






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_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


__snake_case												=  logging.get_logger(__name__)

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

__snake_case												=  {
    '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'
        ),
    },
}
__snake_case												=  {
    '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'
        ),
    },
}
__snake_case												=  {
    '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'
        ),
    },
}

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


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







class 			UpperCAmelCase_						(					lowercase ):



      """simple docstring"""
      UpperCamelCase_				:					str           =VOCAB_FILES_NAMES
      UpperCamelCase_				:					Union[str, Any]           =CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_				:					List[Any]           =CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_				:					List[str]           =CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
      UpperCamelCase_				:					Dict           =DPRContextEncoderTokenizer







class 			UpperCAmelCase_						(					lowercase ):



      """simple docstring"""
      UpperCamelCase_				:					int           =VOCAB_FILES_NAMES
      UpperCamelCase_				:					Any           =QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_				:					List[Any]           =QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_				:					List[str]           =QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
      UpperCamelCase_				:					List[str]           =DPRQuestionEncoderTokenizer







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

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


__snake_case												=  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    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\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    Return:\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(lowercase )
class 			UpperCAmelCase_						:



      """simple docstring"""
      def __call__(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = False    ,				SCREAMING_SNAKE_CASE_ = False    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = None    ,				**SCREAMING_SNAKE_CASE_    ,				)				->      BatchEncoding:
          if titles is None and texts is None:
              return super().__call__(
                  SCREAMING_SNAKE_CASE_    ,				padding=SCREAMING_SNAKE_CASE_    ,				truncation=SCREAMING_SNAKE_CASE_    ,				max_length=SCREAMING_SNAKE_CASE_    ,				return_tensors=SCREAMING_SNAKE_CASE_    ,				return_attention_mask=SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_    ,				)
          elif titles is None or texts is None:
              UpperCamelCase       :int								=   titles if texts is None else texts
              return super().__call__(
                  SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				padding=SCREAMING_SNAKE_CASE_    ,				truncation=SCREAMING_SNAKE_CASE_    ,				max_length=SCREAMING_SNAKE_CASE_    ,				return_tensors=SCREAMING_SNAKE_CASE_    ,				return_attention_mask=SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_    ,				)
          UpperCamelCase       :Optional[Any]								=   titles if not isinstance(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ ) else [titles]
          UpperCamelCase       :int								=   texts if not isinstance(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ ) else [texts]
          UpperCamelCase       :List[str]								=   len(SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :List[str]								=   questions if not isinstance(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ ) else [questions] * n_passages
          assert len(SCREAMING_SNAKE_CASE_ ) == len(
              SCREAMING_SNAKE_CASE_ ), F'''There should be as many titles than texts but got {len(SCREAMING_SNAKE_CASE_ )} titles and {len(SCREAMING_SNAKE_CASE_ )} texts.'''
          UpperCamelCase       :Optional[Any]								=   super().__call__(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				padding=SCREAMING_SNAKE_CASE_    ,				truncation=SCREAMING_SNAKE_CASE_ )['''input_ids''']
          UpperCamelCase       :Union[str, Any]								=   super().__call__(SCREAMING_SNAKE_CASE_    ,				add_special_tokens=SCREAMING_SNAKE_CASE_    ,				padding=SCREAMING_SNAKE_CASE_    ,				truncation=SCREAMING_SNAKE_CASE_ )['''input_ids''']
          UpperCamelCase       :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(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ )
              ]
          }
          if return_attention_mask is not False:
              UpperCamelCase       :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] )
              UpperCamelCase       :str								=   attention_mask
          return self.pad(SCREAMING_SNAKE_CASE_    ,				padding=SCREAMING_SNAKE_CASE_    ,				max_length=SCREAMING_SNAKE_CASE_    ,				return_tensors=SCREAMING_SNAKE_CASE_ )
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = 16    ,				SCREAMING_SNAKE_CASE_ = 64    ,				SCREAMING_SNAKE_CASE_ = 4    ,				)				->      List[DPRSpanPrediction]:
          UpperCamelCase       :List[Any]								=   reader_input['''input_ids''']
          UpperCamelCase       :int								=   reader_output[:3]
          UpperCamelCase       :Dict								=   len(SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :str								=   sorted(range(SCREAMING_SNAKE_CASE_ )    ,				reverse=SCREAMING_SNAKE_CASE_    ,				key=relevance_logits.__getitem__ )
          UpperCamelCase       :List[DPRReaderOutput]								=   []
          for doc_id in sorted_docs:
              UpperCamelCase       :str								=   list(input_ids[doc_id] )
              # assuming question & title information is at the beginning of the sequence
              UpperCamelCase       :Any								=   sequence_ids.index(self.sep_token_id    ,				2 ) + 1  # second sep id
              if sequence_ids[-1] == self.pad_token_id:
                  UpperCamelCase       :int								=   sequence_ids.index(self.pad_token_id )
              else:
                  UpperCamelCase       :Any								=   len(SCREAMING_SNAKE_CASE_ )

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




      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				)				->      List[DPRSpanPrediction]:
          UpperCamelCase       :List[str]								=   []
          for start_index, start_score in enumerate(SCREAMING_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) )
          UpperCamelCase       :int								=   sorted(SCREAMING_SNAKE_CASE_    ,				key=lambda SCREAMING_SNAKE_CASE_ : x[1]    ,				reverse=SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :Dict								=   []
          for (start_index, end_index), score in scores:
              assert start_index <= end_index, F'''Wrong span indices: [{start_index}:{end_index}]'''
              UpperCamelCase       :Any								=   end_index - start_index + 1
              assert length <= max_answer_length, 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(SCREAMING_SNAKE_CASE_ ) == top_spans:
                  break
          return chosen_span_intervals







@add_end_docstrings(lowercase )
class 			UpperCAmelCase_						(					lowercase,	lowercase ):



      """simple docstring"""
      UpperCamelCase_				:					Tuple           =VOCAB_FILES_NAMES
      UpperCamelCase_				:					Optional[Any]           =READER_PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_				:					List[str]           =READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_				:					Optional[int]           =READER_PRETRAINED_INIT_CONFIGURATION
      UpperCamelCase_				:					List[Any]           =['input_ids', 'attention_mask']
      UpperCamelCase_				:					List[Any]           =DPRReaderTokenizer










"""simple docstring"""
from __future__ import annotations

import os
from collections.abc import Mapping

_lowerCAmelCase    :Tuple     =					tuple[int, int]
class 							_UpperCAmelCase							:





     '''simple docstring'''



     def __init__(					self       ,	A       ,	A		)							->					None:
         _UpperCAmelCase							:  set[int]											=    vertices
         _UpperCAmelCase							:  dict[EdgeT, int]											=    {
             (min(A		), max(A		)): weight for edge, weight in edges.items()
         }





     def  __lowerCAmelCase	(					self       ,	A       ,	A		)							->					None:
         self.vertices.add(edge[0]		)
         self.vertices.add(edge[1]		)
         _UpperCAmelCase							:  List[Any]											=    weight





     def  __lowerCAmelCase	(					self		)							->					Graph:
         _UpperCAmelCase							:  Graph											=    Graph({min(self.vertices		)}       ,	{}		)
         _UpperCAmelCase							:  EdgeT
         _UpperCAmelCase							:  int
         _UpperCAmelCase							:  EdgeT
         _UpperCAmelCase							:  int

         while len(subgraph.vertices		) < len(self.vertices		):
             _UpperCAmelCase							:  Any											=    max(self.edges.values()		) + 1
             for edge, weight in self.edges.items():
                 if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
                     if weight < min_weight:
                         _UpperCAmelCase							:  Tuple											=    edge
                         _UpperCAmelCase							:  Optional[int]											=    weight

             subgraph.add_edge(A       ,	A		)

         return subgraph





def  lowerCamelCase_						(UpperCamelCase__		:		str = "p107_network.txt"							):
    _UpperCAmelCase							:  str											=    os.path.abspath(os.path.dirname(UpperCamelCase__							)							)
    _UpperCAmelCase							:  str											=    os.path.join(UpperCamelCase__     ,       UpperCamelCase__							)
    _UpperCAmelCase							:  dict[EdgeT, int]											=    {}
    _UpperCAmelCase							:  list[str]
    _UpperCAmelCase							:  int
    _UpperCAmelCase							:  int

    with open(UpperCamelCase__							) as f:
        _UpperCAmelCase							:  str											=    f.read().strip().split('''\n'''							)

    _UpperCAmelCase							:  List[Any]											=    [line.split(''','''							) for line in data]

    for edgea in range(1     ,       len(UpperCamelCase__							)							):
        for edgea in range(UpperCamelCase__							):
            if adjaceny_matrix[edgea][edgea] != "-":
                _UpperCAmelCase							:  Optional[Any]											=    int(adjaceny_matrix[edgea][edgea]							)

    _UpperCAmelCase							:  Graph											=    Graph(set(range(len(UpperCamelCase__							)							)							)     ,       UpperCamelCase__							)

    _UpperCAmelCase							:  Graph											=    graph.prims_algorithm()

    _UpperCAmelCase							:  int											=    sum(graph.edges.values()							)
    _UpperCAmelCase							:  int											=    sum(subgraph.edges.values()							)

    return initial_total - optimal_total


if __name__ == "__main__":
  print(f"{solution() = }")


