


'''simple docstring'''

import logging
import random

import ray

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


_UpperCAmelCase :       int										= logging.getLogger(__name__)


class 					a__ :







				"""simple docstring"""



				def __init__(self						):
										__lowerCAmelCase											= False




				def     _snake_case     (self				,	__lowercase				,	__lowercase				,	__lowercase				,	__lowercase						):



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




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




				def     _snake_case     (self				,	__lowercase				,	__lowercase						):
										__lowerCAmelCase				,    __lowerCAmelCase											= self.retriever._main_retrieve(__lowerCAmelCase				,	__lowerCAmelCase						)
										return doc_ids, retrieved_doc_embeds


class 					a__ (       __A					):







				"""simple docstring"""



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



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




				def     _snake_case     (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     _snake_case     (self				,	__lowercase				,	__lowercase						):



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




				@classmethod
				def     _snake_case     (cls				,	__lowercase				,	__lowercase=None				,	**__lowercase						):
										return super(__lowerCAmelCase				,	cls						).get_tokenizers(__lowerCAmelCase				,	__lowerCAmelCase				,	**__lowerCAmelCase						)




				@classmethod
				def     _snake_case     (cls				,	__lowercase				,	__lowercase				,	__lowercase=None				,	**__lowercase						):
										__lowerCAmelCase											= kwargs.pop('''config'''				,	__lowerCAmelCase						) or RagConfig.from_pretrained(__lowerCAmelCase				,	**__lowerCAmelCase						)
										__lowerCAmelCase											= RagTokenizer.from_pretrained(__lowerCAmelCase				,	config=__lowerCAmelCase						)
										__lowerCAmelCase											= rag_tokenizer.question_encoder
										__lowerCAmelCase											= rag_tokenizer.generator
										if indexed_dataset is not None:
																__lowerCAmelCase											= '''custom'''
																__lowerCAmelCase											= CustomHFIndex(config.retrieval_vector_size				,	__lowerCAmelCase						)
										else:
																__lowerCAmelCase											= cls._build_index(__lowerCAmelCase						)
										return cls(
										    __lowerCAmelCase				,	question_encoder_tokenizer=__lowerCAmelCase				,	generator_tokenizer=__lowerCAmelCase				,	retrieval_workers=__lowerCAmelCase				,	index=__lowerCAmelCase				,	)


"""simple docstring"""






import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput





class  _A						(       lowerCAmelCase  ,    lowerCAmelCase	):
      snake_case__			:   Tuple							  =	1

      @register_to_config
      def __init__(						self		,       __lowerCAmelCase = 1000		,       __lowerCAmelCase = None						):







            """simple docstring"""



            self.set_timesteps(__lowerCAmelCase						)

            # standard deviation of the initial noise distribution
            lowercase														=	1.0

            # For now we only support F-PNDM, i.e. the runge-kutta method
            # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
            # mainly at formula (9), (12), (13) and the Algorithm 2.
            lowercase														=	4

            # running values
            lowercase														=	[]

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase = None						):







            """simple docstring"""



            lowercase														=	num_inference_steps
            lowercase														=	torch.linspace(1		,       0		,       num_inference_steps + 1						)[:-1]
            lowercase														=	torch.cat([steps, torch.tensor([0.0]						)]						)

            if self.config.trained_betas is not None:
                  lowercase														=	torch.tensor(self.config.trained_betas		,       dtype=torch.floataa						)
            else:
                  lowercase														=	torch.sin(steps * math.pi / 2						) ** 2

            lowercase														=	(1.0 - self.betas**2) ** 0.5

            lowercase														=	(torch.atana(self.betas		,       self.alphas						) / math.pi * 2)[:-1]
            lowercase														=	timesteps.to(__lowerCAmelCase						)

            lowercase														=	[]

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase = True		,       ):







            """simple docstring"""



            if self.num_inference_steps is None:
                  raise ValueError(
                      """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""						)

            lowercase														=	(self.timesteps == timestep).nonzero().item()
            lowercase														=	timestep_index + 1

            lowercase														=	sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
            self.ets.append(__lowerCAmelCase						)

            if len(self.ets						) == 1:
                  lowercase														=	self.ets[-1]
            elif len(self.ets						) == 2:
                  lowercase														=	(3 * self.ets[-1] - self.ets[-2]) / 2
            elif len(self.ets						) == 3:
                  lowercase														=	(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
            else:
                  lowercase														=	(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

            lowercase														=	self._get_prev_sample(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						)

            if not return_dict:
                  return (prev_sample,)

            return SchedulerOutput(prev_sample=__lowerCAmelCase						)

      def 				A__       (						self		,       __lowerCAmelCase		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            return sample

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	self.alphas[timestep_index]
            lowercase														=	self.betas[timestep_index]

            lowercase														=	self.alphas[prev_timestep_index]
            lowercase														=	self.betas[prev_timestep_index]

            lowercase														=	(sample - sigma * ets) / max(__lowerCAmelCase		,       1E-8						)
            lowercase														=	next_alpha * pred + ets * next_sigma

            return prev_sample







      def __len__(						self						):







            """simple docstring"""



            return self.config.num_train_timesteps



