





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   _UpperCamelCase  :







					'''simple docstring'''

					def __init__(						self    :  Dict		):






						'''simple docstring'''







						UpperCAmelCase_								=     False





					def        SCREAMING_SNAKE_CASE							(						self    :  Union[str, Any] ,					__lowercase    :  int ,					__lowercase    :  Any ,					__lowercase    :  Dict ,					__lowercase    :  Union[str, Any]		):






						'''simple docstring'''







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





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any]		):






						'''simple docstring'''







						self.retriever.index.init_index()





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int] ,					__lowercase    :  Optional[Any] ,					__lowercase    :  int		):






						'''simple docstring'''







						UpperCAmelCase_						,	UpperCAmelCase_								=     self.retriever._main_retrieve(A_ ,					A_		)
						return doc_ids, retrieved_doc_embeds







class   _UpperCamelCase  (       _lowercase  ):







					'''simple docstring'''

					def __init__(						self    :  Union[str, Any] ,					__lowercase    :  int ,					__lowercase    :  Any ,					__lowercase    :  Optional[Any] ,					__lowercase    :  Dict ,					__lowercase    :  str=None		):






						'''simple docstring'''







						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_ ,					)
						UpperCAmelCase_								=     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        SCREAMING_SNAKE_CASE							(						self    :  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        SCREAMING_SNAKE_CASE							(						self    :  Optional[int] ,					__lowercase    :  Optional[Any] ,					__lowercase    :  int		):






						'''simple docstring'''







						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(A_ ,					A_		)		)
						else:
							UpperCAmelCase_						,	UpperCAmelCase_								=     self._main_retrieve(A_ ,					A_		)
						return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(A_		)





					@classmethod
					def        SCREAMING_SNAKE_CASE							(						cls    :  Any ,					__lowercase    :  List[str] ,					__lowercase    :  Optional[int]=None ,					**__lowercase    :  Any		):






						'''simple docstring'''







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





					@classmethod
					def        SCREAMING_SNAKE_CASE							(						cls    :  Any ,					__lowercase    :  int ,					__lowercase    :  Union[str, Any] ,					__lowercase    :  int=None ,					**__lowercase    :  List[str]		):






						'''simple docstring'''







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









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__   : List[Any]					  = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__   : List[str]					  = [
				    """SEW_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """SEWForCTC""",
				    """SEWForSequenceClassification""",
				    """SEWModel""",
				    """SEWPreTrainedModel""",
				]

if TYPE_CHECKING:
				from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_sew import (
								    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SEWForCTC,
								    SEWForSequenceClassification,
								    SEWModel,
								    SEWPreTrainedModel,
								)


else:
				import sys

				UpperCamelCase__   : str					  = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


