
"""simple docstring"""





import numpy as np

import datasets


_a		       =							'\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'

_a		       =							'\\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'

_a		       =							'\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 A_	(datasets.Metric      ):




  '''simple docstring'''







  def        UpperCamelCase__					(		self  ):

     """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        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     # convert to numpy arrays
     UpperCAmelCase_					:					Union[str, Any]			     =    np.array(lowercase_  )
     UpperCAmelCase_					:					Any			     =    np.array(lowercase_  )

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

     return {"mahalanobis": mahal_dist}




"""simple docstring"""





import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_a		       =							'src/diffusers'

# Matches is_xxx_available()
_a		       =							re.compile(R'is\_([a-z_]*)_available\(\)')
# Matches from xxx import bla
_a		       =							re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')


_a		       =							'\n{0} = None\n'

_a		       =							'\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n'


_a		       =							'\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'







def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					int			     =    _re_backend.findall(__lowerCamelCase  )
   if len(__lowerCamelCase  ) == 0:
      return None

   return "_and_".join(__lowerCamelCase  )







def 	__a     (		):
   with open(os.path.join(__lowerCamelCase,		"__init__.py"  ),		"r",		encoding="utf-8",		newline="\n"  ) as f:
      UpperCAmelCase_					:					Optional[int]			     =    f.readlines()

   # Get to the point we do the actual imports for type checking
   UpperCAmelCase_					:					Union[str, Any]			     =    0
   UpperCAmelCase_					:					Optional[int]			     =    {}
   # Go through the end of the file
   while line_index < len(__lowerCamelCase  ):
      # If the line contains is_backend_available, we grab all objects associated with the `else` block
      UpperCAmelCase_					:					Union[str, Any]			     =    find_backend(lines[line_index]  )
      if backend is not None:
         while not lines[line_index].startswith("else:"  ):
            line_index += 1
         line_index += 1
         UpperCAmelCase_					:					List[str]			     =    []
         # Until we unindent, add backend objects to the list
         while line_index < len(__lowerCamelCase  ) and len(lines[line_index]  ) > 1:
            UpperCAmelCase_					:					Union[str, Any]			     =    lines[line_index]
            UpperCAmelCase_					:					Optional[Any]			     =    _re_single_line_import.search(__lowerCamelCase  )
            if single_line_import_search is not None:
               objects.extend(single_line_import_search.groups()[0].split(", "  )  )
            elif line.startswith(" " * 8  ):
               objects.append(line[8:-2]  )
            line_index += 1

         if len(__lowerCamelCase  ) > 0:
            UpperCAmelCase_					:					Optional[int]			     =    objects
      else:
         line_index += 1

   return backend_specific_objects







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   if name.isupper():
      return DUMMY_CONSTANT.format(__lowerCamelCase  )
   elif name.islower():
      return DUMMY_FUNCTION.format(__lowerCamelCase,		__lowerCamelCase  )
   else:
      return DUMMY_CLASS.format(__lowerCamelCase,		__lowerCamelCase  )







def 	__a     (		__lowerCamelCase=None  ):
   if backend_specific_objects is None:
      UpperCAmelCase_					:					Tuple			     =    read_init()
   # For special correspondence backend to module name as used in the function requires_modulename
   UpperCAmelCase_					:					str			     =    {}

   for backend, objects in backend_specific_objects.items():
      UpperCAmelCase_					:					int			     =    "[" + ", ".join(f"""\"{b}\"""" for b in backend.split("_and_"  )  ) + "]"
      UpperCAmelCase_					:					Dict			     =    "# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
      dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
      dummy_file += "\n".join([create_dummy_object(__lowerCamelCase,		__lowerCamelCase  ) for o in objects]  )
      UpperCAmelCase_					:					int			     =    dummy_file

   return dummy_files







def 	__a     (		__lowerCamelCase=False  ):
   UpperCAmelCase_					:					Optional[Any]			     =    create_dummy_files()
   # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
   UpperCAmelCase_					:					Union[str, Any]			     =    {"torch": "pt"}

   # Locate actual dummy modules and read their content.
   UpperCAmelCase_					:					List[str]			     =    os.path.join(__lowerCamelCase,		"utils"  )
   UpperCAmelCase_					:					Optional[int]			     =    {
       backend: os.path.join(__lowerCamelCase,		f"""dummy_{short_names.get(__lowerCamelCase,		__lowerCamelCase  )}_objects.py"""  )
       for backend in dummy_files.keys()
   }

   UpperCAmelCase_					:					Any			     =    {}
   for backend, file_path in dummy_file_paths.items():
      if os.path.isfile(__lowerCamelCase  ):
         with open(__lowerCamelCase,		"r",		encoding="utf-8",		newline="\n"  ) as f:
            UpperCAmelCase_					:					Optional[int]			     =    f.read()
      else:
         UpperCAmelCase_					:					Any			     =    ""

   for backend in dummy_files.keys():
      if dummy_files[backend] != actual_dummies[backend]:
         if overwrite:
            print(
                f"""Updating diffusers.utils.dummy_{short_names.get(__lowerCamelCase,		__lowerCamelCase  )}_objects.py as the main """
                "__init__ has new objects."  )
            with open(dummy_file_paths[backend],		"w",		encoding="utf-8",		newline="\n"  ) as f:
               f.write(dummy_files[backend]  )
         else:
            raise ValueError(
                "The main __init__ has objects that are not present in "
                f"""diffusers.utils.dummy_{short_names.get(__lowerCamelCase,		__lowerCamelCase  )}_objects.py. Run `make fix-copies` """
                "to fix this."  )


if __name__ == "__main__":
    _a		       =							argparse.ArgumentParser()
    parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
    _a		       =							parser.parse_args()

    check_dummies(args.fix_and_overwrite)


