






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      :      str     						=						logging.get_logger(__name__)

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

__UpperCamelCase      :      Dict     						=						{
    """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      :      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"""
        ),
    },
}
__UpperCamelCase      :      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"""
        ),
    },
}

__UpperCamelCase      :      Dict     						=						{
    """facebook/dpr-ctx_encoder-single-nq-base""": 512,
    """facebook/dpr-ctx_encoder-multiset-base""": 512,
}
__UpperCamelCase      :      Optional[Any]     						=						{
    """facebook/dpr-question_encoder-single-nq-base""": 512,
    """facebook/dpr-question_encoder-multiset-base""": 512,
}
__UpperCamelCase      :      Tuple     						=						{
    """facebook/dpr-reader-single-nq-base""": 512,
    """facebook/dpr-reader-multiset-base""": 512,
}


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


class        __UpperCamelCase    (     _lowerCAmelCase   ):
 __snake_case	:Optional[Any]   =       VOCAB_FILES_NAMES
 __snake_case	:Optional[Any]   =       CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 __snake_case	:Union[str, Any]   =       CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __snake_case	:Any   =       CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
 __snake_case	:Optional[int]   =       DPRContextEncoderTokenizer


class        __UpperCamelCase    (     _lowerCAmelCase   ):
 __snake_case	:Union[str, Any]   =       VOCAB_FILES_NAMES
 __snake_case	:Dict   =       QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 __snake_case	:Optional[int]   =       QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __snake_case	:Optional[int]   =       QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
 __snake_case	:Tuple   =       DPRQuestionEncoderTokenizer

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

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


__UpperCamelCase      :      str     						=						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      :							Dict       ,				_lowerCAmelCase      :							Optional[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:
  """simple docstring"""







  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             =  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             =  titles if not isinstance(_lowerCAmelCase       ,				_lowerCAmelCase			) else [titles]
  __lowercase             =  texts if not isinstance(_lowerCAmelCase       ,				_lowerCAmelCase			) else [texts]
  __lowercase             =  len(_lowerCAmelCase			)
  __lowercase             =  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             =  super().__call__(_lowerCAmelCase       ,				_lowerCAmelCase       ,				padding=_lowerCAmelCase       ,				truncation=_lowerCAmelCase			)["""input_ids"""]
  __lowercase             =  super().__call__(_lowerCAmelCase       ,				add_special_tokens=_lowerCAmelCase       ,				padding=_lowerCAmelCase       ,				truncation=_lowerCAmelCase			)["""input_ids"""]
  __lowercase             =  {
      """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             =  []
   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             =  attention_mask
  return self.pad(_lowerCAmelCase       ,				padding=_lowerCAmelCase       ,				max_length=_lowerCAmelCase       ,				return_tensors=_lowerCAmelCase			)







 def  _a   (  self      :							Union[str, Any]       ,				_lowerCAmelCase      :							BatchEncoding       ,				_lowerCAmelCase      :							DPRReaderOutput       ,				_lowerCAmelCase      :							int = 16       ,				_lowerCAmelCase      :							int = 64       ,				_lowerCAmelCase      :							int = 4       ,				)		-> List[DPRSpanPrediction]:
  """simple docstring"""







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

   __lowercase             =  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  _a   (  self      :							List[Any]       ,				_lowerCAmelCase      :							List[int]       ,				_lowerCAmelCase      :							List[int]       ,				_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							int       ,				)		-> List[DPRSpanPrediction]:
  """simple docstring"""







  __lowercase             =  []
  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             =  sorted(_lowerCAmelCase       ,				key=lambda _lowerCAmelCase			: x[1]       ,				reverse=_lowerCAmelCase			)
  __lowercase             =  []
  for (start_index, end_index), score in scores:
   assert start_index <= end_index, F'Wrong span indices: [{start_index}:{end_index}]'
   __lowercase             =  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   ):
 __snake_case	:int   =       VOCAB_FILES_NAMES
 __snake_case	:Dict   =       READER_PRETRAINED_VOCAB_FILES_MAP
 __snake_case	:Optional[int]   =       READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __snake_case	:Tuple   =       READER_PRETRAINED_INIT_CONFIGURATION
 __snake_case	:List[Any]   =       ['input_ids', 'attention_mask']
 __snake_case	:Any   =       DPRReaderTokenizer









# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__UpperCamelCase      :      Any     						=						{"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase      :      Dict     						=						[
						    """MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """MraForMaskedLM""",
						    """MraForMultipleChoice""",
						    """MraForQuestionAnswering""",
						    """MraForSequenceClassification""",
						    """MraForTokenClassification""",
						    """MraLayer""",
						    """MraModel""",
						    """MraPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mra import (
												    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MraForMaskedLM,
												    MraForMultipleChoice,
												    MraForQuestionAnswering,
												    MraForSequenceClassification,
												    MraForTokenClassification,
												    MraLayer,
												    MraModel,
												    MraPreTrainedModel,
												)
else:
						import sys

						__UpperCamelCase      :      List[str]     						=						_LazyModule(__name__, globals()["""__file__"""], _import_structure)

