




import unittest

import numpy as np

def     _SCREAMING_SNAKE_CASE    (							lowercase			: np.ndarray			,							lowercase			: np.ndarray			,							lowercase			: np.ndarray			,							lowercase			: np.ndarray | None = None			,							):


      '''simple docstring'''



      lowerCamelCase_	  =					np.shape(lowercase  )
      lowerCamelCase_	  =					np.shape(lowercase  )
      lowerCamelCase_	  =					np.shape(lowercase  )

      if shape_a[0] != shape_b[0]:
            lowerCamelCase_	  =					(
                'Expected the same number of rows for A and B. '
                f"""Instead found A of size {shape_a} and B of size {shape_b}"""
            )
            raise ValueError(lowercase  )

      if shape_b[1] != shape_c[1]:
            lowerCamelCase_	  =					(
                'Expected the same number of columns for B and C. '
                f"""Instead found B of size {shape_b} and C of size {shape_c}"""
            )
            raise ValueError(lowercase  )

      lowerCamelCase_	  =					pseudo_inv
      if a_inv is None:
            try:
                  lowerCamelCase_	  =					np.linalg.inv(lowercase  )
            except np.linalg.LinAlgError:
                  raise ValueError(
                      'Input matrix A is not invertible. Cannot compute Schur complement.'  )

      return mat_c - mat_b.T @ a_inv @ mat_b







class 						A(		unittest.TestCase     ):



 '''simple docstring'''


 def        a__				(       self      :						Optional[Any]						)     ->			None:

       """simple docstring"""






       lowerCamelCase_	  =					np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]						)
       lowerCamelCase_	  =					np.array([[0, 3], [3, 0], [2, 3]]						)
       lowerCamelCase_	  =					np.array([[2, 1], [6, 3]]						)

       lowerCamelCase_	  =					schur_complement(A_		,							A_		,							A_						)

       lowerCamelCase_	  =					np.block([[a, b], [b.T, c]]						)

       lowerCamelCase_	  =					np.linalg.det(A_						)
       lowerCamelCase_	  =					np.linalg.det(A_						)
       lowerCamelCase_	  =					np.linalg.det(A_						)

       self.assertAlmostEqual(A_		,							det_a * det_s						)

 def        a__				(       self      :						Optional[Any]						)     ->			None:

       """simple docstring"""






       lowerCamelCase_	  =					np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]						)
       lowerCamelCase_	  =					np.array([[0, 3], [3, 0], [2, 3]]						)
       lowerCamelCase_	  =					np.array([[2, 1], [6, 3]]						)

       with self.assertRaises(A_						):
             schur_complement(A_		,							A_		,							A_						)





 def        a__				(       self      :						int						)     ->			None:

       """simple docstring"""






       lowerCamelCase_	  =					np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]						)
       lowerCamelCase_	  =					np.array([[0, 3], [3, 0], [2, 3]]						)
       lowerCamelCase_	  =					np.array([[2, 1, 3], [6, 3, 5]]						)

       with self.assertRaises(A_						):
             schur_complement(A_		,							A_		,							A_						)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 unittest.main()









import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[Any]  ):


      '''simple docstring'''



      return EnvironmentCommand()







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 @staticmethod
 def        a__				(       A_      :						ArgumentParser						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					parser.add_parser('env'						)
       download_parser.set_defaults(func=A_						)

 def        a__				(       self      :						Optional[Any]						)     ->			Any:

       """simple docstring"""






       lowerCamelCase_	  =					huggingface_hub.__version__

       lowerCamelCase_	  =					'not installed'
       lowerCamelCase_	  =					'NA'
       if is_torch_available():
             import torch

             lowerCamelCase_	  =					torch.__version__
             lowerCamelCase_	  =					torch.cuda.is_available()

       lowerCamelCase_	  =					'not installed'
       if is_transformers_available():
             import transformers

             lowerCamelCase_	  =					transformers.__version__

       lowerCamelCase_	  =					'not installed'
       if is_accelerate_available():
             import accelerate

             lowerCamelCase_	  =					accelerate.__version__

       lowerCamelCase_	  =					'not installed'
       if is_xformers_available():
             import xformers

             lowerCamelCase_	  =					xformers.__version__

       lowerCamelCase_	  =					{
           '`diffusers` version': version,
           'Platform': platform.platform(),
           'Python version': platform.python_version(),
           'PyTorch version (GPU?)': f"""{pt_version} ({pt_cuda_available})""",
           'Huggingface_hub version': hub_version,
           'Transformers version': transformers_version,
           'Accelerate version': accelerate_version,
           'xFormers version': xformers_version,
           'Using GPU in script?': '<fill in>',
           'Using distributed or parallel set-up in script?': '<fill in>',
       }

       print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'						)
       print(self.format_dict(A_						)						)

       return info





 @staticmethod
 def        a__				(       A_      :						Dict						)     ->			Any:

       """simple docstring"""






       return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()]						) + "\n"



