import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A     :  int					     =       logging.get_logger(__name__)

A     :  str					     =       "▁"

A     :  Dict					     =       {
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
}

A     :  Optional[int]					     =       {
    "vocab_file": {
        "facebook/s2t-small-librispeech-asr": (
            "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json"
        ),
    },
    "spm_file": {
        "facebook/s2t-small-librispeech-asr": (
            "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model"
        )
    },
}

A     :  List[str]					     =       {
    "facebook/s2t-small-librispeech-asr": 10_24,
}

A     :  Union[str, Any]					     =       ["pt", "fr", "ru", "nl", "ro", "it", "es", "de"]

A     :  List[str]					     =       {"mustc": MUSTC_LANGS}


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						VOCAB_FILES_NAMES
  lowerCamelCase__     =						PRETRAINED_VOCAB_FILES_MAP
  lowerCamelCase__     =						MAX_MODEL_INPUT_SIZES
  lowerCamelCase__     =						['''input_ids''', '''attention_mask''']

  lowerCamelCase__     =						[]







  def __init__(							self :       str  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       Any  ,     __magic_name__ :       int="<s>"  ,     __magic_name__ :       List[Any]="</s>"  ,     __magic_name__ :       Dict="<pad>"  ,     __magic_name__ :       int="<unk>"  ,     __magic_name__ :       Any=False  ,     __magic_name__ :       Optional[Any]=False  ,     __magic_name__ :       int=None  ,     __magic_name__ :       Any=None  ,     __magic_name__ :       Optional[Dict[str, Any]] = None  ,     **__magic_name__ :       Optional[Any]  ,     )  ->	None:
        SCREAMING_SNAKE_CASE_          =  {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=__magic_name__  ,     eos_token=__magic_name__  ,     unk_token=__magic_name__  ,     pad_token=__magic_name__  ,     do_upper_case=__magic_name__  ,     do_lower_case=__magic_name__  ,     tgt_lang=__magic_name__  ,     lang_codes=__magic_name__  ,     sp_model_kwargs=self.sp_model_kwargs  ,     **__magic_name__  ,     )
        SCREAMING_SNAKE_CASE_          =  do_upper_case
        SCREAMING_SNAKE_CASE_          =  do_lower_case

        SCREAMING_SNAKE_CASE_          =  load_json(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  {v: k for k, v in self.encoder.items()}
        SCREAMING_SNAKE_CASE_          =  spm_file
        SCREAMING_SNAKE_CASE_          =  load_spm(__magic_name__  ,     self.sp_model_kwargs   )

        if lang_codes is not None:
              SCREAMING_SNAKE_CASE_          =  lang_codes
              SCREAMING_SNAKE_CASE_          =  LANGUAGES[lang_codes]
              SCREAMING_SNAKE_CASE_          =  [F'''<lang:{lang}>''' for lang in self.langs]
              SCREAMING_SNAKE_CASE_          =  {lang: self.sp_model.PieceToId(F'''<lang:{lang}>'''   ) for lang in self.langs}

              SCREAMING_SNAKE_CASE_          =  self.lang_tokens
              SCREAMING_SNAKE_CASE_          =  tgt_lang if tgt_lang is not None else self.langs[0]

              self.set_tgt_lang_special_tokens(self._tgt_lang   )
        else:
              SCREAMING_SNAKE_CASE_          =  {}







  @property
  def    __A     (							self :       Any   )  ->	int:
        return len(self.encoder   )







  @property
  def    __A     (							self :       List[Any]   )  ->	str:
        return self._tgt_lang







  @tgt_lang.setter
  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       str   )  ->	None:
        SCREAMING_SNAKE_CASE_          =  new_tgt_lang
        self.set_tgt_lang_special_tokens(__magic_name__   )







  def    __A     (							self :       List[Any]  ,     __magic_name__ :       str   )  ->	None:
        SCREAMING_SNAKE_CASE_          =  self.lang_code_to_id[tgt_lang]
        SCREAMING_SNAKE_CASE_          =  [lang_code_id]







  def    __A     (							self :       Dict  ,     __magic_name__ :       str   )  ->	List[str]:
        return self.sp_model.encode(__magic_name__  ,     out_type=__magic_name__   )







  def    __A     (							self :       Dict  ,     __magic_name__ :       Union[str, Any]   )  ->	List[Any]:
        return self.encoder.get(__magic_name__  ,     self.encoder[self.unk_token]   )







  def    __A     (							self :       Tuple  ,     __magic_name__ :       int   )  ->	str:
        return self.decoder.get(__magic_name__  ,     self.unk_token   )







  def    __A     (							self :       str  ,     __magic_name__ :       List[str]   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  []
        SCREAMING_SNAKE_CASE_          =  ""
        for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                    SCREAMING_SNAKE_CASE_          =  self.sp_model.decode(__magic_name__   )
                    out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
                    SCREAMING_SNAKE_CASE_          =  []
              else:
                    current_sub_tokens.append(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.sp_model.decode(__magic_name__   )
        out_string += decoded.upper() if self.do_upper_case else decoded
        return out_string.strip()







  def    __A     (							self :       str  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       int=None   )  ->	List[int]:
        if token_ids_a is None:
              return self.prefix_tokens + token_ids_a + [self.eos_token_id]
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       List[int]  ,     __magic_name__ :       Optional[List[int]] = None  ,     __magic_name__ :       bool = False   )  ->	List[int]:

        if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=__magic_name__  ,     token_ids_a=__magic_name__  ,     already_has_special_tokens=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  [1] * len(self.prefix_tokens   )
        SCREAMING_SNAKE_CASE_          =  [1]
        if token_ids_a is None:
              return prefix_ones + ([0] * len(__magic_name__   )) + suffix_ones
        return prefix_ones + ([0] * len(__magic_name__   )) + ([0] * len(__magic_name__   )) + suffix_ones







  def    __A     (							self :       Tuple   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  self.encoder.copy()
        vocab.update(self.added_tokens_encoder   )
        return vocab







  def __getstate__(							self :       List[str]   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  self.__dict__.copy()
        SCREAMING_SNAKE_CASE_          =  None
        return state







  def __setstate__(							self :       Optional[Any]  ,     __magic_name__ :       Dict   )  ->	None:
        SCREAMING_SNAKE_CASE_          =  d

        # for backward compatibility
        if not hasattr(self  ,     "sp_model_kwargs"   ):
              SCREAMING_SNAKE_CASE_          =  {}

        SCREAMING_SNAKE_CASE_          =  load_spm(self.spm_file  ,     self.sp_model_kwargs   )







  def    __A     (							self :       str  ,     __magic_name__ :       str  ,     __magic_name__ :       Optional[str] = None   )  ->	Tuple[str]:
        SCREAMING_SNAKE_CASE_          =  Path(__magic_name__   )
        assert save_dir.is_dir(), F'''{save_directory} should be a directory'''
        SCREAMING_SNAKE_CASE_          =  save_dir / (
            (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
        )
        SCREAMING_SNAKE_CASE_          =  save_dir / (
            (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
        )

        save_json(self.encoder  ,     __magic_name__   )

        if os.path.abspath(self.spm_file   ) != os.path.abspath(__magic_name__   ) and os.path.isfile(self.spm_file   ):
              copyfile(self.spm_file  ,     __magic_name__   )
        elif not os.path.isfile(self.spm_file   ):
              with open(__magic_name__  ,     "wb"   ) as fi:
                    SCREAMING_SNAKE_CASE_          =  self.sp_model.serialized_model_proto()
                    fi.write(__magic_name__   )

        return (str(__magic_name__   ), str(__magic_name__   ))



def      a__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  sentencepiece.SentencePieceProcessor(**__UpperCamelCase				)
      spm.Load(str(__UpperCamelCase				)				)
      return spm


def      a__ (		__UpperCamelCase				):
      with open(__UpperCamelCase     ,      "r"				) as f:
            return json.load(__UpperCamelCase				)





def      a__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      with open(__UpperCamelCase     ,      "w"				) as f:
            json.dump(__UpperCamelCase     ,      __UpperCamelCase     ,      indent=2				)



import copy

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


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


@add_start_docstrings(SCREAMING_SNAKE_CASE__					)
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						'''rag'''
  lowerCamelCase__     =						True







  def __init__(							self :       Any  ,     __magic_name__ :       Optional[int]=None  ,     __magic_name__ :       Dict=True  ,     __magic_name__ :       List[Any]=None  ,     __magic_name__ :       Optional[Any]=None  ,     __magic_name__ :       List[Any]=None  ,     __magic_name__ :       Any=None  ,     __magic_name__ :       Optional[Any]=None  ,     __magic_name__ :       Any=" / "  ,     __magic_name__ :       int=" // "  ,     __magic_name__ :       Any=5  ,     __magic_name__ :       Dict=300  ,     __magic_name__ :       Optional[Any]=768  ,     __magic_name__ :       str=8  ,     __magic_name__ :       List[Any]="wiki_dpr"  ,     __magic_name__ :       Any="train"  ,     __magic_name__ :       Any="compressed"  ,     __magic_name__ :       List[Any]=None  ,     __magic_name__ :       Optional[int]=None  ,     __magic_name__ :       Optional[int]=False  ,     __magic_name__ :       Union[str, Any]=False  ,     __magic_name__ :       List[str]=0.0  ,     __magic_name__ :       Dict=True  ,     __magic_name__ :       str=False  ,     __magic_name__ :       int=False  ,     __magic_name__ :       Tuple=False  ,     __magic_name__ :       Tuple=True  ,     __magic_name__ :       Dict=None  ,     **__magic_name__ :       int  ,     )  ->	List[str]:
        super().__init__(
            bos_token_id=__magic_name__  ,     pad_token_id=__magic_name__  ,     eos_token_id=__magic_name__  ,     decoder_start_token_id=__magic_name__  ,     forced_eos_token_id=__magic_name__  ,     is_encoder_decoder=__magic_name__  ,     prefix=__magic_name__  ,     vocab_size=__magic_name__  ,     **__magic_name__  ,     )
        assert (
            "question_encoder" in kwargs and "generator" in kwargs
        ), "Config has to be initialized with question_encoder and generator config"
        SCREAMING_SNAKE_CASE_          =  kwargs.pop("question_encoder"   )
        SCREAMING_SNAKE_CASE_          =  question_encoder_config.pop("model_type"   )
        SCREAMING_SNAKE_CASE_          =  kwargs.pop("generator"   )
        SCREAMING_SNAKE_CASE_          =  decoder_config.pop("model_type"   )

        from ..auto.configuration_auto import AutoConfig

        SCREAMING_SNAKE_CASE_          =  AutoConfig.for_model(__magic_name__  ,     **__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  AutoConfig.for_model(__magic_name__  ,     **__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  reduce_loss
        SCREAMING_SNAKE_CASE_          =  label_smoothing
        SCREAMING_SNAKE_CASE_          =  exclude_bos_score
        SCREAMING_SNAKE_CASE_          =  do_marginalize

        SCREAMING_SNAKE_CASE_          =  title_sep
        SCREAMING_SNAKE_CASE_          =  doc_sep
        SCREAMING_SNAKE_CASE_          =  n_docs
        SCREAMING_SNAKE_CASE_          =  max_combined_length

        SCREAMING_SNAKE_CASE_          =  dataset
        SCREAMING_SNAKE_CASE_          =  dataset_split
        SCREAMING_SNAKE_CASE_          =  index_name

        SCREAMING_SNAKE_CASE_          =  retrieval_vector_size
        SCREAMING_SNAKE_CASE_          =  retrieval_batch_size
        SCREAMING_SNAKE_CASE_          =  passages_path
        SCREAMING_SNAKE_CASE_          =  index_path
        SCREAMING_SNAKE_CASE_          =  use_dummy_dataset

        SCREAMING_SNAKE_CASE_          =  output_retrieved

        SCREAMING_SNAKE_CASE_          =  do_deduplication

        SCREAMING_SNAKE_CASE_          =  use_cache

        if self.forced_eos_token_id is None:
              SCREAMING_SNAKE_CASE_          =  getattr(self.generator  ,     "forced_eos_token_id"  ,     __magic_name__   )







  @classmethod
  def    __A     (							cls :       Dict  ,     __magic_name__ :       PretrainedConfig  ,     __magic_name__ :       PretrainedConfig  ,     **__magic_name__ :       List[str]   )  ->	PretrainedConfig:
        return cls(question_encoder=question_encoder_config.to_dict()  ,     generator=generator_config.to_dict()  ,     **__magic_name__   )







  def    __A     (							self :       Any   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  copy.deepcopy(self.__dict__   )
        SCREAMING_SNAKE_CASE_          =  self.question_encoder.to_dict()
        SCREAMING_SNAKE_CASE_          =  self.generator.to_dict()
        SCREAMING_SNAKE_CASE_          =  self.__class__.model_type
        return output


