





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			:	Union[str, Any]					    =	logging.get_logger(__name__)

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

__lowerCamelCase			:	Dict					    =	{
    """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			:	Tuple					    =	{
    """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			:	int					    =	{
    """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			:	int					    =	{
    """facebook/dpr-ctx_encoder-single-nq-base""": 512,
    """facebook/dpr-ctx_encoder-multiset-base""": 512,
}
__lowerCamelCase			:	Any					    =	{
    """facebook/dpr-question_encoder-single-nq-base""": 512,
    """facebook/dpr-question_encoder-multiset-base""": 512,
}
__lowerCamelCase			:	List[Any]					    =	{
    """facebook/dpr-reader-single-nq-base""": 512,
    """facebook/dpr-reader-multiset-base""": 512,
}


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



class    A__  (			__snake_case     ):
      _UpperCAmelCase      :List[Any]		       =  VOCAB_FILES_NAMES
      _UpperCAmelCase      :str		       =  CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase      :int		       =  CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _UpperCAmelCase      :Optional[Any]		       =  CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION



class    A__  (			__snake_case     ):
      _UpperCAmelCase      :Optional[Any]		       =  VOCAB_FILES_NAMES
      _UpperCAmelCase      :Dict		       =  QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase      :Dict		       =  QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _UpperCAmelCase      :Union[str, Any]		       =  QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION


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

__lowerCamelCase			:	Any					    =	collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


__lowerCamelCase			:	Optional[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(__snake_case     )
class    A__  :





      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:
        UpperCamelCase      :   Any			      =  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_			,				)
       UpperCamelCase      :   Optional[int]			      =  titles if not isinstance(A_			,				A_  ) else [titles]
       UpperCamelCase      :   List[str]			      =  texts if not isinstance(A_			,				A_  ) else [texts]
       UpperCamelCase      :   str			      =  len(A_  )
       UpperCamelCase      :   int			      =  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."""  )
       UpperCamelCase      :   Any			      =  super().__call__(A_			,				A_			,				padding=A_			,				truncation=A_  )["input_ids"]
       UpperCamelCase      :   Optional[Any]			      =  super().__call__(A_			,				add_special_tokens=A_			,				padding=A_			,				truncation=A_  )["input_ids"]
       UpperCamelCase      :   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:
        UpperCamelCase      :   List[str]			      =  []
        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      :   Optional[Any]			      =  attention_mask
       return self.pad(A_			,				padding=A_			,				max_length=A_			,				return_tensors=A_  )





      def 			__UpperCamelCase(					self			,				A_			,				A_			,				A_ = 16			,				A_ = 64			,				A_ = 4			,				):
       '''simple docstring'''




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

        UpperCamelCase      :   Optional[int]			      =  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 			__UpperCamelCase(					self			,				A_			,				A_			,				A_			,				A_			,				):
       '''simple docstring'''




       UpperCamelCase      :   Optional[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)  )
       UpperCamelCase      :   List[str]			      =  sorted(A_			,				key=lambda A_  : x[1]			,				reverse=A_  )
       UpperCamelCase      :   int			      =  []
       for (start_index, end_index), score in scores:
        if start_index > end_index:
         raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]"""  )
        UpperCamelCase      :   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(__snake_case     )
class    A__  (			__snake_case			,  __snake_case     ):
      _UpperCAmelCase      :Any		       =  VOCAB_FILES_NAMES
      _UpperCAmelCase      :Any		       =  READER_PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase      :Any		       =  READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _UpperCAmelCase      :Tuple		       =  READER_PRETRAINED_INIT_CONFIGURATION
      _UpperCAmelCase      :Union[str, Any]		       =  ['input_ids', 'attention_mask']







# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

__lowerCamelCase			:	str					    =	TypeVar("""T""")



class    A__  (			Generic[T]     ):





      def __init__(					self			,				A_ = True  ):
       '''simple docstring'''




       UpperCamelCase      :   dict[T, list[T]]			      =  {}  # dictionary of lists
       UpperCamelCase      :   Union[str, Any]			      =  directed





      def 			__UpperCamelCase(					self			,				A_			,				A_  ):
       '''simple docstring'''




       if not self.directed:  # For undirected graphs
        # if both source vertex and destination vertex are both present in the
        # adjacency list, add destination vertex to source vertex list of adjacent
        # vertices and add source vertex to destination vertex list of adjacent
        # vertices.
        if source_vertex in self.adj_list and destination_vertex in self.adj_list:
         self.adj_list[source_vertex].append(A_  )
         self.adj_list[destination_vertex].append(A_  )
        # if only source vertex is present in adjacency list, add destination vertex
        # to source vertex list of adjacent vertices, then create a new vertex with
        # destination vertex as key and assign a list containing the source vertex
        # as it's first adjacent vertex.
        elif source_vertex in self.adj_list:
         self.adj_list[source_vertex].append(A_  )
         UpperCamelCase      :   Any			      =  [source_vertex]
        # if only destination vertex is present in adjacency list, add source vertex
        # to destination vertex list of adjacent vertices, then create a new vertex
        # with source vertex as key and assign a list containing the source vertex
        # as it's first adjacent vertex.
        elif destination_vertex in self.adj_list:
         self.adj_list[destination_vertex].append(A_  )
         UpperCamelCase      :   Any			      =  [destination_vertex]
        # if both source vertex and destination vertex are not present in adjacency
        # list, create a new vertex with source vertex as key and assign a list
        # containing the destination vertex as it's first adjacent vertex also
        # create a new vertex with destination vertex as key and assign a list
        # containing the source vertex as it's first adjacent vertex.
        else:
         UpperCamelCase      :   Union[str, Any]			      =  [destination_vertex]
         UpperCamelCase      :   List[str]			      =  [source_vertex]
       else:  # For directed graphs
        # if both source vertex and destination vertex are present in adjacency
        # list, add destination vertex to source vertex list of adjacent vertices.
        if source_vertex in self.adj_list and destination_vertex in self.adj_list:
         self.adj_list[source_vertex].append(A_  )
        # if only source vertex is present in adjacency list, add destination
        # vertex to source vertex list of adjacent vertices and create a new vertex
        # with destination vertex as key, which has no adjacent vertex
        elif source_vertex in self.adj_list:
         self.adj_list[source_vertex].append(A_  )
         UpperCamelCase      :   List[str]			      =  []
        # if only destination vertex is present in adjacency list, create a new
        # vertex with source vertex as key and assign a list containing destination
        # vertex as first adjacent vertex
        elif destination_vertex in self.adj_list:
         UpperCamelCase      :   Union[str, Any]			      =  [destination_vertex]
        # if both source vertex and destination vertex are not present in adjacency
        # list, create a new vertex with source vertex as key and a list containing
        # destination vertex as it's first adjacent vertex. Then create a new vertex
        # with destination vertex as key, which has no adjacent vertex
        else:
         UpperCamelCase      :   Union[str, Any]			      =  [destination_vertex]
         UpperCamelCase      :   Dict			      =  []

       return self





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




       return pformat(self.adj_list  )
