





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

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

   # drop extra batch for now
   lowerCAmelCase:	Union[str, Any]							      =range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
   lowerCAmelCase:	str							      =tr_dataset.shuffle().select(indices)
   lowerCAmelCase:	Optional[int]							      =range(len(val_dataset) - len(val_dataset) % args.batch_size)
   lowerCAmelCase:	Any							      =val_dataset.shuffle().select(indices)

   if os.environ.get("TRAIN_ON_SMALL", "false") == "true":
      lowerCAmelCase:	Optional[Any]							      =tr_dataset.shuffle().select(range(80000))
      lowerCAmelCase:	Optional[int]							      =val_dataset.shuffle().select(range(8000))

   print(tr_dataset)
   print(val_dataset)

   lowerCAmelCase:	Any							      =FlaxBigBirdForNaturalQuestions.from_pretrained(
       args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
   )
   lowerCAmelCase:	Any							      =BigBirdTokenizerFast.from_pretrained(args.model_id)
   lowerCAmelCase:	List[Any]							      =DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

   lowerCAmelCase:	List[str]							      ={
       "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:	Optional[int]							      =build_tx(**tx_args)

   lowerCAmelCase:	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,
   )

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









"""simple docstring"""
import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()




def 			__snake_case						(      __A ,__A ,__A ,__A ,__A    )		->				Optional[int]:
   # Initialise PyTorch model
   lowercase    :			Optional[Any]  =       BertConfig.from_pretrained(
       """bert-large-cased""" ,vocab_size=__A ,max_position_embeddings=512 ,is_decoder=__A ,add_cross_attention=__A ,)
   lowercase    :			List[str]  =       bert_config.to_dict()
   del bert_config_dict["type_vocab_size"]
   lowercase    :			Tuple  =       BertGenerationConfig(**__A    )
   if is_encoder:
      lowercase    :			Dict  =       BertGenerationEncoder(__A    )
   else:
      lowercase    :			Dict  =       BertGenerationDecoder(__A    )
   print(F'''Building PyTorch model from configuration: {config}'''    )

   # Load weights from tf checkpoint
   load_tf_weights_in_bert_generation(
       __A ,__A ,model_class="""bert""" ,is_encoder_named_decoder=__A ,is_encoder=__A ,)

   # Save pytorch-model
   print(F'''Save PyTorch model and config to {pytorch_dump_path}'''    )
   model.save_pretrained(__A    )


if __name__ == "__main__":
   lowerCAmelCase:	Dict							      =argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
   )
   parser.add_argument(
       "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   parser.add_argument(
       "--is_encoder_named_decoder",
       action="store_true",
       help="If decoder has to be renamed to encoder in PyTorch model.",
   )
   parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
   parser.add_argument("--vocab_size", default=50358, type=int, help="Vocab size of model")
   lowerCAmelCase:	Optional[int]							      =parser.parse_args()
   convert_tf_checkpoint_to_pytorch(
       args.tf_hub_path,
       args.pytorch_dump_path,
       args.is_encoder_named_decoder,
       args.vocab_size,
       is_encoder=args.is_encoder,
   )


