

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(8_0_0_0_0))
		lowerCAmelCase__						=	val_dataset.shuffle().select(range(8_0_0_0))

	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_0_9_6)

	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__						=	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)



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__						=	logging.get_logger(__name__)

lowerCAmelCase__						=	{
    """MBZUAI/swiftformer-xs""": """https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json""",
}

class    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    'swiftformer'


			def __init__(      self , lowercase=3 , lowercase=[3, 3, 6, 4] , lowercase=[48, 56, 112, 220] , lowercase=4 , lowercase=[True, True, True, True] , lowercase="gelu" , lowercase=3 , lowercase=2 , lowercase=1 , lowercase=0.0 , lowercase=True , lowercase=1e-5 , lowercase=1e-5 , **lowercase , )					->  Optional[int]:
							'''simple docstring'''






							super().__init__(**lowercase    )
							A__            =     num_channels
							A__            =     depths
							A__            =     embed_dims
							A__            =     mlp_ratio
							A__            =     downsamples
							A__            =     hidden_act
							A__            =     down_patch_size
							A__            =     down_stride
							A__            =     down_pad
							A__            =     drop_path_rate
							A__            =     use_layer_scale
							A__            =     layer_scale_init_value
							A__            =     batch_norm_eps

class    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    version.parse('1.11'     )


			@property
			def        UpperCamelCase						(      self    )					->  Mapping[str, Mapping[int, str]]:
							'''simple docstring'''






							return OrderedDict(
							    [
							        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
							    ]    )


			@property
			def        UpperCamelCase						(      self    )					->  float:
							'''simple docstring'''






							return 1e-4

