


'''simple docstring'''
import logging
import random

import ray

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


lowerCAmelCase  =				logging.getLogger(__name__)






class 					lowerCAmelCase_     :






       def __init__(   self      )->			Dict:
            _A				=       False






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase      )->			Tuple:
            if not self.initialized:
                 _A				=       RagRetriever(
                     __A ,    question_encoder_tokenizer=__A ,    generator_tokenizer=__A ,    index=__A ,    init_retrieval=__A ,    )
                 _A				=       True






       def      UpperCamelCase	(   self      )->			Dict:
            self.retriever.index.init_index()






       def      UpperCamelCase	(   self      )->			List[Any]:
            # delete the old self.retriever object before assigning the new index
            del self.retriever
            _A				=       False






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase      )->			str:
            _A		,							_A				=       self.retriever._main_retrieve(__A ,    __A      )
            _A				=       self.retriever.index.get_doc_dicts(__A      )
            return doc_ids, retrieved_doc_embeds, doc_dicts






class 					lowerCAmelCase_     (    _SCREAMING_SNAKE_CASE  ):






       def __init__(   self ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase=None      )->			int:
            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 ,    )

            _A				=       retrieval_workers
            _A				=       question_encoder_tokenizer
            _A				=       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      UpperCamelCase	(   self      )->			Union[str, Any]:
            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      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase      )->			Dict:
            if len(self.retrieval_workers      ) > 0:
                 # Select a random retrieval actor.
                 _A				=       self.retrieval_workers[random.randint(0 ,    len(self.retrieval_workers      ) - 1      )]
                 _A		,							_A		,							_A				=       ray.get(
                     random_worker.retrieve.remote(__A ,    __A      )      )
            else:
                 _A		,							_A				=       self._main_retrieve(__A ,    __A      )
                 _A				=       self.index.get_doc_dicts(__A      )
            return retrieved_doc_embeds, doc_ids, doc_dicts






       @classmethod
       def      UpperCamelCase	(   cls ,    _UpperCamelCase ,    _UpperCamelCase=None ,    **_UpperCamelCase      )->			Tuple:
            return super(__A ,    cls      ).get_tokenizers(__A ,    __A ,    **__A      )






       @classmethod
       def      UpperCamelCase	(   cls ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase=None ,    **_UpperCamelCase      )->			Optional[Any]:
            _A				=       kwargs.pop('config' ,    __A      ) or RagConfig.from_pretrained(__A ,    **__A      )
            _A				=       RagTokenizer.from_pretrained(__A ,    config=__A      )
            _A				=       rag_tokenizer.question_encoder
            _A				=       rag_tokenizer.generator

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

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






       def      UpperCamelCase	(   self      )->			Union[str, Any]:
            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
            _A				=       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
                ]      )









'''simple docstring'''
import argparse

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


logging.set_verbosity_info()

def 				lowerCamelCase_   (   __UpperCamelCase				:					Tuple       ,						__UpperCamelCase				:					Any       ,						__UpperCamelCase				:					Dict       ,						__UpperCamelCase				:					List[str]       ,						__UpperCamelCase				:					str		)				->  List[str]:







     """simple docstring"""
     # Initialise PyTorch model
     _A				=       BertConfig.from_pretrained(
         'bert-large-cased'       ,						vocab_size=__UpperCamelCase       ,						max_position_embeddings=5_1_2       ,						is_decoder=__UpperCamelCase       ,						add_cross_attention=__UpperCamelCase       ,						)
     _A				=       bert_config.to_dict()
     del bert_config_dict["type_vocab_size"]
     _A				=       BertGenerationConfig(**__UpperCamelCase		)
     if is_encoder:
          _A				=       BertGenerationEncoder(__UpperCamelCase		)
     else:
          _A				=       BertGenerationDecoder(__UpperCamelCase		)
     print(F'Building PyTorch model from configuration: {config}'		)

     # Load weights from tf checkpoint
     load_tf_weights_in_bert_generation(
         __UpperCamelCase       ,						__UpperCamelCase       ,						model_class='bert'       ,						is_encoder_named_decoder=__UpperCamelCase       ,						is_encoder=__UpperCamelCase       ,						)

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


if __name__ == "__main__":
    lowerCAmelCase  =				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=50_358, type=int, help="""Vocab size of model""")
    lowerCAmelCase  =				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,
    )





