




import logging
import random

import ray

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


a_   		=     logging.getLogger(__name__)

class  _lowerCamelCase						:

      """simple docstring"""

      def __init__(     self					: Dict				):
          __UpperCamelCase  		=						False







      def       snake_case	(     self					: Tuple ,       snake_case					: Tuple ,       snake_case					: str ,       snake_case					: Optional[int] ,       snake_case					: Any				):
          if not self.initialized:
              __UpperCamelCase  		=						RagRetriever(
                  snake_case ,       question_encoder_tokenizer=snake_case ,       generator_tokenizer=snake_case ,       index=snake_case ,       init_retrieval=snake_case ,       )
              __UpperCamelCase  		=						True







      def       snake_case	(     self					: Optional[int]				):
          self.retriever.index.init_index()







      def       snake_case	(     self					: Dict ,       snake_case					: int ,       snake_case					: int				):
          __UpperCamelCase		,      __UpperCamelCase  		=						self.retriever._main_retrieve(snake_case ,       snake_case				)
          return doc_ids, retrieved_doc_embeds







class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

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







      def       snake_case	(     self					: List[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       snake_case	(     self					: int ,       snake_case					: Any ,       snake_case					: Any				):
          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(snake_case ,       snake_case				)				)
          else:
              __UpperCamelCase		,      __UpperCamelCase  		=						self._main_retrieve(snake_case ,       snake_case				)
          return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(snake_case				)







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







      @classmethod
      def       snake_case	(     cls					: Any ,       snake_case					: str ,       snake_case					: Any ,       snake_case					: Optional[int]=None ,       **snake_case					: str				):
          __UpperCamelCase  		=						kwargs.pop('''config''' ,       snake_case				) or RagConfig.from_pretrained(snake_case ,       **snake_case				)
          __UpperCamelCase  		=						RagTokenizer.from_pretrained(snake_case ,       config=snake_case				)
          __UpperCamelCase  		=						rag_tokenizer.question_encoder
          __UpperCamelCase  		=						rag_tokenizer.generator
          if indexed_dataset is not None:
              __UpperCamelCase  		=						'''custom'''
              __UpperCamelCase  		=						CustomHFIndex(config.retrieval_vector_size ,       snake_case				)
          else:
              __UpperCamelCase  		=						cls._build_index(snake_case				)
          return cls(
              snake_case ,       question_encoder_tokenizer=snake_case ,       generator_tokenizer=snake_case ,       retrieval_workers=snake_case ,       index=snake_case ,       )












from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Any    =					""
      lowerCAmelCase__			: Any    =					"hf-legacy"  # "hf://"" is reserved for hffs







      def __init__(     self					: str ,       snake_case					: Optional[DatasetInfo] = None ,       snake_case					: Optional[str] = None ,       **snake_case					: List[Any] ,       ):
          super().__init__(self ,       **snake_case				)
          __UpperCamelCase  		=						repo_info
          __UpperCamelCase  		=						token
          __UpperCamelCase  		=						None







      def       snake_case	(     self					: List[Any]				):
          if self.dir_cache is None:
              __UpperCamelCase  		=						{}
              for hf_file in self.repo_info.siblings:
                  # TODO(QL): add sizes
                  __UpperCamelCase  		=						{
                      '''name''': hf_file.rfilename,
                      '''size''': None,
                      '''type''': '''file''',
                  }
                  self.dir_cache.update(
                      {
                          str(snake_case				): {'''name''': str(snake_case				), '''size''': None, '''type''': '''directory'''}
                          for d in list(PurePosixPath(hf_file.rfilename				).parents				)[:-1]
                      }				)







      def       snake_case	(     self					: Dict ,       snake_case					: str ,       snake_case					: str = "rb" ,       **snake_case					: Union[str, Any] ,       ):
          if not isinstance(self.repo_info ,       snake_case				):
              raise NotImplementedError(F"Open is only implemented for dataset repositories, but got {self.repo_info}"				)
          __UpperCamelCase  		=						hf_hub_url(self.repo_info.id ,       snake_case ,       revision=self.repo_info.sha				)
          return fsspec.open(
              snake_case ,       mode=snake_case ,       headers=get_authentication_headers_for_url(snake_case ,       use_auth_token=self.token				) ,       client_kwargs={'''trust_env''': True} ,       ).open()







      def       snake_case	(     self					: Optional[Any] ,       snake_case					: Tuple ,       **snake_case					: List[Any]				):
          self._get_dirs()
          __UpperCamelCase  		=						self._strip_protocol(snake_case				)
          if path in self.dir_cache:
              return self.dir_cache[path]
          else:
              raise FileNotFoundError(snake_case				)







      def       snake_case	(     self					: List[str] ,       snake_case					: int ,       snake_case					: Tuple=False ,       **snake_case					: Dict				):
          self._get_dirs()
          __UpperCamelCase  		=						PurePosixPath(path.strip('''/'''				)				)
          __UpperCamelCase  		=						{}
          for p, f in self.dir_cache.items():
              __UpperCamelCase  		=						PurePosixPath(p.strip('''/'''				)				)
              __UpperCamelCase  		=						p.parent
              if root == path:
                  __UpperCamelCase  		=						f
          __UpperCamelCase  		=						list(paths.values()				)
          if detail:
              return out
          else:
              return sorted(f['''name'''] for f in out				)






