


import logging
import random

import ray

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


A      = logging.getLogger(__name__)


class     lowercase__  :






  def __init__(					self      :					int ):




         """simple docstring"""







         UpperCAmelCase__										=     False






  def 			_UpperCAmelCase  (					self      :					Union[str, Any]		,						_lowercase      :					Dict		,						_lowercase      :					Optional[Any]		,						_lowercase      :					str		,						_lowercase      :					List[str] ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         self.retriever.index.init_index()






  def 			_UpperCAmelCase  (					self      :					Any		,						_lowercase      :					Dict		,						_lowercase      :					int ):




         """simple docstring"""







         UpperCAmelCase__						,			UpperCAmelCase__										=     self.retriever._main_retrieve(_lowercase		,						_lowercase )
         return doc_ids, retrieved_doc_embeds




class     lowercase__  (					__SCREAMING_SNAKE_CASE		):






  def __init__(					self      :					Optional[int]		,						_lowercase      :					Tuple		,						_lowercase      :					Union[str, Any]		,						_lowercase      :					Tuple		,						_lowercase      :					Tuple		,						_lowercase      :					List[Any]=None ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					Optional[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 			_UpperCAmelCase  (					self      :					Optional[Any]		,						_lowercase      :					int		,						_lowercase      :					Optional[int] ):




         """simple docstring"""







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






  @classmethod
  def 			_UpperCAmelCase  (					cls      :					Optional[Any]		,						_lowercase      :					Optional[Any]		,						_lowercase      :					Tuple=None		,						**_lowercase      :					Union[str, Any] ):




         """simple docstring"""







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






  @classmethod
  def 			_UpperCAmelCase  (					cls      :					List[str]		,						_lowercase      :					Optional[int]		,						_lowercase      :					List[Any]		,						_lowercase      :					str=None		,						**_lowercase      :					Tuple ):




         """simple docstring"""







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









import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A      = logging.getLogger(__name__)


@dataclass
class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  field(
      default=0.0					,							metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether to SortishSamler or not.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'whether to use adafactor'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Dropout probability. Goes into model.config.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Attention dropout probability. Goes into model.config.'}		)
  A__=  field(
      default='linear'					,							metadata={'help': f'Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}'}					,							)





