






"""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


_lowerCAmelCase	:	Any				=      logging.get_logger(__name__)

_lowerCAmelCase	:	Tuple				=      {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowerCAmelCase	:	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"""
        ),
    },
}
_lowerCAmelCase	:	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"""
        ),
    },
}
_lowerCAmelCase	:	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"""
        ),
    },
}

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


_lowerCAmelCase	:	Dict				=      {
    """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
_lowerCAmelCase	:	int				=      {
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
_lowerCAmelCase	:	Optional[int]				=      {
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}




class 			lowerCAmelCase__			(    __magic_name__   ):
   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 			lowerCAmelCase__			(    __magic_name__   ):
   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




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

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


_lowerCAmelCase	:	Union[str, 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(__magic_name__   )
class 			lowerCAmelCase__			:
   def __call__(		self      :	Union[str, Any]							,			snake_case__      :	Dict							,			snake_case__      :	Optional[str] = None							,			snake_case__      :	Optional[str] = None							,			snake_case__      :	Union[bool, str] = False							,			snake_case__      :	Union[bool, str] = False							,			snake_case__      :	Optional[int] = None							,			snake_case__      :	Optional[Union[str, TensorType]] = None							,			snake_case__      :	Optional[bool] = None							,			**snake_case__      :	Union[str, Any]							,			):


        '''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:
             UpperCAmelCase__						:    str							=							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__							,			)
        UpperCAmelCase__						:    Dict							=							titles if not isinstance(snake_case__							,			snake_case__							) else [titles]
        UpperCAmelCase__						:    int							=							texts if not isinstance(snake_case__							,			snake_case__							) else [texts]
        UpperCAmelCase__						:    str							=							len(snake_case__							)
        UpperCAmelCase__						:    Union[str, Any]							=							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.'							)
        UpperCAmelCase__						:    Dict							=							super().__call__(snake_case__							,			snake_case__							,			padding=snake_case__							,			truncation=snake_case__							)["input_ids"]
        UpperCAmelCase__						:    Dict							=							super().__call__(snake_case__							,			add_special_tokens=snake_case__							,			padding=snake_case__							,			truncation=snake_case__							)["input_ids"]
        UpperCAmelCase__						:    Optional[int]							=							{
            "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:
             UpperCAmelCase__						:    Tuple							=							[]
             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__						:    Any							=							attention_mask
        return self.pad(snake_case__							,			padding=snake_case__							,			max_length=snake_case__							,			return_tensors=snake_case__							)
   def       __a							(		self      :	Any							,			snake_case__      :	BatchEncoding							,			snake_case__      :	DPRReaderOutput							,			snake_case__      :	int = 1_6							,			snake_case__      :	int = 6_4							,			snake_case__      :	int = 4							,			):


        '''simple docstring'''
        UpperCAmelCase__						:    List[str]							=							reader_input["input_ids"]
        UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    Dict							=							reader_output[:3]
        UpperCAmelCase__						:    List[str]							=							len(snake_case__							)
        UpperCAmelCase__						:    int							=							sorted(range(snake_case__							)							,			reverse=snake_case__							,			key=relevance_logits.__getitem__							)
        UpperCAmelCase__						:    List[DPRReaderOutput]							=							[]
        for doc_id in sorted_docs:
             UpperCAmelCase__						:    Optional[Any]							=							list(input_ids[doc_id]							)
             # assuming question & title information is at the beginning of the sequence
             UpperCAmelCase__						:    List[Any]							=							sequence_ids.index(self.sep_token_id							,			2							) + 1  # second sep id
             if sequence_ids[-1] == self.pad_token_id:
                  UpperCAmelCase__						:    Union[str, Any]							=							sequence_ids.index(self.pad_token_id							)
             else:
                  UpperCAmelCase__						:    Optional[Any]							=							len(snake_case__							)

             UpperCAmelCase__						:    List[str]							=							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       __a							(		self      :	Union[str, Any]							,			snake_case__      :	List[int]							,			snake_case__      :	List[int]							,			snake_case__      :	int							,			snake_case__      :	int							,			):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[Any]							=							[]
        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)							)
        UpperCAmelCase__						:    Union[str, Any]							=							sorted(snake_case__							,			key=lambda snake_case__							: x[1]							,			reverse=snake_case__							)
        UpperCAmelCase__						:    Any							=							[]
        for (start_index, end_index), score in scores:
             if start_index > end_index:
                  raise ValueError(f'Wrong span indices: [{start_index}:{end_index}]'							)
             UpperCAmelCase__						:    Tuple							=							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(__magic_name__   )
class 			lowerCAmelCase__			(    __magic_name__							,						__magic_name__   ):
   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 __future__ import annotations

from math import pow, sqrt


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : float			,					snake_case       : float			,					snake_case       : float				)->		dict[str, float]:






     '''simple docstring'''



     if (resistance, reactance, impedance).count(0				) != 1:
          raise ValueError("One and only one argument must be 0"				)
     if resistance == 0:
          return {"resistance": sqrt(pow(snake_case			,					2				) - pow(snake_case			,					2				)				)}
     elif reactance == 0:
          return {"reactance": sqrt(pow(snake_case			,					2				) - pow(snake_case			,					2				)				)}
     elif impedance == 0:
          return {"impedance": sqrt(pow(snake_case			,					2				) + pow(snake_case			,					2				)				)}
     else:
          raise ValueError("Exactly one argument must be 0"				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


