


"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 					__magic_name__		(			__UpperCAmelCase			,						__UpperCAmelCase			,						__UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      str        =   StableDiffusionInpaintPipeline
       __A			:      Union[str, Any]        =   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
       __A			:      str        =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       __A			:      Tuple        =   frozenset(
           []     )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       __A			:      Any        =   frozenset([]     )






       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              torch.manual_seed(0				)
              lowercase		:Dict       							=				UNetaDConditionModel(
                  block_out_channels=(3_2, 6_4)  ,     layers_per_block=2  ,     sample_size=3_2  ,     in_channels=9  ,     out_channels=4  ,     down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')  ,     up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')  ,     cross_attention_dim=3_2  ,     attention_head_dim=(2, 4)  ,     use_linear_projection=snake_case__  ,     )
              lowercase		:Any       							=				PNDMScheduler(skip_prk_steps=snake_case__				)
              torch.manual_seed(0				)
              lowercase		:str       							=				AutoencoderKL(
                  block_out_channels=[3_2, 6_4]  ,     in_channels=3  ,     out_channels=3  ,     down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']  ,     up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']  ,     latent_channels=4  ,     sample_size=1_2_8  ,     )
              torch.manual_seed(0				)
              lowercase		:List[str]       							=				CLIPTextConfig(
                  bos_token_id=0  ,     eos_token_id=2  ,     hidden_size=3_2  ,     intermediate_size=3_7  ,     layer_norm_eps=1e-0_5  ,     num_attention_heads=4  ,     num_hidden_layers=5  ,     pad_token_id=1  ,     vocab_size=1_0_0_0  ,     hidden_act='''gelu'''  ,     projection_dim=5_1_2  ,     )
              lowercase		:int       							=				CLIPTextModel(snake_case__				)
              lowercase		:List[Any]       							=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

              lowercase		:Optional[Any]       							=				{
                  '''unet''': unet,
                  '''scheduler''': scheduler,
                  '''vae''': vae,
                  '''text_encoder''': text_encoder,
                  '''tokenizer''': tokenizer,
                  '''safety_checker''': None,
                  '''feature_extractor''': None,
              }
              return components






       def  __snake_case						(   self  :    Optional[int]  ,     snake_case__  :    Tuple  ,     snake_case__  :    Optional[Any]=0				):

              '''simple docstring'''






              lowercase		:Dict       							=				floats_tensor((1, 3, 3_2, 3_2)  ,     rng=random.Random(snake_case__				)				).to(snake_case__				)
              lowercase		:Tuple       							=				image.cpu().permute(0  ,     2  ,     3  ,     1				)[0]
              lowercase		:Dict       							=				Image.fromarray(np.uinta(snake_case__				)				).convert('''RGB'''				).resize((6_4, 6_4)				)
              lowercase		:Optional[int]       							=				Image.fromarray(np.uinta(image + 4				)				).convert('''RGB'''				).resize((6_4, 6_4)				)
              if str(snake_case__				).startswith('''mps'''				):
                     lowercase		:Union[str, Any]       							=				torch.manual_seed(snake_case__				)
              else:
                     lowercase		:Dict       							=				torch.Generator(device=snake_case__				).manual_seed(snake_case__				)
              lowercase		:Dict       							=				{
                  '''prompt''': '''A painting of a squirrel eating a burger''',
                  '''image''': init_image,
                  '''mask_image''': mask_image,
                  '''generator''': generator,
                  '''num_inference_steps''': 2,
                  '''guidance_scale''': 6.0,
                  '''output_type''': '''numpy''',
              }
              return inputs






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              lowercase		:Dict       							=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
              lowercase		:Optional[Any]       							=				self.get_dummy_components()
              lowercase		:Optional[Any]       							=				StableDiffusionInpaintPipeline(**snake_case__				)
              lowercase		:List[Any]       							=				sd_pipe.to(snake_case__				)
              sd_pipe.set_progress_bar_config(disable=snake_case__				)

              lowercase		:Optional[int]       							=				self.get_dummy_inputs(snake_case__				)
              lowercase		:Any       							=				sd_pipe(**snake_case__				).images
              lowercase		:Any       							=				image[0, -3:, -3:, -1]

              assert image.shape == (1, 6_4, 6_4, 3)
              lowercase		:Dict       							=				np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76]				)

              assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2







       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              super().test_inference_batch_single_identical(expected_max_diff=3e-3				)







@slow
@require_torch_gpu
class 					__magic_name__		(			unittest.TestCase     ):






       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()






       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                  '''/sd2-inpaint/init_image.png'''				)
              lowercase		:Optional[int]       							=				load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''				)
              lowercase		:Optional[Any]       							=				load_numpy(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
                  '''/yellow_cat_sitting_on_a_park_bench.npy'''				)

              lowercase		:List[Any]       							=				'''stabilityai/stable-diffusion-2-inpainting'''
              lowercase		:Dict       							=				StableDiffusionInpaintPipeline.from_pretrained(snake_case__  ,     safety_checker=snake_case__				)
              pipe.to(snake_case__				)
              pipe.set_progress_bar_config(disable=snake_case__				)
              pipe.enable_attention_slicing()

              lowercase		:Tuple       							=				'''Face of a yellow cat, high resolution, sitting on a park bench'''

              lowercase		:List[str]       							=				torch.manual_seed(0				)
              lowercase		:int       							=				pipe(
                  prompt=snake_case__  ,     image=snake_case__  ,     mask_image=snake_case__  ,     generator=snake_case__  ,     output_type='''np'''  ,     )
              lowercase		:Any       							=				output.images[0]

              assert image.shape == (5_1_2, 5_1_2, 3)
              assert np.abs(expected_image - image				).max() < 9e-3






       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                  '''/sd2-inpaint/init_image.png'''				)
              lowercase		:Dict       							=				load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''				)
              lowercase		:int       							=				load_numpy(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
                  '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''				)

              lowercase		:List[Any]       							=				'''stabilityai/stable-diffusion-2-inpainting'''
              lowercase		:Union[str, Any]       							=				StableDiffusionInpaintPipeline.from_pretrained(
                  snake_case__  ,     torch_dtype=torch.floataa  ,     safety_checker=snake_case__  ,     )
              pipe.to(snake_case__				)
              pipe.set_progress_bar_config(disable=snake_case__				)
              pipe.enable_attention_slicing()

              lowercase		:int       							=				'''Face of a yellow cat, high resolution, sitting on a park bench'''

              lowercase		:Dict       							=				torch.manual_seed(0				)
              lowercase		:Any       							=				pipe(
                  prompt=snake_case__  ,     image=snake_case__  ,     mask_image=snake_case__  ,     generator=snake_case__  ,     output_type='''np'''  ,     )
              lowercase		:List[str]       							=				output.images[0]

              assert image.shape == (5_1_2, 5_1_2, 3)
              assert np.abs(expected_image - image				).max() < 5e-1







       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              torch.cuda.empty_cache()
              torch.cuda.reset_max_memory_allocated()
              torch.cuda.reset_peak_memory_stats()

              lowercase		:Tuple       							=				load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                  '''/sd2-inpaint/init_image.png'''				)
              lowercase		:Optional[Any]       							=				load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''				)

              lowercase		:int       							=				'''stabilityai/stable-diffusion-2-inpainting'''
              lowercase		:Optional[int]       							=				PNDMScheduler.from_pretrained(snake_case__  ,     subfolder='''scheduler'''				)
              lowercase		:int       							=				StableDiffusionInpaintPipeline.from_pretrained(
                  snake_case__  ,     safety_checker=snake_case__  ,     scheduler=snake_case__  ,     torch_dtype=torch.floataa  ,     )
              pipe.to(snake_case__				)
              pipe.set_progress_bar_config(disable=snake_case__				)
              pipe.enable_attention_slicing(1				)
              pipe.enable_sequential_cpu_offload()

              lowercase		:str       							=				'''Face of a yellow cat, high resolution, sitting on a park bench'''

              lowercase		:Any       							=				torch.manual_seed(0				)
              lowercase		:List[Any]       							=				pipe(
                  prompt=snake_case__  ,     image=snake_case__  ,     mask_image=snake_case__  ,     generator=snake_case__  ,     num_inference_steps=2  ,     output_type='''np'''  ,     )

              lowercase		:int       							=				torch.cuda.max_memory_allocated()
              # make sure that less than 2.65 GB is allocated
              assert mem_bytes < 2.65 * 1_0**9





"""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()

