

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

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

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

    if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
        UpperCamelCase__		   =    tr_dataset.shuffle().select(range(80000))
        UpperCamelCase__		   =    val_dataset.shuffle().select(range(8000))

    print(tr_dataset)
    print(val_dataset)

    UpperCamelCase__		   =    FlaxBigBirdForNaturalQuestions.from_pretrained(
        args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
    )
    UpperCamelCase__		   =    BigBirdTokenizerFast.from_pretrained(args.model_id)
    UpperCamelCase__		   =    DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

    UpperCamelCase__		   =    {
        '''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__		   =    build_tx(**tx_args)

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




import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

UpperCamelCase__		   =    {'''UserAgent''': UserAgent().random}
UpperCamelCase__		   =    '''https://www.mywaifulist.moe/random'''







def 	lowerCamelCase__			(   __A						:str  ,__A						:str		):


       """simple docstring"""




       __snake_case					  =       requests.get(__A  ,headers=__A		)
       with open(__A  ,"""wb"""		) as file:
              file.write(image.content		)







def 	lowerCamelCase__			(   ):


       """simple docstring"""




       __snake_case					  =       BeautifulSoup(requests.get(__A  ,headers=__A		).text  ,"""html.parser"""		)
       __snake_case					  =       soup.find("""meta"""  ,attrs={"""property""": """og:title"""}		).attrs["""content"""]
       __snake_case					  =       soup.find("""meta"""  ,attrs={"""property""": """og:image"""}		).attrs["""content"""]
       __snake_case					  =       soup.find("""p"""  ,id="""description"""		).get_text()
       __snake_case  ,						__snake_case					  =       os.path.splitext(os.path.basename(__A		)		)
       __snake_case					  =       title.strip().replace(""" """  ,"""_"""		)
       __snake_case					  =       F'{image_title}{image_extension}'
       save_image(__A  ,__A		)
       return (title, description, image_title)


if __name__ == "__main__":
    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__		   =    random_anime_character()
    print(F'{title}\n\n{desc}\n\nImage saved : {image_title}')

