






import logging
import random

import ray

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


A_						:						str						      =					logging.getLogger(__name__)




class 							_lowerCAmelCase:





       """simple docstring"""







       def __init__(   self				):
         UpperCamelCase_:   Optional[int]        =							False





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         if not self.initialized:
           UpperCamelCase_:   Optional[Any]        =							RagRetriever(
               _lowerCamelCase    ,     question_encoder_tokenizer=_lowerCamelCase    ,     generator_tokenizer=_lowerCamelCase    ,     index=_lowerCamelCase    ,     init_retrieval=_lowerCamelCase    ,     )
           UpperCamelCase_:   str        =							True





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





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         UpperCamelCase_ ,UpperCamelCase_:   Any        =							self.retriever._main_retrieve(_lowerCamelCase    ,     _lowerCamelCase				)
         return doc_ids, retrieved_doc_embeds







class 							_lowerCAmelCase(			UpperCAmelCase_   ):





       """simple docstring"""







       def __init__(   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase=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    ,     )
         UpperCamelCase_:   List[str]        =							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      _a       (   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      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         if len(self.retrieval_workers				) > 0:
           # Select a random retrieval actor.
           UpperCamelCase_:   Union[str, Any]        =							self.retrieval_workers[random.randint(0    ,     len(self.retrieval_workers				) - 1				)]
           UpperCamelCase_ ,UpperCamelCase_:   str        =							ray.get(random_worker.retrieve.remote(_lowerCamelCase    ,     _lowerCamelCase				)				)
         else:
           UpperCamelCase_ ,UpperCamelCase_:   Dict        =							self._main_retrieve(_lowerCamelCase    ,     _lowerCamelCase				)
         return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_lowerCamelCase				)





       @classmethod
       def      _a       (   cls    ,     _lowerCamelCase    ,     _lowerCamelCase=None    ,     **_lowerCamelCase				):
         return super(_lowerCamelCase    ,     cls				).get_tokenizers(_lowerCamelCase    ,     _lowerCamelCase    ,     **_lowerCamelCase				)





       @classmethod
       def      _a       (   cls    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase=None    ,     **_lowerCamelCase				):
         UpperCamelCase_:   List[str]        =							kwargs.pop('config'    ,     _lowerCamelCase				) or RagConfig.from_pretrained(_lowerCamelCase    ,     **_lowerCamelCase				)
         UpperCamelCase_:   Optional[int]        =							RagTokenizer.from_pretrained(_lowerCamelCase    ,     config=_lowerCamelCase				)
         UpperCamelCase_:   List[str]        =							rag_tokenizer.question_encoder
         UpperCamelCase_:   List[Any]        =							rag_tokenizer.generator
         if indexed_dataset is not None:
           UpperCamelCase_:   Union[str, Any]        =							'custom'
           UpperCamelCase_:   int        =							CustomHFIndex(config.retrieval_vector_size    ,     _lowerCamelCase				)
         else:
           UpperCamelCase_:   str        =							cls._build_index(_lowerCamelCase				)
         return cls(
             _lowerCamelCase    ,     question_encoder_tokenizer=_lowerCamelCase    ,     generator_tokenizer=_lowerCamelCase    ,     retrieval_workers=_lowerCamelCase    ,     index=_lowerCamelCase    ,     )
'''simple docstring'''






_UpperCAmelCase    :     Any         =       '''0.21.0'''

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
   from .utils import rich



