





"""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


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': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
UpperCAmelCase__  							={
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
UpperCAmelCase__  							={
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


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    lowerCamelCase__						(		_A    ):
      a :     List[str]											=		VOCAB_FILES_NAMES
      a :     List[Any]											=		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      a :     int											=		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a :     List[str]											=		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class    lowerCamelCase__						(		_A    ):
      a :     Optional[int]											=		VOCAB_FILES_NAMES
      a :     List[Any]											=		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      a :     List[str]											=		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a :     Optional[Any]											=		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    lowerCamelCase__						:
      def __call__(		self	:	List[Any]      ,       A_	:	int      ,       A_	:	Optional[str] = None      ,       A_	:	Optional[str] = None      ,       A_	:	Union[bool, str] = False      ,       A_	:	Union[bool, str] = False      ,       A_	:	Optional[int] = None      ,       A_	:	Optional[Union[str, TensorType]] = None      ,       A_	:	Optional[bool] = None      ,       **A_	:	Optional[Any]      ,       ):






       '''simple docstring'''


       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:
        __lowercase									=	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__      ,       )
       __lowercase									=	titles if not isinstance(UpperCamelCase__      ,       UpperCamelCase__							) else [titles]
       __lowercase									=	texts if not isinstance(UpperCamelCase__      ,       UpperCamelCase__							) else [texts]
       __lowercase									=	len(UpperCamelCase__							)
       __lowercase									=	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.'''							)
       __lowercase									=	super().__call__(UpperCamelCase__      ,       UpperCamelCase__      ,       padding=UpperCamelCase__      ,       truncation=UpperCamelCase__							)["""input_ids"""]
       __lowercase									=	super().__call__(UpperCamelCase__      ,       add_special_tokens=UpperCamelCase__      ,       padding=UpperCamelCase__      ,       truncation=UpperCamelCase__							)["""input_ids"""]
       __lowercase									=	{
           """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:
        __lowercase									=	[]
        for input_ids in encoded_inputs["input_ids"]:
         attention_mask.append([int(input_id != self.pad_token_id							) for input_id in input_ids]							)
        __lowercase									=	attention_mask
       return self.pad(UpperCamelCase__      ,       padding=UpperCamelCase__      ,       max_length=UpperCamelCase__      ,       return_tensors=UpperCamelCase__							)
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]      ,       A_	:	BatchEncoding      ,       A_	:	DPRReaderOutput      ,       A_	:	int = 1_6      ,       A_	:	int = 6_4      ,       A_	:	int = 4      ,       ):






       '''simple docstring'''


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

        __lowercase									=	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 SCREAMING_SNAKE_CASE_	(		self	:	Tuple      ,       A_	:	List[int]      ,       A_	:	List[int]      ,       A_	:	int      ,       A_	:	int      ,       ):






       '''simple docstring'''


       __lowercase									=	[]
       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)							)
       __lowercase									=	sorted(UpperCamelCase__      ,       key=lambda A_							: x[1]      ,       reverse=UpperCamelCase__							)
       __lowercase									=	[]
       for (start_index, end_index), score in scores:
        if start_index > end_index:
         raise ValueError(F'''Wrong span indices: [{start_index}:{end_index}]'''							)
        __lowercase									=	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    lowerCamelCase__						(		_A			,   _A    ):
      a :     str											=		VOCAB_FILES_NAMES
      a :     List[str]											=		READER_PRETRAINED_VOCAB_FILES_MAP
      a :     Optional[int]											=		READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a :     Dict											=		READER_PRETRAINED_INIT_CONFIGURATION
      a :     Dict											=		["""input_ids""", """attention_mask"""]




"""simple docstring"""

import random


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: float				,   UpperCamelCase__	: bool = False ):






 """simple docstring"""







 __lowercase									=	{i: [] for i in range(UpperCamelCase__ )}

 # if probability is greater or equal than 1, then generate a complete graph
 if probability >= 1:
  return complete_graph(UpperCamelCase__ )
 # if probability is lower or equal than 0, then return a graph without edges
 if probability <= 0:
  return graph

 # for each couple of nodes, add an edge from u to v
 # if the number randomly generated is greater than probability probability
 for i in range(UpperCamelCase__ ):
  for j in range(i + 1				,   UpperCamelCase__ ):
   if random.random() < probability:
    graph[i].append(UpperCamelCase__ )
    if not directed:
     # if the graph is undirected, add an edge in from j to i, either
     graph[j].append(UpperCamelCase__ )
 return graph


def lowerCAmelCase_      (				UpperCamelCase__	: int ):






 """simple docstring"""







 return {
     i: [j for j in range(UpperCamelCase__ ) if i != j] for i in range(UpperCamelCase__ )
 }


if __name__ == "__main__":
       import doctest

       doctest.testmod()
