


import numpy as np

import datasets


UpperCamelCase								=	"""
Compute the Mahalanobis Distance

Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introduced by Prof. P. C. Mahalanobis in 1936
and has been used in various statistical applications ever since
[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]
"""

UpperCamelCase								=	"""\
@article{de2000mahalanobis,
  title={The mahalanobis distance},
  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},
  journal={Chemometrics and intelligent laboratory systems},
  volume={50},
  number={1},
  pages={1--18},
  year={2000},
  publisher={Elsevier}
}
"""

UpperCamelCase								=	"""
Args:
    X: List of datapoints to be compared with the `reference_distribution`.
    reference_distribution: List of datapoints from the reference distribution we want to compare to.
Returns:
    mahalanobis: The Mahalonobis distance for each datapoint in `X`.
Examples:

    >>> mahalanobis_metric = datasets.load_metric(\"mahalanobis\")
    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])
    >>> print(results)
    {'mahalanobis': array([0.5])}
"""






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,							_KWARGS_DESCRIPTION			)
class   _lowerCamelCase (					datasets.Metric			):




       """simple docstring"""



       def        _snake_case       (      self		)->Union[str, Any]:




         '''simple docstring'''





         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        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->Union[str, Any]:




         '''simple docstring'''





         A_      :				List[Any]		     =					np.array(_SCREAMING_SNAKE_CASE		)
         A_      :				Optional[Any]		     =					np.array(_SCREAMING_SNAKE_CASE		)

         # 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
         A_      :				str		     =					X - np.mean(_SCREAMING_SNAKE_CASE		)
         A_      :				List[str]		     =					np.cov(reference_distribution.T		)
         try:
           A_      :				Optional[Any]		     =					np.linalg.inv(_SCREAMING_SNAKE_CASE		)
         except np.linalg.LinAlgError:
           A_      :				Optional[int]		     =					np.linalg.pinv(_SCREAMING_SNAKE_CASE		)
         A_      :				Optional[Any]		     =					np.dot(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
         A_      :				Optional[int]		     =					np.dot(_SCREAMING_SNAKE_CASE				,       X_minus_mu.T		).diagonal()

         return {"mahalanobis": mahal_dist}








from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase								=	logging.get_logger(__name__)

UpperCamelCase								=	{
    """vinvino02/glpn-kitti""": """https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json""",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    "glpn"



       def __init__(      self				,       _SCREAMING_SNAKE_CASE=3				,       _SCREAMING_SNAKE_CASE=4				,       _SCREAMING_SNAKE_CASE=[2, 2, 2, 2]				,       _SCREAMING_SNAKE_CASE=[8, 4, 2, 1]				,       _SCREAMING_SNAKE_CASE=[32, 64, 160, 256]				,       _SCREAMING_SNAKE_CASE=[7, 3, 3, 3]				,       _SCREAMING_SNAKE_CASE=[4, 2, 2, 2]				,       _SCREAMING_SNAKE_CASE=[1, 2, 5, 8]				,       _SCREAMING_SNAKE_CASE=[4, 4, 4, 4]				,       _SCREAMING_SNAKE_CASE="gelu"				,       _SCREAMING_SNAKE_CASE=0.0				,       _SCREAMING_SNAKE_CASE=0.0				,       _SCREAMING_SNAKE_CASE=0.0_2				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=1e-6				,       _SCREAMING_SNAKE_CASE=64				,       _SCREAMING_SNAKE_CASE=10				,       _SCREAMING_SNAKE_CASE=-1				,       **_SCREAMING_SNAKE_CASE				,       )->Any:




         '''simple docstring'''





         super().__init__(**_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[int]		     =					num_channels
         A_      :				Union[str, Any]		     =					num_encoder_blocks
         A_      :				int		     =					depths
         A_      :				Dict		     =					sr_ratios
         A_      :				Any		     =					hidden_sizes
         A_      :				int		     =					patch_sizes
         A_      :				Optional[int]		     =					strides
         A_      :				str		     =					mlp_ratios
         A_      :				List[str]		     =					num_attention_heads
         A_      :				str		     =					hidden_act
         A_      :				int		     =					hidden_dropout_prob
         A_      :				List[Any]		     =					attention_probs_dropout_prob
         A_      :				Optional[Any]		     =					initializer_range
         A_      :				Tuple		     =					drop_path_rate
         A_      :				Optional[int]		     =					layer_norm_eps
         A_      :				List[str]		     =					decoder_hidden_size
         A_      :				List[Any]		     =					max_depth
         A_      :				List[Any]		     =					head_in_index




