code: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import logging
  
  # standard libraries
  import sys
  
  import numpy as np
  import pytest  # type: ignore
  
  # Custom/local libraries
  from matrix import matrix_operation as matop
  
  __A :		List[Any]      							=    [[12, 10], [3, 9]]
  __A :		Optional[Any]      							=    [[3, 4], [7, 4]]
  __A :		Optional[int]      							=    [[3, 0, 2], [2, 0, -2], [0, 1, 1]]
  __A :		Optional[Any]      							=    [[3, 0, -2], [2, 0, 2], [0, 1, 1]]
  __A :		List[str]      							=    [[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
  __A :		Any      							=    [1]
  __A :		Union[str, Any]      							=    [2]
  
  __A :		Union[str, Any]      							=    logging.getLogger()
  __A :		Any      							=    logging.DEBUG
  __A :		Any      							=    logging.StreamHandler(sys.stdout)
  logger.addHandler(stream_handler)
  @pytest.mark.mat_ops
  @pytest.mark.parametrize(
      ("""mat1""", """mat2""")						,[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]    )
  def 					A_			(		snake_case_     :       List[str]						,snake_case_     :       List[Any]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      if (np.array(snake_case_    )).shape < (2, 2) or (np.array(snake_case_    )).shape < (2, 2):
          with pytest.raises(snake_case_    ):
              logger.info(f'\n\t{test_addition.__name__} returned integer'    )
              matop.add(snake_case_						,snake_case_    )
      elif (np.array(snake_case_    )).shape == (np.array(snake_case_    )).shape:
          logger.info(f'\n\t{test_addition.__name__} with same matrix dims'    )
          UpperCamelCase				:   List[str]          =							(np.array(snake_case_    ) + np.array(snake_case_    )).tolist()
          UpperCamelCase				:   Any          =							matop.add(snake_case_						,snake_case_    )
          assert theo == act
      else:
          with pytest.raises(snake_case_    ):
              logger.info(f'\n\t{test_addition.__name__} with different matrix dims'    )
              matop.add(snake_case_						,snake_case_    )
  @pytest.mark.mat_ops
  @pytest.mark.parametrize(
      ("""mat1""", """mat2""")						,[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]    )
  def 					A_			(		snake_case_     :       str						,snake_case_     :       Tuple    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      if (np.array(snake_case_    )).shape < (2, 2) or (np.array(snake_case_    )).shape < (2, 2):
          with pytest.raises(snake_case_    ):
              logger.info(f'\n\t{test_subtraction.__name__} returned integer'    )
              matop.subtract(snake_case_						,snake_case_    )
      elif (np.array(snake_case_    )).shape == (np.array(snake_case_    )).shape:
          logger.info(f'\n\t{test_subtraction.__name__} with same matrix dims'    )
          UpperCamelCase				:   Union[str, Any]          =							(np.array(snake_case_    ) - np.array(snake_case_    )).tolist()
          UpperCamelCase				:   List[Any]          =							matop.subtract(snake_case_						,snake_case_    )
          assert theo == act
      else:
          with pytest.raises(snake_case_    ):
              logger.info(f'\n\t{test_subtraction.__name__} with different matrix dims'    )
              assert matop.subtract(snake_case_						,snake_case_    )
  @pytest.mark.mat_ops
  @pytest.mark.parametrize(
      ("""mat1""", """mat2""")						,[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]    )
  def 					A_			(		snake_case_     :       Union[str, Any]						,snake_case_     :       str    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      if (np.array(snake_case_    )).shape < (2, 2) or (np.array(snake_case_    )).shape < (2, 2):
          logger.info(f'\n\t{test_multiplication.__name__} returned integer'    )
          with pytest.raises(snake_case_    ):
              matop.add(snake_case_						,snake_case_    )
      elif (np.array(snake_case_    )).shape == (np.array(snake_case_    )).shape:
          logger.info(f'\n\t{test_multiplication.__name__} meets dim requirements'    )
          UpperCamelCase				:   str          =							(np.matmul(snake_case_						,snake_case_    )).tolist()
          UpperCamelCase				:   str          =							matop.multiply(snake_case_						,snake_case_    )
          assert theo == act
      else:
          with pytest.raises(snake_case_    ):
              logger.info(
                  f'\n\t{test_multiplication.__name__} does not meet dim requirements'    )
              assert matop.subtract(snake_case_						,snake_case_    )
  @pytest.mark.mat_ops
  def 					A_			(		):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Union[str, Any]          =							(3.5 * np.array(snake_case_    )).tolist()
      UpperCamelCase				:   int          =							matop.scalar_multiply(snake_case_						,3.5    )
      assert theo == act
  @pytest.mark.mat_ops
  def 					A_			(		):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Optional[int]          =							(np.identity(5    )).tolist()
      UpperCamelCase				:   Dict          =							matop.identity(5    )
      assert theo == act
  
  
  @pytest.mark.mat_ops
  @pytest.mark.parametrize("""mat"""						,[mat_a, mat_b, mat_c, mat_d, mat_e, mat_f]    )
  def 					A_			(		snake_case_     :       Dict    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      if (np.array(snake_case_    )).shape < (2, 2):
          with pytest.raises(snake_case_    ):
              logger.info(f'\n\t{test_transpose.__name__} returned integer'    )
              matop.transpose(snake_case_    )
      else:
          UpperCamelCase				:   str          =							(np.transpose(snake_case_    )).tolist()
          UpperCamelCase				:   Tuple          =							matop.transpose(snake_case_						,return_map=snake_case_    )
          assert theo == act
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  from ...utils import is_transformers_available
  from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline
  
  
  if is_transformers_available():
    from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline
  
  
  
  
  