




"""simple docstring"""







import numpy as np

import datasets


a      =							"\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"

a      =							"\\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"

a      =							"\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       SCREAMING_SNAKE_CASE__					(					datasets.Metric					):


						def   __lowercase (							self				:			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				:			Optional[int]						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			str					):
										lowerCAmelCase  =				np.array(lowerCAmelCase_					)
										lowerCAmelCase  =				np.array(lowerCAmelCase_					)

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

										return {"mahalanobis": mahal_dist}











"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a      =							{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a      =							[
		    'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'BioGptForCausalLM',
		    'BioGptForTokenClassification',
		    'BioGptForSequenceClassification',
		    'BioGptModel',
		    'BioGptPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
		from .tokenization_biogpt import BioGptTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_biogpt import (
				    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BioGptForCausalLM,
				    BioGptForSequenceClassification,
				    BioGptForTokenClassification,
				    BioGptModel,
				    BioGptPreTrainedModel,
				)


else:
		import sys

		a      =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





