

"""simple docstring"""
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	):


        """simple docstring"""

        UpperCAmelCase__			:							Any							=    False



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        if not self.initialized:
              UpperCAmelCase__			:							List[str]							=    RagRetriever(
                  _lowerCamelCase							,       question_encoder_tokenizer=_lowerCamelCase							,       generator_tokenizer=_lowerCamelCase							,       index=_lowerCamelCase							,       init_retrieval=_lowerCamelCase							,       )
              UpperCAmelCase__			:							Optional[int]							=    True



  def  _a      (self	):


        """simple docstring"""

        self.retriever.index.init_index()




  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__					,		UpperCAmelCase__			:							str							=    self.retriever._main_retrieve(_lowerCamelCase							,       _lowerCamelCase	)
        return doc_ids, retrieved_doc_embeds




class       lowerCamelCase	(       lowerCAmelCase__   ):


  '''simple docstring'''







  def __init__(self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase=None	):


        """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							,       )
        UpperCAmelCase__			:							Optional[Any]							=    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  _a      (self	):


        """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  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        if len(self.retrieval_workers	) > 0:
              # Select a random retrieval actor.
              UpperCAmelCase__			:							List[str]							=    self.retrieval_workers[random.randint(0							,       len(self.retrieval_workers	) - 1	)]
              UpperCAmelCase__					,		UpperCAmelCase__			:							str							=    ray.get(random_worker.retrieve.remote(_lowerCamelCase							,       _lowerCamelCase	)	)
        else:
              UpperCAmelCase__					,		UpperCAmelCase__			:							Dict							=    self._main_retrieve(_lowerCamelCase							,       _lowerCamelCase	)
        return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_lowerCamelCase	)



  @classmethod
  def  _a      (cls							,       _lowerCamelCase							,       _lowerCamelCase=None							,       **_lowerCamelCase	):


        """simple docstring"""

        return super(_lowerCamelCase							,       cls	).get_tokenizers(_lowerCamelCase							,       _lowerCamelCase							,       **_lowerCamelCase	)




  @classmethod
  def  _a      (cls							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase=None							,       **_lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							int							=    kwargs.pop("""config"""							,       _lowerCamelCase	) or RagConfig.from_pretrained(_lowerCamelCase							,       **_lowerCamelCase	)
        UpperCAmelCase__			:							Union[str, Any]							=    RagTokenizer.from_pretrained(_lowerCamelCase							,       config=_lowerCamelCase	)
        UpperCAmelCase__			:							Tuple							=    rag_tokenizer.question_encoder
        UpperCAmelCase__			:							Any							=    rag_tokenizer.generator
        if indexed_dataset is not None:
              UpperCAmelCase__			:							str							=    """custom"""
              UpperCAmelCase__			:							Tuple							=    CustomHFIndex(config.retrieval_vector_size							,       _lowerCamelCase	)
        else:
              UpperCAmelCase__			:							List[str]							=    cls._build_index(_lowerCamelCase	)
        return cls(
            _lowerCamelCase							,       question_encoder_tokenizer=_lowerCamelCase							,       generator_tokenizer=_lowerCamelCase							,       retrieval_workers=_lowerCamelCase							,       index=_lowerCamelCase							,       )




"""simple docstring"""
# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    """pipelines_utils""",
    """0.22.0""",
    """Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
    standard_warn=False,
    stacklevel=3,
)

