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


_UpperCamelCase   :						Optional[int]      =    logging.get_logger(__name__)

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

_UpperCamelCase   :						Union[str, 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'''
        ),
    },
}
_UpperCamelCase   :						Optional[int]      =    {
    '''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   :						Optional[Any]      =    {
    '''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   :						Any      =    {
    '''facebook/dpr-ctx_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-ctx_encoder-multiset-base''': 5_12,
}
_UpperCamelCase   :						Optional[Any]      =    {
    '''facebook/dpr-question_encoder-single-nq-base''': 5_12,
    '''facebook/dpr-question_encoder-multiset-base''': 5_12,
}
_UpperCamelCase   :						Dict      =    {
    '''facebook/dpr-reader-single-nq-base''': 5_12,
    '''facebook/dpr-reader-multiset-base''': 5_12,
}


_UpperCamelCase   :						Optional[int]      =    {
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
_UpperCamelCase   :						Optional[Any]      =    {
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
_UpperCamelCase   :						Any      =    {
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}


class      UpperCAmelCase_	(    lowerCamelCase__):
      lowerCamelCase__		:    str   =  VOCAB_FILES_NAMES
      lowerCamelCase__		:    str   =  CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      lowerCamelCase__		:    Any   =  CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCamelCase__		:    Optional[Any]   =  CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
      lowerCamelCase__		:    Any   =  DPRContextEncoderTokenizer





class      UpperCAmelCase_	(    lowerCamelCase__):
      lowerCamelCase__		:    Dict   =  VOCAB_FILES_NAMES
      lowerCamelCase__		:    Any   =  QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      lowerCamelCase__		:    Tuple   =  QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCamelCase__		:    Union[str, Any]   =  QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
      lowerCamelCase__		:    Dict   =  DPRQuestionEncoderTokenizer





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

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


_UpperCamelCase   :						Any      =    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(lowerCamelCase__)
class      UpperCAmelCase_	:


      def __call__(   self		,			a		,			a = None		,			a = None		,			a = False		,			a = False		,			a = None		,			a = None		,			a = None		,			**a		,			)       ->					str:






             if titles is None and texts is None:
                    return super().__call__(
                        lowerCAmelCase__		,			padding=lowerCAmelCase__		,			truncation=lowerCAmelCase__		,			max_length=lowerCAmelCase__		,			return_tensors=lowerCAmelCase__		,			return_attention_mask=lowerCAmelCase__		,			**lowerCAmelCase__		,			)
             elif titles is None or texts is None:
                    lowercase__			:		List[Any]       =	titles if texts is None else texts
                    return super().__call__(
                        lowerCAmelCase__		,			lowerCAmelCase__		,			padding=lowerCAmelCase__		,			truncation=lowerCAmelCase__		,			max_length=lowerCAmelCase__		,			return_tensors=lowerCAmelCase__		,			return_attention_mask=lowerCAmelCase__		,			**lowerCAmelCase__		,			)
             lowercase__			:		Dict       =	titles if not isinstance(lowerCAmelCase__		,			lowerCAmelCase__							) else [titles]
             lowercase__			:		Tuple       =	texts if not isinstance(lowerCAmelCase__		,			lowerCAmelCase__							) else [texts]
             lowercase__			:		Optional[Any]       =	len(lowerCAmelCase__							)
             lowercase__			:		Tuple       =	questions if not isinstance(lowerCAmelCase__		,			lowerCAmelCase__							) else [questions] * n_passages
             assert len(lowerCAmelCase__							) == len(
                 lowerCAmelCase__							), f"""There should be as many titles than texts but got {len(lowerCAmelCase__							)} titles and {len(lowerCAmelCase__							)} texts."""
             lowercase__			:		int       =	super().__call__(lowerCAmelCase__		,			lowerCAmelCase__		,			padding=lowerCAmelCase__		,			truncation=lowerCAmelCase__							)["""input_ids"""]
             lowercase__			:		str       =	super().__call__(lowerCAmelCase__		,			add_special_tokens=lowerCAmelCase__		,			padding=lowerCAmelCase__		,			truncation=lowerCAmelCase__							)["""input_ids"""]
             lowercase__			:		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(lowerCAmelCase__		,			lowerCAmelCase__							)
                 ]
             }
             if return_attention_mask is not False:
                    lowercase__			:		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]							)
                    lowercase__			:		int       =	attention_mask
             return self.pad(lowerCAmelCase__		,			padding=lowerCAmelCase__		,			max_length=lowerCAmelCase__		,			return_tensors=lowerCAmelCase__							)


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

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


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a		,			a		,			)       ->					Union[str, Any]:
             lowercase__			:		Optional[Any]       =	[]
             for start_index, start_score in enumerate(lowerCAmelCase__							):
                    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__			:		Optional[Any]       =	sorted(lowerCAmelCase__		,			key=lambda a							: x[1]		,			reverse=lowerCAmelCase__							)
             lowercase__			:		Optional[int]       =	[]
             for (start_index, end_index), score in scores:
                    assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
                    lowercase__			:		Optional[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(lowerCAmelCase__							) == top_spans:
                           break
             return chosen_span_intervals





@add_end_docstrings(lowerCamelCase__)
class      UpperCAmelCase_	(    lowerCamelCase__ ,							lowerCamelCase__):
      lowerCamelCase__		:    Optional[int]   =  VOCAB_FILES_NAMES
      lowerCamelCase__		:    int   =  READER_PRETRAINED_VOCAB_FILES_MAP
      lowerCamelCase__		:    str   =  READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCamelCase__		:    str   =  READER_PRETRAINED_INIT_CONFIGURATION
      lowerCamelCase__		:    Dict   =  ['''input_ids''', '''attention_mask''']
      lowerCamelCase__		:    Tuple   =  DPRReaderTokenizer






'''simple docstring'''




from __future__ import annotations


def 				lowerCAmelCase_   (	_lowerCamelCase:   list[int]       ,      _lowerCamelCase:   list[int]       ,      _lowerCamelCase:   int					):
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		list(range(len(_lowerCamelCase					)					)					)
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		[v / w for v, w in zip(_lowerCamelCase       ,      _lowerCamelCase					)]
		index.sort(key=lambda _lowerCamelCase					: ratio[i]       ,      reverse=_lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		float           =		0
		__SCREAMING_SNAKE_CASE       :		list[float]           =		[0] * len(_lowerCamelCase					)
		for i in index:
				if weight[i] <= capacity:
						__SCREAMING_SNAKE_CASE       :		str           =		1
						max_value += value[i]
						capacity -= weight[i]
				else:
						__SCREAMING_SNAKE_CASE       :		int           =		capacity / weight[i]
						max_value += value[i] * capacity / weight[i]
						break

		return max_value, fractions


if __name__ == "__main__":
						import doctest

						doctest.testmod()