




'''simple docstring'''


import argparse
import os
import re


UpperCAmelCase_ 		=      '''src/transformers/models/auto'''


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
UpperCAmelCase_ 		=      re.compile(r"[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict")
# re pattern that matches identifiers in mappings
UpperCAmelCase_ 		=      re.compile(r"\s*\(\s*\"(\S[^\"]+)\"")


def 						SCREAMING_SNAKE_CASE       (      a_	:							int				,					a_	:							bool = False			):






  with open(_lowercase				,					'r'				,					encoding='utf-8'			) as f:
    __a			=					f.read()

  __a			=					content.split('\n'			)
  __a			=					[]
  __a			=					0
  while line_idx < len(_lowercase			):
    if _re_intro_mapping.search(lines[line_idx]			) is not None:
      __a			=					len(re.search(R'^(\s*)\S'				,					lines[line_idx]			).groups()[0]			) + 8
      # Start of a new mapping!
      while not lines[line_idx].startswith(' ' * indent + '('			):
        new_lines.append(lines[line_idx]			)
        line_idx += 1

      __a			=					[]
      while lines[line_idx].strip() != "]":
        # Blocks either fit in one line or not
        if lines[line_idx].strip() == "(":
          __a			=					line_idx
          while not lines[line_idx].startswith(' ' * indent + ')'			):
            line_idx += 1
          blocks.append('\n'.join(lines[start_idx : line_idx + 1]			)			)
        else:
          blocks.append(lines[line_idx]			)
        line_idx += 1

      # Sort blocks by their identifiers
      __a			=					sorted(_lowercase				,					key=lambda a_			: _re_identifier.search(_lowercase			).groups()[0]			)
      new_lines += blocks
    else:
      new_lines.append(lines[line_idx]			)
      line_idx += 1

  if overwrite:
    with open(_lowercase				,					'w'				,					encoding='utf-8'			) as f:
      f.write('\n'.join(_lowercase			)			)
  elif "\n".join(_lowercase			) != content:
    return True


def 						SCREAMING_SNAKE_CASE       (      a_	:							bool = False			):
  __a			=					[os.path.join(_lowercase				,					_lowercase			) for f in os.listdir(_lowercase			) if f.endswith('.py'			)]
  __a			=					[sort_auto_mapping(_lowercase				,					overwrite=_lowercase			) for fname in fnames]

  if not overwrite and any(_lowercase			):
    __a			=					[f for f, d in zip(_lowercase				,					_lowercase			) if d]
    raise ValueError(
        f"The following files have auto mappings that need sorting: {', '.join(_lowercase			)}. Run `make style` to fix"
        ' this.'			)


if __name__ == "__main__":
  UpperCAmelCase_ 		=      argparse.ArgumentParser()
  parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
  UpperCAmelCase_ 		=      parser.parse_args()

  sort_all_auto_mappings(not args.check_only)









'''simple docstring'''


import numpy as np

import datasets


UpperCAmelCase_ 		=      "\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"

UpperCAmelCase_ 		=      "\\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"

UpperCAmelCase_ 		=      "\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    __lowercase						(  datasets.Metric ):







   def 		UpperCamelCase__			(	self      )				->    Optional[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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      )				->    Optional[int]:
     # convert to numpy arrays
     __a			=					np.array(UpperCamelCase      )
     __a			=					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
     __a			=					X - np.mean(UpperCamelCase      )
     __a			=					np.cov(reference_distribution.T      )
     try:
       __a			=					np.linalg.inv(UpperCamelCase      )
     except np.linalg.LinAlgError:
       __a			=					np.linalg.pinv(UpperCamelCase      )
     __a			=					np.dot(UpperCamelCase      ,							UpperCamelCase      )
     __a			=					np.dot(UpperCamelCase      ,							X_minus_mu.T      ).diagonal()

     return {"mahalanobis": mahal_dist}



