
'''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


_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   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				str									=       VOCAB_FILES_NAMES
						_lowercase		:				Tuple									=       CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						_lowercase		:				Optional[int]									=       CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowercase		:				Optional[int]									=       CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				Any									=       VOCAB_FILES_NAMES
						_lowercase		:				Union[str, Any]									=       QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						_lowercase		:				Optional[Any]									=       QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowercase		:				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(_SCREAMING_SNAKE_CASE			)
class   UpperCAmelCase_					:






						'''simple docstring'''






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

												"""simple docstring"""



												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:
																		_lowerCAmelCase  						=			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   ,       )
												_lowerCAmelCase  						=			titles if not isinstance(_lowercase   ,       _lowercase				) else [titles]
												_lowerCAmelCase  						=			texts if not isinstance(_lowercase   ,       _lowercase				) else [texts]
												_lowerCAmelCase  						=			len(_lowercase				)
												_lowerCAmelCase  						=			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.'				)
												_lowerCAmelCase  						=			super().__call__(_lowercase   ,       _lowercase   ,       padding=_lowercase   ,       truncation=_lowercase				)["""input_ids"""]
												_lowerCAmelCase  						=			super().__call__(_lowercase   ,       add_special_tokens=_lowercase   ,       padding=_lowercase   ,       truncation=_lowercase				)["""input_ids"""]
												_lowerCAmelCase  						=			{
												    """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:
																		_lowerCAmelCase  						=			[]
																		for input_ids in encoded_inputs["input_ids"]:
																								attention_mask.append([int(input_id != self.pad_token_id				) for input_id in input_ids]				)
																		_lowerCAmelCase  						=			attention_mask
												return self.pad(_lowercase   ,       padding=_lowercase   ,       max_length=_lowercase   ,       return_tensors=_lowercase				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase = 16   ,       _lowercase = 64   ,       _lowercase = 4   ,       ):

												"""simple docstring"""



												_lowerCAmelCase  						=			reader_input["""input_ids"""]
												_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase  						=			reader_output[:3]
												_lowerCAmelCase  						=			len(_lowercase				)
												_lowerCAmelCase  						=			sorted(range(_lowercase				)   ,       reverse=_lowercase   ,       key=relevance_logits.__getitem__				)
												_lowerCAmelCase  						=			[]
												for doc_id in sorted_docs:
																		_lowerCAmelCase  						=			list(input_ids[doc_id]				)
																		# assuming question & title information is at the beginning of the sequence
																		_lowerCAmelCase  						=			sequence_ids.index(self.sep_token_id   ,       2				) + 1  # second sep id
																		if sequence_ids[-1] == self.pad_token_id:
																								_lowerCAmelCase  						=			sequence_ids.index(self.pad_token_id				)
																		else:
																								_lowerCAmelCase  						=			len(_lowercase				)

																		_lowerCAmelCase  						=			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  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       ):

												"""simple docstring"""



												_lowerCAmelCase  						=			[]
												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)				)
												_lowerCAmelCase  						=			sorted(_lowercase   ,       key=lambda _lowercase				: x[1]   ,       reverse=_lowercase				)
												_lowerCAmelCase  						=			[]
												for (start_index, end_index), score in scores:
																		if start_index > end_index:
																								raise ValueError(F'Wrong span indices: [{start_index}:{end_index}]'				)
																		_lowerCAmelCase  						=			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(_SCREAMING_SNAKE_CASE			)
class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				List[str]									=       VOCAB_FILES_NAMES
						_lowercase		:				str									=       READER_PRETRAINED_VOCAB_FILES_MAP
						_lowercase		:				List[Any]									=       READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowercase		:				Optional[Any]									=       READER_PRETRAINED_INIT_CONFIGURATION
						_lowercase		:				int									=       ['''input_ids''', '''attention_mask''']






'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase    =		logging.get_logger(__name__)

_lowercase    =		{
    """YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json""",
    """YituTech/conv-bert-medium-small""": (
        """https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"""
    ),
    """YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json""",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				List[Any]									=       '''convbert'''




						def __init__( self   ,       _lowercase=30_522   ,       _lowercase=768   ,       _lowercase=12   ,       _lowercase=12   ,       _lowercase=3_072   ,       _lowercase="gelu"   ,       _lowercase=0.1   ,       _lowercase=0.1   ,       _lowercase=512   ,       _lowercase=2   ,       _lowercase=0.02   ,       _lowercase=1e-12   ,       _lowercase=1   ,       _lowercase=0   ,       _lowercase=2   ,       _lowercase=768   ,       _lowercase=2   ,       _lowercase=9   ,       _lowercase=1   ,       _lowercase=None   ,       **_lowercase   ,       ):

												"""simple docstring"""



												super().__init__(
												    pad_token_id=_lowercase   ,       bos_token_id=_lowercase   ,       eos_token_id=_lowercase   ,       **_lowercase   ,       )

												_lowerCAmelCase  						=			vocab_size
												_lowerCAmelCase  						=			hidden_size
												_lowerCAmelCase  						=			num_hidden_layers
												_lowerCAmelCase  						=			num_attention_heads
												_lowerCAmelCase  						=			intermediate_size
												_lowerCAmelCase  						=			hidden_act
												_lowerCAmelCase  						=			hidden_dropout_prob
												_lowerCAmelCase  						=			attention_probs_dropout_prob
												_lowerCAmelCase  						=			max_position_embeddings
												_lowerCAmelCase  						=			type_vocab_size
												_lowerCAmelCase  						=			initializer_range
												_lowerCAmelCase  						=			layer_norm_eps
												_lowerCAmelCase  						=			embedding_size
												_lowerCAmelCase  						=			head_ratio
												_lowerCAmelCase  						=			conv_kernel_size
												_lowerCAmelCase  						=			num_groups
												_lowerCAmelCase  						=			classifier_dropout






class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												if self.task == "multiple-choice":
																		_lowerCAmelCase  						=			{0: """batch""", 1: """choice""", 2: """sequence"""}
												else:
																		_lowerCAmelCase  						=			{0: """batch""", 1: """sequence"""}
												return OrderedDict(
												    [
												        ("""input_ids""", dynamic_axis),
												        ("""attention_mask""", dynamic_axis),
												        ("""token_type_ids""", dynamic_axis),
												    ]				)
