"""simple docstring"""







import logging
import random

import ray

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


lowerCamelCase      : Any       =logging.getLogger(__name__)






class 							__snake_case:


    '''simple docstring'''





    def __init__(  self	):




         '''simple docstring'''



         __A					:					str								=		False
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         if not self.initialized:
              __A					:					Dict								=		RagRetriever(
                  __lowerCamelCase					,       question_encoder_tokenizer=__lowerCamelCase					,       generator_tokenizer=__lowerCamelCase					,       index=__lowerCamelCase					,       init_retrieval=__lowerCamelCase					,       )
              __A					:					Optional[Any]								=		True
    def 						_a							(  self	):




         '''simple docstring'''



         self.retriever.index.init_index()
    def 						_a							(  self	):




         '''simple docstring'''



         del self.retriever
         __A					:					Optional[Any]								=		False

    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         __A						,		__A					:					Optional[int]								=		self.retriever._main_retrieve(__lowerCamelCase					,       __lowerCamelCase	)
         __A					:					Tuple								=		self.retriever.index.get_doc_dicts(__lowerCamelCase	)
         return doc_ids, retrieved_doc_embeds, doc_dicts







class 							__snake_case(					A_	):


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase=None	):




         '''simple docstring'''



         if index is not None and index.is_initialized() and len(__lowerCamelCase	) > 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__(
             __lowerCamelCase					,       question_encoder_tokenizer=__lowerCamelCase					,       generator_tokenizer=__lowerCamelCase					,       index=__lowerCamelCase					,       init_retrieval=__lowerCamelCase					,       )

         __A					:					Tuple								=		retrieval_workers
         __A					:					Any								=		question_encoder_tokenizer
         __A					:					Any								=		generator_tokenizer
         if len(self.retrieval_workers	) > 0:
              ray.get(
                  [
                      worker.create_rag_retriever.remote(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase	)
                      for worker in self.retrieval_workers
                  ]	)
    def 						_a							(  self	):




         '''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 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         if len(self.retrieval_workers	) > 0:
              # Select a random retrieval actor.
              __A					:					List[Any]								=		self.retrieval_workers[random.randint(0					,       len(self.retrieval_workers	) - 1	)]
              __A						,		__A						,		__A					:					int								=		ray.get(
                  random_worker.retrieve.remote(__lowerCamelCase					,       __lowerCamelCase	)	)
         else:
              __A						,		__A					:					List[Any]								=		self._main_retrieve(__lowerCamelCase					,       __lowerCamelCase	)
              __A					:					Optional[Any]								=		self.index.get_doc_dicts(__lowerCamelCase	)
         return retrieved_doc_embeds, doc_ids, doc_dicts
    @classmethod
    def 						_a							(  cls					,       __lowerCamelCase					,       __lowerCamelCase=None					,       **__lowerCamelCase	):




         '''simple docstring'''



         return super(__lowerCamelCase					,       cls	).get_tokenizers(__lowerCamelCase					,       __lowerCamelCase					,       **__lowerCamelCase	)
    @classmethod
    def 						_a							(  cls					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase=None					,       **__lowerCamelCase	):




         '''simple docstring'''



         __A					:					Tuple								=		kwargs.pop('config'					,       __lowerCamelCase	) or RagConfig.from_pretrained(__lowerCamelCase					,       **__lowerCamelCase	)
         __A					:					int								=		RagTokenizer.from_pretrained(__lowerCamelCase					,       config=__lowerCamelCase	)
         __A					:					str								=		rag_tokenizer.question_encoder
         __A					:					Any								=		rag_tokenizer.generator

         if indexed_dataset is not None:
              __A					:					Dict								=		'custom'
              __A					:					List[Any]								=		CustomHFIndex(config.retrieval_vector_size					,       __lowerCamelCase	)
         else:
              __A					:					Optional[int]								=		cls._build_index(__lowerCamelCase	)

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

    def 						_a							(  self	):




         '''simple docstring'''



         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					:					Any								=		self._build_index(self.config	)

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




"""simple docstring"""







from typing import Dict, Optional, Tuple

import torch




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				torch.Tensor						)    ->     torch.Tensor:







     '''simple docstring'''


     __A					:					List[str]								=		boundaries[1] - boundaries[0]
     __A					:					int								=		boundaries + step / 2
     __A					:					Union[str, Any]								=		torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1						)]  ,       dim=0						)
     return bin_centers




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				torch.Tensor  ,       _SCREAMING_SNAKE_CASE				:				torch.Tensor  ,       )    ->     Tuple[torch.Tensor, torch.Tensor]:







     '''simple docstring'''


     __A					:					str								=		_calculate_bin_centers(_SCREAMING_SNAKE_CASE						)
     return (
         torch.sum(aligned_distance_error_probs * bin_centers  ,       dim=-1						),
         bin_centers[-1],
     )




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				torch.Tensor  ,       _SCREAMING_SNAKE_CASE				:				int = 31  ,       _SCREAMING_SNAKE_CASE				:				int = 64  ,       **_SCREAMING_SNAKE_CASE				:				Optional[Any]  ,       )    ->     Dict[str, torch.Tensor]:







     '''simple docstring'''


     __A					:					int								=		torch.linspace(0  ,       _SCREAMING_SNAKE_CASE  ,       steps=(no_bins - 1)  ,       device=logits.device						)

     __A					:					Any								=		torch.nn.functional.softmax(_SCREAMING_SNAKE_CASE  ,       dim=-1						)
     __A						,		__A					:					Tuple								=		_calculate_expected_aligned_error(
         alignment_confidence_breaks=_SCREAMING_SNAKE_CASE  ,       aligned_distance_error_probs=_SCREAMING_SNAKE_CASE  ,       )

     return {
         "aligned_confidence_probs": aligned_confidence_probs,
         "predicted_aligned_error": predicted_aligned_error,
         "max_predicted_aligned_error": max_predicted_aligned_error,
     }




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				torch.Tensor  ,       _SCREAMING_SNAKE_CASE				:				Optional[torch.Tensor] = None  ,       _SCREAMING_SNAKE_CASE				:				int = 31  ,       _SCREAMING_SNAKE_CASE				:				int = 64  ,       _SCREAMING_SNAKE_CASE				:				float = 1E-8  ,       **_SCREAMING_SNAKE_CASE				:				Union[str, Any]  ,       )    ->     torch.Tensor:







     '''simple docstring'''


     if residue_weights is None:
          __A					:					Optional[int]								=		logits.new_ones(logits.shape[-2]						)

     __A					:					List[str]								=		torch.linspace(0  ,       _SCREAMING_SNAKE_CASE  ,       steps=(no_bins - 1)  ,       device=logits.device						)

     __A					:					Tuple								=		_calculate_bin_centers(_SCREAMING_SNAKE_CASE						)
     torch.sum(_SCREAMING_SNAKE_CASE						)
     __A					:					int								=		logits.shape[-2]
     __A					:					str								=		max(_SCREAMING_SNAKE_CASE  ,       19						)

     __A					:					Tuple								=		1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

     __A					:					Tuple								=		torch.nn.functional.softmax(_SCREAMING_SNAKE_CASE  ,       dim=-1						)

     __A					:					List[str]								=		1.0 / (1 + (bin_centers**2) / (da**2))
     __A					:					int								=		torch.sum(probs * tm_per_bin  ,       dim=-1						)

     __A					:					Any								=		residue_weights / (eps + residue_weights.sum())
     __A					:					List[Any]								=		torch.sum(predicted_tm_term * normed_residue_mask  ,       dim=-1						)

     __A					:					int								=		per_alignment * residue_weights

     __A					:					Optional[int]								=		(weighted == torch.max(_SCREAMING_SNAKE_CASE						)).nonzero()[0]
     return per_alignment[tuple(_SCREAMING_SNAKE_CASE						)]



