


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      lowercase__     :

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







							'''simple docstring'''





							lowerCAmelCase__       =						False

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					Optional[int]:







							'''simple docstring'''





							if not self.initialized:
													lowerCAmelCase__       =						RagRetriever(
													    __UpperCAmelCase		,    question_encoder_tokenizer=__UpperCAmelCase		,    generator_tokenizer=__UpperCAmelCase		,    index=__UpperCAmelCase		,    init_retrieval=__UpperCAmelCase		,    )
													lowerCAmelCase__       =						True

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







							'''simple docstring'''





							self.retriever.index.init_index()


	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.retriever._main_retrieve(__UpperCAmelCase		,    __UpperCAmelCase       )
							return doc_ids, retrieved_doc_embeds





class      lowercase__     (			_UpperCAmelCase	):

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase=None       )->					Optional[Any]:







							'''simple docstring'''





							if index is not None and index.is_initialized() and len(__UpperCAmelCase       ) > 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__(
							    __UpperCAmelCase		,    question_encoder_tokenizer=__UpperCAmelCase		,    generator_tokenizer=__UpperCAmelCase		,    index=__UpperCAmelCase		,    init_retrieval=__UpperCAmelCase		,    )
							lowerCAmelCase__       =						retrieval_workers
							if len(self.retrieval_workers       ) > 0:
													ray.get(
													    [
													        worker.create_rag_retriever.remote(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )
													        for worker in self.retrieval_workers
													    ]       )

	def 		UpperCAmelCase  (							self       )->					List[str]:







							'''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		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					Union[str, Any]:







							'''simple docstring'''





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

	@classmethod
	def 		UpperCAmelCase  (							cls		,    __UpperCAmelCase		,    __UpperCAmelCase=None		,    **__UpperCAmelCase       )->					Tuple:







							'''simple docstring'''





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


	@classmethod
	def 		UpperCAmelCase  (							cls		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase=None		,    **__UpperCAmelCase       )->					Dict:







							'''simple docstring'''





							lowerCAmelCase__       =						kwargs.pop("config"		,    __UpperCAmelCase       ) or RagConfig.from_pretrained(__UpperCAmelCase		,    **__UpperCAmelCase       )
							lowerCAmelCase__       =						RagTokenizer.from_pretrained(__UpperCAmelCase		,    config=__UpperCAmelCase       )
							lowerCAmelCase__       =						rag_tokenizer.question_encoder
							lowerCAmelCase__       =						rag_tokenizer.generator
							if indexed_dataset is not None:
													lowerCAmelCase__       =						"custom"
													lowerCAmelCase__       =						CustomHFIndex(config.retrieval_vector_size		,    __UpperCAmelCase       )
							else:
													lowerCAmelCase__       =						cls._build_index(__UpperCAmelCase       )
							return cls(
							    __UpperCAmelCase		,    question_encoder_tokenizer=__UpperCAmelCase		,    generator_tokenizer=__UpperCAmelCase		,    retrieval_workers=__UpperCAmelCase		,    index=__UpperCAmelCase		,    )














# 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,
)






