





import logging
import random

import ray

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


UpperCamelCase__   : List[Any]					  = logging.getLogger(__name__)







class   _UpperCamelCase  :







					'''simple docstring'''

					def __init__(						self    :  List[str]		):






						'''simple docstring'''







						UpperCAmelCase_								=     False





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






						'''simple docstring'''







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





					def        SCREAMING_SNAKE_CASE							(						self    :  str		):






						'''simple docstring'''







						self.retriever.index.init_index()





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






						'''simple docstring'''







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







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					def __init__(						self    :  Tuple ,					__lowercase    :  Dict ,					__lowercase    :  str ,					__lowercase    :  Dict ,					__lowercase    :  List[Any] ,					__lowercase    :  List[str]=None		):






						'''simple docstring'''







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





					def        SCREAMING_SNAKE_CASE							(						self    :  Dict		):






						'''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    :  List[str] ,					__lowercase    :  Tuple ,					__lowercase    :  Tuple		):






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





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






						'''simple docstring'''







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





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






						'''simple docstring'''







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









from torch import nn







class   _UpperCamelCase  (       nn.Module  ):







					'''simple docstring'''

					def __init__(						self    :  Dict ,					__lowercase    :  List[str] ,					__lowercase    :  Dict		):






						'''simple docstring'''







						super().__init__()
						UpperCAmelCase_								=     class_size
						UpperCAmelCase_								=     embed_size
						# self.mlp1 = nn.Linear(embed_size, embed_size)
						# self.mlp2 = (nn.Linear(embed_size, class_size))
						UpperCAmelCase_								=     nn.Linear(__lowercase ,					__lowercase		)





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






						'''simple docstring'''







						UpperCAmelCase_								=     self.mlp(__lowercase		)
						return logits


