


"""simple docstring"""



import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
 import onnxruntime as ort
class 					__magic_name__		(			__UpperCAmelCase			,						unittest.TestCase     ):
       # FIXME: add fast tests
       pass







@nightly
@require_onnxruntime
@require_torch_gpu
class 					__magic_name__		(			unittest.TestCase     ):






       @property
       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              return (
                  "CUDAExecutionProvider",
                  {
                      "gpu_mem_limit": "15000000000",  # 15GB
                      "arena_extend_strategy": "kSameAsRequested",
                  },
              )






       @property
       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              lowercase		:str       							=				ort.SessionOptions()
              lowercase		:int       							=				False
              return options






       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              lowercase		:Dict       							=				load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                  '''/in_paint/overture-creations-5sI6fQgYIuo.png'''				)
              lowercase		:Union[str, Any]       							=				load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                  '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''				)
              lowercase		:Union[str, Any]       							=				OnnxStableDiffusionInpaintPipeline.from_pretrained(
                  '''runwayml/stable-diffusion-inpainting'''  ,     revision='''onnx'''  ,     safety_checker=snake_case__  ,     feature_extractor=snake_case__  ,     provider=self.gpu_provider  ,     sess_options=self.gpu_options  ,     )
              pipe.set_progress_bar_config(disable=snake_case__				)

              lowercase		:Optional[Any]       							=				'''A red cat sitting on a park bench'''

              lowercase		:Optional[Any]       							=				np.random.RandomState(0				)
              lowercase		:Any       							=				pipe(
                  prompt=snake_case__  ,     image=snake_case__  ,     mask_image=snake_case__  ,     guidance_scale=7.5  ,     num_inference_steps=1_0  ,     generator=snake_case__  ,     output_type='''np'''  ,     )
              lowercase		:Any       							=				output.images
              lowercase		:List[Any]       							=				images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

              assert images.shape == (1, 5_1_2, 5_1_2, 3)
              lowercase		:List[Any]       							=				np.array([0.25_14, 0.30_07, 0.35_17, 0.17_90, 0.23_82, 0.31_67, 0.19_44, 0.22_73, 0.24_64]				)

              assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-3







       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                  '''/in_paint/overture-creations-5sI6fQgYIuo.png'''				)
              lowercase		:Dict       							=				load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                  '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''				)
              lowercase		:Optional[int]       							=				LMSDiscreteScheduler.from_pretrained(
                  '''runwayml/stable-diffusion-inpainting'''  ,     subfolder='''scheduler'''  ,     revision='''onnx'''				)
              lowercase		:Tuple       							=				OnnxStableDiffusionInpaintPipeline.from_pretrained(
                  '''runwayml/stable-diffusion-inpainting'''  ,     revision='''onnx'''  ,     scheduler=snake_case__  ,     safety_checker=snake_case__  ,     feature_extractor=snake_case__  ,     provider=self.gpu_provider  ,     sess_options=self.gpu_options  ,     )
              pipe.set_progress_bar_config(disable=snake_case__				)

              lowercase		:Dict       							=				'''A red cat sitting on a park bench'''

              lowercase		:Union[str, Any]       							=				np.random.RandomState(0				)
              lowercase		:int       							=				pipe(
                  prompt=snake_case__  ,     image=snake_case__  ,     mask_image=snake_case__  ,     guidance_scale=7.5  ,     num_inference_steps=2_0  ,     generator=snake_case__  ,     output_type='''np'''  ,     )
              lowercase		:Optional[int]       							=				output.images
              lowercase		:Optional[int]       							=				images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

              assert images.shape == (1, 5_1_2, 5_1_2, 3)
              lowercase		:Optional[Any]       							=				np.array([0.00_86, 0.00_77, 0.00_83, 0.00_93, 0.01_07, 0.01_39, 0.00_94, 0.00_97, 0.01_25]				)

              assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-3





"""simple docstring"""



import unittest

import numpy as np






def       lowerCamelCase					(a_						:np.ndarray				,				a_						:np.ndarray				,				a_						:np.ndarray				,				a_						:np.ndarray | None = None				,				)     ->							np.ndarray:
       lowercase		:str       							=				np.shape(a_)
       lowercase		:Dict       							=				np.shape(a_)
       lowercase		:Optional[int]       							=				np.shape(a_)

       if shape_a[0] != shape_b[0]:
              lowercase		:List[str]       							=				(
                  '''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(a_)

       if shape_b[1] != shape_c[1]:
              lowercase		:List[str]       							=				(
                  '''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(a_)

       lowercase		:Optional[Any]       							=				pseudo_inv
       if a_inv is None:
              try:
                     lowercase		:int       							=				np.linalg.inv(a_)
              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 					__magic_name__		(			unittest.TestCase     ):






       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              lowercase		:Dict       							=				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]				)
              lowercase		:List[str]       							=				np.array([[0, 3], [3, 0], [2, 3]]				)
              lowercase		:List[str]       							=				np.array([[2, 1], [6, 3]]				)

              lowercase		:Any       							=				schur_complement(snake_case__  ,     snake_case__  ,     snake_case__				)

              lowercase		:int       							=				np.block([[a, b], [b.T, c]]				)

              lowercase		:Tuple       							=				np.linalg.det(snake_case__				)
              lowercase		:Any       							=				np.linalg.det(snake_case__				)
              lowercase		:List[Any]       							=				np.linalg.det(snake_case__				)

              self.assertAlmostEqual(snake_case__  ,     det_a * det_s				)






       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]				)
              lowercase		:Union[str, Any]       							=				np.array([[0, 3], [3, 0], [2, 3]]				)
              lowercase		:List[str]       							=				np.array([[2, 1], [6, 3]]				)

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







       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              lowercase		:List[str]       							=				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]				)
              lowercase		:int       							=				np.array([[0, 3], [3, 0], [2, 3]]				)
              lowercase		:List[Any]       							=				np.array([[2, 1, 3], [6, 3, 5]]				)

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


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 unittest.main()

