





import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin



class 				__snake_case  (     __SCREAMING_SNAKE_CASE ,    unittest.TestCase						):


    '''simple docstring'''







    lowerCamelCase__						:		Tuple							=  SpeechaTextaTokenizer
    lowerCamelCase__						:		Any							=  False



    def    lowercase_				(					self    ):





        '''simple docstring'''

        super().setUp()

        SCREAMING_SNAKE_CASE__       							= '''<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'''.split(''' '''    )
        SCREAMING_SNAKE_CASE__       							= ['''he re</w> 123''', '''here a 1456''']
        SCREAMING_SNAKE_CASE__       							= dict(zip(A_      ,							range(len(A_    )    )    )    )

        SCREAMING_SNAKE_CASE__       							= {'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}

        SCREAMING_SNAKE_CASE__       							= tempfile.mkdtemp()
        SCREAMING_SNAKE_CASE__       							= os.path.join(self.tmpdirname      ,							VOCAB_FILES_NAMES['''vocab_file''']    )
        SCREAMING_SNAKE_CASE__       							= os.path.join(self.tmpdirname      ,							VOCAB_FILES_NAMES['''merges_file''']    )

        with open(self.vocab_file      ,							'''w'''      ,							encoding='''utf-8'''    ) as fp:
            fp.write(json.dumps(A_    ) + '''\n'''    )

        with open(self.merges_file      ,							'''w'''    ) as fp:
            fp.write('''\n'''.join(A_    )    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= list(self.get_tokenizer().get_vocab().keys()    )

        self.assertEqual(vocab_keys[0]      ,							'''<s>'''    )
        self.assertEqual(vocab_keys[1]      ,							'''<pad>'''    )
        self.assertEqual(vocab_keys[-1]      ,							'''vocab'''    )
        self.assertEqual(len(A_    )      ,							14    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        self.assertEqual(self.get_tokenizer().vocab_size      ,							14    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= SpeechaTextaTokenizer.from_pretrained(self.tmpdirname    )

        # make sure @@ is correctly concatenated
        SCREAMING_SNAKE_CASE__       							= [4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
        SCREAMING_SNAKE_CASE__       							= tokenizer.decode(A_    )

        self.assertTrue(output_string == '''herecouple words ofthe'''    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= SpeechaTextaTokenizer.from_pretrained(self.tmpdirname    )

        with tempfile.TemporaryDirectory() as tmp_dirname:
            tokenizer.save_pretrained(A_    )
            os.remove(os.path.join(A_      ,							'''merges.txt'''    )    )

            # load tokenizer without merges file should not throw an error
            SCREAMING_SNAKE_CASE__       							= SpeechaTextaTokenizer.from_pretrained(A_    )

        with tempfile.TemporaryDirectory() as tmp_dirname:
            # save tokenizer and load again
            tokenizer.save_pretrained(A_    )
            SCREAMING_SNAKE_CASE__       							= SpeechaTextaTokenizer.from_pretrained(A_    )

        self.assertIsNotNone(A_    )




    def    lowercase_				(					self    ):





        '''simple docstring'''

        if not self.test_slow_tokenizer:
            return

        SCREAMING_SNAKE_CASE__       							= inspect.signature(self.tokenizer_class.__init__    )
        SCREAMING_SNAKE_CASE__       							= self.get_tokenizer()

        for parameter_name, parameter in signature.parameters.items():
            if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
                self.assertIn(A_      ,							tokenizer.init_kwargs    )







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

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

  # drop extra batch for now
  _A				:							Dict							= range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
  _A				:							int							= tr_dataset.shuffle().select(indices)
  _A				:							Optional[int]							= range(len(val_dataset) - len(val_dataset) % args.batch_size)
  _A				:							Dict							= val_dataset.shuffle().select(indices)

  if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
    _A				:							Optional[Any]							= tr_dataset.shuffle().select(range(8_00_00))
    _A				:							Tuple							= val_dataset.shuffle().select(range(80_00))

  print(tr_dataset)
  print(val_dataset)

  _A				:							int							= FlaxBigBirdForNaturalQuestions.from_pretrained(
      args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
  )
  _A				:							Any							= BigBirdTokenizerFast.from_pretrained(args.model_id)
  _A				:							Optional[Any]							= DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

  _A				:							int							= {
      """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				:							Union[str, Any]							= build_tx(**tx_args)

  _A				:							str							= 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				:							Tuple							= None
  _A				:							Dict							= 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				:							Tuple							= jax_utils.unreplicate(state.params)
  model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)
