

"""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


A_							:	List[str]      =       logging.get_logger(__name__)

A_							:	Optional[int]      =       {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

A_							:	Optional[Any]      =       {
    """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"""
        ),
    },
}
A_							:	Dict      =       {
    """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"""
        ),
    },
}
A_							:	str      =       {
    """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"""
        ),
    },
}

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


A_							:	Optional[Any]      =       {
    """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
A_							:	List[str]      =       {
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
A_							:	Tuple      =       {
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}





class       lowerCamelCase  (UpperCAmelCase__	):
			lowerCamelCase__      :						str    						=							VOCAB_FILES_NAMES
			lowerCamelCase__      :						Optional[int]    						=							CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__      :						str    						=							CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__      :						Tuple    						=							CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION





class       lowerCamelCase  (UpperCAmelCase__	):
			lowerCamelCase__      :						Any    						=							VOCAB_FILES_NAMES
			lowerCamelCase__      :						Any    						=							QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__      :						Optional[int]    						=							QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__      :						Any    						=							QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
A_							:	List[str]      =       collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

A_							:	str      =       collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


A_							:	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(UpperCAmelCase__	)
class       lowerCamelCase  :

			def __call__(    self :  Union[str, Any]			, __UpperCAmelCase :  List[Any]			, __UpperCAmelCase :  Optional[str] = None			, __UpperCAmelCase :  Optional[str] = None			, __UpperCAmelCase :  Union[bool, str] = False			, __UpperCAmelCase :  Union[bool, str] = False			, __UpperCAmelCase :  Optional[int] = None			, __UpperCAmelCase :  Optional[Union[str, TensorType]] = None			, __UpperCAmelCase :  Optional[bool] = None			, **__UpperCAmelCase :  List[str]			, )      ->			BatchEncoding:

						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:
									SCREAMING_SNAKE_CASE__									=					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__			, )
						SCREAMING_SNAKE_CASE__									=					titles if not isinstance(UpperCAmelCase__			, UpperCAmelCase__				) else [titles]
						SCREAMING_SNAKE_CASE__									=					texts if not isinstance(UpperCAmelCase__			, UpperCAmelCase__				) else [texts]
						SCREAMING_SNAKE_CASE__									=					len(UpperCAmelCase__				)
						SCREAMING_SNAKE_CASE__									=					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."""				)
						SCREAMING_SNAKE_CASE__									=					super().__call__(UpperCAmelCase__			, UpperCAmelCase__			, padding=UpperCAmelCase__			, truncation=UpperCAmelCase__				)["""input_ids"""]
						SCREAMING_SNAKE_CASE__									=					super().__call__(UpperCAmelCase__			, add_special_tokens=UpperCAmelCase__			, padding=UpperCAmelCase__			, truncation=UpperCAmelCase__				)["""input_ids"""]
						SCREAMING_SNAKE_CASE__									=					{
						    """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:
									SCREAMING_SNAKE_CASE__									=					[]
									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__									=					attention_mask
						return self.pad(UpperCAmelCase__			, padding=UpperCAmelCase__			, max_length=UpperCAmelCase__			, return_tensors=UpperCAmelCase__				)

			def     SCREAMING_SNAKE_CASE (    self :  Dict			, __UpperCAmelCase :  BatchEncoding			, __UpperCAmelCase :  DPRReaderOutput			, __UpperCAmelCase :  int = 1_6			, __UpperCAmelCase :  int = 6_4			, __UpperCAmelCase :  int = 4			, )      ->			List[DPRSpanPrediction]:
						SCREAMING_SNAKE_CASE__									=					reader_input["""input_ids"""]
						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									=					reader_output[:3]
						SCREAMING_SNAKE_CASE__									=					len(UpperCAmelCase__				)
						SCREAMING_SNAKE_CASE__									=					sorted(range(UpperCAmelCase__				)			, reverse=UpperCAmelCase__			, key=relevance_logits.__getitem__				)
						SCREAMING_SNAKE_CASE__									=					[]
						for doc_id in sorted_docs:
									SCREAMING_SNAKE_CASE__									=					list(input_ids[doc_id]				)
									# assuming question & title information is at the beginning of the sequence
									SCREAMING_SNAKE_CASE__									=					sequence_ids.index(self.sep_token_id			, 2				) + 1  # second sep id
									if sequence_ids[-1] == self.pad_token_id:
												SCREAMING_SNAKE_CASE__									=					sequence_ids.index(self.pad_token_id				)
									else:
												SCREAMING_SNAKE_CASE__									=					len(UpperCAmelCase__				)

									SCREAMING_SNAKE_CASE__									=					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 :  Dict			, __UpperCAmelCase :  List[int]			, __UpperCAmelCase :  List[int]			, __UpperCAmelCase :  int			, __UpperCAmelCase :  int			, )      ->			List[DPRSpanPrediction]:
						SCREAMING_SNAKE_CASE__									=					[]
						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)				)
						SCREAMING_SNAKE_CASE__									=					sorted(UpperCAmelCase__			, key=lambda __UpperCAmelCase				: x[1]			, reverse=UpperCAmelCase__				)
						SCREAMING_SNAKE_CASE__									=					[]
						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__									=					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(UpperCAmelCase__	)
class       lowerCamelCase  (UpperCAmelCase__	,UpperCAmelCase__	):
			lowerCamelCase__      :						List[Any]    						=							VOCAB_FILES_NAMES
			lowerCamelCase__      :						Tuple    						=							READER_PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__      :						Tuple    						=							READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__      :						int    						=							READER_PRETRAINED_INIT_CONFIGURATION
			lowerCamelCase__      :						Optional[int]    						=							['input_ids', 'attention_mask']








def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Any:



       """simple docstring"""

       A__       =    [0] * len(lowercase_						)
       A__       =    []
       A__       =    [1] * len(lowercase_						)

       for values in graph.values():
              for i in values:
                     indegree[i] += 1

       for i in range(len(lowercase_						)						):
              if indegree[i] == 0:
                     queue.append(lowercase_						)

       while queue:
              A__       =    queue.pop(0						)
              for x in graph[vertex]:
                     indegree[x] -= 1

                     if long_dist[vertex] + 1 > long_dist[x]:
                            A__       =    long_dist[vertex] + 1

                     if indegree[x] == 0:
                            queue.append(lowercase_						)

       print(max(lowercase_						)						)


# Adjacency list of Graph
_lowerCamelCase	:		Optional[int]							       =   {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)


