



'''simple docstring'''





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 				snake_case :



       def __init__(				self      )  ->							str:
           lowercase__		       =						False



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							int:
           if not self.initialized:
               lowercase__		       =						RagRetriever(
                   UpperCAmelCase_     ,question_encoder_tokenizer=UpperCAmelCase_     ,generator_tokenizer=UpperCAmelCase_     ,index=UpperCAmelCase_     ,init_retrieval=UpperCAmelCase_     ,)
               lowercase__		       =						True



       def 							_a      (				self      )  ->							Any:
           self.retriever.index.init_index()







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Dict:
           lowercase__       ,       lowercase__		       =						self.retriever._main_retrieve(UpperCAmelCase_     ,UpperCAmelCase_      )
           return doc_ids, retrieved_doc_embeds





class 				snake_case (UpperCamelCase   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_=None      )  ->							int:
           if index is not None and index.is_initialized() and len(UpperCAmelCase_      ) > 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__(
               UpperCAmelCase_     ,question_encoder_tokenizer=UpperCAmelCase_     ,generator_tokenizer=UpperCAmelCase_     ,index=UpperCAmelCase_     ,init_retrieval=UpperCAmelCase_     ,)
           lowercase__		       =						retrieval_workers
           if len(self.retrieval_workers      ) > 0:
               ray.get(
                   [
                       worker.create_rag_retriever.remote(UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )
                       for worker in self.retrieval_workers
                   ]      )



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



       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,UpperCAmelCase_=None     ,**UpperCAmelCase_      )  ->							int:
           return super(UpperCAmelCase_     ,cls      ).get_tokenizers(UpperCAmelCase_     ,UpperCAmelCase_     ,**UpperCAmelCase_      )







       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_=None     ,**UpperCAmelCase_      )  ->							Any:
           lowercase__		       =						kwargs.pop("config"     ,UpperCAmelCase_      ) or RagConfig.from_pretrained(UpperCAmelCase_     ,**UpperCAmelCase_      )
           lowercase__		       =						RagTokenizer.from_pretrained(UpperCAmelCase_     ,config=UpperCAmelCase_      )
           lowercase__		       =						rag_tokenizer.question_encoder
           lowercase__		       =						rag_tokenizer.generator
           if indexed_dataset is not None:
               lowercase__		       =						"custom"
               lowercase__		       =						CustomHFIndex(config.retrieval_vector_size     ,UpperCAmelCase_      )
           else:
               lowercase__		       =						cls._build_index(UpperCAmelCase_      )
           return cls(
               UpperCAmelCase_     ,question_encoder_tokenizer=UpperCAmelCase_     ,generator_tokenizer=UpperCAmelCase_     ,retrieval_workers=UpperCAmelCase_     ,index=UpperCAmelCase_     ,)









'''simple docstring'''





import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer


class 				snake_case (tf.keras.layers.Layer   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = None      )  ->							List[Any]:
           super().__init__()
           lowercase__		       =						pad_token_id
           lowercase__		       =						max_length
           lowercase__		       =						vocab
           lowercase__		       =						merges
           lowercase__		       =						BytePairTokenizer(UpperCAmelCase_     ,UpperCAmelCase_     ,sequence_length=UpperCAmelCase_      )



       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							List[str]:
           lowercase__		       =						[" ".join(UpperCAmelCase_      ) for m in tokenizer.bpe_ranks.keys()]
           lowercase__		       =						tokenizer.get_vocab()
           return cls(UpperCAmelCase_     ,UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )



       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							Optional[Any]:
           lowercase__		       =						GPTaTokenizer.from_pretrained(UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )
           return cls.from_tokenizer(UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )



       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_      )  ->							Optional[Any]:
           return cls(**UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Tuple:
           return {
               "vocab": self.vocab,
               "merges": self.merges,
               "max_length": self.max_length,
               "pad_token_id": self.pad_token_id,
           }







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_ = None      )  ->							Tuple:
           lowercase__		       =						self.tf_tokenizer(UpperCAmelCase_      )
           lowercase__		       =						tf.ones_like(UpperCAmelCase_      )

           if self.pad_token_id is not None:
               # pad the tokens up to max length
               lowercase__		       =						max_length if max_length is not None else self.max_length

               if max_length is not None:
                   lowercase__       ,       lowercase__		       =						pad_model_inputs(
                       UpperCAmelCase_     ,max_seq_length=UpperCAmelCase_     ,pad_value=self.pad_token_id      )

           return {"attention_mask": attention_mask, "input_ids": input_ids}




