





import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


_A				:							List[Any]							= logging.getLogger(__name__)



class 				__snake_case  :


    '''simple docstring'''







    def __init__(					self    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= False



    def    lowercase_				(					self      ,							A_      ,							A_      ,							A_      ,							A_    ):





        '''simple docstring'''

        if not self.initialized:
            SCREAMING_SNAKE_CASE__       							= RagRetriever(
                A_      ,							question_encoder_tokenizer=A_      ,							generator_tokenizer=A_      ,							index=A_      ,							init_retrieval=A_      ,							)
            SCREAMING_SNAKE_CASE__       							= True



    def    lowercase_				(					self    ):





        '''simple docstring'''

        self.retriever.index.init_index()



    def    lowercase_				(					self    ):





        '''simple docstring'''

        del self.retriever
        SCREAMING_SNAKE_CASE__       							= False




    def    lowercase_				(					self      ,							A_      ,							A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= self.retriever._main_retrieve(A_      ,							A_    )
        SCREAMING_SNAKE_CASE__       							= self.retriever.index.get_doc_dicts(A_    )
        return doc_ids, retrieved_doc_embeds, doc_dicts





class 				__snake_case  (     a_						):


    '''simple docstring'''







    def __init__(					self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_=None    ):





        '''simple docstring'''

        if index is not None and index.is_initialized() and len(A_    ) > 0:
            raise ValueError(
                '''When using Ray for distributed fine-tuning, '''
                '''you\'ll need to provide the paths instead, '''
                '''as the dataset and the index are loaded '''
                '''separately. More info in examples/rag/use_own_knowledge_dataset.py '''    )

        super().__init__(
            A_      ,							question_encoder_tokenizer=A_      ,							generator_tokenizer=A_      ,							index=A_      ,							init_retrieval=A_      ,							)

        SCREAMING_SNAKE_CASE__       							= retrieval_workers
        SCREAMING_SNAKE_CASE__       							= question_encoder_tokenizer
        SCREAMING_SNAKE_CASE__       							= generator_tokenizer
        if len(self.retrieval_workers    ) > 0:
            ray.get(
                [
                    worker.create_rag_retriever.remote(A_      ,							A_      ,							A_      ,							A_    )
                    for worker in self.retrieval_workers
                ]    )



    def    lowercase_				(					self    ):





        '''simple docstring'''

        logger.info('''initializing retrieval'''    )

        if len(self.retrieval_workers    ) > 0:
            ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]    )
        else:
            # Non-distributed training. Load index into this same process.
            self.index.init_index()



    def    lowercase_				(					self      ,							A_      ,							A_    ):





        '''simple docstring'''

        if len(self.retrieval_workers    ) > 0:
            # Select a random retrieval actor.
            SCREAMING_SNAKE_CASE__       							= self.retrieval_workers[random.randint(0      ,							len(self.retrieval_workers    ) - 1    )]
            SCREAMING_SNAKE_CASE__       							= ray.get(
                random_worker.retrieve.remote(A_      ,							A_    )    )
        else:
            SCREAMING_SNAKE_CASE__       							= self._main_retrieve(A_      ,							A_    )
            SCREAMING_SNAKE_CASE__       							= self.index.get_doc_dicts(A_    )
        return retrieved_doc_embeds, doc_ids, doc_dicts



    @classmethod
    def    lowercase_				(					cls      ,							A_      ,							A_=None      ,							**A_    ):





        '''simple docstring'''

        return super(A_      ,							cls    ).get_tokenizers(A_      ,							A_      ,							**A_    )



    @classmethod
    def    lowercase_				(					cls      ,							A_      ,							A_      ,							A_=None      ,							**A_    ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= kwargs.pop('''config'''      ,							A_    ) or RagConfig.from_pretrained(A_      ,							**A_    )
        SCREAMING_SNAKE_CASE__       							= RagTokenizer.from_pretrained(A_      ,							config=A_    )
        SCREAMING_SNAKE_CASE__       							= rag_tokenizer.question_encoder
        SCREAMING_SNAKE_CASE__       							= rag_tokenizer.generator

        if indexed_dataset is not None:
            SCREAMING_SNAKE_CASE__       							= 'custom'
            SCREAMING_SNAKE_CASE__       							= CustomHFIndex(config.retrieval_vector_size      ,							A_    )
        else:
            SCREAMING_SNAKE_CASE__       							= cls._build_index(A_    )

        return cls(
            A_      ,							question_encoder_tokenizer=A_      ,							generator_tokenizer=A_      ,							retrieval_workers=A_      ,							index=A_      ,							)




    def    lowercase_				(					self    ):





        '''simple docstring'''

        logger.info('''re-loading the new dataset with embeddings'''    )
        # access from the training loop

        ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]    )

        # build the index object again
        SCREAMING_SNAKE_CASE__       							= self._build_index(self.config    )

        ray.get(
            [
                worker.create_rag_retriever.remote(
                    self.config      ,							self.question_encoder_tokenizer      ,							self.generator_tokenizer      ,							A_    )
                for worker in self.retrieval_workers
            ]    )







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)
