




'''simple docstring'''




lowercase__  							=					{
    "Pillow": "Pillow<10.0.0",
    "accelerate": "accelerate>=0.20.3",
    "av": "av==9.2.0",
    "beautifulsoup4": "beautifulsoup4",
    "black": "black~=23.1",
    "codecarbon": "codecarbon==1.2.0",
    "cookiecutter": "cookiecutter==1.7.3",
    "dataclasses": "dataclasses",
    "datasets": "datasets!=2.5.0",
    "decord": "decord==0.6.0",
    "deepspeed": "deepspeed>=0.9.3",
    "diffusers": "diffusers",
    "dill": "dill<0.3.5",
    "evaluate": "evaluate>=0.2.0",
    "fairscale": "fairscale>0.3",
    "faiss-cpu": "faiss-cpu",
    "fastapi": "fastapi",
    "filelock": "filelock",
    "flax": "flax>=0.4.1,<=0.7.0",
    "ftfy": "ftfy",
    "fugashi": "fugashi>=1.0",
    "GitPython": "GitPython<3.1.19",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.14.1,<1.0",
    "importlib_metadata": "importlib_metadata",
    "ipadic": "ipadic>=1.0.0,<2.0",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2,<=0.4.13",
    "jaxlib": "jaxlib>=0.1.65,<=0.4.13",
    "jieba": "jieba",
    "kenlm": "kenlm",
    "keras-nlp": "keras-nlp>=0.3.1",
    "librosa": "librosa",
    "nltk": "nltk",
    "natten": "natten>=0.14.6",
    "numpy": "numpy>=1.17",
    "onnxconverter-common": "onnxconverter-common",
    "onnxruntime-tools": "onnxruntime-tools>=1.4.2",
    "onnxruntime": "onnxruntime>=1.4.0",
    "opencv-python": "opencv-python",
    "optuna": "optuna",
    "optax": "optax>=0.0.8,<=0.1.4",
    "packaging": "packaging>=20.0",
    "parameterized": "parameterized",
    "phonemizer": "phonemizer",
    "protobuf": "protobuf",
    "psutil": "psutil",
    "pyyaml": "pyyaml>=5.1",
    "pydantic": "pydantic<2",
    "pytest": "pytest>=7.2.0",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "python": "python>=3.8.0",
    "ray[tune]": "ray[tune]",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "rhoknp": "rhoknp>=1.1.0,<1.3.1",
    "rjieba": "rjieba",
    "rouge-score": "rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1",
    "ruff": "ruff>=0.0.241,<=0.0.259",
    "sacrebleu": "sacrebleu>=1.4.12,<2.0.0",
    "sacremoses": "sacremoses",
    "safetensors": "safetensors>=0.3.1",
    "sagemaker": "sagemaker>=2.31.0",
    "scikit-learn": "scikit-learn",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "sigopt": "sigopt",
    "starlette": "starlette",
    "sudachipy": "sudachipy>=0.6.6",
    "sudachidict_core": "sudachidict_core>=20220729",
    "tensorflow-cpu": "tensorflow-cpu>=2.6,<2.14",
    "tensorflow": "tensorflow>=2.6,<2.14",
    "tensorflow-text": "tensorflow-text<2.14",
    "tf2onnx": "tf2onnx",
    "timeout-decorator": "timeout-decorator",
    "timm": "timm",
    "tokenizers": "tokenizers>=0.11.1,!=0.11.3,<0.14",
    "torch": "torch>=1.9,!=1.12.0",
    "torchaudio": "torchaudio",
    "torchvision": "torchvision",
    "pyctcdecode": "pyctcdecode>=0.4.0",
    "tqdm": "tqdm>=4.27",
    "unidic": "unidic>=1.0.2",
    "unidic_lite": "unidic_lite>=1.0.7",
    "urllib3": "urllib3<2.0.0",
    "uvicorn": "uvicorn",
}






import numpy as np

import datasets


SCREAMING_SNAKE_CASE		:     Optional[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"

SCREAMING_SNAKE_CASE		:     Dict         =	"\\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"

SCREAMING_SNAKE_CASE		:     List[str]         =	"\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        UpperCamelCase						(				datasets.Metric				):





						def        A_							(self						)				->				str:
												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			,     __UpperCamelCase			,     __UpperCamelCase						)				->				int:
												# convert to numpy arrays
												UpperCamelCase_		:							int  =		np.array(__UpperCamelCase						)
												UpperCamelCase_		:							Union[str, Any]  =		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
												UpperCamelCase_		:							str  =		X - np.mean(__UpperCamelCase						)
												UpperCamelCase_		:							Dict  =		np.cov(reference_distribution.T						)
												try:
																		UpperCamelCase_		:							str  =		np.linalg.inv(__UpperCamelCase						)
												except np.linalg.LinAlgError:
																		UpperCamelCase_		:							List[Any]  =		np.linalg.pinv(__UpperCamelCase						)
												UpperCamelCase_		:							Tuple  =		np.dot(__UpperCamelCase			,     __UpperCamelCase						)
												UpperCamelCase_		:							Tuple  =		np.dot(__UpperCamelCase			,     X_minus_mu.T						).diagonal()

												return {"mahalanobis": mahal_dist}






