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

   __lowerCamelCase							= load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
   __lowerCamelCase							= load_dataset("""json""", data_files=args.val_data_path)["""train"""]

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

   if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
      __lowerCamelCase							= tr_dataset.shuffle().select(range(8_00_00))
      __lowerCamelCase							= val_dataset.shuffle().select(range(80_00))

   print(tr_dataset)
   print(val_dataset)

   __lowerCamelCase							= FlaxBigBirdForNaturalQuestions.from_pretrained(
       args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
   )
   __lowerCamelCase							= BigBirdTokenizerFast.from_pretrained(args.model_id)
   __lowerCamelCase							= DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

   __lowerCamelCase							= {
       """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,
   }
   __lowerCamelCase,     __lowerCamelCase							= build_tx(**tx_args)

   __lowerCamelCase							= 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,
   )

   __lowerCamelCase							= None
   __lowerCamelCase							= 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`""")
   __lowerCamelCase							= jax_utils.unreplicate(state.params)
   model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)







from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest






@custom_tokenizers
class 						UpperCAmelCase	(		A_				):



   def        _SCREAMING_SNAKE_CASE     (self	:      List[str]					,		snake_case__	:      Dict					,		snake_case__	:      Tuple					,		snake_case__	:      Union[str, Any]					,		snake_case__	:      List[str]					,		snake_case__	:      List[Any]				)	->			Any:


          '''simple docstring'''


          return True



   def        _SCREAMING_SNAKE_CASE     (self	:      str				)	->			Dict:


          '''simple docstring'''


          snake_case :     Union[str, Any]           =   CpmTokenizer.from_pretrained("TsinghuaAI/CPM-Generate"				)
          snake_case :     str           =   "Hugging Face大法好，谁用谁知道。"
          snake_case :     Optional[Any]           =   "Hugging Face大法好,谁用谁知道。<unk>"
          snake_case :     int           =   "▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。".split()

          snake_case :     Union[str, Any]           =   tokenizer.tokenize(snake_case__				)
          self.assertListEqual(snake_case__					,		snake_case__				)

          snake_case :     Tuple           =   tokens + [tokenizer.unk_token]

          snake_case :     int           =   [1_37_89, 1_32_83, 14_21, 8, 10, 11_64, 1_36_08, 1_65_28, 63, 8, 9, 4_40, 1_08, 4_40, 1_21, 90, 8, 12, 0]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__				)					,		snake_case__				)

          snake_case :     Dict           =   tokenizer.decode(snake_case__				)
          self.assertEqual(snake_case__					,		snake_case__				)





