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


__lowercase							 =					logging.get_logger(__name__)

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

__lowercase							 =					{
    '''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'''
        ),
    },
}
__lowercase							 =					{
    '''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'''
        ),
    },
}
__lowercase							 =					{
    '''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'''
        ),
    },
}

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


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


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						List[Any]						=      VOCAB_FILES_NAMES
    a__	:						Optional[int]						=      CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    a__	:						Tuple						=      CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__	:						List[str]						=      CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Dict						=      VOCAB_FILES_NAMES
    a__	:						Optional[int]						=      QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    a__	:						Optional[int]						=      QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__	:						Optional[Any]						=      QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
__lowercase							 =					collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

__lowercase							 =					collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


__lowercase							 =					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_     :






    '''simple docstring'''






    def __call__(	self , __lowercase , __lowercase = None , __lowercase = None , __lowercase = False , __lowercase = False , __lowercase = None , __lowercase = None , __lowercase = None , **__lowercase , )       ->	BatchEncoding:
        if titles is None and texts is None:
            return super().__call__(
                __lowercase , padding=__lowercase , truncation=__lowercase , max_length=__lowercase , return_tensors=__lowercase , return_attention_mask=__lowercase , **__lowercase , )
        elif titles is None or texts is None:
            __UpperCamelCase     :List[Any]			  =		titles if texts is None else texts
            return super().__call__(
                __lowercase , __lowercase , padding=__lowercase , truncation=__lowercase , max_length=__lowercase , return_tensors=__lowercase , return_attention_mask=__lowercase , **__lowercase , )
        __UpperCamelCase     :List[Any]			  =		titles if not isinstance(__lowercase , __lowercase) else [titles]
        __UpperCamelCase     :int			  =		texts if not isinstance(__lowercase , __lowercase) else [texts]
        __UpperCamelCase     :Optional[Any]			  =		len(__lowercase)
        __UpperCamelCase     :Union[str, Any]			  =		questions if not isinstance(__lowercase , __lowercase) else [questions] * n_passages
        if len(__lowercase) != len(__lowercase):
            raise ValueError(
                f"""There should be as many titles than texts but got {len(__lowercase)} titles and {len(__lowercase)} texts.""")
        __UpperCamelCase     :Optional[int]			  =		super().__call__(__lowercase , __lowercase , padding=__lowercase , truncation=__lowercase)['''input_ids''']
        __UpperCamelCase     :Tuple			  =		super().__call__(__lowercase , add_special_tokens=__lowercase , padding=__lowercase , truncation=__lowercase)['''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(__lowercase , __lowercase)
            ]
        }
        if return_attention_mask is not False:
            __UpperCamelCase     :List[str]			  =		[]
            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     :Union[str, Any]			  =		attention_mask
        return self.pad(__lowercase , padding=__lowercase , max_length=__lowercase , return_tensors=__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase = 16 , __lowercase = 64 , __lowercase = 4 , )       ->	List[DPRSpanPrediction]:
        __UpperCamelCase     :Union[str, Any]			  =		reader_input['''input_ids''']
        __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :Any			  =		reader_output[:3]
        __UpperCamelCase     :Optional[Any]			  =		len(__lowercase)
        __UpperCamelCase     :List[Any]			  =		sorted(range(__lowercase) , reverse=__lowercase , key=relevance_logits.__getitem__)
        __UpperCamelCase     :List[DPRReaderOutput]			  =		[]
        for doc_id in sorted_docs:
            __UpperCamelCase     :Dict			  =		list(input_ids[doc_id])
            # assuming question & title information is at the beginning of the sequence
            __UpperCamelCase     :List[Any]			  =		sequence_ids.index(self.sep_token_id , 2) + 1  # second sep id
            if sequence_ids[-1] == self.pad_token_id:
                __UpperCamelCase     :Any			  =		sequence_ids.index(self.pad_token_id)
            else:
                __UpperCamelCase     :Optional[int]			  =		len(__lowercase)

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







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase , __lowercase , )       ->	List[DPRSpanPrediction]:
        __UpperCamelCase     :Optional[int]			  =		[]
        for start_index, start_score in enumerate(__lowercase):
            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     :Tuple			  =		sorted(__lowercase , key=lambda __lowercase: x[1] , reverse=__lowercase)
        __UpperCamelCase     :str			  =		[]
        for (start_index, end_index), score in scores:
            if start_index > end_index:
                raise ValueError(f"""Wrong span indices: [{start_index}:{end_index}]""")
            __UpperCamelCase     :List[str]			  =		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(__lowercase) == top_spans:
                break
        return chosen_span_intervals


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






    '''simple docstring'''






    a__	:						Optional[int]						=      VOCAB_FILES_NAMES
    a__	:						Dict						=      READER_PRETRAINED_VOCAB_FILES_MAP
    a__	:						Any						=      READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__	:						int						=      READER_PRETRAINED_INIT_CONFIGURATION
    a__	:						int						=      ["""input_ids""", """attention_mask"""]






def lowerCamelCase  (   SCREAMING_SNAKE_CASE = 1  ,			SCREAMING_SNAKE_CASE = 1_000	):
    '''simple docstring'''







    __UpperCamelCase     :Union[str, Any]			  =		1
    __UpperCamelCase     :Any			  =		0

    for divide_by_number in range(SCREAMING_SNAKE_CASE  ,			digit + 1	):
        __UpperCamelCase     :list[int]			  =		[]
        __UpperCamelCase     :Optional[int]			  =		numerator
        for _ in range(1  ,			digit + 1	):
            if now_divide in has_been_divided:
                if longest_list_length < len(SCREAMING_SNAKE_CASE	):
                    __UpperCamelCase     :Optional[Any]			  =		len(SCREAMING_SNAKE_CASE	)
                    __UpperCamelCase     :int			  =		divide_by_number
            else:
                has_been_divided.append(SCREAMING_SNAKE_CASE	)
                __UpperCamelCase     :Any			  =		now_divide * 10 % divide_by_number

    return the_digit


# Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()





