"""simple docstring"""


import logging
import random

import ray

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


UpperCAmelCase__			=    logging.getLogger(__name__)


class 							a					:


    def __init__(       self						:	Optional[Any]       ):
      _UpperCAmelCase    			=   False


    def   lowerCAmelCase_	(       self						:	str ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	Tuple ,					__lowerCAmelCase						:	Dict       ):
      if not self.initialized:
        _UpperCAmelCase    			=   RagRetriever(
            __lowerCAmelCase ,					question_encoder_tokenizer=__lowerCAmelCase ,					generator_tokenizer=__lowerCAmelCase ,					index=__lowerCAmelCase ,					init_retrieval=__lowerCAmelCase ,					)
        _UpperCAmelCase    			=   True


    def   lowerCAmelCase_	(       self						:	str       ):
      self.retriever.index.init_index()


    def   lowerCAmelCase_	(       self						:	Tuple ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	int       ):
      _UpperCAmelCase			,					_UpperCAmelCase    			=   self.retriever._main_retrieve(__lowerCAmelCase ,					__lowerCAmelCase       )
      return doc_ids, retrieved_doc_embeds



class 							a					(		lowerCAmelCase_ ):


    def __init__(       self						:	List[str] ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Union[str, Any] ,					__lowerCAmelCase						:	Tuple=None       ):
      if index is not None and index.is_initialized() and len(__lowerCAmelCase       ) > 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__(
          __lowerCAmelCase ,					question_encoder_tokenizer=__lowerCAmelCase ,					generator_tokenizer=__lowerCAmelCase ,					index=__lowerCAmelCase ,					init_retrieval=__lowerCAmelCase ,					)
      _UpperCAmelCase    			=   retrieval_workers
      if len(self.retrieval_workers       ) > 0:
        ray.get(
            [
                worker.create_rag_retriever.remote(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase       )
                for worker in self.retrieval_workers
            ]       )


    def   lowerCAmelCase_	(       self						:	Optional[int]       ):
      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   lowerCAmelCase_	(       self						:	Any ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	List[str]       ):
      if len(self.retrieval_workers       ) > 0:
        # Select a random retrieval actor.
        _UpperCAmelCase    			=   self.retrieval_workers[random.randint(0 ,					len(self.retrieval_workers       ) - 1       )]
        _UpperCAmelCase			,					_UpperCAmelCase    			=   ray.get(random_worker.retrieve.remote(__lowerCAmelCase ,					__lowerCAmelCase       )       )
      else:
        _UpperCAmelCase			,					_UpperCAmelCase    			=   self._main_retrieve(__lowerCAmelCase ,					__lowerCAmelCase       )
      return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__lowerCAmelCase       )


    @classmethod
    def   lowerCAmelCase_	(       cls						:	List[Any] ,					__lowerCAmelCase						:	Union[str, Any] ,					__lowerCAmelCase						:	Tuple=None ,					**__lowerCAmelCase						:	List[str]       ):
      return super(__lowerCAmelCase ,					cls       ).get_tokenizers(__lowerCAmelCase ,					__lowerCAmelCase ,					**__lowerCAmelCase       )


    @classmethod
    def   lowerCAmelCase_	(       cls						:	List[str] ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	Union[str, Any]=None ,					**__lowerCAmelCase						:	Dict       ):
      _UpperCAmelCase    			=   kwargs.pop("""config""" ,					__lowerCAmelCase       ) or RagConfig.from_pretrained(__lowerCAmelCase ,					**__lowerCAmelCase       )
      _UpperCAmelCase    			=   RagTokenizer.from_pretrained(__lowerCAmelCase ,					config=__lowerCAmelCase       )
      _UpperCAmelCase    			=   rag_tokenizer.question_encoder
      _UpperCAmelCase    			=   rag_tokenizer.generator
      if indexed_dataset is not None:
        _UpperCAmelCase    			=   """custom"""
        _UpperCAmelCase    			=   CustomHFIndex(config.retrieval_vector_size ,					__lowerCAmelCase       )
      else:
        _UpperCAmelCase    			=   cls._build_index(__lowerCAmelCase       )
      return cls(
          __lowerCAmelCase ,					question_encoder_tokenizer=__lowerCAmelCase ,					generator_tokenizer=__lowerCAmelCase ,					retrieval_workers=__lowerCAmelCase ,					index=__lowerCAmelCase ,					)

"""simple docstring"""


from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
    import datasets


class 							a					(		lowerCAmelCase_ ):


    @slow
    @require_torch
    def   lowerCAmelCase_	(       self						:	str       ):
      _UpperCAmelCase    			=   EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny""" ,					"""prajjwal1/bert-tiny"""       )
      _UpperCAmelCase    			=   BertTokenizer.from_pretrained("""bert-base-uncased"""       )

      _UpperCAmelCase    			=   bertabert.config.encoder.vocab_size
      _UpperCAmelCase    			=   tokenizer.sep_token_id
      _UpperCAmelCase    			=   tokenizer.cls_token_id
      _UpperCAmelCase    			=   128

      _UpperCAmelCase    			=   datasets.load_dataset("""cnn_dailymail""" ,					"""3.0.0""" ,					split="""train[:1%]"""       )
      _UpperCAmelCase    			=   datasets.load_dataset("""cnn_dailymail""" ,					"""3.0.0""" ,					split="""validation[:1%]"""       )

      _UpperCAmelCase    			=   train_dataset.select(range(32       )       )
      _UpperCAmelCase    			=   val_dataset.select(range(16       )       )

      _UpperCAmelCase    			=   4

      def _map_to_encoder_decoder_inputs(__lowerCAmelCase						:	Dict       ):
        # Tokenizer will automatically set [BOS] <text> [EOS]
        _UpperCAmelCase    			=   tokenizer(batch["""article"""] ,					padding="""max_length""" ,					truncation=__lowerCAmelCase ,					max_length=512       )
        _UpperCAmelCase    			=   tokenizer(batch["""highlights"""] ,					padding="""max_length""" ,					truncation=__lowerCAmelCase ,					max_length=128       )
        _UpperCAmelCase    			=   inputs.input_ids
        _UpperCAmelCase    			=   inputs.attention_mask

        _UpperCAmelCase    			=   outputs.input_ids
        _UpperCAmelCase    			=   outputs.input_ids.copy()
        _UpperCAmelCase    			=   [
            [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
        ]
        _UpperCAmelCase    			=   outputs.attention_mask

        assert all(len(__lowerCAmelCase       ) == 512 for x in inputs.input_ids       )
        assert all(len(__lowerCAmelCase       ) == 128 for x in outputs.input_ids       )

        return batch

      def _compute_metrics(__lowerCAmelCase						:	int       ):
        _UpperCAmelCase    			=   pred.label_ids
        _UpperCAmelCase    			=   pred.predictions

        # all unnecessary tokens are removed
        _UpperCAmelCase    			=   tokenizer.batch_decode(__lowerCAmelCase ,					skip_special_tokens=__lowerCAmelCase       )
        _UpperCAmelCase    			=   tokenizer.batch_decode(__lowerCAmelCase ,					skip_special_tokens=__lowerCAmelCase       )

        _UpperCAmelCase    			=   sum([int(pred_str[i] == label_str[i]       ) for i in range(len(__lowerCAmelCase       )       )]       ) / len(__lowerCAmelCase       )

        return {"accuracy": accuracy}

      # map train dataset
      _UpperCAmelCase    			=   train_dataset.map(
          _map_to_encoder_decoder_inputs ,					batched=__lowerCAmelCase ,					batch_size=__lowerCAmelCase ,					remove_columns=["""article""", """highlights"""] ,					)
      train_dataset.set_format(
          type="""torch""" ,					columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] ,					)

      # same for validation dataset
      _UpperCAmelCase    			=   val_dataset.map(
          _map_to_encoder_decoder_inputs ,					batched=__lowerCAmelCase ,					batch_size=__lowerCAmelCase ,					remove_columns=["""article""", """highlights"""] ,					)
      val_dataset.set_format(
          type="""torch""" ,					columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] ,					)

      _UpperCAmelCase    			=   self.get_auto_remove_tmp_dir()

      _UpperCAmelCase    			=   SeqaSeqTrainingArguments(
          output_dir=__lowerCAmelCase ,					per_device_train_batch_size=__lowerCAmelCase ,					per_device_eval_batch_size=__lowerCAmelCase ,					predict_with_generate=__lowerCAmelCase ,					evaluation_strategy="""steps""" ,					do_train=__lowerCAmelCase ,					do_eval=__lowerCAmelCase ,					warmup_steps=0 ,					eval_steps=2 ,					logging_steps=2 ,					)

      # instantiate trainer
      _UpperCAmelCase    			=   SeqaSeqTrainer(
          model=__lowerCAmelCase ,					args=__lowerCAmelCase ,					compute_metrics=_compute_metrics ,					train_dataset=__lowerCAmelCase ,					eval_dataset=__lowerCAmelCase ,					tokenizer=__lowerCAmelCase ,					)

      # start training
      trainer.train()
