'''simple docstring'''

import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class    lowerCamelCase__  (   __snake_case       ,				__snake_case       ,				__snake_case     ):



   @register_to_config
   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = False					,      )				->     Dict:
        """simple docstring"""





        super().__init__()

        _UpperCamelCase  :List[Any]       =nn.Linear(lowerCAmelCase__					,      lowerCAmelCase__					,      bias=lowerCAmelCase__     )

        _UpperCamelCase  :List[str]       =nn.Embedding(lowerCAmelCase__					,      lowerCAmelCase__     )
        _UpperCamelCase  :Any       =False

        _UpperCamelCase  :Optional[Any]       =nn.Dropout(p=lowerCAmelCase__     )

        _UpperCamelCase  :List[str]       =TaConfig(
            d_model=lowerCAmelCase__					,      num_heads=lowerCAmelCase__					,      d_kv=lowerCAmelCase__					,      d_ff=lowerCAmelCase__					,      feed_forward_proj=lowerCAmelCase__					,      dropout_rate=lowerCAmelCase__					,      is_decoder=lowerCAmelCase__					,      is_encoder_decoder=lowerCAmelCase__					,      )
        _UpperCamelCase  :Dict       =nn.ModuleList()
        for lyr_num in range(lowerCAmelCase__     ):
             _UpperCamelCase  :List[str]       =TaBlock(lowerCAmelCase__     )
             self.encoders.append(lowerCAmelCase__     )

        _UpperCamelCase  :str       =TaLayerNorm(lowerCAmelCase__     )
        _UpperCamelCase  :List[str]       =nn.Dropout(p=lowerCAmelCase__     )




   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Any:
        """simple docstring"""





        _UpperCamelCase  :str       =self.input_proj(lowerCAmelCase__     )

        # terminal relative positional encodings
        _UpperCamelCase  :List[str]       =encoder_inputs.shape[1]
        _UpperCamelCase  :Union[str, Any]       =torch.arange(lowerCAmelCase__					,      device=encoder_inputs.device     )

        _UpperCamelCase  :Dict       =encoder_inputs_mask.sum(-1     )
        _UpperCamelCase  :Optional[Any]       =torch.roll(input_positions.unsqueeze(0     )					,      tuple(seq_lens.tolist()     )					,      dims=0     )
        x += self.position_encoding(lowerCAmelCase__     )

        _UpperCamelCase  :List[str]       =self.dropout_pre(lowerCAmelCase__     )

        # inverted the attention mask
        _UpperCamelCase  :Dict       =encoder_inputs.size()
        _UpperCamelCase  :Any       =self.get_extended_attention_mask(lowerCAmelCase__					,      lowerCAmelCase__     )

        for lyr in self.encoders:
             _UpperCamelCase  :Union[str, Any]       =lyr(lowerCAmelCase__					,      lowerCAmelCase__     )[0]
        _UpperCamelCase  :str       =self.layer_norm(lowerCAmelCase__     )

        return self.dropout_post(lowerCAmelCase__     ), encoder_inputs_mask



"""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__				:  Optional[Any]						= logging.getLogger(__name__)


class 	A_       :





  """simple docstring"""

  def __init__(						self						)				->					Dict:
     a   :	List[str]						=    False
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					List[str]:
     if not self.initialized:
        a   :	Dict						=    RagRetriever(
            __UpperCAmelCase  ,					question_encoder_tokenizer=__UpperCAmelCase  ,					generator_tokenizer=__UpperCAmelCase  ,					index=__UpperCAmelCase  ,					init_retrieval=__UpperCAmelCase  ,					)
        a   :	Dict						=    True
  def 							lowercase_     (						self						)				->					List[Any]:
     self.retriever.index.init_index()
  def 							lowercase_     (						self						)				->					List[Any]:
     # delete the old self.retriever object before assigning the new index
     del self.retriever
     a   :	Dict						=    False






  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Optional[int]:
     a	, a   :	List[str]						=    self.retriever._main_retrieve(__UpperCAmelCase  ,					__UpperCAmelCase						)
     a   :	List[Any]						=    self.retriever.index.get_doc_dicts(__UpperCAmelCase						)
     return doc_ids, retrieved_doc_embeds, doc_dicts



class 	A_       (     _UpperCAmelCase   ):





  """simple docstring"""

  def __init__(						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase=None						)				->					Tuple:
     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  ,					)

     a   :	Tuple						=    retrieval_workers
     a   :	List[str]						=    question_encoder_tokenizer
     a   :	Union[str, Any]						=    generator_tokenizer
     if len(self.retrieval_workers						) > 0:
        ray.get(
            [
                worker.create_rag_retriever.remote(__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)
                for worker in self.retrieval_workers
            ]						)
  def 							lowercase_     (						self						)				->					Tuple:
     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 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Tuple:
     if len(self.retrieval_workers						) > 0:
        # Select a random retrieval actor.
        a   :	Dict						=    self.retrieval_workers[random.randint(0  ,					len(self.retrieval_workers						) - 1						)]
        a	, a	, a   :	Any						=    ray.get(
            random_worker.retrieve.remote(__UpperCAmelCase  ,					__UpperCAmelCase						)						)
     else:
        a	, a   :	str						=    self._main_retrieve(__UpperCAmelCase  ,					__UpperCAmelCase						)
        a   :	str						=    self.index.get_doc_dicts(__UpperCAmelCase						)
     return retrieved_doc_embeds, doc_ids, doc_dicts
  @classmethod
  def 							lowercase_     (						cls  ,					__UpperCAmelCase  ,					__UpperCAmelCase=None  ,					**__UpperCAmelCase						)				->					int:
     return super(__UpperCAmelCase  ,					cls						).get_tokenizers(__UpperCAmelCase  ,					__UpperCAmelCase  ,					**__UpperCAmelCase						)
  @classmethod
  def 							lowercase_     (						cls  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase=None  ,					**__UpperCAmelCase						)				->					str:
     a   :	Union[str, Any]						=    kwargs.pop('config'  ,					__UpperCAmelCase						) or RagConfig.from_pretrained(__UpperCAmelCase  ,					**__UpperCAmelCase						)
     a   :	Optional[Any]						=    RagTokenizer.from_pretrained(__UpperCAmelCase  ,					config=__UpperCAmelCase						)
     a   :	List[str]						=    rag_tokenizer.question_encoder
     a   :	int						=    rag_tokenizer.generator

     if indexed_dataset is not None:
        a   :	Optional[int]						=    'custom'
        a   :	str						=    CustomHFIndex(config.retrieval_vector_size  ,					__UpperCAmelCase						)
     else:
        a   :	Dict						=    cls._build_index(__UpperCAmelCase						)

     return cls(
         __UpperCAmelCase  ,					question_encoder_tokenizer=__UpperCAmelCase  ,					generator_tokenizer=__UpperCAmelCase  ,					retrieval_workers=__UpperCAmelCase  ,					index=__UpperCAmelCase  ,					)






  def 							lowercase_     (						self						)				->					List[Any]:
     logger.info('re-loading the new dataset with embeddings'						)
     # access from the training loop

     ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]						)

     # build the index object again
     a   :	Optional[Any]						=    self._build_index(self.config						)

     ray.get(
         [
             worker.create_rag_retriever.remote(
                 self.config  ,					self.question_encoder_tokenizer  ,					self.generator_tokenizer  ,					__UpperCAmelCase						)
             for worker in self.retrieval_workers
         ]						)





