from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE		:     str         =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:     Tuple         =	{
    "microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json",
}

class        UpperCamelCase						(				__a	,	__a				):
						a__		:Any      =			'''resnet'''
						a__		:Union[str, Any]      =			['''basic''', '''bottleneck''']





						def __init__(self			,     __UpperCamelCase=3			,     __UpperCamelCase=64			,     __UpperCamelCase=[256, 512, 1_024, 2_048]			,     __UpperCamelCase=[3, 4, 6, 3]			,     __UpperCamelCase="bottleneck"			,     __UpperCamelCase="relu"			,     __UpperCamelCase=False			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     **__UpperCamelCase			,     )				->				List[str]:
												super().__init__(**__UpperCamelCase						)
												if layer_type not in self.layer_types:
																		raise ValueError(f'''layer_type={layer_type} is not one of {",".join(self.layer_types						)}'''						)
												UpperCamelCase_		:							Union[str, Any]  =		num_channels
												UpperCamelCase_		:							Optional[Any]  =		embedding_size
												UpperCamelCase_		:							Optional[Any]  =		hidden_sizes
												UpperCamelCase_		:							int  =		depths
												UpperCamelCase_		:							Optional[Any]  =		layer_type
												UpperCamelCase_		:							Any  =		hidden_act
												UpperCamelCase_		:							int  =		downsample_in_first_stage
												UpperCamelCase_		:							List[Any]  =		["""stem"""] + [f'''stage{idx}''' for idx in range(1			,     len(__UpperCamelCase						) + 1						)]
												UpperCamelCase_,UpperCamelCase_		:							List[str]  =		get_aligned_output_features_output_indices(
												    out_features=__UpperCamelCase			,     out_indices=__UpperCamelCase			,     stage_names=self.stage_names						)


class        UpperCamelCase						(				__a				):
						a__		:Dict      =			version.parse('''1.11'''				)





						@property
						def        A_							(self						)				->				Mapping[str, Mapping[int, str]]:
												return OrderedDict(
												    [
												        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
												    ]						)





						@property
						def        A_							(self						)				->				float:
												return 1E-3







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}






