




"""simple docstring"""


import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


A_						=				logging.get_logger(__name__)






class 					lowercase:







 '''simple docstring'''





 lowercase__ 					=					None







@experimental
def 					UpperCAmelCase__       (snake_case__							:					Dict		,   snake_case__							:					Any		,   snake_case__							:					Optional[int]		,   snake_case__							:					Tuple		,   snake_case__							:					str		,   snake_case__							:					Optional[Any]		,   snake_case__							:					int    ):
  """simple docstring"""

  if ParallelBackendConfig.backend_name is None:
    return _map_with_multiprocessing_pool(
        snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__    )

  return _map_with_joblib(snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__    )


def 					UpperCAmelCase__       (snake_case__							:					Union[str, Any]		,   snake_case__							:					Any		,   snake_case__							:					Tuple		,   snake_case__							:					Optional[Any]		,   snake_case__							:					Optional[int]		,   snake_case__							:					int		,   snake_case__							:					Tuple    ):
  """simple docstring"""

  _snake_case							:		str								=  num_proc if num_proc <= len(snake_case__    ) else len(snake_case__    )
  _snake_case							:		Dict								=  []  # We organize the splits ourselve (contiguous splits)
  for index in range(snake_case__    ):
    _snake_case							:		str								=  len(snake_case__    ) // num_proc
    _snake_case							:		Tuple								=  len(snake_case__    ) % num_proc
    _snake_case							:		Tuple								=  div * index + min(snake_case__		,   snake_case__    )
    _snake_case							:		str								=  start + div + (1 if index < mod else 0)
    split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)    )

  if len(snake_case__    ) != sum(len(i[1]    ) for i in split_kwds    ):
    raise ValueError(
        F"Error dividing inputs iterable among processes. "
        F"Total number of objects {len(snake_case__    )}, "
        F"length: {sum(len(i[1]    ) for i in split_kwds    )}"    )

  logger.info(
      F"Spawning {num_proc} processes for {len(snake_case__    )} objects in slices of {[len(i[1]    ) for i in split_kwds]}"    )
  _snake_case		,					_snake_case							:		List[str]								=  None, None
  if not disable_tqdm:
    _snake_case		,					_snake_case							:		List[Any]								=  (RLock(),), tqdm.set_lock
  with Pool(snake_case__		,   initargs=snake_case__		,   initializer=snake_case__    ) as pool:
    _snake_case							:		List[Any]								=  pool.map(snake_case__		,   snake_case__    )
  logger.info(F"Finished {num_proc} processes"    )
  _snake_case							:		List[Any]								=  [obj for proc_res in mapped for obj in proc_res]
  logger.info(F"Unpacked {len(snake_case__    )} objects"    )

  return mapped


def 					UpperCAmelCase__       (snake_case__							:					Any		,   snake_case__							:					List[Any]		,   snake_case__							:					str		,   snake_case__							:					Dict		,   snake_case__							:					Any		,   snake_case__							:					Union[str, Any]		,   snake_case__							:					List[Any]    ):
  """simple docstring"""

  import joblib

  with joblib.parallel_backend(ParallelBackendConfig.backend_name		,   n_jobs=snake_case__    ):
    return joblib.Parallel()(
        joblib.delayed(snake_case__    )((function, obj, types, None, True, None)    ) for obj in iterable    )






@experimental
@contextlib.contextmanager
def 					UpperCAmelCase__       (snake_case__							:					str    ):
  """simple docstring"""

  _snake_case							:		List[Any]								=  backend_name

  if backend_name == "spark":
    from joblibspark import register_spark

    register_spark()

    # TODO: call create_cache_and_write_probe if "download" in steps
    # TODO: raise NotImplementedError when Dataset.map etc is called

  try:
    yield
  finally:
    _snake_case							:		Tuple								=  None








"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


A_						=				r'''
    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.

    Args:
        title_sep (`str`, *optional*, defaults to  `" / "`):
            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].
        doc_sep (`str`, *optional*, defaults to  `" // "`):
            Separator inserted between the text of the retrieved document and the original input when calling
            [`RagRetriever`].
        n_docs (`int`, *optional*, defaults to 5):
            Number of documents to retrieve.
        max_combined_length (`int`, *optional*, defaults to 300):
            Max length of contextualized input returned by [`~RagRetriever.__call__`].
        retrieval_vector_size (`int`, *optional*, defaults to 768):
            Dimensionality of the document embeddings indexed by [`RagRetriever`].
        retrieval_batch_size (`int`, *optional*, defaults to 8):
            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated
            [`RagRetriever`].
        dataset (`str`, *optional*, defaults to `"wiki_dpr"`):
            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids
            using `datasets.list_datasets()`).
        dataset_split (`str`, *optional*, defaults to `"train"`)
            Which split of the `dataset` to load.
        index_name (`str`, *optional*, defaults to `"compressed"`)
            The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and
            `"compressed"`.
        index_path (`str`, *optional*)
            The path to the serialized faiss index on disk.
        passages_path (`str`, *optional*):
            A path to text passages compatible with the faiss index. Required if using
            [`~models.rag.retrieval_rag.LegacyIndex`]
        use_dummy_dataset (`bool`, *optional*, defaults to `False`)
            Whether to load a "dummy" variant of the dataset specified by `dataset`.
        label_smoothing (`float`, *optional*, defaults to 0.0):
            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing
            in the loss calculation. If set to 0, no label smoothing is performed.
        do_marginalize (`bool`, *optional*, defaults to `False`):
            If `True`, the logits are marginalized over all documents by making use of
            `torch.nn.functional.log_softmax`.
        reduce_loss (`bool`, *optional*, defaults to `False`):
            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.
        do_deduplication (`bool`, *optional*, defaults to `True`):
            Whether or not to deduplicate the generations from different context documents for a given input. Has to be
            set to `False` if used while training with distributed backend.
        exclude_bos_score (`bool`, *optional*, defaults to `False`):
            Whether or not to disregard the BOS token when computing the loss.
        output_retrieved(`bool`, *optional*, defaults to `False`):
            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and
            `context_attention_mask` are returned. See returned tensors for more detail.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        forced_eos_token_id (`int`, *optional*):
            The id of the token to force as the last generated token when `max_length` is reached. Usually set to
            `eos_token_id`.
'''






@add_start_docstrings(__a )
class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"rag"
 lowercase__ 					=					True


 def __init__(				self:		Union[str, Any],      a_:		int=None,      a_:		Tuple=True,      a_:		Optional[int]=None,      a_:		List[str]=None,      a_:		int=None,      a_:		Optional[Any]=None,      a_:		List[str]=None,      a_:		Optional[Any]=" / ",      a_:		Tuple=" // ",      a_:		List[Any]=5,      a_:		Dict=300,      a_:		Tuple=768,      a_:		Optional[Any]=8,      a_:		int="wiki_dpr",      a_:		Any="train",      a_:		Optional[int]="compressed",      a_:		Optional[int]=None,      a_:		List[Any]=None,      a_:		Optional[Any]=False,      a_:		str=False,      a_:		Dict=0.0,      a_:		Union[str, Any]=True,      a_:		Union[str, Any]=False,      a_:		str=False,      a_:		List[str]=False,      a_:		Union[str, Any]=True,      a_:		Any=None,      **a_:		List[Any],      ):




   '''simple docstring'''



   super().__init__(
       bos_token_id=a_,      pad_token_id=a_,      eos_token_id=a_,      decoder_start_token_id=a_,      forced_eos_token_id=a_,      is_encoder_decoder=a_,      prefix=a_,      vocab_size=a_,      **a_,      )
   assert (
       "question_encoder" in kwargs and "generator" in kwargs
   ), "Config has to be initialized with question_encoder and generator config"
   _snake_case							:		Union[str, Any]								=  kwargs.pop("""question_encoder"""		)
   _snake_case							:		List[str]								=  question_encoder_config.pop("""model_type"""		)
   _snake_case							:		Union[str, Any]								=  kwargs.pop("""generator"""		)
   _snake_case							:		Any								=  decoder_config.pop("""model_type"""		)

   from ..auto.configuration_auto import AutoConfig

   _snake_case							:		Union[str, Any]								=  AutoConfig.for_model(a_,      **a_		)
   _snake_case							:		Optional[Any]								=  AutoConfig.for_model(a_,      **a_		)

   _snake_case							:		Any								=  reduce_loss
   _snake_case							:		Optional[int]								=  label_smoothing
   _snake_case							:		Dict								=  exclude_bos_score
   _snake_case							:		int								=  do_marginalize

   _snake_case							:		Optional[Any]								=  title_sep
   _snake_case							:		Any								=  doc_sep
   _snake_case							:		List[str]								=  n_docs
   _snake_case							:		Tuple								=  max_combined_length

   _snake_case							:		Optional[Any]								=  dataset
   _snake_case							:		Union[str, Any]								=  dataset_split
   _snake_case							:		Tuple								=  index_name

   _snake_case							:		Any								=  retrieval_vector_size
   _snake_case							:		Union[str, Any]								=  retrieval_batch_size
   _snake_case							:		str								=  passages_path
   _snake_case							:		Tuple								=  index_path
   _snake_case							:		List[Any]								=  use_dummy_dataset

   _snake_case							:		Optional[Any]								=  output_retrieved

   _snake_case							:		Tuple								=  do_deduplication

   _snake_case							:		Union[str, Any]								=  use_cache

   if self.forced_eos_token_id is None:
     _snake_case							:		Dict								=  getattr(self.generator,      """forced_eos_token_id""",      a_		)


 @classmethod
 def  UpperCamelCase_   (				cls:		Any,      a_:		PretrainedConfig,      a_:		PretrainedConfig,      **a_:		Optional[Any]		):




   '''simple docstring'''



   return cls(question_encoder=question_encoder_config.to_dict(),      generator=generator_config.to_dict(),      **a_		)


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  copy.deepcopy(self.__dict__		)
   _snake_case							:		List[str]								=  self.question_encoder.to_dict()
   _snake_case							:		Tuple								=  self.generator.to_dict()
   _snake_case							:		Dict								=  self.__class__.model_type
   return output


