"""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_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


_a      :		str           =   logging.get_logger(__name__)

_a      :		Union[str, Any]           =   {'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      :		List[str]           =   {
    '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      :		List[str]           =   {
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
_a      :		Optional[Any]           =   {
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
_a      :		Union[str, Any]           =   {
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


_a      :		Optional[int]           =   {
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
_a      :		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 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Optional[int]							=		VOCAB_FILES_NAMES
     _UpperCamelCase							:			Optional[int]							=		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase							:			List[str]							=		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCamelCase							:			int							=		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
     _UpperCamelCase							:			Tuple							=		DPRContextEncoderTokenizer







class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Any							=		VOCAB_FILES_NAMES
     _UpperCamelCase							:			Optional[int]							=		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase							:			Tuple							=		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCamelCase							:			List[Any]							=		QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
     _UpperCamelCase							:			Tuple							=		DPRQuestionEncoderTokenizer







_a      :		List[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      :		List[str]           =   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    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\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    Return:\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(SCREAMING_SNAKE_CASE_		)
class 						__A							:






     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:
         _lowerCAmelCase						:       Union[str, Any]  			=     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__				,     )
       _lowerCAmelCase						:       Union[str, Any]  			=     titles if not isinstance(a__				,     a__  ) else [titles]
       _lowerCAmelCase						:       List[Any]  			=     texts if not isinstance(a__				,     a__  ) else [texts]
       _lowerCAmelCase						:       Union[str, Any]  			=     len(a__  )
       _lowerCAmelCase						:       Union[str, Any]  			=     questions if not isinstance(a__				,     a__  ) else [questions] * n_passages
       assert len(a__  ) == len(
           a__  ), F"There should be as many titles than texts but got {len(a__  )} titles and {len(a__  )} texts."
       _lowerCAmelCase						:       str  			=     super().__call__(a__				,     a__				,     padding=a__				,     truncation=a__  )["""input_ids"""]
       _lowerCAmelCase						:       str  			=     super().__call__(a__				,     add_special_tokens=a__				,     padding=a__				,     truncation=a__  )["""input_ids"""]
       _lowerCAmelCase						:       Dict  			=     {
           """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:
         _lowerCAmelCase						:       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]  )
         _lowerCAmelCase						:       Optional[Any]  			=     attention_mask
       return self.pad(a__				,     padding=a__				,     max_length=a__				,     return_tensors=a__  )






     def     __A  (		self				,     a__				,     a__				,     a__ = 16				,     a__ = 64				,     a__ = 4				,     ):
       _lowerCAmelCase						:       Dict  			=     reader_input["""input_ids"""]
       _lowerCAmelCase			,	_lowerCAmelCase			,	_lowerCAmelCase						:       Optional[Any]  			=     reader_output[:3]
       _lowerCAmelCase						:       List[str]  			=     len(a__  )
       _lowerCAmelCase						:       Union[str, Any]  			=     sorted(range(a__  )				,     reverse=a__				,     key=relevance_logits.__getitem__  )
       _lowerCAmelCase						:       List[DPRReaderOutput]  			=     []
       for doc_id in sorted_docs:
         _lowerCAmelCase						:       Tuple  			=     list(input_ids[doc_id]  )
         # assuming question & title information is at the beginning of the sequence
         _lowerCAmelCase						:       List[str]  			=     sequence_ids.index(self.sep_token_id				,     2  ) + 1  # second sep id
         if sequence_ids[-1] == self.pad_token_id:
           _lowerCAmelCase						:       List[str]  			=     sequence_ids.index(self.pad_token_id  )
         else:
           _lowerCAmelCase						:       Tuple  			=     len(a__  )

         _lowerCAmelCase						:       int  			=     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     __A  (		self				,     a__				,     a__				,     a__				,     a__				,     ):
       _lowerCAmelCase						:       List[str]  			=     []
       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)  )
       _lowerCAmelCase						:       Optional[Any]  			=     sorted(a__				,     key=lambda a__  : x[1]				,     reverse=a__  )
       _lowerCAmelCase						:       Union[str, Any]  			=     []
       for (start_index, end_index), score in scores:
         assert start_index <= end_index, F"Wrong span indices: [{start_index}:{end_index}]"
         _lowerCAmelCase						:       Union[str, Any]  			=     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(a__  ) == top_spans:
           break
       return chosen_span_intervals







@add_end_docstrings(SCREAMING_SNAKE_CASE_		)
class 						__A							( SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Any							=		VOCAB_FILES_NAMES
     _UpperCamelCase							:			Optional[Any]							=		READER_PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase							:			Union[str, Any]							=		READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCamelCase							:			Optional[int]							=		READER_PRETRAINED_INIT_CONFIGURATION
     _UpperCamelCase							:			int							=		["input_ids", "attention_mask"]
     _UpperCamelCase							:			Optional[int]							=		DPRReaderTokenizer





"""simple docstring"""




from scipy.stats import pearsonr

import datasets


_a      :		str           =   '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.\nThe p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.\n'


_a      :		List[str]           =   '\nArgs:\n    predictions (`list` of `int`): Predicted class labels, as returned by a model.\n    references (`list` of `int`): Ground truth labels.\n    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.\n    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.\n\nExamples:\n\n    Example 1-A simple example using only predictions and references.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n\n    Example 2-The same as Example 1, but that also returns the `p-value`.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n        >>> print(sorted(list(results.keys())))\n        [\'p-value\', \'pearsonr\']\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n        >>> print(round(results[\'p-value\'], 2))\n        0.15\n'


_a      :		List[Any]           =   '\n@article{2020SciPy-NMeth,\nauthor  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n      Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n      Kern, Robert and Larson, Eric and Carey, C J and\n      Polat, Ilhan and Feng, Yu and Moore, Eric W. and\n      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n      Harris, Charles R. and Archibald, Anne M. and\n      Ribeiro, Antonio H. and Pedregosa, Fabian and\n      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n      Computing in Python}},\njournal = {Nature Methods},\nyear    = {2020},\nvolume  = {17},\npages   = {261--272},\nadsurl  = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,	_KWARGS_DESCRIPTION		)
class 						__A							( datasets.Metric		):






     def     __A  (		self  ):
       return datasets.MetricInfo(
           description=_DESCRIPTION				,     citation=_CITATION				,     inputs_description=_KWARGS_DESCRIPTION				,     features=datasets.Features(
               {
                   """predictions""": datasets.Value("""float"""  ),
                   """references""": datasets.Value("""float"""  ),
               }  )				,     reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html"""]				,     )






     def     __A  (		self				,     a__				,     a__				,     a__=False  ):
       if return_pvalue:
         _lowerCAmelCase						:       List[Any]  			=     pearsonr(a__				,     a__  )
         return {"pearsonr": results[0], "p-value": results[1]}
       else:
         return {"pearsonr": float(pearsonr(a__				,     a__  )[0]  )}




