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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ="""▁"""

_lowerCamelCase					   ={"""vocab_file""": """prophetnet.tokenizer"""}

_lowerCamelCase					   ={
    """vocab_file""": {
        """microsoft/xprophetnet-large-wiki100-cased""": (
            """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"""
        ),
    }
}

_lowerCamelCase					   ={
    """microsoft/xprophetnet-large-wiki100-cased""": {"""do_lower_case""": False},
}

_lowerCamelCase					   ={
    """microsoft/xprophetnet-large-wiki100-cased""": 5_1_2,
}





def     _a					(						lowerCamelCase							):
 lowerCamelCase		:	str							=      collections.OrderedDict()
 with open(lowerCamelCase,	"""r""",	encoding="""utf-8"""							) as reader:
  lowerCamelCase		:	int							=      reader.readlines()
 for index, token in enumerate(lowerCamelCase							):
  lowerCamelCase		:	List[Any]							=      token.rstrip("""\n"""							)
  lowerCamelCase		:	List[str]							=      index
 return vocab





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Tuple           =			VOCAB_FILES_NAMES
      _UpperCAmelCase				:    Optional[int]           =			PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase				:    int           =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _UpperCAmelCase				:    str           =			["""input_ids""", """attention_mask"""]
      def __init__(      self    ,  __magic_name__    ,  __magic_name__="[SEP]"    ,  __magic_name__="[SEP]"    ,  __magic_name__="[SEP]"    ,  __magic_name__="[UNK]"    ,  __magic_name__="[PAD]"    ,  __magic_name__="[CLS]"    ,  __magic_name__="[MASK]"    ,  __magic_name__ = None    ,  **__magic_name__    ,  ):
       lowerCamelCase		:	Tuple							=      {} if sp_model_kwargs is None else sp_model_kwargs

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

       try:
        import sentencepiece as spm
       except ImportError:
        logger.warning(
            """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
            """ pip install sentencepiece"""      )
        raise

       lowerCamelCase		:	Dict							=      spm.SentencePieceProcessor(**self.sp_model_kwargs      )
       self.sp_model.Load(str(__magic_name__      )      )
       lowerCamelCase		:	Optional[int]							=      vocab_file

       # Original fairseq vocab and spm vocab must be "aligned":
       # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
       # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
       # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
       # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

       # put special tokens and [unused] tokens into the vocab
       lowerCamelCase		:	Any							=      {"""[PAD]""": 0, """[CLS]""": 1, """[SEP]""": 2, """[UNK]""": 3, """[MASK]""": 4}

       for i in range(1_0      ):
        lowerCamelCase		:	Dict							=      F'''[unused{i}]'''
        lowerCamelCase		:	List[Any]							=      5 + i

       # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
       lowerCamelCase		:	Any							=      1_2
       lowerCamelCase		:	Optional[int]							=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}
       for k in self.fairseq_tokens_to_ids.keys():
        self.unique_no_split_tokens.append(__magic_name__      )
      def __getstate__(      self      ):
       lowerCamelCase		:	List[Any]							=      self.__dict__.copy()
       lowerCamelCase		:	Union[str, Any]							=      None
       return state
      def __setstate__(      self    ,  __magic_name__      ):
       lowerCamelCase		:	str							=      d
       try:
        import sentencepiece as spm
       except ImportError:
        logger.warning(
            """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
            """ pip install sentencepiece"""      )
        raise

       # for backward compatibility
       if not hasattr(self    ,  """sp_model_kwargs"""      ):
        lowerCamelCase		:	int							=      {}

       lowerCamelCase		:	List[Any]							=      spm.SentencePieceProcessor(**self.sp_model_kwargs      )
       self.sp_model.Load(self.vocab_file      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = None    ,  __magic_name__ = False      ):

       if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=__magic_name__    ,  token_ids_a=__magic_name__    ,  already_has_special_tokens=__magic_name__      )

       if token_ids_a is None:
        return ([0] * len(__magic_name__      )) + [1]
       return ([0] * len(__magic_name__      )) + [1] + ([0] * len(__magic_name__      )) + [1]
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = None      ):
       lowerCamelCase		:	Tuple							=      [self.sep_token_id]

       if token_ids_a is None:
        return len(token_ids_a + sep      ) * [0]
       return len(token_ids_a + sep + sep + token_ids_a + sep      ) * [0]
      @property
      def 						UpperCamelCase__							(      self      ):
       return len(self.sp_model      ) + self.fairseq_offset
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[str]							=      {self.convert_ids_to_tokens(__magic_name__      ): i for i in range(self.vocab_size      )}
       vocab.update(self.added_tokens_encoder      )
       return vocab
      def 						UpperCamelCase__							(      self    ,  __magic_name__      ):
       return self.sp_model.encode(__magic_name__    ,  out_type=__magic_name__      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__      ):
       if token in self.fairseq_tokens_to_ids:
        return self.fairseq_tokens_to_ids[token]
       lowerCamelCase		:	List[str]							=      self.sp_model.PieceToId(__magic_name__      )

       # Need to return unknown token if the SP model returned 0
       return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
      def 						UpperCamelCase__							(      self    ,  __magic_name__      ):
       if index in self.fairseq_ids_to_tokens:
        return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__      ):
       lowerCamelCase		:	Any							=      """""".join(__magic_name__      ).replace(__magic_name__    ,  """ """      ).strip()
       return out_string
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = None      ):
       if not os.path.isdir(__magic_name__      ):
        logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
        return
       lowerCamelCase		:	List[Any]							=      os.path.join(
           __magic_name__    ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

       if os.path.abspath(self.vocab_file      ) != os.path.abspath(__magic_name__      ) and os.path.isfile(self.vocab_file      ):
        copyfile(self.vocab_file    ,  __magic_name__      )
       elif not os.path.isfile(self.vocab_file      ):
        with open(__magic_name__    ,  """wb"""      ) as fi:
         lowerCamelCase		:	Optional[Any]							=      self.sp_model.serialized_model_proto()
         fi.write(__magic_name__      )

       return (out_vocab_file,)






      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = None      ):

       if token_ids_a is None:
        return token_ids_a + [self.sep_token_id]
       lowerCamelCase		:	List[str]							=      [self.sep_token_id]
       return token_ids_a + sep + token_ids_a + sep






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]      )}




