






import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__UpperCAmelCase      =				logging.get_logger(__name__)

__UpperCAmelCase      =				["UtteranceLevel"]





@torch.no_grad()
def 						A_					(						lowercase_	,  lowercase_	,  lowercase_	,  lowercase_			)				->Union[str, Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		torch.load(lowercase_	,  map_location='cpu'			)
 if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
  raise NotImplementedError(f'''The supported s3prl models are {SUPPORTED_MODELS}'''			)

 SCREAMING_SNAKE_CASE				    =		checkpoint['Downstream']

 SCREAMING_SNAKE_CASE				    =		HubertConfig.from_pretrained(lowercase_			)
 SCREAMING_SNAKE_CASE				    =		HubertForSequenceClassification.from_pretrained(lowercase_	,  config=lowercase_			)
 SCREAMING_SNAKE_CASE				    =		WavaVecaFeatureExtractor.from_pretrained(
     lowercase_	,  return_attention_mask=lowercase_	,  do_normalize=lowercase_			)

 if hf_congfig.use_weighted_layer_sum:
  SCREAMING_SNAKE_CASE				    =		checkpoint['Featurizer']['weights']

 SCREAMING_SNAKE_CASE				    =		downstream_dict['projector.weight']
 SCREAMING_SNAKE_CASE				    =		downstream_dict['projector.bias']
 SCREAMING_SNAKE_CASE				    =		downstream_dict['model.post_net.linear.weight']
 SCREAMING_SNAKE_CASE				    =		downstream_dict['model.post_net.linear.bias']

 hf_feature_extractor.save_pretrained(lowercase_			)
 hf_model.save_pretrained(lowercase_			)


if __name__ == "__main__":
      __UpperCAmelCase      =				argparse.ArgumentParser()
      parser.add_argument(
          "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
      )
      parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
      parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
      parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
      __UpperCAmelCase      =				parser.parse_args()
      convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)












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
          ])




