
"""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


lowercase_       :						int     					=	logging.get_logger(__name__)

lowercase_       :						str     					=	{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase_       :						List[str]     					=	{
    '''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'''
        ),
    },
}
lowercase_       :						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'''
        ),
    },
}
lowercase_       :						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'''
        ),
    },
}

lowercase_       :						Dict     					=	{
    '''facebook/dpr-ctx_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_12,
}
lowercase_       :						Union[str, Any]     					=	{
    '''facebook/dpr-question_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-question_encoder-multiset-base''': 5_12,
}
lowercase_       :						Any     					=	{
    '''facebook/dpr-reader-single-nq-base''': 5_12,
    '''facebook/dpr-reader-multiset-base''': 5_12,
}


lowercase_       :						Dict     					=	{
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
lowercase_       :						Optional[Any]     					=	{
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
lowercase_       :						Tuple     					=	{
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			VOCAB_FILES_NAMES
     A__      =			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     A__      =			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A__      =			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			VOCAB_FILES_NAMES
     A__      =			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     A__      =			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A__      =			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
lowercase_       :						Optional[Any]     					=	collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

lowercase_       :						Dict     					=	collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


lowercase_       :						int     					=	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(__SCREAMING_SNAKE_CASE  )
class 			UpperCamelCase      :
     def __call__(     self  ,     snake_case__  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = False  ,     snake_case__ = False  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     **snake_case__  ,     ):




       """simple docstring"""


       if titles is None and texts is None:
         return super().__call__(
             snake_case__  ,     padding=snake_case__  ,     truncation=snake_case__  ,     max_length=snake_case__  ,     return_tensors=snake_case__  ,     return_attention_mask=snake_case__  ,     **snake_case__  ,     )
       elif titles is None or texts is None:
         _SCREAMING_SNAKE_CASE   :				str							    = titles if texts is None else texts
         return super().__call__(
             snake_case__  ,     snake_case__  ,     padding=snake_case__  ,     truncation=snake_case__  ,     max_length=snake_case__  ,     return_tensors=snake_case__  ,     return_attention_mask=snake_case__  ,     **snake_case__  ,     )
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = titles if not isinstance(snake_case__  ,     snake_case__				) else [titles]
       _SCREAMING_SNAKE_CASE   :				Dict							    = texts if not isinstance(snake_case__  ,     snake_case__				) else [texts]
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = len(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				List[str]							    = questions if not isinstance(snake_case__  ,     snake_case__				) else [questions] * n_passages
       if len(snake_case__				) != len(snake_case__				):
         raise ValueError(
             F'''There should be as many titles than texts but got {len(snake_case__				)} titles and {len(snake_case__				)} texts.'''				)
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = super().__call__(snake_case__  ,     snake_case__  ,     padding=snake_case__  ,     truncation=snake_case__				)["input_ids"]
       _SCREAMING_SNAKE_CASE   :				List[str]							    = super().__call__(snake_case__  ,     add_special_tokens=snake_case__  ,     padding=snake_case__  ,     truncation=snake_case__				)["input_ids"]
       _SCREAMING_SNAKE_CASE   :				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(snake_case__  ,     snake_case__				)
           ]
       }
       if return_attention_mask is not False:
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = []
         for input_ids in encoded_inputs["input_ids"]:
           attention_mask.append([int(input_id != self.pad_token_id				) for input_id in input_ids]				)
         _SCREAMING_SNAKE_CASE   :				Tuple							    = attention_mask
       return self.pad(snake_case__  ,     padding=snake_case__  ,     max_length=snake_case__  ,     return_tensors=snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__ = 16  ,     snake_case__ = 64  ,     snake_case__ = 4  ,     ):




       """simple docstring"""


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

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




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = []
       for start_index, start_score in enumerate(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)				)
       _SCREAMING_SNAKE_CASE   :				Any							    = sorted(snake_case__  ,     key=lambda snake_case__				: x[1]  ,     reverse=snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = []
       for (start_index, end_index), score in scores:
         if start_index > end_index:
           raise ValueError(F'''Wrong span indices: [{start_index}:{end_index}]'''				)
         _SCREAMING_SNAKE_CASE   :				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(snake_case__				) == top_spans:
           break
       return chosen_span_intervals
@add_end_docstrings(__SCREAMING_SNAKE_CASE  )
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE      ,       __SCREAMING_SNAKE_CASE  ):
     A__      =			VOCAB_FILES_NAMES
     A__      =			READER_PRETRAINED_VOCAB_FILES_MAP
     A__      =			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A__      =			READER_PRETRAINED_INIT_CONFIGURATION
     A__      =			["""input_ids""", """attention_mask"""]





"""simple docstring"""
from typing import List

from .keymap import KEYMAP, get_character





def    _lowerCAmelCase    (     lowerCamelCase__							:							str				)  ->    Optional[int]:

  def decorator(lowerCamelCase__							:							int				):
    _SCREAMING_SNAKE_CASE   :				Optional[int]							    = getattr(lowerCamelCase__,						"handle_key",						[]				)
    handle += [key]
    setattr(lowerCamelCase__,						"handle_key",						lowerCamelCase__				)
    return func

  return decorator





def    _lowerCAmelCase    (     *lowerCamelCase__							:							List[str]				)  ->    Tuple:

  def decorator(lowerCamelCase__							:							Dict				):
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = getattr(lowerCamelCase__,						"handle_key",						[]				)
    handle += keys
    setattr(lowerCamelCase__,						"handle_key",						lowerCamelCase__				)
    return func

  return decorator
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     def __new__(     cls  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Tuple							    = super().__new__(cls  ,     snake_case__  ,     snake_case__  ,     snake_case__				)
       if not hasattr(snake_case__  ,     "key_handler"				):
         setattr(snake_case__  ,     "key_handler"  ,     {}				)
       setattr(snake_case__  ,     "handle_input"  ,     KeyHandler.handle_input				)

       for value in attrs.values():
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = getattr(snake_case__  ,     "handle_key"  ,     []				)
         for key in handled_keys:
           _SCREAMING_SNAKE_CASE   :				Tuple							    = value
       return new_cls




     @staticmethod
     def 				__SCREAMING_SNAKE_CASE							(     cls				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = get_character()
       if char != KEYMAP["undefined"]:
         _SCREAMING_SNAKE_CASE   :				Dict							    = ord(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = cls.key_handler.get(snake_case__				)
       if handler:
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = char
         return handler(cls				)
       else:
         return None





def    _lowerCAmelCase    (     cls							:							List[Any]				)  ->    str:
  return KeyHandler(cls.__name__,						cls.__bases__,						cls.__dict__.copy()				)



