






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


__snake_case      :      Union[str, Any]             =       logging.get_logger(__name__)

__snake_case      :      Any             =       {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

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


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


class       __SCREAMING_SNAKE_CASE   (				_a):
      _SCREAMING_SNAKE_CASE		:    str						=   VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE		:    Optional[int]						=   CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE		:    Tuple						=   CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE		:    List[Any]						=   CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION




class       __SCREAMING_SNAKE_CASE   (				_a):
      _SCREAMING_SNAKE_CASE		:    Optional[Any]						=   VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE		:    List[Any]						=   QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE		:    Optional[int]						=   QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE		:    Optional[Any]						=   QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION




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

__snake_case      :      str             =       collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


__snake_case      :      Union[str, 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    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\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    Returns:\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(_a)
class       __SCREAMING_SNAKE_CASE   :






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






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






      def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = 16       ,  _UpperCamelCase = 64       ,  _UpperCamelCase = 4       ,  ):






            """simple docstring"""
            lowerCAmelCase__        =  reader_input["""input_ids"""]
            lowerCAmelCase__        =  reader_output[:3]
            lowerCAmelCase__        =  len(snake_case_				)
            lowerCAmelCase__        =  sorted(range(snake_case_				)       ,  reverse=snake_case_       ,  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(snake_case_				)

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







      def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  ):






            """simple docstring"""
            lowerCAmelCase__        =  []
            for start_index, start_score in enumerate(snake_case_				):
                  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(snake_case_       ,  key=lambda _UpperCamelCase				: x[1]       ,  reverse=snake_case_				)
            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(snake_case_				) == top_spans:
                        break
            return chosen_span_intervals




@add_end_docstrings(_a)
class       __SCREAMING_SNAKE_CASE   (				_a						,   _a):
      _SCREAMING_SNAKE_CASE		:    Tuple						=   VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE		:    Tuple						=   READER_PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE		:    str						=   READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE		:    List[Any]						=   READER_PRETRAINED_INIT_CONFIGURATION
      _SCREAMING_SNAKE_CASE		:    str						=   ['''input_ids''', '''attention_mask''']













from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case      :      Union[str, Any]             =       {
    """configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
    """tokenization_convbert""": ["""ConvBertTokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case      :      List[str]             =       ["""ConvBertTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case      :      Optional[Any]             =       [
					    """CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """ConvBertForMaskedLM""",
					    """ConvBertForMultipleChoice""",
					    """ConvBertForQuestionAnswering""",
					    """ConvBertForSequenceClassification""",
					    """ConvBertForTokenClassification""",
					    """ConvBertLayer""",
					    """ConvBertModel""",
					    """ConvBertPreTrainedModel""",
					    """load_tf_weights_in_convbert""",
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case      :      Union[str, Any]             =       [
					    """TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFConvBertForMaskedLM""",
					    """TFConvBertForMultipleChoice""",
					    """TFConvBertForQuestionAnswering""",
					    """TFConvBertForSequenceClassification""",
					    """TFConvBertForTokenClassification""",
					    """TFConvBertLayer""",
					    """TFConvBertModel""",
					    """TFConvBertPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
					from .tokenization_convbert import ConvBertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_convbert_fast import ConvBertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_convbert import (
										    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ConvBertForMaskedLM,
										    ConvBertForMultipleChoice,
										    ConvBertForQuestionAnswering,
										    ConvBertForSequenceClassification,
										    ConvBertForTokenClassification,
										    ConvBertLayer,
										    ConvBertModel,
										    ConvBertPreTrainedModel,
										    load_tf_weights_in_convbert,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_convbert import (
										    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFConvBertForMaskedLM,
										    TFConvBertForMultipleChoice,
										    TFConvBertForQuestionAnswering,
										    TFConvBertForSequenceClassification,
										    TFConvBertForTokenClassification,
										    TFConvBertLayer,
										    TFConvBertModel,
										    TFConvBertPreTrainedModel,
										)


else:
					import sys

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





