




"""simple docstring"""




from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION

class        UpperCAmelCase_  (	snake_case ,			snake_case						):
		def __init__(		self   )		->   Dict:
					super().__init__()

					self.register_buffer('''watermark_image'''							,		torch.zeros((62, 62, 4)   )   )
					__lowercase	:       Union[str, Any]				=   None
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_=None   )		->   List[str]:
					# copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287

					__lowercase	:       Dict				=   images[0].height
					__lowercase	:       Tuple				=   images[0].width

					__lowercase	:       Optional[Any]				=   sample_size or h

					__lowercase	:       str				=   min(h / sample_size							,		w / sample_size   )
					__lowercase	,__lowercase	:       Optional[Any]				=   (int(h / coef   ), int(w / coef   )) if coef < 1 else (h, w)

					__lowercase	,__lowercase	:       List[str]				=   10_24**2, img_w * img_h
					__lowercase	:       Union[str, Any]				=   (Sa / Sa) ** 0.5
					__lowercase	,__lowercase	,__lowercase	:       List[Any]				=   int(K * 62   ), img_w - int(14 * K   ), img_h - int(14 * K   )

					if self.watermark_image_as_pil is None:
								__lowercase	:       Dict				=   self.watermark_image.to(torch.uinta   ).cpu().numpy()
								__lowercase	:       Optional[int]				=   Image.fromarray(UpperCamelCase_							,		mode='''RGBA'''   )
								__lowercase	:       Tuple				=   watermark_image

					__lowercase	:       str				=   self.watermark_image_as_pil.resize(
					    (wm_size, wm_size)							,		PIL_INTERPOLATION['''bicubic''']							,		reducing_gap=UpperCamelCase_   )

					for pil_img in images:
								pil_img.paste(UpperCamelCase_							,		box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)							,		mask=wm_img.split()[-1]   )

					return images









"""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)



