

"""simple docstring"""






import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin







class 	__UpperCamelCase    (    a__							,							unittest.TestCase						):
  _UpperCAmelCase          =		PhobertTokenizer
  _UpperCAmelCase          =		False

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      _lowerCAmelCase       :   str  =    ['T@@', 'i', 'I', 'R@@', 'r', 'e@@']
      _lowerCAmelCase       :   Union[str, Any]  =    dict(zip(_A    ,range(len(_A      )      )      )      )
      _lowerCAmelCase       :   Optional[int]  =    ['#version: 0.2', 'l à</w>']
      _lowerCAmelCase       :   Tuple  =    {'unk_token': '<unk>'}

      _lowerCAmelCase       :   List[Any]  =    os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['vocab_file']      )
      _lowerCAmelCase       :   int  =    os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['merges_file']      )

      with open(self.vocab_file    ,'w'    ,encoding='utf-8'      ) as fp:
          for token in vocab_tokens:
              fp.write(F"""{token} {vocab_tokens[token]}\n"""      )
      with open(self.merges_file    ,'w'    ,encoding='utf-8'      ) as fp:
          fp.write('\n'.join(_A      )      )

  def __lowerCamelCase		(		self    ,**_A      ):
      '''simple docstring'''

      kwargs.update(self.special_tokens_map      )
      return PhobertTokenizer.from_pretrained(self.tmpdirname    ,**_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    'Tôi là VinAI Research'
      _lowerCAmelCase       :   Any  =    'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
      return input_text, output_text







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    PhobertTokenizer(self.vocab_file    ,self.merges_file    ,**self.special_tokens_map      )
      _lowerCAmelCase       :   List[Any]  =    'Tôi là VinAI Research'
      _lowerCAmelCase       :   Dict  =    'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
      _lowerCAmelCase       :   Optional[int]  =    tokenizer.tokenize(_A      )
      print(_A      )
      self.assertListEqual(_A    ,_A      )

      _lowerCAmelCase       :   List[Any]  =    tokens + [tokenizer.unk_token]

      _lowerCAmelCase       :   Dict  =    [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(_A      )    ,_A      )








"""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


_lowerCAmelCase						=		logging.get_logger(__name__)

_lowerCAmelCase						=		{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowerCAmelCase						=		{
    """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"""
        ),
    },
}
_lowerCAmelCase						=		{
    """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"""
        ),
    },
}
_lowerCAmelCase						=		{
    """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"""
        ),
    },
}

_lowerCAmelCase						=		{
    """facebook/dpr-ctx_encoder-single-nq-base""": 5_1_2,
    """facebook/dpr-ctx_encoder-multiset-base""": 5_1_2,
}
_lowerCAmelCase						=		{
    """facebook/dpr-question_encoder-single-nq-base""": 5_1_2,
    """facebook/dpr-question_encoder-multiset-base""": 5_1_2,
}
_lowerCAmelCase						=		{
    """facebook/dpr-reader-single-nq-base""": 5_1_2,
    """facebook/dpr-reader-multiset-base""": 5_1_2,
}


_lowerCAmelCase						=		{
    """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
_lowerCAmelCase						=		{
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
_lowerCAmelCase						=		{
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}







class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		VOCAB_FILES_NAMES
  _UpperCAmelCase          =		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  _UpperCAmelCase          =		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _UpperCAmelCase          =		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		VOCAB_FILES_NAMES
  _UpperCAmelCase          =		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  _UpperCAmelCase          =		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _UpperCAmelCase          =		QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION





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

_lowerCAmelCase						=		collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


_lowerCAmelCase						=		r"""
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    ```
    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
    ```

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer's default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Returns:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    """







@add_start_docstrings(a__						)
class 	__UpperCamelCase    :

  def __call__(		self    ,_A    ,_A = None    ,_A = None    ,_A = False    ,_A = False    ,_A = None    ,_A = None    ,_A = None    ,**_A    ,):
      '''simple docstring'''

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

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A = 16    ,_A = 64    ,_A = 4    ,):
      '''simple docstring'''

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

          _lowerCAmelCase       :   Optional[Any]  =    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=_A    ,top_spans=_A    ,)
          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=_A    ,start_index=_A    ,end_index=_A    ,text=self.decode(sequence_ids[start_index : end_index + 1]      )    ,)      )
          if len(_A      ) >= num_spans:
              break
      return nbest_spans_predictions[:num_spans]







  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    []
      for start_index, start_score in enumerate(_A      ):
          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)      )
      _lowerCAmelCase       :   Tuple  =    sorted(_A    ,key=lambda _A      : x[1]    ,reverse=_A      )
      _lowerCAmelCase       :   int  =    []
      for (start_index, end_index), score in scores:
          if start_index > end_index:
              raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]"""      )
          _lowerCAmelCase       :   List[str]  =    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(_A      ) == top_spans:
              break
      return chosen_span_intervals







@add_end_docstrings(a__						)
class 	__UpperCamelCase    (    a__							,							a__						):
  _UpperCAmelCase          =		VOCAB_FILES_NAMES
  _UpperCAmelCase          =		READER_PRETRAINED_VOCAB_FILES_MAP
  _UpperCAmelCase          =		READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _UpperCAmelCase          =		READER_PRETRAINED_INIT_CONFIGURATION
  _UpperCAmelCase          =		["input_ids", "attention_mask"]





