




"""simple docstring"""




import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey

class        UpperCAmelCase_  :
		def __init__(		self							,		UpperCamelCase_							,		UpperCamelCase_   )		->   Optional[Any]:
					__lowercase	:       Any				=   0
					# max greyscale value for #FFFFFF
					__lowercase	:       List[str]				=   int(self.get_greyscale(2_55							,		2_55							,		2_55   )   )

					if not self.min_threshold < threshold < self.max_threshold:
								__lowercase	:       Any				=   F"""Factor value should be from 0 to {self.max_threshold}"""
								raise ValueError(UpperCamelCase_   )

					__lowercase	:       str				=   input_img
					__lowercase	:       List[Any]				=   threshold
					__lowercase	:       List[Any]				=   self.input_img.shape[1], self.input_img.shape[0]

					# error table size (+4 columns and +1 row) greater than input image because of
					# lack of if statements
					__lowercase	:       Tuple				=   [
					    [0 for _ in range(self.height + 4   )] for __ in range(self.width + 1   )
					]
					__lowercase	:       Any				=   np.ones((self.width, self.height, 3)							,		np.uinta   ) * 2_55
		@classmethod
		def 					_lowerCamelCase    (		cls							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )		->   float:
					return 0.1_1_4 * blue + 0.5_8_7 * green + 0.2_1_2_6 * red
		def 					_lowerCamelCase    (		self   )		->   None:
					for y in range(self.height   ):
								for x in range(self.width   ):
											__lowercase	:       Union[str, Any]				=   int(self.get_greyscale(*self.input_img[y][x]   )   )
											if self.threshold > greyscale + self.error_table[y][x]:
														__lowercase	:       List[Any]				=   (0, 0, 0)
														__lowercase	:       Optional[int]				=   greyscale + self.error_table[x][y]
											else:
														__lowercase	:       Any				=   (2_55, 2_55, 2_55)
														__lowercase	:       Any				=   greyscale + self.error_table[x][y] - 2_55
											self.error_table[y][x + 1] += int(8 / 32 * current_error   )
											self.error_table[y][x + 2] += int(4 / 32 * current_error   )
											self.error_table[y + 1][x] += int(8 / 32 * current_error   )
											self.error_table[y + 1][x + 1] += int(4 / 32 * current_error   )
											self.error_table[y + 1][x + 2] += int(2 / 32 * current_error   )
											self.error_table[y + 1][x - 1] += int(4 / 32 * current_error   )
											self.error_table[y + 1][x - 2] += int(2 / 32 * current_error   )


if __name__ == "__main__":
				# create Burke's instances with original images in greyscale
				a_            =   [
				    Burkes(imread('image_data/lena.jpg', 1), threshold)
				    for threshold in (1, 1_2_6, 1_3_0, 1_4_0)
				]

				for burkes in burkes_instances:
								burkes.process()

				for burkes in burkes_instances:
								imshow(
								    F"Original image with dithering threshold: {burkes.threshold}",
								    burkes.output_img,
								)

				waitKey(0)
				destroyAllWindows()









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

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

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

			if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
						a_            =   tr_dataset.shuffle().select(range(8_0_0_0_0))
						a_            =   val_dataset.shuffle().select(range(8_0_0_0))

			print(tr_dataset)
			print(val_dataset)

			a_            =   FlaxBigBirdForNaturalQuestions.from_pretrained(
			    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
			)
			a_            =   BigBirdTokenizerFast.from_pretrained(args.model_id)
			a_            =   DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

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

			a_            =   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,
			)

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



