'''simple docstring'''




import numpy as np

import datasets


_lowercase		:				int	       =		'\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'

_lowercase		:				Optional[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'

_lowercase		:				Union[str, Any]	       =		'\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 					__magic_name__		(					datasets.Metric):







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										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 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]       ,    lowercase_       :						Optional[int]       ,    lowercase_       :						List[str]					):
										lowercase_      :    Optional[int]       		= np.array(UpperCamelCase__					)
										lowercase_      :    List[str]       		= 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
										lowercase_      :    Optional[Any]       		= X - np.mean(UpperCamelCase__					)
										lowercase_      :    Optional[Any]       		= np.cov(reference_distribution.T					)
										try:
																	lowercase_      :    Tuple       		= np.linalg.inv(UpperCamelCase__					)
										except np.linalg.LinAlgError:
																	lowercase_      :    Any       		= np.linalg.pinv(UpperCamelCase__					)
										lowercase_      :    Optional[int]       		= np.dot(UpperCamelCase__       ,    UpperCamelCase__					)
										lowercase_      :    Any       		= np.dot(UpperCamelCase__       ,    X_minus_mu.T					).diagonal()

										return {"mahalanobis": mahal_dist}






'''simple docstring'''




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
						import tensorflow as tf

						from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM





@require_tf
@require_sentencepiece
@require_tokenizers
class 					__magic_name__		(					unittest.TestCase):







			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										lowercase_      :    Any       		= TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""					)
										lowercase_      :    Dict       		= AutoTokenizer.from_pretrained("""google/mt5-small"""					)

										lowercase_      :    Union[str, Any]       		= tokenizer("""Hello there"""       ,    return_tensors="""tf"""					).input_ids
										lowercase_      :    List[str]       		= tokenizer("""Hi I am"""       ,    return_tensors="""tf"""					).input_ids

										lowercase_      :    Optional[Any]       		= model(lowercase_       ,    labels=lowercase_					).loss
										lowercase_      :    Optional[int]       		= -tf.math.reduce_mean(lowercase_					).numpy()

										lowercase_      :    Optional[int]       		= -21.22_81_68
										self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 2E-4					)





