'''simple docstring'''

import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer




class 		SCREAMING_SNAKE_CASE_					(nn.Linear      ):



			"""simple docstring"""






			def __init__(							self				,			snake_case_				,			snake_case_				,			snake_case_ = True				,			snake_case_ = "constant"				,			snake_case_ = 0.0				,			snake_case_ = "topK"				,			):
									super(_a				,			self					).__init__(in_features=_a				,			out_features=_a				,			bias=_a					)
									assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
									snake_case :       Tuple				=    pruning_method

									if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
															snake_case :       Dict				=    mask_scale
															snake_case :       List[Any]				=    mask_init
															snake_case :       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				,			snake_case_				,			snake_case_					):
									# Get the mask
									if self.pruning_method == "topK":
															snake_case :       Dict				=    TopKBinarizer.apply(self.mask_scores				,			_a					)
									elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
															snake_case :       str				=    "sigmoied" in self.pruning_method
															snake_case :       Any				=    ThresholdBinarizer.apply(self.mask_scores				,			_a				,			_a					)
									elif self.pruning_method == "magnitude":
															snake_case :       List[str]				=    MagnitudeBinarizer.apply(self.weight				,			_a					)
									elif self.pruning_method == "l0":
															snake_case :       List[str]				=    -0.1, 1.1, 2 / 3
															if self.training:
																					snake_case :       Optional[Any]				=    torch.zeros_like(self.mask_scores					).uniform_().clamp(0.0_001				,			0.9_999					)
																					snake_case :       List[Any]				=    torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b					)
															else:
																					snake_case :       Tuple				=    torch.sigmoid(self.mask_scores					)
															snake_case :       Optional[Any]				=    s * (r - l) + l
															snake_case :       Dict				=    s_bar.clamp(min=0.0				,			max=1.0					)
									# Mask weights with computed mask
									snake_case :       Union[str, Any]				=    mask * self.weight
									# Compute output (linear layer) with masked weights
									return nn.functional.linear(_a				,			_a				,			self.bias					)










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 						lowerCamelCase__			:


						def __init__(			self    :	Optional[Any]      ):
													a__:							Tuple									=False


						def 	_lowerCamelCase      (			self    :	Dict   ,							_a    :	Any   ,							_a    :	Union[str, Any]   ,							_a    :	List[str]   ,							_a    :	Optional[int]      ):
													if not self.initialized:
																				a__:							Any									=RagRetriever(
																				    _a   ,							question_encoder_tokenizer=_a   ,							generator_tokenizer=_a   ,							index=_a   ,							init_retrieval=_a   ,							)
																				a__:							str									=True


						def 	_lowerCamelCase      (			self    :	List[str]      ):
													self.retriever.index.init_index()


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													# delete the old self.retriever object before assigning the new index
													del self.retriever
													a__:							Dict									=False







						def 	_lowerCamelCase      (			self    :	str   ,							_a    :	List[Any]   ,							_a    :	Any      ):
													a__				,      a__:							List[str]									=self.retriever._main_retrieve(_a   ,							_a      )
													a__:							List[str]									=self.retriever.index.get_doc_dicts(_a      )
													return doc_ids, retrieved_doc_embeds, doc_dicts


class 						lowerCamelCase__			(		_a ):


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

													a__:							Optional[Any]									=retrieval_workers
													a__:							str									=question_encoder_tokenizer
													a__:							Optional[int]									=generator_tokenizer
													if len(self.retrieval_workers      ) > 0:
																				ray.get(
																				    [
																				        worker.create_rag_retriever.remote(_a   ,							_a   ,							_a   ,							_a      )
																				        for worker in self.retrieval_workers
																				    ]      )


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


						@classmethod
						def 	_lowerCamelCase      (			cls    :	str   ,							_a    :	Union[str, Any]   ,							_a    :	Any=None   ,							**_a    :	Dict      ):
													return super(_a   ,							cls      ).get_tokenizers(_a   ,							_a   ,							**_a      )


						@classmethod
						def 	_lowerCamelCase      (			cls    :	Dict   ,							_a    :	Optional[int]   ,							_a    :	Tuple   ,							_a    :	int=None   ,							**_a    :	List[str]      ):
													a__:							Union[str, Any]									=kwargs.pop("config"   ,							_a      ) or RagConfig.from_pretrained(_a   ,							**_a      )
													a__:							List[Any]									=RagTokenizer.from_pretrained(_a   ,							config=_a      )
													a__:							str									=rag_tokenizer.question_encoder
													a__:							Any									=rag_tokenizer.generator

													if indexed_dataset is not None:
																				a__:							Dict									="custom"
																				a__:							Dict									=CustomHFIndex(config.retrieval_vector_size   ,							_a      )
													else:
																				a__:							List[str]									=cls._build_index(_a      )

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







						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):
													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
													a__:							str									=self._build_index(self.config      )

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





