



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


__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__							):
       UpperCAmelCase__				:					List[str]        =  VOCAB_FILES_NAMES
       UpperCAmelCase__				:					List[str]        =  CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       UpperCAmelCase__				:					Dict        =  CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCAmelCase__				:					Any        =  CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
       UpperCAmelCase__				:					int        =  DPRContextEncoderTokenizer






class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					Union[str, Any]        =  VOCAB_FILES_NAMES
       UpperCAmelCase__				:					Any        =  QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       UpperCAmelCase__				:					Tuple        =  QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCAmelCase__				:					Optional[Any]        =  QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
       UpperCAmelCase__				:					Tuple        =  DPRQuestionEncoderTokenizer






__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'''
    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)

    Return:
        `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(a__							)
class 				lowerCAmelCase_		:
       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    :  List[Any]							=	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    :  Dict							=	titles if not isinstance(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ) else [titles]
          UpperCamelCase    :  Optional[int]							=	texts if not isinstance(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ) else [texts]
          UpperCamelCase    :  str							=	len(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Union[str, Any]							=	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    :  int							=	super().__call__(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			padding=SCREAMING_SNAKE_CASE_,			truncation=SCREAMING_SNAKE_CASE_       )['input_ids']
          UpperCamelCase    :  Optional[Any]							=	super().__call__(SCREAMING_SNAKE_CASE_,			add_special_tokens=SCREAMING_SNAKE_CASE_,			padding=SCREAMING_SNAKE_CASE_,			truncation=SCREAMING_SNAKE_CASE_       )['input_ids']
          UpperCamelCase    :  Optional[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    :  Dict							=	[]
             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    :  Tuple							=	attention_mask
          return self.pad(SCREAMING_SNAKE_CASE_,			padding=SCREAMING_SNAKE_CASE_,			max_length=SCREAMING_SNAKE_CASE_,			return_tensors=SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			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	,     UpperCamelCase	,     UpperCamelCase    :  Optional[int]							=	reader_output[:3]
          UpperCamelCase    :  List[Any]							=	len(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  int							=	sorted(range(SCREAMING_SNAKE_CASE_       ),			reverse=SCREAMING_SNAKE_CASE_,			key=relevance_logits.__getitem__       )
          UpperCamelCase    :  List[DPRReaderOutput]							=	[]
          for doc_id in sorted_docs:
             UpperCamelCase    :  Any							=	list(input_ids[doc_id]       )
             # assuming question & title information is at the beginning of the sequence
             UpperCamelCase    :  Tuple							=	sequence_ids.index(self.sep_token_id,			2       ) + 1  # second sep id
             if sequence_ids[-1] == self.pad_token_id:
                UpperCamelCase    :  str							=	sequence_ids.index(self.pad_token_id       )
             else:
                UpperCamelCase    :  Tuple							=	len(SCREAMING_SNAKE_CASE_       )

             UpperCamelCase    :  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=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 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			)     ->		List[DPRSpanPrediction]:
          UpperCamelCase    :  Any							=	[]
          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    :  List[Any]							=	[]
          for (start_index, end_index), score in scores:
             assert start_index <= end_index, F"""Wrong span indices: [{start_index}:{end_index}]"""
             UpperCamelCase    :  str							=	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(a__							)
class 				lowerCAmelCase_		(  a__       ,    a__							):
       UpperCAmelCase__				:					List[str]        =  VOCAB_FILES_NAMES
       UpperCAmelCase__				:					Dict        =  READER_PRETRAINED_VOCAB_FILES_MAP
       UpperCAmelCase__				:					List[Any]        =  READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCAmelCase__				:					Tuple        =  READER_PRETRAINED_INIT_CONFIGURATION
       UpperCAmelCase__				:					Union[str, Any]        =  ["input_ids", "attention_mask"]
       UpperCAmelCase__				:					Any        =  DPRReaderTokenizer








from __future__ import annotations

__UpperCAmelCase          =       {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}



class 				lowerCAmelCase_		:
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		None:
          UpperCamelCase    :  Union[str, Any]							=	graph
          # mapping node to its parent in resulting breadth first tree
          UpperCamelCase    :  dict[str, str | None]							=	{}
          UpperCamelCase    :  Union[str, Any]							=	source_vertex
       def 					snake_case_       (			self       )     ->		None:
          UpperCamelCase    :  str							=	{self.source_vertex}
          UpperCamelCase    :  str							=	None
          UpperCamelCase    :  int							=	[self.source_vertex]  # first in first out queue

          while queue:
             UpperCamelCase    :  Dict							=	queue.pop(0       )
             for adjacent_vertex in self.graph[vertex]:
                if adjacent_vertex not in visited:
                   visited.add(SCREAMING_SNAKE_CASE_       )
                   UpperCamelCase    :  Tuple							=	vertex
                   queue.append(SCREAMING_SNAKE_CASE_       )






       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		str:
          if target_vertex == self.source_vertex:
             return self.source_vertex

          UpperCamelCase    :  Optional[Any]							=	self.parent.get(SCREAMING_SNAKE_CASE_       )
          if target_vertex_parent is None:
             UpperCamelCase    :  Union[str, Any]							=	(
                 F"""No path from vertex: {self.source_vertex} to vertex: {target_vertex}"""
             )
             raise ValueError(SCREAMING_SNAKE_CASE_       )

          return self.shortest_path(SCREAMING_SNAKE_CASE_       ) + F"""->{target_vertex}"""


if __name__ == "__main__":
    __UpperCAmelCase          =       Graph(graph, '''G''')
    g.breath_first_search()
    print(g.shortest_path('''D'''))
    print(g.shortest_path('''G'''))
    print(g.shortest_path('''Foo'''))



