


"""simple docstring"""






import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__snake_case				=				logging.get_logger(__name__)

__snake_case				=				{'''vocab_file''': '''spiece.model'''}

__snake_case				=				{
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    }
}

__snake_case				=				{
    '''google/bigbird-roberta-base''': 4096,
    '''google/bigbird-roberta-large''': 4096,
    '''google/bigbird-base-trivia-itc''': 4096,
}


class 			__lowerCamelCase     (    __SCREAMING_SNAKE_CASE ):

     '''simple docstring'''
     A_				:		int			=						VOCAB_FILES_NAMES
     A_				:		int			=						PRETRAINED_VOCAB_FILES_MAP
     A_				:		Optional[int]			=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A_				:		Tuple			=						["""input_ids""", """attention_mask"""]
     A_				:		List[int]			=						[]


     def __init__(					self			, __UpperCAmelCase			, __UpperCAmelCase="<unk>"			, __UpperCAmelCase="<s>"			, __UpperCAmelCase="</s>"			, __UpperCAmelCase="<pad>"			, __UpperCAmelCase="[SEP]"			, __UpperCAmelCase="[MASK]"			, __UpperCAmelCase="[CLS]"			, __UpperCAmelCase = None			, **__UpperCAmelCase			, )				->							Union[str, Any]:
          _a					= AddedToken(__UpperCAmelCase			, lstrip=__UpperCAmelCase			, rstrip=__UpperCAmelCase							) if isinstance(__UpperCAmelCase			, __UpperCAmelCase							) else bos_token
          _a					= AddedToken(__UpperCAmelCase			, lstrip=__UpperCAmelCase			, rstrip=__UpperCAmelCase							) if isinstance(__UpperCAmelCase			, __UpperCAmelCase							) else eos_token
          _a					= AddedToken(__UpperCAmelCase			, lstrip=__UpperCAmelCase			, rstrip=__UpperCAmelCase							) if isinstance(__UpperCAmelCase			, __UpperCAmelCase							) else unk_token
          _a					= AddedToken(__UpperCAmelCase			, lstrip=__UpperCAmelCase			, rstrip=__UpperCAmelCase							) if isinstance(__UpperCAmelCase			, __UpperCAmelCase							) else pad_token
          _a					= AddedToken(__UpperCAmelCase			, lstrip=__UpperCAmelCase			, rstrip=__UpperCAmelCase							) if isinstance(__UpperCAmelCase			, __UpperCAmelCase							) else cls_token
          _a					= AddedToken(__UpperCAmelCase			, lstrip=__UpperCAmelCase			, rstrip=__UpperCAmelCase							) if isinstance(__UpperCAmelCase			, __UpperCAmelCase							) else sep_token

          # Mask token behave like a normal word, i.e. include the space before it
          _a					= AddedToken(__UpperCAmelCase			, lstrip=__UpperCAmelCase			, rstrip=__UpperCAmelCase							) if isinstance(__UpperCAmelCase			, __UpperCAmelCase							) else mask_token

          _a					= {} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              bos_token=__UpperCAmelCase			, eos_token=__UpperCAmelCase			, unk_token=__UpperCAmelCase			, pad_token=__UpperCAmelCase			, sep_token=__UpperCAmelCase			, mask_token=__UpperCAmelCase			, cls_token=__UpperCAmelCase			, sp_model_kwargs=self.sp_model_kwargs			, **__UpperCAmelCase			, )

          _a					= vocab_file

          _a					= spm.SentencePieceProcessor(**self.sp_model_kwargs							)
          self.sp_model.Load(__UpperCAmelCase							)


     @property
     def 							_UpperCAmelCase					(					self							)				->							List[Any]:
          return self.sp_model.get_piece_size()


     def 							_UpperCAmelCase					(					self							)				->							Tuple:
          _a					= {self.convert_ids_to_tokens(__UpperCAmelCase							): i for i in range(self.vocab_size							)}
          vocab.update(self.added_tokens_encoder							)
          return vocab


     def __getstate__(					self							)				->							str:
          _a					= self.__dict__.copy()
          _a					= None
          return state


     def __setstate__(					self			, __UpperCAmelCase							)				->							Dict:
          _a					= d

          # for backward compatibility
          if not hasattr(self			, '''sp_model_kwargs'''							):
               _a					= {}

          _a					= spm.SentencePieceProcessor(**self.sp_model_kwargs							)
          self.sp_model.Load(self.vocab_file							)


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase							)				->							Tuple:
          return self.sp_model.encode(__UpperCAmelCase			, out_type=__UpperCAmelCase							)


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase							)				->							List[Any]:
          return self.sp_model.piece_to_id(__UpperCAmelCase							)


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase							)				->							Any:
          _a					= self.sp_model.IdToPiece(__UpperCAmelCase							)
          return token


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase							)				->							Tuple:
          _a					= []
          _a					= """"""
          _a					= False
          for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                    if not prev_is_special:
                         out_string += " "
                    out_string += self.sp_model.decode(__UpperCAmelCase							) + token
                    _a					= True
                    _a					= []
               else:
                    current_sub_tokens.append(__UpperCAmelCase							)
                    _a					= False
          out_string += self.sp_model.decode(__UpperCAmelCase							)
          return out_string.strip()


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase = False			, __UpperCAmelCase = None			, __UpperCAmelCase = True			, **__UpperCAmelCase			, )				->							Dict:
          _a					= kwargs.pop('''use_source_tokenizer'''			, __UpperCAmelCase							)

          _a					= self.convert_ids_to_tokens(__UpperCAmelCase			, skip_special_tokens=__UpperCAmelCase							)

          # To avoid mixing byte-level and unicode for byte-level BPT
          # we need to build string separately for added tokens and byte-level tokens
          # cf. https://github.com/huggingface/transformers/issues/1133
          _a					= []
          _a					= []
          for token in filtered_tokens:
               if skip_special_tokens and token in self.all_special_ids:
                    continue
               if token in self.added_tokens_encoder:
                    if current_sub_text:
                         sub_texts.append(self.convert_tokens_to_string(__UpperCAmelCase							)							)
                         _a					= []
                    sub_texts.append(__UpperCAmelCase							)
               else:
                    current_sub_text.append(__UpperCAmelCase							)
          if current_sub_text:
               sub_texts.append(self.convert_tokens_to_string(__UpperCAmelCase							)							)

          # Mimic the behavior of the Rust tokenizer:
          # No space before [MASK] and [SEP]
          if spaces_between_special_tokens:
               _a					= re.sub(r''' (\[(MASK|SEP)\])'''			, r'''\1'''			, ''' '''.join(__UpperCAmelCase							)							)
          else:
               _a					= """""".join(__UpperCAmelCase							)

          _a					= (
              clean_up_tokenization_spaces
              if clean_up_tokenization_spaces is not None
              else self.clean_up_tokenization_spaces
          )
          if clean_up_tokenization_spaces:
               _a					= self.clean_up_tokenization(__UpperCAmelCase							)
               return clean_text
          else:
               return text


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase = None							)				->							Dict:
          if not os.path.isdir(__UpperCAmelCase							):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
               return
          _a					= os.path.join(
              __UpperCAmelCase			, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

          if os.path.abspath(self.vocab_file							) != os.path.abspath(__UpperCAmelCase							) and os.path.isfile(self.vocab_file							):
               copyfile(self.vocab_file			, __UpperCAmelCase							)
          elif not os.path.isfile(self.vocab_file							):
               with open(__UpperCAmelCase			, '''wb'''							) as fi:
                    _a					= self.sp_model.serialized_model_proto()
                    fi.write(__UpperCAmelCase							)

          return (out_vocab_file,)


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase = None							)				->							Optional[Any]:
          if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          _a					= [self.cls_token_id]
          _a					= [self.sep_token_id]
          return cls + token_ids_a + sep + token_ids_a + sep


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase = None			, __UpperCAmelCase = False							)				->							Optional[Any]:
          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__UpperCAmelCase			, token_ids_a=__UpperCAmelCase			, already_has_special_tokens=__UpperCAmelCase							)

          if token_ids_a is None:
               return [1] + ([0] * len(__UpperCAmelCase							)) + [1]
          return [1] + ([0] * len(__UpperCAmelCase							)) + [1] + ([0] * len(__UpperCAmelCase							)) + [1]





     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase = None							)				->							Tuple:
          _a					= [self.sep_token_id]
          _a					= [self.cls_token_id]
          if token_ids_a is None:
               return len(cls + token_ids_a + sep							) * [0]
          return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]

from scipy.stats import pearsonr

import datasets


_lowerCamelCase					   ="""
Pearson correlation coefficient and p-value for testing non-correlation.
The 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.
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.
"""


_lowerCamelCase					   ="""
Args:
    predictions (`list` of `int`): Predicted class labels, as returned by a model.
    references (`list` of `int`): Ground truth labels.
    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.

Returns:
    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.
    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.

Examples:

    Example 1-A simple example using only predictions and references.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
        >>> print(round(results['pearsonr'], 2))
        -0.74

    Example 2-The same as Example 1, but that also returns the `p-value`.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
        >>> print(sorted(list(results.keys())))
        ['p-value', 'pearsonr']
        >>> print(round(results['pearsonr'], 2))
        -0.74
        >>> print(round(results['p-value'], 2))
        0.15
"""


_lowerCamelCase					   ="""
@article{2020SciPy-NMeth,
author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
      Haberland, Matt and Reddy, Tyler and Cournapeau, David and
      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
      Kern, Robert and Larson, Eric and Carey, C J and
      Polat, Ilhan and Feng, Yu and Moore, Eric W. and
      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
      Harris, Charles R. and Archibald, Anne M. and
      Ribeiro, Antonio H. and Pedregosa, Fabian and
      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
      Computing in Python}},
journal = {Nature Methods},
year    = {2020},
volume  = {17},
pages   = {261--272},
adsurl  = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
"""





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,   _KWARGS_DESCRIPTION)
class 							A__		(	datasets.Metric):
      def 						UpperCamelCase__							(      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 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__=False      ):
       if return_pvalue:
        lowerCamelCase		:	Optional[Any]							=      pearsonr(__magic_name__    ,  __magic_name__      )
        return {"pearsonr": results[0], "p-value": results[1]}
       else:
        return {"pearsonr": float(pearsonr(__magic_name__    ,  __magic_name__      )[0]      )}




