






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_:



     """simple docstring"""



     def __init__(		self					:	Any)							->	Optional[int]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		False





     def     __UpperCamelCase						(		self					:	Dict  ,  lowerCAmelCase__					:	Union[str, Any]  ,  lowerCAmelCase__					:	List[Any]  ,  lowerCAmelCase__					:	Optional[Any]  ,  lowerCAmelCase__					:	List[Any])							->	Optional[Any]:

      """simple docstring"""
      if not self.initialized:
       SCREAMING_SNAKE_CASE				    =		RagRetriever(
           lowerCAmelCase__  ,  question_encoder_tokenizer=lowerCAmelCase__  ,  generator_tokenizer=lowerCAmelCase__  ,  index=lowerCAmelCase__  ,  init_retrieval=lowerCAmelCase__  ,  )
       SCREAMING_SNAKE_CASE				    =		True





     def     __UpperCamelCase						(		self					:	List[str])							->	Any:

      """simple docstring"""
      self.retriever.index.init_index()





     def     __UpperCamelCase						(		self					:	int)							->	Dict:

      """simple docstring"""
      # delete the old self.retriever object before assigning the new index
      del self.retriever
      SCREAMING_SNAKE_CASE				    =		False





     def     __UpperCamelCase						(		self					:	List[str]  ,  lowerCAmelCase__					:	Any  ,  lowerCAmelCase__					:	Optional[int])							->	Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.retriever._main_retrieve(lowerCAmelCase__  ,  lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		self.retriever.index.get_doc_dicts(lowerCAmelCase__)
      return doc_ids, retrieved_doc_embeds, doc_dicts

class 	a_(		lowercase__	):



     """simple docstring"""



     def __init__(		self					:	List[str]  ,  lowerCAmelCase__					:	Tuple  ,  lowerCAmelCase__					:	Any  ,  lowerCAmelCase__					:	Union[str, Any]  ,  lowerCAmelCase__					:	Optional[Any]  ,  lowerCAmelCase__					:	Optional[Any]=None)							->	Tuple:

      """simple docstring"""
      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__  ,  )

      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(lowerCAmelCase__  ,  lowerCAmelCase__  ,  lowerCAmelCase__  ,  lowerCAmelCase__)
               for worker in self.retrieval_workers
           ])





     def     __UpperCamelCase						(		self					:	Any)							->	Dict:

      """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     __UpperCamelCase						(		self					:	Optional[int]  ,  lowerCAmelCase__					:	List[str]  ,  lowerCAmelCase__					:	Union[str, Any])							->	List[str]:

      """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    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		ray.get(
           random_worker.retrieve.remote(lowerCAmelCase__  ,  lowerCAmelCase__))
      else:
       SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self._main_retrieve(lowerCAmelCase__  ,  lowerCAmelCase__)
       SCREAMING_SNAKE_CASE				    =		self.index.get_doc_dicts(lowerCAmelCase__)
      return retrieved_doc_embeds, doc_ids, doc_dicts





     @classmethod
     def     __UpperCamelCase						(		cls					:	List[str]  ,  lowerCAmelCase__					:	Optional[int]  ,  lowerCAmelCase__					:	Any=None  ,  **lowerCAmelCase__					:	Optional[Any])							->	Union[str, Any]:

      """simple docstring"""
      return super(lowerCAmelCase__  ,  cls).get_tokenizers(lowerCAmelCase__  ,  lowerCAmelCase__  ,  **lowerCAmelCase__)





     @classmethod
     def     __UpperCamelCase						(		cls					:	Any  ,  lowerCAmelCase__					:	Dict  ,  lowerCAmelCase__					:	List[Any]  ,  lowerCAmelCase__					:	Optional[Any]=None  ,  **lowerCAmelCase__					:	str)							->	Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		kwargs.pop('config'  ,  lowerCAmelCase__) or RagConfig.from_pretrained(lowerCAmelCase__  ,  **lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		RagTokenizer.from_pretrained(lowerCAmelCase__  ,  config=lowerCAmelCase__)
      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  ,  lowerCAmelCase__)
      else:
       SCREAMING_SNAKE_CASE				    =		cls._build_index(lowerCAmelCase__)

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





     def     __UpperCamelCase						(		self					:	List[Any])							->	Union[str, Any]:

      """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  ,  lowerCAmelCase__)
              for worker in self.retrieval_workers
          ])










import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()



def    UpperCAmelCase							(  _snake_case    ,			_snake_case				):
							# Initialize PyTorch model
							lowerCAmelCase =		CanineConfig()
							lowerCAmelCase =		CanineModel(_snake_case				)
							model.eval()

							print(F"""Building PyTorch model from configuration: {config}"""				)

							# Load weights from tf checkpoint
							load_tf_weights_in_canine(_snake_case    ,			_snake_case    ,			_snake_case				)

							# Save pytorch-model (weights and configuration)
							print(F"""Save PyTorch model to {pytorch_dump_path}"""				)
							model.save_pretrained(_snake_case				)

							# Save tokenizer files
							lowerCAmelCase =		CanineTokenizer()
							print(F"""Save tokenizer files to {pytorch_dump_path}"""				)
							tokenizer.save_pretrained(_snake_case				)


if __name__ == "__main__":
		UpperCAmelCase_    =argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--tf_checkpoint_path""",
		    default=None,
		    type=str,
		    required=True,
		    help="""Path to the TensorFlow checkpoint. Should end with model.ckpt""",
		)
		parser.add_argument(
		    """--pytorch_dump_path""",
		    default=None,
		    type=str,
		    required=True,
		    help="""Path to a folder where the PyTorch model will be placed.""",
		)
		UpperCAmelCase_    =parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)



