
"""simple docstring"""







from __future__ import annotations



class 		UpperCAmelCase_						:
 """simple docstring"""




 def __init__(			self							:			List[Any]  , a_							:			int			)->       None:



      """simple docstring"""

      UpperCAmelCase_    :     Tuple            =	order

      # a_{0} ... a_{k}
      UpperCAmelCase_    :     Any            =	[1.0] + [0.0] * order
      # b_{0} ... b_{k}
      UpperCAmelCase_    :     int            =	[1.0] + [0.0] * order

      # x[n-1] ... x[n-k]
      UpperCAmelCase_    :     Optional[int]            =	[0.0] * self.order
      # y[n-1] ... y[n-k]
      UpperCAmelCase_    :     Optional[int]            =	[0.0] * self.order




 def 	a   (			self							:			List[Any]  , a_							:			list[float]  , a_							:			list[float]			)->       None:



      """simple docstring"""

      if len(a_			) < self.order:
           UpperCAmelCase_    :     Optional[Any]            =	[1.0, *a_coeffs]

      if len(a_			) != self.order + 1:
           UpperCAmelCase_    :     int            =	(
               f'''Expected a_coeffs to have {self.order + 1} elements '''
               f'''for {self.order}-order filter, got {len(a_			)}'''
           )
           raise ValueError(a_			)

      if len(a_			) != self.order + 1:
           UpperCAmelCase_    :     Optional[int]            =	(
               f'''Expected b_coeffs to have {self.order + 1} elements '''
               f'''for {self.order}-order filter, got {len(a_			)}'''
           )
           raise ValueError(a_			)

      UpperCAmelCase_    :     Union[str, Any]            =	a_coeffs
      UpperCAmelCase_    :     Dict            =	b_coeffs






 def 	a   (			self							:			List[Any]  , a_							:			float			)->       float:



      """simple docstring"""

      UpperCAmelCase_    :     List[str]            =	0.0

      # Start at index 1 and do index 0 at the end.
      for i in range(1  , self.order + 1			):
           result += (
               self.b_coeffs[i] * self.input_history[i - 1]
               - self.a_coeffs[i] * self.output_history[i - 1]
           )

      UpperCAmelCase_    :     Optional[Any]            =	(result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

      UpperCAmelCase_    :     List[Any]            =	self.input_history[:-1]
      UpperCAmelCase_    :     Optional[int]            =	self.output_history[:-1]

      UpperCAmelCase_    :     List[str]            =	sample
      UpperCAmelCase_    :     str            =	result

      return result



"""simple docstring"""







import logging
import random

import ray

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


lowercase_         =       logging.getLogger(__name__)



class 		UpperCAmelCase_						:
 """simple docstring"""




 def __init__(			self							:			Dict			)->       Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_    :     List[Any]            =	False




 def 	a   (			self							:			Optional[int]  , a_							:			Union[str, Any]  , a_							:			Tuple  , a_							:			Dict  , a_							:			Tuple			)->       Any:



      """simple docstring"""

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




 def 	a   (			self							:			Dict			)->       Union[str, Any]:



      """simple docstring"""

      self.retriever.index.init_index()






 def 	a   (			self							:			Any  , a_							:			Any  , a_							:			Dict			)->       str:



      """simple docstring"""

      UpperCAmelCase_       ,UpperCAmelCase_    :     List[Any]            =	self.retriever._main_retrieve(a_  , a_			)
      return doc_ids, retrieved_doc_embeds



class 		UpperCAmelCase_						(lowerCamelCase_    ):
 """simple docstring"""




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



      """simple docstring"""

      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_  , )
      UpperCAmelCase_    :     Union[str, Any]            =	retrieval_workers
      if len(self.retrieval_workers			) > 0:
           ray.get(
               [
                   worker.create_rag_retriever.remote(a_  , a_  , a_  , a_			)
                   for worker in self.retrieval_workers
               ]			)




 def 	a   (			self							:			List[str]			)->       str:



      """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							:			Optional[Any]  , a_							:			Optional[Any]  , a_							:			List[str]			)->       Tuple:



      """simple docstring"""

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




 @classmethod
 def 	a   (			cls							:			Dict  , a_							:			List[Any]  , a_							:			Union[str, Any]=None  , **a_							:			int			)->       str:



      """simple docstring"""

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






 @classmethod
 def 	a   (			cls							:			Tuple  , a_							:			Tuple  , a_							:			List[str]  , a_							:			Optional[int]=None  , **a_							:			int			)->       List[Any]:



      """simple docstring"""

      UpperCAmelCase_    :     List[str]            =	kwargs.pop("""config"""  , a_			) or RagConfig.from_pretrained(a_  , **a_			)
      UpperCAmelCase_    :     Optional[int]            =	RagTokenizer.from_pretrained(a_  , config=a_			)
      UpperCAmelCase_    :     Optional[Any]            =	rag_tokenizer.question_encoder
      UpperCAmelCase_    :     List[str]            =	rag_tokenizer.generator
      if indexed_dataset is not None:
           UpperCAmelCase_    :     Dict            =	"""custom"""
           UpperCAmelCase_    :     List[Any]            =	CustomHFIndex(config.retrieval_vector_size  , a_			)
      else:
           UpperCAmelCase_    :     int            =	cls._build_index(a_			)
      return cls(
          a_  , question_encoder_tokenizer=a_  , generator_tokenizer=a_  , retrieval_workers=a_  , index=a_  , )

