






import logging
import random

import ray

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


UpperCAmelCase_					:		str           = logging.getLogger(__name__)

class        _SCREAMING_SNAKE_CASE			:





	def __init__(						self							:   List[str]      ):
		UpperCamelCase	:Optional[int]		=      False





	def 		_A					(						self							:   Optional[int]					,    __lowerCamelCase							:   Optional[int]					,    __lowerCamelCase							:   int					,    __lowerCamelCase							:   Optional[int]					,    __lowerCamelCase							:   List[str]      ):
		if not self.initialized:
			UpperCamelCase	:Tuple		=      RagRetriever(
			    _UpperCAmelCase					,    question_encoder_tokenizer=_UpperCAmelCase					,    generator_tokenizer=_UpperCAmelCase					,    index=_UpperCAmelCase					,    init_retrieval=_UpperCAmelCase					,    )
			UpperCamelCase	:Tuple		=      True





	def 		_A					(						self							:   List[Any]      ):
		self.retriever.index.init_index()





	def 		_A					(						self							:   Tuple      ):
		# delete the old self.retriever object before assigning the new index
		del self.retriever
		UpperCamelCase	:Union[str, Any]		=      False





	def 		_A					(						self							:   int					,    __lowerCamelCase							:   Union[str, Any]					,    __lowerCamelCase							:   List[Any]      ):
		UpperCamelCase	:str		=      self.retriever._main_retrieve(_UpperCAmelCase					,    _UpperCAmelCase      )
		UpperCamelCase	:Optional[Any]		=      self.retriever.index.get_doc_dicts(_UpperCAmelCase      )
		return doc_ids, retrieved_doc_embeds, doc_dicts

class        _SCREAMING_SNAKE_CASE			(     UpperCamelCase__ ):





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

		UpperCamelCase	:Union[str, Any]		=      retrieval_workers
		UpperCamelCase	:List[str]		=      question_encoder_tokenizer
		UpperCamelCase	:int		=      generator_tokenizer
		if len(self.retrieval_workers      ) > 0:
			ray.get(
			    [
			        worker.create_rag_retriever.remote(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase      )
			        for worker in self.retrieval_workers
			    ]      )





	def 		_A					(						self							:   Any      ):
		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							:   Union[str, Any]					,    __lowerCamelCase							:   Tuple					,    __lowerCamelCase							:   List[str]      ):
		if len(self.retrieval_workers      ) > 0:
			# Select a random retrieval actor.
			UpperCamelCase	:Dict		=      self.retrieval_workers[random.randint(0					,    len(self.retrieval_workers      ) - 1      )]
			UpperCamelCase	:Optional[int]		=      ray.get(
			    random_worker.retrieve.remote(_UpperCAmelCase					,    _UpperCAmelCase      )      )
		else:
			UpperCamelCase	:str		=      self._main_retrieve(_UpperCAmelCase					,    _UpperCAmelCase      )
			UpperCamelCase	:List[Any]		=      self.index.get_doc_dicts(_UpperCAmelCase      )
		return retrieved_doc_embeds, doc_ids, doc_dicts





	@classmethod
	def 		_A					(						cls							:   Optional[int]					,    __lowerCamelCase							:   Optional[int]					,    __lowerCamelCase							:   int=None					,    **__lowerCamelCase							:   Optional[int]      ):
		return super(_UpperCAmelCase					,    cls      ).get_tokenizers(_UpperCAmelCase					,    _UpperCAmelCase					,    **_UpperCAmelCase      )





	@classmethod
	def 		_A					(						cls							:   Optional[Any]					,    __lowerCamelCase							:   Tuple					,    __lowerCamelCase							:   Dict					,    __lowerCamelCase							:   Any=None					,    **__lowerCamelCase							:   str      ):
		UpperCamelCase	:str		=      kwargs.pop("""config"""					,    _UpperCAmelCase      ) or RagConfig.from_pretrained(_UpperCAmelCase					,    **_UpperCAmelCase      )
		UpperCamelCase	:Optional[Any]		=      RagTokenizer.from_pretrained(_UpperCAmelCase					,    config=_UpperCAmelCase      )
		UpperCamelCase	:Dict		=      rag_tokenizer.question_encoder
		UpperCamelCase	:Any		=      rag_tokenizer.generator

		if indexed_dataset is not None:
			UpperCamelCase	:str		=      '''custom'''
			UpperCamelCase	:Any		=      CustomHFIndex(config.retrieval_vector_size					,    _UpperCAmelCase      )
		else:
			UpperCamelCase	:int		=      cls._build_index(_UpperCAmelCase      )

		return cls(
		    _UpperCAmelCase					,    question_encoder_tokenizer=_UpperCAmelCase					,    generator_tokenizer=_UpperCAmelCase					,    retrieval_workers=_UpperCAmelCase					,    index=_UpperCAmelCase					,    )





	def 		_A					(						self							:   Tuple      ):
		logger.info("""re-loading the new dataset with embeddings"""      )
		# access from the training loop

		ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]      )

		# build the index object again
		UpperCamelCase	:str		=      self._build_index(self.config      )

		ray.get(
		    [
		        worker.create_rag_retriever.remote(
		            self.config					,    self.question_encoder_tokenizer					,    self.generator_tokenizer					,    _UpperCAmelCase      )
		        for worker in self.retrieval_workers
		    ]      )








'''simple docstring'''







import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer




class 					__UpperCAmelCase (		nn.Linear	):

      '''simple docstring'''





      def __init__(					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase = True  ,		_UpperCAmelCase = "constant"  ,		_UpperCAmelCase = 0.0  ,		_UpperCAmelCase = "topK"  ,		):
             super(_UpperCAmelCase  ,		self						).__init__(in_features=_UpperCAmelCase  ,		out_features=_UpperCAmelCase  ,		bias=_UpperCAmelCase						)
             assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
             UpperCAmelCase__	:   Union[str, Any]												=	pruning_method

             if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
                    UpperCAmelCase__	:   Union[str, Any]												=	mask_scale
                    UpperCAmelCase__	:   Optional[int]												=	mask_init
                    UpperCAmelCase__	:   List[str]												=	nn.Parameter(torch.empty(self.weight.size()						)						)
                    self.init_mask()




      def 							lowerCamelCase     (					self						):
             if self.mask_init == "constant":
                    init.constant_(self.mask_scores  ,		val=self.mask_scale						)
             elif self.mask_init == "uniform":
                    init.uniform_(self.mask_scores  ,		a=-self.mask_scale  ,		b=self.mask_scale						)
             elif self.mask_init == "kaiming":
                    init.kaiming_uniform_(self.mask_scores  ,		a=math.sqrt(5						)						)




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase						):
             # Get the mask
             if self.pruning_method == "topK":
                    UpperCAmelCase__	:   Union[str, Any]												=	TopKBinarizer.apply(self.mask_scores  ,		_UpperCAmelCase						)
             elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
                    UpperCAmelCase__	:   List[Any]												=	'''sigmoied''' in self.pruning_method
                    UpperCAmelCase__	:   Tuple												=	ThresholdBinarizer.apply(self.mask_scores  ,		_UpperCAmelCase  ,		_UpperCAmelCase						)
             elif self.pruning_method == "magnitude":
                    UpperCAmelCase__	:   Optional[int]												=	MagnitudeBinarizer.apply(self.weight  ,		_UpperCAmelCase						)
             elif self.pruning_method == "l0":
                    UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:   int												=	-0.1, 1.1, 2 / 3
                    if self.training:
                           UpperCAmelCase__	:   Optional[int]												=	torch.zeros_like(self.mask_scores						).uniform_().clamp(0.00_01  ,		0.99_99						)
                           UpperCAmelCase__	:   Tuple												=	torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b						)
                    else:
                           UpperCAmelCase__	:   Any												=	torch.sigmoid(self.mask_scores						)
                    UpperCAmelCase__	:   List[Any]												=	s * (r - l) + l
                    UpperCAmelCase__	:   Optional[int]												=	s_bar.clamp(min=0.0  ,		max=1.0						)
             # Mask weights with computed mask
             UpperCAmelCase__	:   Union[str, Any]												=	mask * self.weight
             # Compute output (linear layer) with masked weights
             return nn.functional.linear(_UpperCAmelCase  ,		_UpperCAmelCase  ,		self.bias						)