





import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
      import transformers



def        __lowercase       (				_SCREAMING_SNAKE_CASE     ):


      '''simple docstring'''


      random.seed(_SCREAMING_SNAKE_CASE     )
      np.random.seed(_SCREAMING_SNAKE_CASE     )
      torch.manual_seed(_SCREAMING_SNAKE_CASE     )
      torch.cuda.manual_seed_all(_SCREAMING_SNAKE_CASE     )
      # ^^ safe to call this function even if cuda is not available



class 		UpperCamelCase__			:



     '''simple docstring'''

     def __init__(							self						:   Optional[Any]						,lowerCamelCase__						:   Iterable[torch.nn.Parameter]						,lowerCamelCase__						:   float = 0.9999						,lowerCamelCase__						:   float = 0.0						,lowerCamelCase__						:   int = 0						,lowerCamelCase__						:   bool = False						,lowerCamelCase__						:   Union[float, int] = 1.0						,lowerCamelCase__						:   Union[float, int] = 2 / 3						,lowerCamelCase__						:   Optional[Any] = None						,lowerCamelCase__						:   Dict[str, Any] = None						,**lowerCamelCase__						:   Any						,)					->		Union[str, Any]:




           '''simple docstring'''


           if isinstance(lowerCamelCase__						,torch.nn.Module	):
                 SCREAMING_SNAKE_CASE		=						(
                     """Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. """
                     """Please pass the parameters of the module instead."""
                 )
                 deprecate(
                     """passing a `torch.nn.Module` to `ExponentialMovingAverage`"""						,"""1.0.0"""						,lowerCamelCase__						,standard_warn=lowerCamelCase__						,)
                 SCREAMING_SNAKE_CASE		=						parameters.parameters()

                 # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
                 SCREAMING_SNAKE_CASE		=						True

           if kwargs.get("""max_value"""						,lowerCamelCase__	) is not None:
                 SCREAMING_SNAKE_CASE		=						"""The `max_value` argument is deprecated. Please use `decay` instead."""
                 deprecate("""max_value"""						,"""1.0.0"""						,lowerCamelCase__						,standard_warn=lowerCamelCase__	)
                 SCREAMING_SNAKE_CASE		=						kwargs["""max_value"""]

           if kwargs.get("""min_value"""						,lowerCamelCase__	) is not None:
                 SCREAMING_SNAKE_CASE		=						"""The `min_value` argument is deprecated. Please use `min_decay` instead."""
                 deprecate("""min_value"""						,"""1.0.0"""						,lowerCamelCase__						,standard_warn=lowerCamelCase__	)
                 SCREAMING_SNAKE_CASE		=						kwargs["""min_value"""]

           SCREAMING_SNAKE_CASE		=						list(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						[p.clone().detach() for p in parameters]

           if kwargs.get("""device"""						,lowerCamelCase__	) is not None:
                 SCREAMING_SNAKE_CASE		=						"""The `device` argument is deprecated. Please use `to` instead."""
                 deprecate("""device"""						,"""1.0.0"""						,lowerCamelCase__						,standard_warn=lowerCamelCase__	)
                 self.to(device=kwargs["""device"""]	)

           SCREAMING_SNAKE_CASE		=						None

           SCREAMING_SNAKE_CASE		=						decay
           SCREAMING_SNAKE_CASE		=						min_decay
           SCREAMING_SNAKE_CASE		=						update_after_step
           SCREAMING_SNAKE_CASE		=						use_ema_warmup
           SCREAMING_SNAKE_CASE		=						inv_gamma
           SCREAMING_SNAKE_CASE		=						power
           SCREAMING_SNAKE_CASE		=						0
           SCREAMING_SNAKE_CASE		=						None  # set in `step()`

           SCREAMING_SNAKE_CASE		=						model_cls
           SCREAMING_SNAKE_CASE		=						model_config






     @classmethod
     def     SCREAMING_SNAKE_CASE__  (							cls						:   Optional[int]						,lowerCamelCase__						:   Optional[int]						,lowerCamelCase__						:   Union[str, Any]	)					->		"EMAModel":




           '''simple docstring'''


           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						model_cls.load_config(lowerCamelCase__						,return_unused_kwargs=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						model_cls.from_pretrained(lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						cls(model.parameters()						,model_cls=lowerCamelCase__						,model_config=model.config	)

           ema_model.load_state_dict(lowerCamelCase__	)
           return ema_model






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]						,lowerCamelCase__						:   str	)					->		Tuple:




           '''simple docstring'''


           if self.model_cls is None:
                 raise ValueError("""`save_pretrained` can only be used if `model_cls` was defined at __init__."""	)

           if self.model_config is None:
                 raise ValueError("""`save_pretrained` can only be used if `model_config` was defined at __init__."""	)

           SCREAMING_SNAKE_CASE		=						self.model_cls.from_config(self.model_config	)
           SCREAMING_SNAKE_CASE		=						self.state_dict()
           state_dict.pop("""shadow_params"""						,lowerCamelCase__	)

           model.register_to_config(**lowerCamelCase__	)
           self.copy_to(model.parameters()	)
           model.save_pretrained(lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   int	)					->		float:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						max(0						,optimization_step - self.update_after_step - 1	)

           if step <= 0:
                 return 0.0

           if self.use_ema_warmup:
                 SCREAMING_SNAKE_CASE		=						1 - (1 + step / self.inv_gamma) ** -self.power
           else:
                 SCREAMING_SNAKE_CASE		=						(1 + step) / (10 + step)

           SCREAMING_SNAKE_CASE		=						min(lowerCamelCase__						,self.decay	)
           # make sure decay is not smaller than min_decay
           SCREAMING_SNAKE_CASE		=						max(lowerCamelCase__						,self.min_decay	)
           return cur_decay_value






     @torch.no_grad()
     def     SCREAMING_SNAKE_CASE__  (							self						:   str						,lowerCamelCase__						:   Iterable[torch.nn.Parameter]	)					->		Optional[Any]:




           '''simple docstring'''


           if isinstance(lowerCamelCase__						,torch.nn.Module	):
                 SCREAMING_SNAKE_CASE		=						(
                     """Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. """
                     """Please pass the parameters of the module instead."""
                 )
                 deprecate(
                     """passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"""						,"""1.0.0"""						,lowerCamelCase__						,standard_warn=lowerCamelCase__						,)
                 SCREAMING_SNAKE_CASE		=						parameters.parameters()

           SCREAMING_SNAKE_CASE		=						list(lowerCamelCase__	)

           self.optimization_step += 1

           # Compute the decay factor for the exponential moving average.
           SCREAMING_SNAKE_CASE		=						self.get_decay(self.optimization_step	)
           SCREAMING_SNAKE_CASE		=						decay
           SCREAMING_SNAKE_CASE		=						1 - decay

           SCREAMING_SNAKE_CASE		=						contextlib.nullcontext
           if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                 import deepspeed

           for s_param, param in zip(self.shadow_params						,lowerCamelCase__	):
                 if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                       SCREAMING_SNAKE_CASE		=						deepspeed.zero.GatheredParameters(lowerCamelCase__						,modifier_rank=lowerCamelCase__	)

                 with context_manager():
                       if param.requires_grad:
                             s_param.sub_(one_minus_decay * (s_param - param)	)
                       else:
                             s_param.copy_(lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]						,lowerCamelCase__						:   Iterable[torch.nn.Parameter]	)					->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						list(lowerCamelCase__	)
           for s_param, param in zip(self.shadow_params						,lowerCamelCase__	):
                 param.data.copy_(s_param.to(param.device	).data	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]						,lowerCamelCase__						:   str=None						,lowerCamelCase__						:   int=None	)					->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[
               p.to(device=lowerCamelCase__						,dtype=lowerCamelCase__	) if p.is_floating_point() else p.to(device=lowerCamelCase__	)
               for p in self.shadow_params
           ]






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]	)					->		dict:




           '''simple docstring'''


           return {
               "decay": self.decay,
               "min_decay": self.min_decay,
               "optimization_step": self.optimization_step,
               "update_after_step": self.update_after_step,
               "use_ema_warmup": self.use_ema_warmup,
               "inv_gamma": self.inv_gamma,
               "power": self.power,
               "shadow_params": self.shadow_params,
           }






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]						,lowerCamelCase__						:   Iterable[torch.nn.Parameter]	)					->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[param.detach().cpu().clone() for param in parameters]






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]						,lowerCamelCase__						:   Iterable[torch.nn.Parameter]	)					->		None:




           '''simple docstring'''


           if self.temp_stored_params is None:
                 raise RuntimeError("""This ExponentialMovingAverage has no `store()`ed weights """ """to `restore()`"""	)
           for c_param, param in zip(self.temp_stored_params						,lowerCamelCase__	):
                 param.data.copy_(c_param.data	)

           # Better memory-wise.
           SCREAMING_SNAKE_CASE		=						None






     def     SCREAMING_SNAKE_CASE__  (							self						:   str						,lowerCamelCase__						:   dict	)					->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						copy.deepcopy(lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						state_dict.get("""decay"""						,self.decay	)
           if self.decay < 0.0 or self.decay > 1.0:
                 raise ValueError("""Decay must be between 0 and 1"""	)

           SCREAMING_SNAKE_CASE		=						state_dict.get("""min_decay"""						,self.min_decay	)
           if not isinstance(self.min_decay						,lowerCamelCase__	):
                 raise ValueError("""Invalid min_decay"""	)

           SCREAMING_SNAKE_CASE		=						state_dict.get("""optimization_step"""						,self.optimization_step	)
           if not isinstance(self.optimization_step						,lowerCamelCase__	):
                 raise ValueError("""Invalid optimization_step"""	)

           SCREAMING_SNAKE_CASE		=						state_dict.get("""update_after_step"""						,self.update_after_step	)
           if not isinstance(self.update_after_step						,lowerCamelCase__	):
                 raise ValueError("""Invalid update_after_step"""	)

           SCREAMING_SNAKE_CASE		=						state_dict.get("""use_ema_warmup"""						,self.use_ema_warmup	)
           if not isinstance(self.use_ema_warmup						,lowerCamelCase__	):
                 raise ValueError("""Invalid use_ema_warmup"""	)

           SCREAMING_SNAKE_CASE		=						state_dict.get("""inv_gamma"""						,self.inv_gamma	)
           if not isinstance(self.inv_gamma						,(float, int)	):
                 raise ValueError("""Invalid inv_gamma"""	)

           SCREAMING_SNAKE_CASE		=						state_dict.get("""power"""						,self.power	)
           if not isinstance(self.power						,(float, int)	):
                 raise ValueError("""Invalid power"""	)

           SCREAMING_SNAKE_CASE		=						state_dict.get("""shadow_params"""						,lowerCamelCase__	)
           if shadow_params is not None:
                 SCREAMING_SNAKE_CASE		=						shadow_params
                 if not isinstance(self.shadow_params						,lowerCamelCase__	):
                       raise ValueError("""shadow_params must be a list"""	)
                 if not all(isinstance(lowerCamelCase__						,torch.Tensor	) for p in self.shadow_params	):
                       raise ValueError("""shadow_params must all be Tensors"""	)









import logging
import random

import ray

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


SCREAMING_SNAKE_CASE_						       =	logging.getLogger(__name__)

class 		UpperCamelCase__			:



     '''simple docstring'''

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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						False






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   int						,lowerCamelCase__						:   Dict						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   Tuple	)					->		Optional[Any]:




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		Any:




           '''simple docstring'''


           self.retriever.index.init_index()






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   List[str]	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.retriever._main_retrieve(lowerCamelCase__						,lowerCamelCase__	)
           return doc_ids, retrieved_doc_embeds

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

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




           '''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__						,)
           SCREAMING_SNAKE_CASE		=						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     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		Union[str, Any]:




           '''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     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   Any						,lowerCamelCase__						:   int	)					->		Dict:




           '''simple docstring'''


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






     @classmethod
     def     SCREAMING_SNAKE_CASE__  (							cls						:   Optional[Any]						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   Union[str, Any]=None						,**lowerCamelCase__						:   Optional[Any]	)					->		Any:




           '''simple docstring'''


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






     @classmethod
     def     SCREAMING_SNAKE_CASE__  (							cls						:   Any						,lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   List[Any]=None						,**lowerCamelCase__						:   Any	)					->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						kwargs.pop("""config"""						,lowerCamelCase__	) or RagConfig.from_pretrained(lowerCamelCase__						,**lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						RagTokenizer.from_pretrained(lowerCamelCase__						,config=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						rag_tokenizer.question_encoder
           SCREAMING_SNAKE_CASE		=						rag_tokenizer.generator
           if indexed_dataset is not None:
                 SCREAMING_SNAKE_CASE		=						"""custom"""
                 SCREAMING_SNAKE_CASE		=						CustomHFIndex(config.retrieval_vector_size						,lowerCamelCase__	)
           else:
                 SCREAMING_SNAKE_CASE		=						cls._build_index(lowerCamelCase__	)
           return cls(
               lowerCamelCase__						,question_encoder_tokenizer=lowerCamelCase__						,generator_tokenizer=lowerCamelCase__						,retrieval_workers=lowerCamelCase__						,index=lowerCamelCase__						,)


