


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

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

	# drop extra batch for now
	_UpperCAmelCase					:			Any				     =					range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
	_UpperCAmelCase					:			str				     =					tr_dataset.shuffle().select(indices)
	_UpperCAmelCase					:			Tuple				     =					range(len(val_dataset) - len(val_dataset) % args.batch_size)
	_UpperCAmelCase					:			str				     =					val_dataset.shuffle().select(indices)

	if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
		_UpperCAmelCase					:			Tuple				     =					tr_dataset.shuffle().select(range(8_0_0_0_0))
		_UpperCAmelCase					:			Optional[int]				     =					val_dataset.shuffle().select(range(8_0_0_0))

	print(tr_dataset)
	print(val_dataset)

	_UpperCAmelCase					:			Any				     =					FlaxBigBirdForNaturalQuestions.from_pretrained(
	    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
	)
	_UpperCAmelCase					:			List[str]				     =					BigBirdTokenizerFast.from_pretrained(args.model_id)
	_UpperCAmelCase					:			Dict				     =					DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

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

	_UpperCAmelCase					:			Any				     =					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,
	)

	_UpperCAmelCase					:			int				     =					None
	_UpperCAmelCase					:			List[Any]				     =					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`""")
	_UpperCAmelCase					:			Optional[int]				     =					jax_utils.unreplicate(state.params)
	model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)





'''simple docstring'''

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class    a__					(			__A   ):







				"""simple docstring"""



				__UpperCamelCase     :		Tuple						=						{
				    'sld_warmup_steps': 15,
				    'sld_guidance_scale': 20,
				    'sld_threshold': 0.0,
				    'sld_momentum_scale': 0.0,
				    'sld_mom_beta': 0.0,
				}
				__UpperCamelCase     :		Optional[Any]						=						{
				    'sld_warmup_steps': 10,
				    'sld_guidance_scale': 1_000,
				    'sld_threshold': 0.01,
				    'sld_momentum_scale': 0.3,
				    'sld_mom_beta': 0.4,
				}
				__UpperCamelCase     :		Tuple						=						{
				    'sld_warmup_steps': 7,
				    'sld_guidance_scale': 2_000,
				    'sld_threshold': 0.025,
				    'sld_momentum_scale': 0.5,
				    'sld_mom_beta': 0.7,
				}
				__UpperCamelCase     :		str						=						{
				    'sld_warmup_steps': 0,
				    'sld_guidance_scale': 5_000,
				    'sld_threshold': 1.0,
				    'sld_momentum_scale': 0.5,
				    'sld_mom_beta': 0.7,
				}


@dataclass
class    a__					(			__A   ):







				"""simple docstring"""



				__UpperCamelCase     :		Union[List[PIL.Image.Image], np.ndarray]
				__UpperCamelCase     :		Optional[List[bool]]
				__UpperCamelCase     :		Optional[Union[List[PIL.Image.Image], np.ndarray]]
				__UpperCamelCase     :		Optional[str]


if is_transformers_available() and is_torch_available():
	from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
	from .safety_checker import SafeStableDiffusionSafetyChecker

