




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 							__lowercase   :



     def __init__(  self   :							List[str]       ):
       a_		    =  False



     def 				__magic_name__		(  self   :							Union[str, Any]      ,		lowercase__   :							List[str]      ,		lowercase__   :							Optional[int]      ,		lowercase__   :							Optional[Any]      ,		lowercase__   :							Union[str, Any]       ):
       if not self.initialized:
         a_		    =  RagRetriever(
             lowercase__      ,		question_encoder_tokenizer=lowercase__      ,		generator_tokenizer=lowercase__      ,		index=lowercase__      ,		init_retrieval=lowercase__      ,		)
         a_		    =  True



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



     def 				__magic_name__		(  self   :							List[Any]       ):
       # delete the old self.retriever object before assigning the new index
       del self.retriever
       a_		    =  False



     def 				__magic_name__		(  self   :							Optional[Any]      ,		lowercase__   :							List[str]      ,		lowercase__   :							Dict       ):
       a_    , a_		    =  self.retriever._main_retrieve(lowercase__      ,		lowercase__       )
       a_		    =  self.retriever.index.get_doc_dicts(lowercase__       )
       return doc_ids, retrieved_doc_embeds, doc_dicts



class 							__lowercase   (			a__    ):



     def __init__(  self   :							List[Any]      ,		lowercase__   :							Optional[int]      ,		lowercase__   :							List[str]      ,		lowercase__   :							Tuple      ,		lowercase__   :							Dict      ,		lowercase__   :							Dict=None       ):
       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__      ,		)

       a_		    =  retrieval_workers
       a_		    =  question_encoder_tokenizer
       a_		    =  generator_tokenizer
       if len(self.retrieval_workers       ) > 0:
         ray.get(
             [
                 worker.create_rag_retriever.remote(lowercase__      ,		lowercase__      ,		lowercase__      ,		lowercase__       )
                 for worker in self.retrieval_workers
             ]       )



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



     @classmethod
     def 				__magic_name__		(  cls   :							Dict      ,		lowercase__   :							Optional[Any]      ,		lowercase__   :							List[str]=None      ,		**lowercase__   :							int       ):
       return super(lowercase__      ,		cls       ).get_tokenizers(lowercase__      ,		lowercase__      ,		**lowercase__       )



     @classmethod
     def 				__magic_name__		(  cls   :							Tuple      ,		lowercase__   :							Any      ,		lowercase__   :							str      ,		lowercase__   :							List[Any]=None      ,		**lowercase__   :							Union[str, Any]       ):
       a_		    =  kwargs.pop('''config'''      ,		lowercase__       ) or RagConfig.from_pretrained(lowercase__      ,		**lowercase__       )
       a_		    =  RagTokenizer.from_pretrained(lowercase__      ,		config=lowercase__       )
       a_		    =  rag_tokenizer.question_encoder
       a_		    =  rag_tokenizer.generator

       if indexed_dataset is not None:
         a_		    =  '''custom'''
         a_		    =  CustomHFIndex(config.retrieval_vector_size      ,		lowercase__       )
       else:
         a_		    =  cls._build_index(lowercase__       )

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



     def 				__magic_name__		(  self   :							Optional[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_		    =  self._build_index(self.config       )

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






from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


UpperCamelCase__       				=	logging.get_logger(__name__)
class 							__lowercase   (			a__    ):
     _lowerCAmelCase		       =						["input_values", "attention_mask"]



     def __init__(  self   :							Union[str, Any]      ,		lowercase__   :							str=1      ,		lowercase__   :							int=1_6_0_0_0      ,		lowercase__   :							Optional[int]=0.0      ,		lowercase__   :							List[Any]=False      ,		lowercase__   :							Dict=True      ,		**lowercase__   :							Any      ,		):
       super().__init__(feature_size=lowercase__      ,		sampling_rate=lowercase__      ,		padding_value=lowercase__      ,		**lowercase__       )
       a_		    =  return_attention_mask
       a_		    =  do_normalize



     @staticmethod
     def 				__magic_name__		(  lowercase__   :							List[np.ndarray]      ,		lowercase__   :							List[np.ndarray]      ,		lowercase__   :							float = 0.0       ):
       if attention_mask is not None:
         a_		    =  np.array(lowercase__      ,		np.intaa       )
         a_		    =  []

         for vector, length in zip(lowercase__      ,		attention_mask.sum(-1       )       ):
           a_		    =  (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7       )
           if length < normed_slice.shape[0]:
             a_		    =  padding_value

           normed_input_values.append(lowercase__       )
       else:
         a_		    =  [(x - x.mean()) / np.sqrt(x.var() + 1e-7       ) for x in input_values]

       return normed_input_values



     def __call__(  self   :							Any      ,		lowercase__   :							Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,		lowercase__   :							Union[bool, str, PaddingStrategy] = False      ,		lowercase__   :							Optional[int] = None      ,		lowercase__   :							bool = False      ,		lowercase__   :							Optional[int] = None      ,		lowercase__   :							Optional[bool] = None      ,		lowercase__   :							Optional[Union[str, TensorType]] = None      ,		lowercase__   :							Optional[int] = None      ,		**lowercase__   :							List[str]      ,		):

       if sampling_rate is not None:
         if sampling_rate != self.sampling_rate:
           raise ValueError(
               f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
               f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
               f" {self.sampling_rate} and not {sampling_rate}."       )
       else:
         logger.warning(
             '''It is strongly recommended to pass the ``sampling_rate`` argument to this function. '''
             '''Failing to do so can result in silent errors that might be hard to debug.'''       )

       a_		    =  isinstance(lowercase__      ,		np.ndarray       ) and len(raw_speech.shape       ) > 1
       if is_batched_numpy and len(raw_speech.shape       ) > 2:
         raise ValueError(f"Only mono-channel audio is supported for input to {self}"       )
       a_		    =  is_batched_numpy or (
           isinstance(lowercase__      ,		(list, tuple)       ) and (isinstance(raw_speech[0]      ,		(np.ndarray, tuple, list)       ))
       )

       # always return batch
       if not is_batched:
         a_		    =  [raw_speech]

       # convert into correct format for padding
       a_		    =  BatchFeature({'''input_values''': raw_speech}       )

       a_		    =  self.pad(
           lowercase__      ,		padding=lowercase__      ,		max_length=lowercase__      ,		truncation=lowercase__      ,		pad_to_multiple_of=lowercase__      ,		return_attention_mask=lowercase__      ,		)

       # convert input values to correct format
       a_		    =  padded_inputs['''input_values''']
       if not isinstance(input_values[0]      ,		np.ndarray       ):
         a_		    =  [np.asarray(lowercase__      ,		dtype=np.floataa       ) for array in input_values]
       elif (
           not isinstance(lowercase__      ,		np.ndarray       )
           and isinstance(input_values[0]      ,		np.ndarray       )
           and input_values[0].dtype is np.dtype(np.floataa       )
       ):
         a_		    =  [array.astype(np.floataa       ) for array in input_values]
       elif isinstance(lowercase__      ,		np.ndarray       ) and input_values.dtype is np.dtype(np.floataa       ):
         a_		    =  input_values.astype(np.floataa       )

       # convert attention_mask to correct format
       a_		    =  padded_inputs.get('''attention_mask'''       )
       if attention_mask is not None:
         a_		    =  [np.asarray(lowercase__      ,		dtype=np.intaa       ) for array in attention_mask]

       # zero-mean and unit-variance normalization
       if self.do_normalize:
         a_		    =  (
             attention_mask
             if self._get_padding_strategies(lowercase__      ,		max_length=lowercase__       ) is not PaddingStrategy.DO_NOT_PAD
             else None
         )
         a_		    =  self.zero_mean_unit_var_norm(
             padded_inputs['''input_values''']      ,		attention_mask=lowercase__      ,		padding_value=self.padding_value       )

       if return_tensors is not None:
         a_		    =  padded_inputs.convert_to_tensors(lowercase__       )

       return padded_inputs
