


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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_rembert import RemBertTokenizer
else:
			UpperCAmelCase      =   None

UpperCAmelCase      =   logging.get_logger(__name__)
UpperCAmelCase      =   {"""vocab_file""": """sentencepiece.model""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase      =   {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
    """tokenizer_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/tokenizer.json""",
    },
}

UpperCAmelCase      =   {
    """google/rembert""": 256,
}

UpperCAmelCase      =   """▁"""







class lowerCAmelCase_						(			lowerCamelCase__	):

	'''simple docstring'''

	__snake_case												=  VOCAB_FILES_NAMES
	__snake_case												=  PRETRAINED_VOCAB_FILES_MAP
	__snake_case												=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__snake_case												=  RemBertTokenizer



	def __init__(     self		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=True		,				_UpperCAmelCase=True		,				_UpperCAmelCase=False		,				_UpperCAmelCase="[CLS]"		,				_UpperCAmelCase="[SEP]"		,				_UpperCAmelCase="<unk>"		,				_UpperCAmelCase="[SEP]"		,				_UpperCAmelCase="<pad>"		,				_UpperCAmelCase="[CLS]"		,				_UpperCAmelCase="[MASK]"		,				**_UpperCAmelCase		,				):
							# Mask token behave like a normal word, i.e. include the space before it
							snake_case_	=				AddedToken(_UpperCAmelCase		,				lstrip=_UpperCAmelCase		,				rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase		,				_UpperCAmelCase ) else mask_token

							super().__init__(
							    _UpperCAmelCase		,				tokenizer_file=_UpperCAmelCase		,				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		,				**_UpperCAmelCase		,				)

							snake_case_	=				do_lower_case
							snake_case_	=				remove_space
							snake_case_	=				keep_accents
							snake_case_	=				vocab_file
							snake_case_	=				False if not self.vocab_file else True



	def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase = None ):
							snake_case_	=				[self.sep_token_id]
							snake_case_	=				[self.cls_token_id]
							if token_ids_a is None:
													return cls + token_ids_a + sep
							return cls + token_ids_a + sep + token_ids_a + sep



	def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase = None		,				_UpperCAmelCase = False ):

							if already_has_special_tokens:
													if token_ids_a is not None:
																			raise ValueError(
																			    '''You should not supply a second sequence if the provided sequence of '''
																			    '''ids is already formatted with special tokens for the model.''' )
													return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

							if token_ids_a is not None:
													return [1] + ([0] * len(_UpperCAmelCase )) + [1] + ([0] * len(_UpperCAmelCase )) + [1]
							return [1] + ([0] * len(_UpperCAmelCase )) + [1]



	def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase = None ):
							snake_case_	=				[self.sep_token_id]
							snake_case_	=				[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]





	def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase = None ):
							if not os.path.isdir(_UpperCAmelCase ):
													logger.error('''Vocabulary path ({}) should be a directory'''.format(_UpperCAmelCase ) )
													return
							snake_case_	=				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 ):
													copyfile(self.vocab_file		,				_UpperCAmelCase )

							return (out_vocab_file,)



import numpy as np

import datasets


UpperCAmelCase      =   """
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      =   """\
@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      =   """
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 lowerCAmelCase_						(			datasets.Metric	):

 '''simple docstring'''

 def 						UpperCamelCase__					(     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 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       # convert to numpy arrays
       snake_case_	=				np.array(_UpperCAmelCase )
       snake_case_	=				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
       snake_case_	=				X - np.mean(_UpperCAmelCase )
       snake_case_	=				np.cov(reference_distribution.T )
       try:
             snake_case_	=				np.linalg.inv(_UpperCAmelCase )
       except np.linalg.LinAlgError:
             snake_case_	=				np.linalg.pinv(_UpperCAmelCase )
       snake_case_	=				np.dot(_UpperCAmelCase		,				_UpperCAmelCase )
       snake_case_	=				np.dot(_UpperCAmelCase		,				X_minus_mu.T ).diagonal()

       return {"mahalanobis": mahal_dist}