
'''simple docstring'''
import logging
import random

import ray

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


__lowerCAmelCase												=	logging.getLogger(__name__)


class       _lowerCAmelCase   :







							'''simple docstring'''

							def __init__(self  )					->       Union[str, Any]:
														_snake_case       =  False

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       int:
														if not self.initialized:
																					_snake_case       =  RagRetriever(
																					    _a  ,  question_encoder_tokenizer=_a  ,  generator_tokenizer=_a  ,  index=_a  ,  init_retrieval=_a  ,  )
																					_snake_case       =  True

							def 	lowercase							(self  )					->       str:
														self.retriever.index.init_index()





							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Any:
														_snake_case       =  self.retriever._main_retrieve(_a  ,  _a  )
														return doc_ids, retrieved_doc_embeds


class       _lowerCAmelCase   (   __lowercase				):







							'''simple docstring'''

							def __init__(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=None  )					->       Optional[int]:
														if index is not None and index.is_initialized() and len(_a  ) > 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__(
														    _a  ,  question_encoder_tokenizer=_a  ,  generator_tokenizer=_a  ,  index=_a  ,  init_retrieval=_a  ,  )
														_snake_case       =  retrieval_workers
														if len(self.retrieval_workers  ) > 0:
																					ray.get(
																					    [
																					        worker.create_rag_retriever.remote(_a  ,  _a  ,  _a  ,  _a  )
																					        for worker in self.retrieval_workers
																					    ]  )

							def 	lowercase							(self  )					->       str:
														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 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       int:
														if len(self.retrieval_workers  ) > 0:
																					# Select a random retrieval actor.
																					_snake_case       =  self.retrieval_workers[random.randint(0  ,  len(self.retrieval_workers  ) - 1  )]
																					_snake_case       =  ray.get(random_worker.retrieve.remote(_a  ,  _a  )  )
														else:
																					_snake_case       =  self._main_retrieve(_a  ,  _a  )
														return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_a  )

							@classmethod
							def 	lowercase							(cls  ,  UpperCAmelCase  ,  UpperCAmelCase=None  ,  **UpperCAmelCase  )					->       Optional[int]:
														return super(_a  ,  cls  ).get_tokenizers(_a  ,  _a  ,  **_a  )





							@classmethod
							def 	lowercase							(cls  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=None  ,  **UpperCAmelCase  )					->       List[str]:
														_snake_case       =  kwargs.pop("""config"""  ,  _a  ) or RagConfig.from_pretrained(_a  ,  **_a  )
														_snake_case       =  RagTokenizer.from_pretrained(_a  ,  config=_a  )
														_snake_case       =  rag_tokenizer.question_encoder
														_snake_case       =  rag_tokenizer.generator
														if indexed_dataset is not None:
																					_snake_case       =  '''custom'''
																					_snake_case       =  CustomHFIndex(config.retrieval_vector_size  ,  _a  )
														else:
																					_snake_case       =  cls._build_index(_a  )
														return cls(
														    _a  ,  question_encoder_tokenizer=_a  ,  generator_tokenizer=_a  ,  retrieval_workers=_a  ,  index=_a  ,  )







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__lowerCAmelCase												=	{
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'EncodecModel',
				    'EncodecPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_encodec import (
				    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    EncodecConfig,
				)
				from .feature_extraction_encodec import EncodecFeatureExtractor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_encodec import (
								    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
								    EncodecModel,
								    EncodecPreTrainedModel,
								)

else:
				import sys

				__lowerCAmelCase												=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)