




import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property





class 						SCREAMING_SNAKE_CASE__		(unittest.TestCase ):
     @cached_property
     def 					A__			(	self  :		int	):



       """simple docstring"""
       return BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''	)
     @cached_property
     def 					A__			(	self  :		int	):



       """simple docstring"""
       return BlenderbotTokenizerFast.from_pretrained('''facebook/blenderbot-3B'''	)
     def 					A__			(	self  :		Optional[int]	):



       """simple docstring"""
       lowerCAmelCase__								=			self.tokenizer_ab
       lowerCAmelCase__								=			''' I am a small frog.'''
       lowerCAmelCase__								=			tok([src_text]					,	padding=__lowerCamelCase					,	truncation=__lowerCamelCase	)['''input_ids''']
       lowerCAmelCase__								=			tok.batch_decode(__lowerCamelCase					,	skip_special_tokens=__lowerCamelCase					,	clean_up_tokenization_spaces=__lowerCamelCase	)[0]
       assert src_text == decoded
     def 					A__			(	self  :		Tuple	):



       """simple docstring"""
       lowerCAmelCase__								=			self.rust_tokenizer_ab
       lowerCAmelCase__								=			''' I am a small frog.'''
       lowerCAmelCase__								=			tok([src_text]					,	padding=__lowerCamelCase					,	truncation=__lowerCamelCase	)['''input_ids''']
       lowerCAmelCase__								=			tok.batch_decode(__lowerCamelCase					,	skip_special_tokens=__lowerCamelCase					,	clean_up_tokenization_spaces=__lowerCamelCase	)[0]
       assert src_text == decoded
     def 					A__			(	self  :		str	):



       """simple docstring"""
       assert self.tokenizer_ab.add_prefix_space
       assert self.tokenizer_ab([''' Sam''', '''Sam''']	).input_ids == [[55_02, 2], [55_02, 2]]



     def 					A__			(	self  :		List[Any]	):



       """simple docstring"""
       assert self.rust_tokenizer_ab.add_prefix_space
       assert self.rust_tokenizer_ab([''' Sam''', '''Sam''']	).input_ids == [[55_02, 2], [55_02, 2]]







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

      __magic_name__     :     Union[str, Any]  							=					load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
      __magic_name__     :     List[Any]  							=					load_dataset("""json""", data_files=args.val_data_path)["""train"""]

      # drop extra batch for now
      __magic_name__     :     List[str]  							=					range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
      __magic_name__     :     Dict  							=					tr_dataset.shuffle().select(indices)
      __magic_name__     :     Any  							=					range(len(val_dataset) - len(val_dataset) % args.batch_size)
      __magic_name__     :     Dict  							=					val_dataset.shuffle().select(indices)

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

      print(tr_dataset)
      print(val_dataset)

      __magic_name__     :     Union[str, Any]  							=					FlaxBigBirdForNaturalQuestions.from_pretrained(
          args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
      )
      __magic_name__     :     Tuple  							=					BigBirdTokenizerFast.from_pretrained(args.model_id)
      __magic_name__     :     Tuple  							=					DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

      __magic_name__     :     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,
      }
      __magic_name__							,   __magic_name__     :     Optional[int]  							=					build_tx(**tx_args)

      __magic_name__     :     List[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,
      )

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

