"""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_0000))
		a							=val_dataset.shuffle().select(range(8000))

	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=4096)

	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)



"""simple docstring"""


import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


a							=logging.get_logger(__name__)






class    __UpperCAmelCase						(	__lowerCAmelCase	):
				A__      :   Any  							=       '''hybrid-clip'''
				A__      :   Optional[Any]  							=       True

				def __init__(      self	, _lowerCamelCase=512	, **_lowerCamelCase      ):
										super().__init__(**_lowerCamelCase      )

										if "text_config" not in kwargs:
																raise ValueError("`text_config` can not be `None`."      )

										if "vision_config" not in kwargs:
																raise ValueError("`vision_config` can not be `None`."      )

										lowerCamelCase__					=kwargs.pop("text_config"      )
										lowerCamelCase__					=kwargs.pop("vision_config"      )

										lowerCamelCase__					=text_config.pop("model_type"      )
										lowerCamelCase__					=vision_config.pop("model_type"      )

										from transformers import AutoConfig

										lowerCamelCase__					=AutoConfig.for_model(_lowerCamelCase	, **_lowerCamelCase      )

										if vision_model_type == "clip":
																lowerCamelCase__					=AutoConfig.for_model(_lowerCamelCase	, **_lowerCamelCase      ).vision_config
										elif vision_model_type == "clip_vision_model":
																from transformers import CLIPVisionConfig

																lowerCamelCase__					=CLIPVisionConfig(**_lowerCamelCase      )
										else:
																lowerCamelCase__					=AutoConfig.for_model(_lowerCamelCase	, **_lowerCamelCase      )

										lowerCamelCase__					=projection_dim
										lowerCamelCase__					=1.0

				@classmethod
				def 		_a	(      cls	, _lowerCamelCase	, _lowerCamelCase	, **_lowerCamelCase      ):
										return cls(text_config=text_config.to_dict()	, vision_config=vision_config.to_dict()	, **_lowerCamelCase      )






				def 		_a	(      self      ):
										lowerCamelCase__					=copy.deepcopy(self.__dict__      )
										lowerCamelCase__					=self.text_config.to_dict()
										lowerCamelCase__					=self.vision_config.to_dict()
										lowerCamelCase__					=self.__class__.model_type
										return output


