





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					:				Tuple				=			logging.get_logger(__name__)

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

_A					:				List[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					:				int				=			{
    """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					:				str				=			{
    """facebook/dpr-ctx_encoder-single-nq-base""": 5_12,
    """facebook/dpr-ctx_encoder-multiset-base""": 5_12,
}
_A					:				int				=			{
    """facebook/dpr-question_encoder-single-nq-base""": 5_12,
    """facebook/dpr-question_encoder-multiset-base""": 5_12,
}
_A					:				Optional[int]				=			{
    """facebook/dpr-reader-single-nq-base""": 5_12,
    """facebook/dpr-reader-multiset-base""": 5_12,
}


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



class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		Union[str, Any]				=	VOCAB_FILES_NAMES
    lowerCamelCase__    :		Any				=	CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase__    :		List[Any]				=	CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase__    :		Optional[int]				=	CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION





class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		List[str]				=	VOCAB_FILES_NAMES
    lowerCamelCase__    :		Dict				=	QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase__    :		Dict				=	QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase__    :		Union[str, Any]				=	QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION





_A					:				Optional[Any]				=			collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

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


_A					:				Optional[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)

    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(__SCREAMING_SNAKE_CASE    )
class 					__snake_case	:


    '''simple docstring'''







    def __call__(       self   ,				A_   ,				A_ = None   ,				A_ = None   ,				A_ = False   ,				A_ = False   ,				A_ = None   ,				A_ = None   ,				A_ = None   ,				**A_   ,				):





        '''simple docstring'''

        if titles is None and texts is None:
            return super().__call__(
                A_   ,				padding=A_   ,				truncation=A_   ,				max_length=A_   ,				return_tensors=A_   ,				return_attention_mask=A_   ,				**A_   ,				)
        elif titles is None or texts is None:
            SCREAMING_SNAKE_CASE__											=			titles if texts is None else texts
            return super().__call__(
                A_   ,				A_   ,				padding=A_   ,				truncation=A_   ,				max_length=A_   ,				return_tensors=A_   ,				return_attention_mask=A_   ,				**A_   ,				)
        SCREAMING_SNAKE_CASE__											=			titles if not isinstance(A_   ,				A_     ) else [titles]
        SCREAMING_SNAKE_CASE__											=			texts if not isinstance(A_   ,				A_     ) else [texts]
        SCREAMING_SNAKE_CASE__											=			len(A_     )
        SCREAMING_SNAKE_CASE__											=			questions if not isinstance(A_   ,				A_     ) else [questions] * n_passages
        if len(A_     ) != len(A_     ):
            raise ValueError(
                f'''There should be as many titles than texts but got {len(A_     )} titles and {len(A_     )} texts.'''     )
        SCREAMING_SNAKE_CASE__											=			super().__call__(A_   ,				A_   ,				padding=A_   ,				truncation=A_     )['''input_ids''']
        SCREAMING_SNAKE_CASE__											=			super().__call__(A_   ,				add_special_tokens=A_   ,				padding=A_   ,				truncation=A_     )['''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(A_   ,				A_     )
            ]
        }
        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(A_   ,				padding=A_   ,				max_length=A_   ,				return_tensors=A_     )



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_ = 16   ,				A_ = 64   ,				A_ = 4   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			reader_input['''input_ids''']
        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			reader_output[:3]
        SCREAMING_SNAKE_CASE__											=			len(A_     )
        SCREAMING_SNAKE_CASE__											=			sorted(range(A_     )   ,				reverse=A_   ,				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(A_     )

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




    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			[]
        for start_index, start_score in enumerate(A_     ):
            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(A_   ,				key=lambda A_     : x[1]   ,				reverse=A_     )
        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(A_     ) == top_spans:
                break
        return chosen_span_intervals





@add_end_docstrings(__SCREAMING_SNAKE_CASE    )
class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		Any				=	VOCAB_FILES_NAMES
    lowerCamelCase__    :		Dict				=	READER_PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase__    :		Any				=	READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase__    :		Optional[Any]				=	READER_PRETRAINED_INIT_CONFIGURATION
    lowerCamelCase__    :		str				=	["""input_ids""", """attention_mask"""]







import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
_A					:				Optional[int]				=			object()

# For specifying empty leaf dict `{}`
_A					:				Tuple				=			object()


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Dict:
    SCREAMING_SNAKE_CASE__											=			tuple((re.compile(x + '''$'''	) for x in qs)	)
    for i in range(len(lowerCAmelCase_	) - len(lowerCAmelCase_	) + 1	):
        SCREAMING_SNAKE_CASE__											=			[x.match(lowerCAmelCase_	) for x, y in zip(lowerCAmelCase_ ,							ks[i:]	)]
        if matches and all(lowerCAmelCase_	):
            return True
    return False


def 							__snake_case							(    lowerCAmelCase_	)				->						int:
    def replace(lowerCAmelCase_ ,							lowerCAmelCase_	):
        for rule, replacement in rules:
            if _match(lowerCAmelCase_ ,							lowerCAmelCase_	):
                return replacement
        return val

    return replace


def 							__snake_case							(    )				->						Optional[int]:
    return [
        # embeddings
        (("transformer", "wpe", "embedding"), P('''mp''' ,							lowerCAmelCase_	)),
        (("transformer", "wte", "embedding"), P('''mp''' ,							lowerCAmelCase_	)),
        # atention
        (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(lowerCAmelCase_ ,							'''mp'''	)),
        (("attention", "out_proj", "kernel"), P('''mp''' ,							lowerCAmelCase_	)),
        (("attention", "out_proj", "bias"), None),
        # mlp
        (("mlp", "c_fc", "kernel"), P(lowerCAmelCase_ ,							'''mp'''	)),
        (("mlp", "c_fc", "bias"), P('''mp'''	)),
        (("mlp", "c_proj", "kernel"), P('''mp''' ,							lowerCAmelCase_	)),
        (("mlp", "c_proj", "bias"), None),
        # layer norms
        ((r"ln_\d+", "bias"), None),
        ((r"\d+", r"ln_\d+", "scale"), None),
        (("ln_f", "bias"), None),
        (("ln_f", "scale"), None),
    ]



def 							__snake_case							(    lowerCAmelCase_	)				->						Optional[Any]:
    SCREAMING_SNAKE_CASE__											=			_get_partition_rules()
    SCREAMING_SNAKE_CASE__											=			_replacement_rules(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			{k: _unmatched for k in flatten_dict(lowerCAmelCase_	)}
    SCREAMING_SNAKE_CASE__											=			{k: replace(lowerCAmelCase_ ,							lowerCAmelCase_	) for k, v in initd.items()}
    assert _unmatched not in result.values(), "Incomplete partition spec."
    return freeze(unflatten_dict(lowerCAmelCase_	)	)
