'''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)








"""simple docstring"""


import shutil

import requests

def 					_lowerCAmelCase (  lowercase_						,      lowercase_ = False						,      lowercase_ = "."		):
						UpperCAmelCase			 =					'https://api.nasa.gov/planetary/apod'
						return requests.get(lowercase_						,      params={'api_key': api_key}		).json()

def 					_lowerCAmelCase (  lowercase_						,      lowercase_ = "."		):
						UpperCAmelCase			 =					get_apod_data(lowercase_		)
						UpperCAmelCase			 =					apod_data['url']
						UpperCAmelCase			 =					img_url.split('/'		)[-1]
						UpperCAmelCase			 =					requests.get(lowercase_						,      stream=lowercase_		)

						with open(F"""{path}/{img_name}"""						,      'wb+'		) as img_file:
												shutil.copyfileobj(response.raw						,      lowercase_		)
						del response
						return apod_data

def 					_lowerCAmelCase (  lowercase_		):
						UpperCAmelCase			 =					'https://images-api.nasa.gov/search'
						return requests.get(lowercase_						,      params={'q': query}		).json()


if __name__ == "__main__":
				print(save_apod("""YOUR API KEY"""))
				snake_case_								=			get_archive_data("""apollo 2011""")["""collection"""]["""items"""]
				print(apollo_aaaa_items[0]["""data"""][0]["""description"""])





