

import numpy as np

import datasets


snake_case_        =			'''
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/]
'''

snake_case_        =			'''\
@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}
}
'''

snake_case_        =			'''
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 				SCREAMING_SNAKE_CASE__ (datasets.Metric					):





    def 				snake_case_    (				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 				snake_case_    (				self						,       a						,       a):
         # convert to numpy arrays
         lowercase__   :       Optional[int]				=	np.array(a)
         lowercase__   :       str				=	np.array(a)

         # 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__   :       Any				=	X - np.mean(a)
         lowercase__   :       int				=	np.cov(reference_distribution.T)
         try:
              lowercase__   :       List[str]				=	np.linalg.inv(a)
         except np.linalg.LinAlgError:
              lowercase__   :       List[Any]				=	np.linalg.pinv(a)
         lowercase__   :       List[Any]				=	np.dot(a						,       a)
         lowercase__   :       Optional[Any]				=	np.dot(a						,       X_minus_mu.T).diagonal()

         return {"mahalanobis": mahal_dist}




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	Tuple														=					["""image_processor""", """tokenizer"""]
    __lowerCamelCase						:	int														=					"""ViltImageProcessor"""
    __lowerCamelCase						:	Union[str, Any]														=					("""BertTokenizer""", """BertTokenizerFast""")





    def __init__(				self						,       a=None						,       a=None						,       **a):
         lowercase__   :       Any				=	None
         if "feature_extractor" in kwargs:
              warnings.warn(
                  'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                  ' instead.'						,       a						,       )
              lowercase__   :       Union[str, Any]				=	kwargs.pop('feature_extractor')

         lowercase__   :       List[Any]				=	image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
              raise ValueError('You need to specify an `image_processor`.')
         if tokenizer is None:
              raise ValueError('You need to specify a `tokenizer`.')

         super().__init__(a						,       a)
         lowercase__   :       str				=	self.image_processor





    def __call__(				self						,       a						,       a = None						,       a = True						,       a = False						,       a = None						,       a = None						,       a = 0						,       a = None						,       a = None						,       a = None						,       a = False						,       a = False						,       a = False						,       a = False						,       a = True						,       a = None						,       **a						,       ):
         lowercase__   :       Optional[Any]				=	self.tokenizer(
             text=a						,       add_special_tokens=a						,       padding=a						,       truncation=a						,       max_length=a						,       stride=a						,       pad_to_multiple_of=a						,       return_token_type_ids=a						,       return_attention_mask=a						,       return_overflowing_tokens=a						,       return_special_tokens_mask=a						,       return_offsets_mapping=a						,       return_length=a						,       verbose=a						,       return_tensors=a						,       **a						,       )
         # add pixel_values + pixel_mask
         lowercase__   :       str				=	self.image_processor(a						,       return_tensors=a)
         encoding.update(a)

         return encoding





    def 				snake_case_    (				self						,       *a						,       **a):
         return self.tokenizer.batch_decode(*a						,       **a)





    def 				snake_case_    (				self						,       *a						,       **a):
         return self.tokenizer.decode(*a						,       **a)





    @property
    def 				snake_case_    (				self):
         lowercase__   :       int				=	self.tokenizer.model_input_names
         lowercase__   :       List[str]				=	self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))





    @property
    def 				snake_case_    (				self):
         warnings.warn(
             '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'						,       a						,       )
         return self.image_processor_class





    @property
    def 				snake_case_    (				self):
         warnings.warn(
             '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						,       a						,       )
         return self.image_processor

