





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


UpperCAmelCase_           =	logging.get_logger(__name__)

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

UpperCAmelCase_           =	{
    "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"
        ),
    },
}
UpperCAmelCase_           =	{
    "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"
        ),
    },
}
UpperCAmelCase_           =	{
    "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"
        ),
    },
}

UpperCAmelCase_           =	{
    "facebook/dpr-ctx_encoder-single-nq-base": 5_12,
    "facebook/dpr-ctx_encoder-multiset-base": 5_12,
}
UpperCAmelCase_           =	{
    "facebook/dpr-question_encoder-single-nq-base": 5_12,
    "facebook/dpr-question_encoder-multiset-base": 5_12,
}
UpperCAmelCase_           =	{
    "facebook/dpr-reader-single-nq-base": 5_12,
    "facebook/dpr-reader-multiset-base": 5_12,
}


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




class 			__UpperCamelCase    (							A__    ):
     __A	:  Optional[int]							  =   VOCAB_FILES_NAMES
     __A	:  int							  =   CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     __A	:  List[str]							  =   CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __A	:  Optional[int]							  =   CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION




class 			__UpperCamelCase    (							A__    ):
     __A	:  Dict							  =   VOCAB_FILES_NAMES
     __A	:  List[str]							  =   QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     __A	:  Any							  =   QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __A	:  str							  =   QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION




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

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


UpperCAmelCase_           =	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(A__    )
class 			__UpperCamelCase    :





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





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = 16				,							_UpperCamelCase = 64				,							_UpperCamelCase = 4				,							):
        _UpperCAmelCase           =   reader_input['''input_ids''']
        _UpperCAmelCase       ,      _UpperCAmelCase       ,      _UpperCAmelCase           =   reader_output[:3]
        _UpperCAmelCase           =   len(_UpperCamelCase					)
        _UpperCAmelCase           =   sorted(range(_UpperCamelCase					)				,							reverse=_UpperCamelCase				,							key=relevance_logits.__getitem__					)
        _UpperCAmelCase           =   []
        for doc_id in sorted_docs:
           _UpperCAmelCase           =   list(input_ids[doc_id]					)
           # assuming question & title information is at the beginning of the sequence
           _UpperCAmelCase           =   sequence_ids.index(self.sep_token_id				,							2					) + 1  # second sep id
           if sequence_ids[-1] == self.pad_token_id:
              _UpperCAmelCase           =   sequence_ids.index(self.pad_token_id					)
           else:
              _UpperCAmelCase           =   len(_UpperCamelCase					)

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





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							):
        _UpperCAmelCase           =   []
        for start_index, start_score in enumerate(_UpperCamelCase					):
           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)					)
        _UpperCAmelCase           =   sorted(_UpperCamelCase				,							key=lambda _UpperCamelCase					: x[1]				,							reverse=_UpperCamelCase					)
        _UpperCAmelCase           =   []
        for (start_index, end_index), score in scores:
           if start_index > end_index:
              raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]'''					)
           _UpperCAmelCase           =   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(_UpperCamelCase					) == top_spans:
              break
        return chosen_span_intervals




@add_end_docstrings(A__    )
class 			__UpperCamelCase    (							A__	, A__    ):
     __A	:  Dict							  =   VOCAB_FILES_NAMES
     __A	:  Dict							  =   READER_PRETRAINED_VOCAB_FILES_MAP
     __A	:  int							  =   READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __A	:  Optional[Any]							  =   READER_PRETRAINED_INIT_CONFIGURATION
     __A	:  Tuple							  =   ["""input_ids""", """attention_mask"""]






from typing import List

from .keymap import KEYMAP, get_character
def      A__       (    SCREAMING_SNAKE_CASE_   :      str						)  ->  List[str]:






   """simple docstring"""







   def decorator(SCREAMING_SNAKE_CASE_   :      List[Any]						):
      _UpperCAmelCase           =   getattr(SCREAMING_SNAKE_CASE_				,	'''handle_key'''				,	[]						)
      handle += [key]
      setattr(SCREAMING_SNAKE_CASE_				,	'''handle_key'''				,	SCREAMING_SNAKE_CASE_						)
      return func

   return decorator
def      A__       (    *SCREAMING_SNAKE_CASE_   :      List[str]						)  ->  Dict:






   """simple docstring"""







   def decorator(SCREAMING_SNAKE_CASE_   :      Any						):
      _UpperCAmelCase           =   getattr(SCREAMING_SNAKE_CASE_				,	'''handle_key'''				,	[]						)
      handle += keys
      setattr(SCREAMING_SNAKE_CASE_				,	'''handle_key'''				,	SCREAMING_SNAKE_CASE_						)
      return func

   return decorator




class 			__UpperCamelCase    (							A__    ):





     def __new__(     cls				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					):
        _UpperCAmelCase           =   super().__new__(cls				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					)
        if not hasattr(_UpperCamelCase				,							'''key_handler'''					):
           setattr(_UpperCamelCase				,							'''key_handler'''				,							{}					)
        setattr(_UpperCamelCase				,							'''handle_input'''				,							KeyHandler.handle_input					)

        for value in attrs.values():
           _UpperCAmelCase           =   getattr(_UpperCamelCase				,							'''handle_key'''				,							[]					)
           for key in handled_keys:
              _UpperCAmelCase           =   value
        return new_cls





     @staticmethod
     def 		UpperCamelCase(     cls					):
        _UpperCAmelCase           =   get_character()
        if char != KEYMAP["undefined"]:
           _UpperCAmelCase           =   ord(_UpperCamelCase					)
        _UpperCAmelCase           =   cls.key_handler.get(_UpperCamelCase					)
        if handler:
           _UpperCAmelCase           =   char
           return handler(cls					)
        else:
           return None




def      A__       (    cls   :      Union[str, Any]						)  ->  Any:






   """simple docstring"""







   return KeyHandler(cls.__name__				,	cls.__bases__				,	cls.__dict__.copy()						)