

'''simple docstring'''
import numpy as np

import datasets


_UpperCAmelCase  :		Optional[int]  =		'''
Compute the Mahalanobis Distance

Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introduced by Prof. P. C. Mahalanobis in 1936
and has been used in various statistical applications ever since
[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]
'''

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

_UpperCAmelCase  :		List[Any]  =		'''
Args:
    X: List of datapoints to be compared with the `reference_distribution`.
    reference_distribution: List of datapoints from the reference distribution we want to compare to.
Returns:
    mahalanobis: The Mahalonobis distance for each datapoint in `X`.
Examples:

    >>> mahalanobis_metric = datasets.load_metric("mahalanobis")
    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])
    >>> print(results)
    {\'mahalanobis\': array([0.5])}
'''

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







 def 						_A(       self   ):
   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 						_A(       self  , snake_case_  , snake_case_   ):
   # convert to numpy arrays
   lowercase									=np.array(snake_case_   )
   lowercase									=np.array(snake_case_   )

   # 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
   lowercase									=X - np.mean(snake_case_   )
   lowercase									=np.cov(reference_distribution.T   )
   try:
     lowercase									=np.linalg.inv(snake_case_   )
   except np.linalg.LinAlgError:
     lowercase									=np.linalg.pinv(snake_case_   )
   lowercase									=np.dot(snake_case_  , snake_case_   )
   lowercase									=np.dot(snake_case_  , X_minus_mu.T   ).diagonal()

   return {"mahalanobis": mahal_dist}








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

import sentencepiece as spm

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


__a   :							List[str]						= "▁"

__a   :							int						= {"vocab_file": "spiece.model"}

__a   :							Any						= {
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

__a   :							Any						= {
    "google/pegasus-xsum": 512,
}


__a   :							Union[str, Any]						= logging.get_logger(__name__)


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					VOCAB_FILES_NAMES

     SCREAMING_SNAKE_CASE     =					VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE     =					PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE     =					["input_ids", "attention_mask"]




     def __init__(					self							:      List[str]			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      Dict="<pad>"			,						UpperCamelCase_							:      Dict="</s>"			,						UpperCamelCase_							:      Optional[Any]="<unk>"			,						UpperCamelCase_							:      List[Any]="<mask_2>"			,						UpperCamelCase_							:      List[Any]="<mask_1>"			,						UpperCamelCase_							:      str=None			,						UpperCamelCase_							:      Dict=103			,						UpperCamelCase_							:      Optional[Dict[str, Any]] = None			,						**UpperCamelCase_							:      int			,						):


         """simple docstring"""



         __A       =						offset
         if additional_special_tokens is not None:
             if not isinstance(UpperCamelCase_			,						UpperCamelCase_					):
                 raise TypeError(
                     F"additional_special_tokens should be of type {type(UpperCamelCase_					)}, but is"
                     F" {type(UpperCamelCase_					)}"					)

             __A       =						(
                 ([mask_token_sent] + additional_special_tokens)
                 if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                 else additional_special_tokens
             )
             # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
             additional_special_tokens_extended += [
                 F"<unk_{i}>" for i in range(len(UpperCamelCase_					)			,						self.offset - 1					)
             ]

             if len(set(UpperCamelCase_					)					) != len(UpperCamelCase_					):
                 raise ValueError(
                     """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
                     F" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."					)
             __A       =						additional_special_tokens_extended
         else:
             __A       =						[mask_token_sent] if mask_token_sent is not None else []
             additional_special_tokens += [F"<unk_{i}>" for i in range(2			,						self.offset					)]

         __A       =						{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             eos_token=UpperCamelCase_			,						unk_token=UpperCamelCase_			,						mask_token=UpperCamelCase_			,						pad_token=UpperCamelCase_			,						mask_token_sent=UpperCamelCase_			,						offset=UpperCamelCase_			,						additional_special_tokens=UpperCamelCase_			,						sp_model_kwargs=self.sp_model_kwargs			,						**UpperCamelCase_			,						)
         __A       =						mask_token_sent
         __A       =						vocab_file
         __A       =						spm.SentencePieceProcessor(**self.sp_model_kwargs					)
         self.sp_model.Load(UpperCamelCase_					)

         # add special tokens to encoder dict
         __A       =						{
             0: self.pad_token,
             1: self.eos_token,
         }

         if self.mask_token_sent is not None:
             self.encoder.update(
                 {
                     2: self.mask_token_sent,
                     3: self.mask_token,
                 }					)

         if self.offset > 0:
             # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
             # mask_token_sent is already added to list -> so start at 1
             self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1			,						self.offset - 1					)}					)

         __A       =						{v: k for k, v in self.encoder.items()}




     @property
     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         return len(self.sp_model					) + self.offset




     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         __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							:      Tuple					):


         """simple docstring"""



         __A       =						self.__dict__.copy()
         __A       =						None
         return state




     def __setstate__(					self							:      Tuple			,						UpperCamelCase_							:      Dict					):


         """simple docstring"""



         __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 		lowerCAmelCase_  (					self							:      Tuple			,						UpperCamelCase_							:      str					):


         """simple docstring"""



         return self.sp_model.encode(UpperCamelCase_			,						out_type=UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      str			,						UpperCamelCase_							:      str					):


         """simple docstring"""



         if token in self.decoder:
             return self.decoder[token]
         elif token in self.added_tokens_decoder:
             return self.added_tokens_decoder[token]
         __A       =						self.sp_model.piece_to_id(UpperCamelCase_					)
         return sp_id + self.offset




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      int					):


         """simple docstring"""



         if index in self.encoder:
             return self.encoder[index]
         elif index in self.added_tokens_encoder:
             return self.added_tokens_encoder[index]
         else:
             __A       =						self.sp_model.IdToPiece(index - self.offset					)
         return token




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      List[str]					):


         """simple docstring"""



         __A       =						[]
         __A       =						""""""
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                 out_string += self.sp_model.decode(UpperCamelCase_					) + token
                 __A       =						[]
             else:
                 current_sub_tokens.append(UpperCamelCase_					)
         out_string += self.sp_model.decode(UpperCamelCase_					)
         return out_string.strip()




     def 		lowerCAmelCase_  (					self							:      Any			,						UpperCamelCase_							:      List[Any]=False					):


         """simple docstring"""



         return 1




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      Tuple					):


         """simple docstring"""



         __A       =						set(self.all_special_ids					)  # call it once instead of inside list comp
         all_special_ids.remove(self.unk_token_id					)  # <unk> is only sometimes special

         return [1 if x in all_special_ids else 0 for x in seq]




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      List			,						UpperCamelCase_							:      Optional[List] = None			,						UpperCamelCase_							:      bool = False					):


         """simple docstring"""



         if already_has_special_tokens:
             return self._special_token_mask(UpperCamelCase_					)
         elif token_ids_a is None:
             return self._special_token_mask(UpperCamelCase_					) + [1]
         else:
             return self._special_token_mask(token_ids_a + token_ids_a					) + [1]




     def 		lowerCAmelCase_  (					self							:      List[Any]			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Optional[Any]=None					):


         """simple docstring"""



         if token_ids_a is None:
             return token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_a + token_ids_a + [self.eos_token_id]





     def 		lowerCAmelCase_  (					self							:      List[Any]			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Optional[str] = None					):


         """simple docstring"""



         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,)




