import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
SCREAMING_SNAKE_CASE		:     List[str]         =	object()

# For specifying empty leaf dict `{}`
SCREAMING_SNAKE_CASE		:     Dict         =	object()






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Union[str, Any]		, _SCREAMING_SNAKE_CASE     :				Any			):
						UpperCamelCase_		:							List[str]  =		tuple((re.compile(x + """$"""			) for x in qs)			)
						for i in range(len(_SCREAMING_SNAKE_CASE			) - len(_SCREAMING_SNAKE_CASE			) + 1			):
												UpperCamelCase_		:							List[str]  =		[x.match(_SCREAMING_SNAKE_CASE			) for x, y in zip(_SCREAMING_SNAKE_CASE		, ks[i:]			)]
												if matches and all(_SCREAMING_SNAKE_CASE			):
																		return True
						return False






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Optional[Any]			):
						def replace(_SCREAMING_SNAKE_CASE     :				Dict		, _SCREAMING_SNAKE_CASE     :				Dict			):
												for rule, replacement in rules:
																		if _match(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			):
																								return replacement
												return val

						return replace






def     lowerCAmelCase_			(   ):
						return [
						    # embeddings
						    (("transformer", "wpe", "embedding"), P("""mp"""		, _SCREAMING_SNAKE_CASE			)),
						    (("transformer", "wte", "embedding"), P("""mp"""		, _SCREAMING_SNAKE_CASE			)),
						    # atention
						    (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(_SCREAMING_SNAKE_CASE		, """mp"""			)),
						    (("attention", "out_proj", "kernel"), P("""mp"""		, _SCREAMING_SNAKE_CASE			)),
						    (("attention", "out_proj", "bias"), None),
						    # mlp
						    (("mlp", "c_fc", "kernel"), P(_SCREAMING_SNAKE_CASE		, """mp"""			)),
						    (("mlp", "c_fc", "bias"), P("""mp"""			)),
						    (("mlp", "c_proj", "kernel"), P("""mp"""		, _SCREAMING_SNAKE_CASE			)),
						    (("mlp", "c_proj", "bias"), None),
						    # layer norms
						    ((r"ln_\d+", "bias"), None),
						    ((r"\d+", r"ln_\d+", "scale"), None),
						    (("ln_f", "bias"), None),
						    (("ln_f", "scale"), None),
						]







def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Tuple			):
						UpperCamelCase_		:							Optional[int]  =		_get_partition_rules()
						UpperCamelCase_		:							int  =		_replacement_rules(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							int  =		{k: _unmatched for k in flatten_dict(_SCREAMING_SNAKE_CASE			)}
						UpperCamelCase_		:							List[str]  =		{k: replace(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			) for k, v in initd.items()}
						assert _unmatched not in result.values(), "Incomplete partition spec."
						return freeze(unflatten_dict(_SCREAMING_SNAKE_CASE			)			)







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}






