
'''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
						_snake_case					: Union[str, Any]     			=					Args()
						_snake_case					: int     			=					wandb.init(project="""bigbird-natural-questions""", config=args.__dict__)
						_snake_case					: Optional[Any]     			=					dict(logger.config)
						del wandb_args["batch_size"]
						_snake_case					: Optional[int]     			=					replace(args, **wandb_args)
						_snake_case					: List[str]     			=					args.base_dir + """-""" + wandb.run.id
						_snake_case					: str     			=					replace(args, base_dir=base_dir)
						print(args)

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

						# drop extra batch for now
						_snake_case					: Any     			=					range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
						_snake_case					: Dict     			=					tr_dataset.shuffle().select(indices)
						_snake_case					: Optional[Any]     			=					range(len(val_dataset) - len(val_dataset) % args.batch_size)
						_snake_case					: int     			=					val_dataset.shuffle().select(indices)

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

						print(tr_dataset)
						print(val_dataset)

						_snake_case					: str     			=					FlaxBigBirdForNaturalQuestions.from_pretrained(
						    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
						)
						_snake_case					: Union[str, Any]     			=					BigBirdTokenizerFast.from_pretrained(args.model_id)
						_snake_case					: str     			=					DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_096)

						_snake_case					: Any     			=					{
						    """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,
						}
						_snake_case			,		_snake_case					: Union[str, Any]     			=					build_tx(**tx_args)

						_snake_case					: str     			=					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,
						)

						_snake_case					: Any     			=					None
						_snake_case					: str     			=					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`""")
						_snake_case					: int     			=					jax_utils.unreplicate(state.params)
						model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)

'''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

_snake_case					: Union[str, Any]     			=					180



def       _a		(						_SCREAMING_SNAKE_CASE		: Optional[int]						,      _SCREAMING_SNAKE_CASE		: Dict	):
						_SCREAMING_SNAKE_CASE					=			k_size // 2
						_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
						_SCREAMING_SNAKE_CASE					=			(
						    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		: int						,      _SCREAMING_SNAKE_CASE		: Tuple						,      _SCREAMING_SNAKE_CASE		: Dict	):
						_SCREAMING_SNAKE_CASE					=			np.zeros(_SCREAMING_SNAKE_CASE	)

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

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

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

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

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

						return destination



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



def       _a		(						_SCREAMING_SNAKE_CASE		: List[Any]						,      _SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: Dict						,      _SCREAMING_SNAKE_CASE		: Any	):
						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],
																								):
																														_SCREAMING_SNAKE_CASE					=			strong
																								else:
																														_SCREAMING_SNAKE_CASE					=			0



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

						_SCREAMING_SNAKE_CASE					=			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
						_snake_case					: Any     			=					cva.imread(R"""../image_data/lena.jpg""", 0)
						# canny edge detection
						_snake_case					: int     			=					canny(lena)
						cva.imshow("""canny""", canny_destination)
						cva.waitKey(0)