





'''simple docstring'''




_A:					List[str]							=				'\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

_A:					str							=				[{'type': 'code', 'content': INSTALL_CONTENT}]
_A:					Any							=				{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}












'''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


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

_A:					Dict							=				{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_A:					int							=				{
    """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"""
        ),
    },
}
_A:					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"""
        ),
    },
}
_A:					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"""
        ),
    },
}

_A:					Any							=				{
    """facebook/dpr-ctx_encoder-single-nq-base""": 512,
    """facebook/dpr-ctx_encoder-multiset-base""": 512,
}
_A:					Tuple							=				{
    """facebook/dpr-question_encoder-single-nq-base""": 512,
    """facebook/dpr-question_encoder-multiset-base""": 512,
}
_A:					int							=				{
    """facebook/dpr-reader-single-nq-base""": 512,
    """facebook/dpr-reader-multiset-base""": 512,
}


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






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			Tuple						   =       VOCAB_FILES_NAMES
    _A :			Dict						   =       CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    _A :			Any						   =       CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _A :			Optional[Any]						   =       CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			List[str]						   =       VOCAB_FILES_NAMES
    _A :			str						   =       QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    _A :			Union[str, Any]						   =       QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _A :			int						   =       QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION



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

_A:					Tuple							=				collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


_A:					Optional[Any]							=				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(UpperCAmelCase_							)
class UpperCAmelCase							:



    def __call__(  self     ,			__A     ,			__A = None     ,			__A = None     ,			__A = False     ,			__A = False     ,			__A = None     ,			__A = None     ,			__A = None     ,			**__A     ,			):
     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:
      __UpperCAmelCase					=   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     ,			)
     __UpperCAmelCase					=   titles if not isinstance(__A     ,			__A					) else [titles]
     __UpperCAmelCase					=   texts if not isinstance(__A     ,			__A					) else [texts]
     __UpperCAmelCase					=   len(__A					)
     __UpperCAmelCase					=   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.'					)
     __UpperCAmelCase					=   super().__call__(__A     ,			__A     ,			padding=__A     ,			truncation=__A					)['input_ids']
     __UpperCAmelCase					=   super().__call__(__A     ,			add_special_tokens=__A     ,			padding=__A     ,			truncation=__A					)['input_ids']
     __UpperCAmelCase					=   {
         '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:
      __UpperCAmelCase					=   []
      for input_ids in encoded_inputs["input_ids"]:
       attention_mask.append([int(input_id != self.pad_token_id					) for input_id in input_ids]					)
      __UpperCAmelCase					=   attention_mask
     return self.pad(__A     ,			padding=__A     ,			max_length=__A     ,			return_tensors=__A					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A = 16     ,			__A = 64     ,			__A = 4     ,			):
     __UpperCAmelCase					=   reader_input['input_ids']
     __UpperCAmelCase					,    __UpperCAmelCase					,    __UpperCAmelCase					=   reader_output[:3]
     __UpperCAmelCase					=   len(__A					)
     __UpperCAmelCase					=   sorted(range(__A					)     ,			reverse=__A     ,			key=relevance_logits.__getitem__					)
     __UpperCAmelCase					=   []
     for doc_id in sorted_docs:
      __UpperCAmelCase					=   list(input_ids[doc_id]					)
      # assuming question & title information is at the beginning of the sequence
      __UpperCAmelCase					=   sequence_ids.index(self.sep_token_id     ,			2					) + 1  # second sep id
      if sequence_ids[-1] == self.pad_token_id:
       __UpperCAmelCase					=   sequence_ids.index(self.pad_token_id					)
      else:
       __UpperCAmelCase					=   len(__A					)

      __UpperCAmelCase					=   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 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			):
     __UpperCAmelCase					=   []
     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)					)
     __UpperCAmelCase					=   sorted(__A     ,			key=lambda __A					: x[1]     ,			reverse=__A					)
     __UpperCAmelCase					=   []
     for (start_index, end_index), score in scores:
      if start_index > end_index:
       raise ValueError(f'Wrong span indices: [{start_index}:{end_index}]'					)
      __UpperCAmelCase					=   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(UpperCAmelCase_							)
class UpperCAmelCase							(		UpperCAmelCase_					,			UpperCAmelCase_							):
    _A :			Optional[int]						   =       VOCAB_FILES_NAMES
    _A :			Optional[int]						   =       READER_PRETRAINED_VOCAB_FILES_MAP
    _A :			Dict						   =       READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _A :			Union[str, Any]						   =       READER_PRETRAINED_INIT_CONFIGURATION
    _A :			List[Any]						   =       ["""input_ids""", """attention_mask"""]





