
'''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


UpperCamelCase__      :		Optional[Any]       							=  logging.get_logger(__name__)

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

UpperCamelCase__      :		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"
        ),
    },
}
UpperCamelCase__      :		int       							=  {
    "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__      :		Optional[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__      :		List[Any]       							=  {
    "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__      :		Optional[int]       							=  {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


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

class    _a				(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE      =  CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE      =  CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE      =  CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class    _a				(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE      =  QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE      =  QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE      =  QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION






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

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


UpperCamelCase__      :		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(_lowerCamelCase)
class    _a				:





       """simple docstring"""




       def __call__(							self       ,							A__       ,							A__ = None       ,							A__ = None       ,							A__ = False       ,							A__ = False       ,							A__ = None       ,							A__ = None       ,							A__ = None       ,							**A__       ,							)    ->			BatchEncoding:
          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        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__ = 16       ,							A__ = 64       ,							A__ = 4       ,							)    ->			List[DPRSpanPrediction]:
          _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        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__       ,							A__       ,							)    ->			List[DPRSpanPrediction]:
          _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(_lowerCamelCase)
class    _a				(_lowerCamelCase  ,					_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE      =  READER_PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE      =  READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE      =  READER_PRETRAINED_INIT_CONFIGURATION
       SCREAMING_SNAKE_CASE      =  ['input_ids', 'attention_mask']








'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase__      :		str       							=  {
    "configuration_canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig"],
    "tokenization_canine": ["CanineTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__      :		List[Any]       							=  [
         "CANINE_PRETRAINED_MODEL_ARCHIVE_LIST",
         "CanineForMultipleChoice",
         "CanineForQuestionAnswering",
         "CanineForSequenceClassification",
         "CanineForTokenClassification",
         "CanineLayer",
         "CanineModel",
         "CaninePreTrainedModel",
         "load_tf_weights_in_canine",
     ]


if TYPE_CHECKING:
     from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
     from .tokenization_canine import CanineTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_canine import (
              CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
              CanineForMultipleChoice,
              CanineForQuestionAnswering,
              CanineForSequenceClassification,
              CanineForTokenClassification,
              CanineLayer,
              CanineModel,
              CaninePreTrainedModel,
              load_tf_weights_in_canine,
          )


else:
     import sys

     UpperCamelCase__      :		str       							=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






