'''simple docstring'''

import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


__a:			Tuple	       =       logging.get_logger(__name__)

__a:			List[Any]	       =       {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__a:			Any	       =       {
    'vocab_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
__a:			List[Any]	       =       {
    'vocab_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
__a:			Dict	       =       {
    'vocab_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}

__a:			List[Any]	       =       {
    'facebook/dpr-ctx_encoder-single-nq-base': 5_12,
    'facebook/dpr-ctx_encoder-multiset-base': 5_12,
}
__a:			Optional[Any]	       =       {
    'facebook/dpr-question_encoder-single-nq-base': 5_12,
    'facebook/dpr-question_encoder-multiset-base': 5_12,
}
__a:			Optional[int]	       =       {
    'facebook/dpr-reader-single-nq-base': 5_12,
    'facebook/dpr-reader-multiset-base': 5_12,
}


__a:			str	       =       {
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
__a:			Union[str, Any]	       =       {
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
__a:			List[Any]	       =       {
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}



class       UpperCAmelCase  (						_lowerCAmelCase ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE 		=   CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE 		=   CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE 		=   CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION



class       UpperCAmelCase  (						_lowerCAmelCase ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE 		=   QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE 		=   QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE 		=   QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION


__a:			Any	       =       collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

__a:			str	       =       collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


__a:			Optional[Any]	       =       r'\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Returns:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '



@add_start_docstrings(_lowerCAmelCase )
class       UpperCAmelCase  :





       '''simple docstring'''



       def __call__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = False     ,       __lowerCAmelCase = False     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       **__lowerCAmelCase     ,       )  ->  BatchEncoding:
        if titles is None and texts is None:
         return super().__call__(
             SCREAMING_SNAKE_CASE_     ,       padding=SCREAMING_SNAKE_CASE_     ,       truncation=SCREAMING_SNAKE_CASE_     ,       max_length=SCREAMING_SNAKE_CASE_     ,       return_tensors=SCREAMING_SNAKE_CASE_     ,       return_attention_mask=SCREAMING_SNAKE_CASE_     ,       **SCREAMING_SNAKE_CASE_     ,       )
        elif titles is None or texts is None:
         lowercase__    :   str						= titles if texts is None else texts
         return super().__call__(
             SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_     ,       padding=SCREAMING_SNAKE_CASE_     ,       truncation=SCREAMING_SNAKE_CASE_     ,       max_length=SCREAMING_SNAKE_CASE_     ,       return_tensors=SCREAMING_SNAKE_CASE_     ,       return_attention_mask=SCREAMING_SNAKE_CASE_     ,       **SCREAMING_SNAKE_CASE_     ,       )
        lowercase__    :   int						= titles if not isinstance(SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_			) else [titles]
        lowercase__    :   List[Any]						= texts if not isinstance(SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_			) else [texts]
        lowercase__    :   str						= len(SCREAMING_SNAKE_CASE_			)
        lowercase__    :   Tuple						= questions if not isinstance(SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_			) else [questions] * n_passages
        if len(SCREAMING_SNAKE_CASE_			) != len(SCREAMING_SNAKE_CASE_			):
         raise ValueError(
             F"""There should be as many titles than texts but got {len(SCREAMING_SNAKE_CASE_			)} titles and {len(SCREAMING_SNAKE_CASE_			)} texts."""			)
        lowercase__    :   Dict						= super().__call__(SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_     ,       padding=SCREAMING_SNAKE_CASE_     ,       truncation=SCREAMING_SNAKE_CASE_			)["""input_ids"""]
        lowercase__    :   str						= super().__call__(SCREAMING_SNAKE_CASE_     ,       add_special_tokens=SCREAMING_SNAKE_CASE_     ,       padding=SCREAMING_SNAKE_CASE_     ,       truncation=SCREAMING_SNAKE_CASE_			)["""input_ids"""]
        lowercase__    :   Optional[Any]						= {
            """input_ids""": [
                (encoded_question_and_title + encoded_text)[:max_length]
                if max_length is not None and truncation
                else encoded_question_and_title + encoded_text
                for encoded_question_and_title, encoded_text in zip(SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_			)
            ]
        }
        if return_attention_mask is not False:
         lowercase__    :   Union[str, Any]						= []
         for input_ids in encoded_inputs["input_ids"]:
          attention_mask.append([int(input_id != self.pad_token_id			) for input_id in input_ids]			)
         lowercase__    :   int						= attention_mask
        return self.pad(SCREAMING_SNAKE_CASE_     ,       padding=SCREAMING_SNAKE_CASE_     ,       max_length=SCREAMING_SNAKE_CASE_     ,       return_tensors=SCREAMING_SNAKE_CASE_			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase = 16     ,       __lowerCAmelCase = 64     ,       __lowerCAmelCase = 4     ,       )  ->  List[DPRSpanPrediction]:
        lowercase__    :   Tuple						= reader_input["""input_ids"""]
        lowercase__    :   List[Any]						= reader_output[:3]
        lowercase__    :   Union[str, Any]						= len(SCREAMING_SNAKE_CASE_			)
        lowercase__    :   List[Any]						= sorted(range(SCREAMING_SNAKE_CASE_			)     ,       reverse=SCREAMING_SNAKE_CASE_     ,       key=relevance_logits.__getitem__			)
        lowercase__    :   List[DPRReaderOutput]						= []
        for doc_id in sorted_docs:
         lowercase__    :   Tuple						= list(input_ids[doc_id]			)
         # assuming question & title information is at the beginning of the sequence
         lowercase__    :   Union[str, Any]						= sequence_ids.index(self.sep_token_id     ,       2			) + 1  # second sep id
         if sequence_ids[-1] == self.pad_token_id:
          lowercase__    :   List[Any]						= sequence_ids.index(self.pad_token_id			)
         else:
          lowercase__    :   Optional[int]						= len(SCREAMING_SNAKE_CASE_			)

         lowercase__    :   Tuple						= self._get_best_spans(
             start_logits=start_logits[doc_id][passage_offset:sequence_len]     ,       end_logits=end_logits[doc_id][passage_offset:sequence_len]     ,       max_answer_length=SCREAMING_SNAKE_CASE_     ,       top_spans=SCREAMING_SNAKE_CASE_     ,       )
         for start_index, end_index in best_spans:
          start_index += passage_offset
          end_index += passage_offset
          nbest_spans_predictions.append(
              DPRSpanPrediction(
                  span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]     ,       relevance_score=relevance_logits[doc_id]     ,       doc_id=SCREAMING_SNAKE_CASE_     ,       start_index=SCREAMING_SNAKE_CASE_     ,       end_index=SCREAMING_SNAKE_CASE_     ,       text=self.decode(sequence_ids[start_index : end_index + 1]			)     ,       )			)
         if len(SCREAMING_SNAKE_CASE_			) >= num_spans:
          break
        return nbest_spans_predictions[:num_spans]


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       )  ->  List[DPRSpanPrediction]:
        lowercase__    :   Optional[Any]						= []
        for start_index, start_score in enumerate(SCREAMING_SNAKE_CASE_			):
         for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]			):
          scores.append(((start_index, start_index + answer_length), start_score + end_score)			)
        lowercase__    :   Tuple						= sorted(SCREAMING_SNAKE_CASE_     ,       key=lambda __lowerCAmelCase			: x[1]     ,       reverse=SCREAMING_SNAKE_CASE_			)
        lowercase__    :   Optional[Any]						= []
        for (start_index, end_index), score in scores:
         if start_index > end_index:
          raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]"""			)
         lowercase__    :   int						= end_index - start_index + 1
         if length > max_answer_length:
          raise ValueError(F"""Span is too long: {length} > {max_answer_length}"""			)
         if any(
             start_index <= prev_start_index <= prev_end_index <= end_index
             or prev_start_index <= start_index <= end_index <= prev_end_index
             for (prev_start_index, prev_end_index) in chosen_span_intervals			):
          continue
         chosen_span_intervals.append((start_index, end_index)			)

         if len(SCREAMING_SNAKE_CASE_			) == top_spans:
          break
        return chosen_span_intervals



@add_end_docstrings(_lowerCAmelCase )
class       UpperCAmelCase  (						_lowerCAmelCase   ,						_lowerCAmelCase ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE 		=   READER_PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE 		=   READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE 		=   READER_PRETRAINED_INIT_CONFIGURATION
       SCREAMING_SNAKE_CASE 		=   ["input_ids", "attention_mask"]


'''simple docstring'''

import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

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


__a:			List[str]	       =       logging.get_logger(__name__)

__a:			int	       =       """▁"""

__a:			Optional[int]	       =       {"""vocab_file""": """prophetnet.tokenizer"""}

__a:			Optional[int]	       =       {
    """vocab_file""": {
        """microsoft/xprophetnet-large-wiki100-cased""": (
            """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"""
        ),
    }
}

__a:			List[str]	       =       {
    """microsoft/xprophetnet-large-wiki100-cased""": {"""do_lower_case""": False},
}

__a:			Tuple	       =       {
    """microsoft/xprophetnet-large-wiki100-cased""": 5_12,
}
def       __UpperCamelCase	(       UpperCAmelCase					):
 lowercase__    :   Dict						= collections.OrderedDict()
 with open(UpperCAmelCase		,					'''r'''		,					encoding='''utf-8'''					) as reader:
  lowercase__    :   List[Any]						= reader.readlines()
 for index, token in enumerate(UpperCAmelCase					):
  lowercase__    :   List[Any]						= token.rstrip('''\n'''					)
  lowercase__    :   Tuple						= index
 return vocab





class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE 		=   PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE 		=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE 		=   ["input_ids", "attention_mask"]

       def __init__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase="[SEP]"     ,       __lowerCAmelCase="[SEP]"     ,       __lowerCAmelCase="[SEP]"     ,       __lowerCAmelCase="[UNK]"     ,       __lowerCAmelCase="[PAD]"     ,       __lowerCAmelCase="[CLS]"     ,       __lowerCAmelCase="[MASK]"     ,       __lowerCAmelCase = None     ,       **__lowerCAmelCase     ,       )  ->  None:
        lowercase__    :   Dict						= {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=__lowerCAmelCase     ,       eos_token=__lowerCAmelCase     ,       sep_token=__lowerCAmelCase     ,       unk_token=__lowerCAmelCase     ,       pad_token=__lowerCAmelCase     ,       cls_token=__lowerCAmelCase     ,       mask_token=__lowerCAmelCase     ,       sp_model_kwargs=self.sp_model_kwargs     ,       **__lowerCAmelCase     ,       )

        try:
         import sentencepiece as spm
        except ImportError:
         logger.warning(
             '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
             ''' pip install sentencepiece'''			)
         raise

        lowercase__    :   str						= spm.SentencePieceProcessor(**self.sp_model_kwargs			)
        self.sp_model.Load(str(__lowerCAmelCase			)			)
        lowercase__    :   Optional[int]						= vocab_file

        # Original fairseq vocab and spm vocab must be "aligned":
        # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
        # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

        # put special tokens and [unused] tokens into the vocab
        lowercase__    :   str						= {'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

        for i in range(10			):
         lowercase__    :   Tuple						= F"""[unused{i}]"""
         lowercase__    :   List[Any]						= 5 + i

        # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
        lowercase__    :   Optional[Any]						= 12
        lowercase__    :   Tuple						= {v: k for k, v in self.fairseq_tokens_to_ids.items()}
        for k in self.fairseq_tokens_to_ids.keys():
         self.unique_no_split_tokens.append(__lowerCAmelCase			)

       def __getstate__(						self			)  ->  Union[str, Any]:
        lowercase__    :   Dict						= self.__dict__.copy()
        lowercase__    :   Optional[Any]						= None
        return state

       def __setstate__(						self     ,       __lowerCAmelCase			)  ->  Dict:
        lowercase__    :   Any						= d
        try:
         import sentencepiece as spm
        except ImportError:
         logger.warning(
             '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
             ''' pip install sentencepiece'''			)
         raise

        # for backward compatibility
        if not hasattr(self     ,       '''sp_model_kwargs'''			):
         lowercase__    :   List[Any]						= {}

        lowercase__    :   int						= spm.SentencePieceProcessor(**self.sp_model_kwargs			)
        self.sp_model.Load(self.vocab_file			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = False			)  ->  List[int]:

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

        if token_ids_a is None:
         return ([0] * len(__lowerCAmelCase			)) + [1]
        return ([0] * len(__lowerCAmelCase			)) + [1] + ([0] * len(__lowerCAmelCase			)) + [1]

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  List[int]:
        lowercase__    :   List[str]						= [self.sep_token_id]

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

       @property
       def _lowerCAmelCase(						self			)  ->  List[Any]:
        return len(self.sp_model			) + self.fairseq_offset

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   str						= {self.convert_ids_to_tokens(__lowerCAmelCase			): i for i in range(self.vocab_size			)}
        vocab.update(self.added_tokens_encoder			)
        return vocab

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  str:
        return self.sp_model.encode(__lowerCAmelCase     ,       out_type=__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
        lowercase__    :   Tuple						= self.sp_model.PieceToId(__lowerCAmelCase			)

        # Need to return unknown token if the SP model returned 0
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  str:
        if index in self.fairseq_ids_to_tokens:
         return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Union[str, Any]:
        lowercase__    :   Optional[Any]						= ''''''.join(__lowerCAmelCase			).replace(__lowerCAmelCase     ,       ''' '''			).strip()
        return out_string

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  Tuple[str]:
        if not os.path.isdir(__lowerCAmelCase			):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
         return
        lowercase__    :   str						= os.path.join(
            __lowerCAmelCase     ,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

        if os.path.abspath(self.vocab_file			) != os.path.abspath(__lowerCAmelCase			) and os.path.isfile(self.vocab_file			):
         copyfile(self.vocab_file     ,       __lowerCAmelCase			)
        elif not os.path.isfile(self.vocab_file			):
         with open(__lowerCAmelCase     ,       '''wb'''			) as fi:
          lowercase__    :   List[Any]						= self.sp_model.serialized_model_proto()
          fi.write(__lowerCAmelCase			)

        return (out_vocab_file,)


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  List[int]:

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

