"""simple docstring"""




import logging
import random

import ray

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


_a      :		Any           =   logging.getLogger(__name__)
class 						__A							:






     def __init__(		self  ):
       _lowerCAmelCase						:       Tuple  			=     False






     def     __A  (		self				,     a__				,     a__				,     a__				,     a__  ):
       if not self.initialized:
         _lowerCAmelCase						:       Any  			=     RagRetriever(
             a__				,     question_encoder_tokenizer=a__				,     generator_tokenizer=a__				,     index=a__				,     init_retrieval=a__				,     )
         _lowerCAmelCase						:       Optional[int]  			=     True






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






     def     __A  (		self				,     a__				,     a__  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       Any  			=     self.retriever._main_retrieve(a__				,     a__  )
       return doc_ids, retrieved_doc_embeds







class 						__A							( SCREAMING_SNAKE_CASE_		):






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






     @classmethod
     def     __A  (		cls				,     a__				,     a__=None				,     **a__  ):
       return super(a__				,     cls  ).get_tokenizers(a__				,     a__				,     **a__  )






     @classmethod
     def     __A  (		cls				,     a__				,     a__				,     a__=None				,     **a__  ):
       _lowerCAmelCase						:       str  			=     kwargs.pop("""config"""				,     a__  ) or RagConfig.from_pretrained(a__				,     **a__  )
       _lowerCAmelCase						:       Tuple  			=     RagTokenizer.from_pretrained(a__				,     config=a__  )
       _lowerCAmelCase						:       str  			=     rag_tokenizer.question_encoder
       _lowerCAmelCase						:       Union[str, Any]  			=     rag_tokenizer.generator
       if indexed_dataset is not None:
         _lowerCAmelCase						:       Any  			=     """custom"""
         _lowerCAmelCase						:       Any  			=     CustomHFIndex(config.retrieval_vector_size				,     a__  )
       else:
         _lowerCAmelCase						:       Union[str, Any]  			=     cls._build_index(a__  )
       return cls(
           a__				,     question_encoder_tokenizer=a__				,     generator_tokenizer=a__				,     retrieval_workers=a__				,     index=a__				,     )










# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin







class 						lowercase							(   UpperCamelCase__,UpperCamelCase__		):
     _a							 =						1

     @register_to_config
     def __init__(  self		,     _a=2000		,     _a=0.1		,     _a=20		,     _a=1e-3				)						->							List[Any]:
           _A							:     Dict			=						None
           _A							:     List[Any]			=						None
           _A							:     Dict			=						None

     def 					a__	(  self		,     _a		,     _a = None				)						->							Union[str, Any]:
           _A							:     Union[str, Any]			=						torch.linspace(1		,     self.config.sampling_eps		,     _a		,     device=_a				)

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a=None				)						->							Dict:
           if self.timesteps is None:
                 raise ValueError(
                     """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""				)

           # TODO(Patrick) better comments + non-PyTorch
           # postprocess model score
           _A							:     Any			=						(
               -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
           )
           _A							:     List[Any]			=						torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff				)				)
           _A							:     List[str]			=						std.flatten()
           while len(std.shape				) < len(score.shape				):
                 _A							:     List[Any]			=						std.unsqueeze(-1				)
           _A							:     int			=						-score / std

           # compute
           _A							:     Tuple			=						-1.0 / len(self.timesteps				)

           _A							:     str			=						self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
           _A							:     List[str]			=						beta_t.flatten()
           while len(beta_t.shape				) < len(x.shape				):
                 _A							:     Union[str, Any]			=						beta_t.unsqueeze(-1				)
           _A							:     Tuple			=						-0.5 * beta_t * x

           _A							:     Tuple			=						torch.sqrt(_a				)
           _A							:     Dict			=						drift - diffusion**2 * score
           _A							:     Dict			=						x + drift * dt

           # add noise
           _A							:     Any			=						randn_tensor(x.shape		,     layout=x.layout		,     generator=_a		,     device=x.device		,     dtype=x.dtype				)
           _A							:     str			=						x_mean + diffusion * math.sqrt(-dt				) * noise

           return x, x_mean



     def __len__(  self				)						->							Optional[Any]:
           return self.config.num_train_timesteps

