

"""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							: str	=     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							: str	=     texts if not isinstance(_a     ,_a   ) else [texts]
      _lowerCAmelCase							: Optional[int]	=     len(_a   )
      _lowerCAmelCase							: Union[str, 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							: str	=     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							: str	=     {
          "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							: Optional[int]	=     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							: Tuple	=     reader_input["input_ids"]
      _lowerCAmelCase							: Optional[Any]	=     reader_output[:3]
      _lowerCAmelCase							: List[Any]	=     len(_a   )
      _lowerCAmelCase							: Optional[Any]	=     sorted(range(_a   )     ,reverse=_a     ,key=relevance_logits.__getitem__   )
      _lowerCAmelCase							: List[DPRReaderOutput]	=     []
      for doc_id in sorted_docs:
          _lowerCAmelCase							: str	=     list(input_ids[doc_id]   )
          # assuming question & title information is at the beginning of the sequence
          _lowerCAmelCase							: Optional[int]	=     sequence_ids.index(self.sep_token_id     ,2   ) + 1  # second sep id
          if sequence_ids[-1] == self.pad_token_id:
              _lowerCAmelCase							: Optional[Any]	=     sequence_ids.index(self.pad_token_id   )
          else:
              _lowerCAmelCase							: str	=     len(_a   )

          _lowerCAmelCase							: 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=_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							: 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							: Optional[int]	=     sorted(_a     ,key=lambda _A   : x[1]     ,reverse=_a   )
      _lowerCAmelCase							: List[Any]	=     []
      for (start_index, end_index), score in scores:
          if start_index > end_index:
              raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]"""   )
          _lowerCAmelCase							: Any	=     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"]







"""simple docstring"""




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class 						a__		( unittest.TestCase		):
   @slow
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"      ,       return_dict=_a							).to(_a							)
          lowercase       :							Union[str, Any]									=				AutoTokenizer.from_pretrained("google/mt5-small"							)

          lowercase       :							Optional[Any]									=				tokenizer("Hello there"      ,       return_tensors="pt"							).input_ids
          lowercase       :							Union[str, Any]									=				tokenizer("Hi I am"      ,       return_tensors="pt"							).input_ids

          lowercase       :							int									=				model(input_ids.to(_a							)      ,       labels=labels.to(_a							)							).loss
          lowercase       :							str									=				-(labels.shape[-1] * loss.item())

          lowercase       :							List[str]									=				-8_4.9_1_2_7
          self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 1E-4							)


