'''simple docstring'''





import numpy as np

import datasets


SCREAMING_SNAKE_CASE_:     List[str]  ='\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. Mahalanobis in 1936\nand has been used in various statistical applications ever since\n[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]\n'

SCREAMING_SNAKE_CASE_:     Union[str, Any]  ='\\n@article{de2000mahalanobis,\n  title={The mahalanobis distance},\n  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},\n  journal={Chemometrics and intelligent laboratory systems},\n  volume={50},\n  number={1},\n  pages={1--18},\n  year={2000},\n  publisher={Elsevier}\n}\n'

SCREAMING_SNAKE_CASE_:     int  ='\nArgs:\n    X: List of datapoints to be compared with the `reference_distribution`.\n    reference_distribution: List of datapoints from the reference distribution we want to compare to.\nReturns:\n    mahalanobis: The Mahalonobis distance for each datapoint in `X`.\nExamples:\n\n    >>> mahalanobis_metric = datasets.load_metric("mahalanobis")\n    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])\n    >>> print(results)\n    {\'mahalanobis\': array([0.5])}\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,	_KWARGS_DESCRIPTION	)
class        __A      ( datasets.Metric	):



    def 					_lowercase						(self    :    Union[str, Any] ):
         return datasets.MetricInfo(
             description=_DESCRIPTION   ,			citation=_CITATION   ,			inputs_description=_KWARGS_DESCRIPTION   ,			features=datasets.Features(
                 {
                     "X": datasets.Sequence(datasets.Value("float"   ,			id="sequence" )   ,			id="X" ),
                 } )   ,			)






    def 					_lowercase						(self    :    str   ,			__a    :    Optional[Any]   ,			__a    :    List[Any] ):
         # convert to numpy arrays
         UpperCAmelCase_           =   np.array(UpperCamelCase__ )
         UpperCAmelCase_           =   np.array(UpperCamelCase__ )

         # Assert that arrays are 2D
         if len(X.shape ) != 2:
              raise ValueError("Expected `X` to be a 2D vector" )
         if len(reference_distribution.shape ) != 2:
              raise ValueError("Expected `reference_distribution` to be a 2D vector" )
         if reference_distribution.shape[0] < 2:
              raise ValueError(
                  "Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension" )

         # Get mahalanobis distance for each prediction
         UpperCAmelCase_           =   X - np.mean(UpperCamelCase__ )
         UpperCAmelCase_           =   np.cov(reference_distribution.T )
         try:
              UpperCAmelCase_           =   np.linalg.inv(UpperCamelCase__ )
         except np.linalg.LinAlgError:
              UpperCAmelCase_           =   np.linalg.pinv(UpperCamelCase__ )
         UpperCAmelCase_           =   np.dot(UpperCamelCase__   ,			UpperCamelCase__ )
         UpperCAmelCase_           =   np.dot(UpperCamelCase__   ,			X_minus_mu.T ).diagonal()

         return {"mahalanobis": mahal_dist}










import os
import unicodedata
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 SPIECE_UNDERLINE, logging


SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       :      Tuple  =   {'vocab_file': 'spiece.model'}

SCREAMING_SNAKE_CASE__       :      int  =   {
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    }
}

SCREAMING_SNAKE_CASE__       :      str  =   {
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

# Segments (not really needed)
SCREAMING_SNAKE_CASE__       :      Dict  =   0
SCREAMING_SNAKE_CASE__       :      Tuple  =   1
SCREAMING_SNAKE_CASE__       :      Optional[int]  =   2
SCREAMING_SNAKE_CASE__       :      List[str]  =   3
SCREAMING_SNAKE_CASE__       :      Optional[int]  =   4




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Dict	     =      VOCAB_FILES_NAMES
    lowerCamelCase_      :	Optional[Any]	     =      PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase_      :	Optional[int]	     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase_      :	List[str]	     =      """left"""







    def __init__(    self     ,  UpperCamelCase__     ,  UpperCamelCase__=False     ,  UpperCamelCase__=True     ,  UpperCamelCase__=False     ,  UpperCamelCase__="<s>"     ,  UpperCamelCase__="</s>"     ,  UpperCamelCase__="<unk>"     ,  UpperCamelCase__="<sep>"     ,  UpperCamelCase__="<pad>"     ,  UpperCamelCase__="<cls>"     ,  UpperCamelCase__="<mask>"     ,  UpperCamelCase__=["<eop>", "<eod>"]     ,  UpperCamelCase__ = None     ,  **UpperCamelCase__     ,  )     ->      None:
           # Mask token behave like a normal word, i.e. include the space before it
           lowerCamelCase   :      str			 =					AddedToken(UpperCamelCase__     ,  lstrip=UpperCamelCase__     ,  rstrip=UpperCamelCase__     ) if isinstance(UpperCamelCase__     ,  UpperCamelCase__     ) else mask_token

           lowerCamelCase   :      Dict			 =					{} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               do_lower_case=UpperCamelCase__     ,  remove_space=UpperCamelCase__     ,  keep_accents=UpperCamelCase__     ,  bos_token=UpperCamelCase__     ,  eos_token=UpperCamelCase__     ,  unk_token=UpperCamelCase__     ,  sep_token=UpperCamelCase__     ,  pad_token=UpperCamelCase__     ,  cls_token=UpperCamelCase__     ,  mask_token=UpperCamelCase__     ,  additional_special_tokens=UpperCamelCase__     ,  sp_model_kwargs=self.sp_model_kwargs     ,  **UpperCamelCase__     ,  )

           lowerCamelCase   :      Any			 =					3

           lowerCamelCase   :      Optional[Any]			 =					do_lower_case
           lowerCamelCase   :      List[Any]			 =					remove_space
           lowerCamelCase   :      str			 =					keep_accents
           lowerCamelCase   :      List[Any]			 =					vocab_file

           lowerCamelCase   :      int			 =					spm.SentencePieceProcessor(**self.sp_model_kwargs     )
           self.sp_model.Load(UpperCamelCase__     )







    @property
    def     _lowercase     (    self     )     ->      Optional[Any]:
           return len(self.sp_model     )







    def     _lowercase     (    self     )     ->      Optional[int]:
           lowerCamelCase   :      int			 =					{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     )     ->      Optional[Any]:
           lowerCamelCase   :      Optional[int]			 =					self.__dict__.copy()
           lowerCamelCase   :      Union[str, Any]			 =					None
           return state







    def __setstate__(    self     ,  UpperCamelCase__     )     ->      int:
           lowerCamelCase   :      int			 =					d

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

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







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      Any:
           if self.remove_space:
                  lowerCamelCase   :      Dict			 =					" ".join(inputs.strip().split()     )
           else:
                  lowerCamelCase   :      Union[str, Any]			 =					inputs
           lowerCamelCase   :      Optional[Any]			 =					outputs.replace("``"     ,  "\""     ).replace("''"     ,  "\""     )

           if not self.keep_accents:
                  lowerCamelCase   :      Optional[int]			 =					unicodedata.normalize("NFKD"     ,  UpperCamelCase__     )
                  lowerCamelCase   :      List[Any]			 =					"".join([c for c in outputs if not unicodedata.combining(UpperCamelCase__     )]     )
           if self.do_lower_case:
                  lowerCamelCase   :      List[str]			 =					outputs.lower()

           return outputs







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      List[str]:
           lowerCamelCase   :      Optional[Any]			 =					self.preprocess_text(UpperCamelCase__     )
           lowerCamelCase   :      Dict			 =					self.sp_model.encode(UpperCamelCase__     ,  out_type=UpperCamelCase__     )
           lowerCamelCase   :      Dict			 =					[]
           for piece in pieces:
                  if len(UpperCamelCase__     ) > 1 and piece[-1] == str(","     ) and piece[-2].isdigit():
                         lowerCamelCase   :      List[Any]			 =					self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCamelCase__     ,  ""     )     )
                         if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                                if len(cur_pieces[0]     ) == 1:
                                       lowerCamelCase   :      Union[str, Any]			 =					cur_pieces[1:]
                                else:
                                       lowerCamelCase   :      Optional[int]			 =					cur_pieces[0][1:]
                         cur_pieces.append(piece[-1]     )
                         new_pieces.extend(UpperCamelCase__     )
                  else:
                         new_pieces.append(UpperCamelCase__     )

           return new_pieces







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      int:
           return self.sp_model.PieceToId(UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      Tuple:
           return self.sp_model.IdToPiece(UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      List[str]:
           lowerCamelCase   :      Union[str, Any]			 =					"".join(UpperCamelCase__     ).replace(UpperCamelCase__     ,  " "     ).strip()
           return out_string







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = False     ,  UpperCamelCase__ = None     ,  UpperCamelCase__ = True     ,  **UpperCamelCase__     ,  )     ->      str:
           lowerCamelCase   :      Optional[int]			 =					kwargs.pop("use_source_tokenizer"     ,  UpperCamelCase__     )

           lowerCamelCase   :      Optional[int]			 =					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
           lowerCamelCase   :      Any			 =					[]
           lowerCamelCase   :      Any			 =					[]
           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__     )     )
                                lowerCamelCase   :      int			 =					[]
                         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:
           # By default, there are no spaces between special tokens
           lowerCamelCase   :      Union[str, Any]			 =					"".join(UpperCamelCase__     )

           lowerCamelCase   :      Tuple			 =					(
               clean_up_tokenization_spaces
               if clean_up_tokenization_spaces is not None
               else self.clean_up_tokenization_spaces
           )
           if clean_up_tokenization_spaces:
                  lowerCamelCase   :      int			 =					self.clean_up_tokenization(UpperCamelCase__     )
                  return clean_text
           else:
                  return text







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = None     )     ->      List[int]:
           lowerCamelCase   :      str			 =					[self.sep_token_id]
           lowerCamelCase   :      Optional[int]			 =					[self.cls_token_id]
           if token_ids_a is None:
                  return token_ids_a + sep + cls
           return token_ids_a + sep + token_ids_a + sep + cls







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = None     ,  UpperCamelCase__ = False     )     ->      List[int]:

           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 not None:
                  return ([0] * len(UpperCamelCase__     )) + [1] + ([0] * len(UpperCamelCase__     )) + [1, 1]
           return ([0] * len(UpperCamelCase__     )) + [1, 1]







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = None     )     ->      List[int]:
           lowerCamelCase   :      Any			 =					[self.sep_token_id]
           lowerCamelCase   :      List[str]			 =					[2]

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







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = None     )     ->      Tuple[str]:
           if not os.path.isdir(UpperCamelCase__     ):
                  logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
                  return
           lowerCamelCase   :      Union[str, Any]			 =					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:
                         lowerCamelCase   :      str			 =					self.sp_model.serialized_model_proto()
                         fi.write(UpperCamelCase__     )

           return (out_vocab_file,)






