowerCAmelCase_      =				tr_dataset.shuffle().select(range(80_000))
        lowerCAmelCase_      =				val_dataset.shuffle().select(range(8_000))

    print(tr_dataset)
    print(val_dataset)

    lowerCAmelCase_      =				FlaxBigBirdForNaturalQuestions.from_pretrained(
        args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
    )
    lowerCAmelCase_      =				BigBirdTokenizerFast.from_pretrained(args.model_id)
    lowerCAmelCase_      =				DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_096)

    lowerCAmelCase_      =				{
        '''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,
    }
    lowerCAmelCase_      ,					lowerCAmelCase_      =				build_tx(**tx_args)

    lowerCAmelCase_      =				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,
    )

    lowerCAmelCase_      =				None
    lowerCAmelCase_      =				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`''')
    lowerCAmelCase_      =				jax_utils.unreplicate(state.params)
    model.save_pretrained(os.path.join(args.base_dir, '''final-weights'''), params=params)






