





import copy

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


lowercase_					     =	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(UpperCAmelCase   )
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	Union[str, Any]				      = 'rag'
   _UpperCamelCase					:	Optional[int]				      = True


   def __init__(					self    :       List[Any]    ,  a    :       List[str]=None    ,  a    :       Dict=True    ,  a    :       Any=None    ,  a    :       List[Any]=None    ,  a    :       Union[str, Any]=None    ,  a    :       str=None    ,  a    :       Tuple=None    ,  a    :       List[Any]=" / "    ,  a    :       int=" // "    ,  a    :       str=5    ,  a    :       List[Any]=300    ,  a    :       Dict=768    ,  a    :       int=8    ,  a    :       str="wiki_dpr"    ,  a    :       List[Any]="train"    ,  a    :       str="compressed"    ,  a    :       Tuple=None    ,  a    :       Union[str, Any]=None    ,  a    :       Optional[int]=False    ,  a    :       Dict=False    ,  a    :       List[Any]=0.0    ,  a    :       int=True    ,  a    :       Optional[int]=False    ,  a    :       str=False    ,  a    :       str=False    ,  a    :       List[str]=True    ,  a    :       Union[str, Any]=None    ,  **a    :       List[Any]    ,  )->     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"
     lowercase__			    =						kwargs.pop('question_encoder' )
     lowercase__			    =						question_encoder_config.pop('model_type' )
     lowercase__			    =						kwargs.pop('generator' )
     lowercase__			    =						decoder_config.pop('model_type' )

     from ..auto.configuration_auto import AutoConfig

     lowercase__			    =						AutoConfig.for_model(a    ,  **a )
     lowercase__			    =						AutoConfig.for_model(a    ,  **a )

     lowercase__			    =						reduce_loss
     lowercase__			    =						label_smoothing
     lowercase__			    =						exclude_bos_score
     lowercase__			    =						do_marginalize

     lowercase__			    =						title_sep
     lowercase__			    =						doc_sep
     lowercase__			    =						n_docs
     lowercase__			    =						max_combined_length

     lowercase__			    =						dataset
     lowercase__			    =						dataset_split
     lowercase__			    =						index_name

     lowercase__			    =						retrieval_vector_size
     lowercase__			    =						retrieval_batch_size
     lowercase__			    =						passages_path
     lowercase__			    =						index_path
     lowercase__			    =						use_dummy_dataset

     lowercase__			    =						output_retrieved

     lowercase__			    =						do_deduplication

     lowercase__			    =						use_cache

     if self.forced_eos_token_id is None:
       lowercase__			    =						getattr(self.generator    ,  'forced_eos_token_id'    ,  a )


   @classmethod
   def  SCREAMING_SNAKE_CASE_							(					cls    :       Optional[int]    ,  a    :       PretrainedConfig    ,  a    :       PretrainedConfig    ,  **a    :       Any )->     PretrainedConfig:





     """simple docstring"""







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


   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict )->     str:





     """simple docstring"""







     lowercase__			    =						copy.deepcopy(self.__dict__ )
     lowercase__			    =						self.question_encoder.to_dict()
     lowercase__			    =						self.generator.to_dict()
     lowercase__			    =						self.__class__.model_type
     return output









import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_barthez import BarthezTokenizer
else:
      lowercase_					     =	None

lowercase_					     =	logging.get_logger(__name__)

lowercase_					     =	{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

lowercase_					     =	{
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"""
        ),
    },
}

lowercase_					     =	{
    """moussaKam/mbarthez""": 1_024,
    """moussaKam/barthez""": 1_024,
    """moussaKam/barthez-orangesum-title""": 1_024,
}

lowercase_					     =	"""▁"""
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	Dict				      = VOCAB_FILES_NAMES
   _UpperCamelCase					:	str				      = PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase					:	Optional[int]				      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase					:	Optional[Any]				      = ['input_ids', 'attention_mask']
   _UpperCamelCase					:	int				      = BarthezTokenizer


   def __init__(					self    :       List[Any]    ,  a    :       Union[str, Any]=None    ,  a    :       Optional[Any]=None    ,  a    :       Dict="<s>"    ,  a    :       Union[str, Any]="</s>"    ,  a    :       List[str]="</s>"    ,  a    :       Optional[Any]="<s>"    ,  a    :       int="<unk>"    ,  a    :       str="<pad>"    ,  a    :       Optional[int]="<mask>"    ,  **a    :       Union[str, Any]    ,  )->     Tuple:





     """simple docstring"""







     lowercase__			    =						AddedToken(a    ,  lstrip=a    ,  rstrip=a ) if isinstance(a    ,  a ) else mask_token

     super().__init__(
         a    ,  tokenizer_file=a    ,  bos_token=a    ,  eos_token=a    ,  unk_token=a    ,  sep_token=a    ,  cls_token=a    ,  pad_token=a    ,  mask_token=a    ,  **a    ,  )

     lowercase__			    =						vocab_file
     lowercase__			    =						False if not self.vocab_file else True


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any]    ,  a    :       List[int]    ,  a    :       Optional[List[int]] = None )->     List[int]:





     """simple docstring"""







     if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     lowercase__			    =						[self.cls_token_id]
     lowercase__			    =						[self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any]    ,  a    :       List[int]    ,  a    :       Optional[List[int]] = None )->     List[int]:





     """simple docstring"""







     lowercase__			    =						[self.sep_token_id]
     lowercase__			    =						[self.cls_token_id]

     if token_ids_a is None:
       return len(cls + token_ids_a + sep ) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]


   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict    ,  a    :       str    ,  a    :       Optional[str] = None )->     Tuple[str]:





     """simple docstring"""







     if not self.can_save_slow_tokenizer:
       raise ValueError(
           'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
           'tokenizer.' )

     if not os.path.isdir(a ):
       logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
       return
     lowercase__			    =						os.path.join(
         a    ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

     if os.path.abspath(self.vocab_file ) != os.path.abspath(a ):
       copyfile(self.vocab_file    ,  a )

     return (out_vocab_file,)


