





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


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

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

_snake_case    :       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'
        ),
    },
}
_snake_case    :       Union[str, Any]		       =    {
    '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'
        ),
    },
}
_snake_case    :       Dict		       =    {
    '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'
        ),
    },
}

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


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



class 							_UpperCAmelCase       (       _UpperCamelCase       ):

  """simple docstring"""


  a_              =					VOCAB_FILES_NAMES
  a_              =					CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  a_              =					CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_              =					CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
  a_              =					DPRContextEncoderTokenizer





class 							_UpperCAmelCase       (       _UpperCamelCase       ):

  """simple docstring"""


  a_              =					VOCAB_FILES_NAMES
  a_              =					QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  a_              =					QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_              =					QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
  a_              =					DPRQuestionEncoderTokenizer





_snake_case    :       Tuple		       =    collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

_snake_case    :       Optional[Any]		       =    collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


_snake_case    :       Any		       =    R'\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '



@add_start_docstrings(_UpperCamelCase       )
class 							_UpperCAmelCase       :

  """simple docstring"""


  def __call__(							self					:						Any   ,    lowerCAmelCase_					:						Any   ,    lowerCAmelCase_					:						Optional[str] = None   ,    lowerCAmelCase_					:						Optional[str] = None   ,    lowerCAmelCase_					:						Union[bool, str] = False   ,    lowerCAmelCase_					:						Union[bool, str] = False   ,    lowerCAmelCase_					:						Optional[int] = None   ,    lowerCAmelCase_					:						Optional[Union[str, TensorType]] = None   ,    lowerCAmelCase_					:						Optional[bool] = None   ,    **lowerCAmelCase_					:						Optional[Any]   ,    )				->		BatchEncoding:
     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:
        __lowerCAmelCase				      =      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_   ,    )
     __lowerCAmelCase				      =      titles if not isinstance(lowerCAmelCase_   ,    lowerCAmelCase_	) else [titles]
     __lowerCAmelCase				      =      texts if not isinstance(lowerCAmelCase_   ,    lowerCAmelCase_	) else [texts]
     __lowerCAmelCase				      =      len(lowerCAmelCase_	)
     __lowerCAmelCase				      =      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."""
     __lowerCAmelCase				      =      super().__call__(lowerCAmelCase_   ,    lowerCAmelCase_   ,    padding=lowerCAmelCase_   ,    truncation=lowerCAmelCase_	)['input_ids']
     __lowerCAmelCase				      =      super().__call__(lowerCAmelCase_   ,    add_special_tokens=lowerCAmelCase_   ,    padding=lowerCAmelCase_   ,    truncation=lowerCAmelCase_	)['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(lowerCAmelCase_   ,    lowerCAmelCase_	)
         ]
     }
     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(lowerCAmelCase_   ,    padding=lowerCAmelCase_   ,    max_length=lowerCAmelCase_   ,    return_tensors=lowerCAmelCase_	)






  def 							lowercase						(							self					:						int   ,    lowerCAmelCase_					:						BatchEncoding   ,    lowerCAmelCase_					:						DPRReaderOutput   ,    lowerCAmelCase_					:						int = 1_6   ,    lowerCAmelCase_					:						int = 6_4   ,    lowerCAmelCase_					:						int = 4   ,    )				->		List[DPRSpanPrediction]:
     __lowerCAmelCase				      =      reader_input['input_ids']
     __lowerCAmelCase      ,      __lowerCAmelCase      ,      __lowerCAmelCase				      =      reader_output[:3]
     __lowerCAmelCase				      =      len(lowerCAmelCase_	)
     __lowerCAmelCase				      =      sorted(range(lowerCAmelCase_	)   ,    reverse=lowerCAmelCase_   ,    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(lowerCAmelCase_	)

        __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=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 							lowercase						(							self					:						Any   ,    lowerCAmelCase_					:						List[int]   ,    lowerCAmelCase_					:						List[int]   ,    lowerCAmelCase_					:						int   ,    lowerCAmelCase_					:						int   ,    )				->		List[DPRSpanPrediction]:
     __lowerCAmelCase				      =      []
     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)	)
     __lowerCAmelCase				      =      sorted(lowerCAmelCase_   ,    key=lambda lowerCAmelCase_	: x[1]   ,    reverse=lowerCAmelCase_	)
     __lowerCAmelCase				      =      []
     for (start_index, end_index), score in scores:
        assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
        __lowerCAmelCase				      =      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(_UpperCamelCase       )
class 							_UpperCAmelCase       (       _UpperCamelCase						,       _UpperCamelCase       ):

  """simple docstring"""


  a_              =					VOCAB_FILES_NAMES
  a_              =					READER_PRETRAINED_VOCAB_FILES_MAP
  a_              =					READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_              =					READER_PRETRAINED_INIT_CONFIGURATION
  a_              =					["""input_ids""", """attention_mask"""]
  a_              =					DPRReaderTokenizer




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase						:  int									=			{
    """configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase						:  str									=			[
					    """LILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """LiltForQuestionAnswering""",
					    """LiltForSequenceClassification""",
					    """LiltForTokenClassification""",
					    """LiltModel""",
					    """LiltPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_lilt import (
										    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LiltForQuestionAnswering,
										    LiltForSequenceClassification,
										    LiltForTokenClassification,
										    LiltModel,
										    LiltPreTrainedModel,
										)

else:
					import sys

					_UpperCAmelCase						:  Dict									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




