
'''simple docstring'''
import unittest

import numpy as np

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE = None,		):
    """simple docstring"""






    snake_case_ :    Any     			=						np.shape(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    int     			=						np.shape(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    str     			=						np.shape(__SCREAMING_SNAKE_CASE       )

    if shape_a[0] != shape_b[0]:
        snake_case_ :    Union[str, Any]     			=						(
            "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(__SCREAMING_SNAKE_CASE       )

    if shape_b[1] != shape_c[1]:
        snake_case_ :    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(__SCREAMING_SNAKE_CASE       )

    snake_case_ :    Any     			=						pseudo_inv
    if a_inv is None:
        try:
            snake_case_ :    Optional[Any]     			=						np.linalg.inv(__SCREAMING_SNAKE_CASE       )
        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    UpperCAmelCase_				(	unittest.TestCase					):





 def        snake_case__		(    self):
     snake_case_ :    int     			=						np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]])
     snake_case_ :    Dict     			=						np.array([[0, 3], [3, 0], [2, 3]])
     snake_case_ :    List[Any]     			=						np.array([[2, 1], [6, 3]])

     snake_case_ :    str     			=						schur_complement(lowercase_ ,       lowercase_ ,       lowercase_)

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

     snake_case_ :    int     			=						np.linalg.det(lowercase_)
     snake_case_ :    int     			=						np.linalg.det(lowercase_)
     snake_case_ :    str     			=						np.linalg.det(lowercase_)

     self.assertAlmostEqual(lowercase_ ,       det_a * det_s)





 def        snake_case__		(    self):
     snake_case_ :    Optional[int]     			=						np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]])
     snake_case_ :    Dict     			=						np.array([[0, 3], [3, 0], [2, 3]])
     snake_case_ :    List[Any]     			=						np.array([[2, 1], [6, 3]])

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





 def        snake_case__		(    self):
     snake_case_ :    Tuple     			=						np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]])
     snake_case_ :    Any     			=						np.array([[0, 3], [3, 0], [2, 3]])
     snake_case_ :    List[Any]     			=						np.array([[2, 1, 3], [6, 3, 5]])

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


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  unittest.main()






'''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,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class    UpperCAmelCase_				(	snake_case__    ,						snake_case__    ,						snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						StableDiffusionInstructPixaPixPipeline
 UpperCAmelCase_		    =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
 UpperCAmelCase_		    =						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 UpperCAmelCase_		    =						IMAGE_TO_IMAGE_IMAGE_PARAMS
 UpperCAmelCase_		    =						IMAGE_TO_IMAGE_IMAGE_PARAMS





 def        snake_case__		(    self):
     torch.manual_seed(0)
     snake_case_ :    List[str]     			=						UNetaDConditionModel(
         block_out_channels=(32, 64) ,       layers_per_block=2 ,       sample_size=32 ,       in_channels=8 ,       out_channels=4 ,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") ,       up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") ,       cross_attention_dim=32 ,       )
     snake_case_ :    Any     			=						PNDMScheduler(skip_prk_steps=lowercase_)
     torch.manual_seed(0)
     snake_case_ :    Tuple     			=						AutoencoderKL(
         block_out_channels=[32, 64] ,       in_channels=3 ,       out_channels=3 ,       down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] ,       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] ,       latent_channels=4 ,       )
     torch.manual_seed(0)
     snake_case_ :    Union[str, Any]     			=						CLIPTextConfig(
         bos_token_id=0 ,       eos_token_id=2 ,       hidden_size=32 ,       intermediate_size=37 ,       layer_norm_eps=1E-05 ,       num_attention_heads=4 ,       num_hidden_layers=5 ,       pad_token_id=1 ,       vocab_size=10_00 ,       )
     snake_case_ :    Union[str, Any]     			=						CLIPTextModel(lowercase_)
     snake_case_ :    Optional[Any]     			=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

     snake_case_ :    str     			=						{
         "unet": unet,
         "scheduler": scheduler,
         "vae": vae,
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "safety_checker": None,
         "feature_extractor": None,
     }
     return components





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_=0):
     snake_case_ :    int     			=						floats_tensor((1, 3, 32, 32) ,       rng=random.Random(lowercase_)).to(lowercase_)
     snake_case_ :    Any     			=						image.cpu().permute(0 ,       2 ,       3 ,       1)[0]
     snake_case_ :    str     			=						Image.fromarray(np.uinta(lowercase_)).convert("RGB")
     if str(lowercase_).startswith("mps"):
         snake_case_ :    Union[str, Any]     			=						torch.manual_seed(lowercase_)
     else:
         snake_case_ :    Any     			=						torch.Generator(device=lowercase_).manual_seed(lowercase_)
     snake_case_ :    List[str]     			=						{
         "prompt": "A painting of a squirrel eating a burger",
         "image": image,
         "generator": generator,
         "num_inference_steps": 2,
         "guidance_scale": 6.0,
         "image_guidance_scale": 1,
         "output_type": "numpy",
     }
     return inputs





 def        snake_case__		(    self):
     snake_case_ :    Union[str, Any]     			=						"cpu"  # ensure determinism for the device-dependent torch.Generator
     snake_case_ :    Tuple     			=						self.get_dummy_components()
     snake_case_ :    Any     			=						StableDiffusionInstructPixaPixPipeline(**lowercase_)
     snake_case_ :    Union[str, Any]     			=						sd_pipe.to(lowercase_)
     sd_pipe.set_progress_bar_config(disable=lowercase_)

     snake_case_ :    Optional[Any]     			=						self.get_dummy_inputs(lowercase_)
     snake_case_ :    List[Any]     			=						sd_pipe(**lowercase_).images
     snake_case_ :    List[Any]     			=						image[0, -3:, -3:, -1]
     assert image.shape == (1, 32, 32, 3)
     snake_case_ :    Optional[int]     			=						np.array([0.7_526, 0.3_750, 0.4_547, 0.6_117, 0.5_866, 0.5_016, 0.4_327, 0.5_642, 0.4_815])

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





 def        snake_case__		(    self):
     snake_case_ :    Optional[int]     			=						"cpu"  # ensure determinism for the device-dependent torch.Generator
     snake_case_ :    Union[str, Any]     			=						self.get_dummy_components()
     snake_case_ :    str     			=						StableDiffusionInstructPixaPixPipeline(**lowercase_)
     snake_case_ :    List[str]     			=						sd_pipe.to(lowercase_)
     sd_pipe.set_progress_bar_config(disable=lowercase_)

     snake_case_ :    List[Any]     			=						self.get_dummy_inputs(lowercase_)
     snake_case_ :    List[Any]     			=						"french fries"
     snake_case_ :    Union[str, Any]     			=						sd_pipe(**lowercase_ ,       negative_prompt=lowercase_)
     snake_case_ :    Optional[int]     			=						output.images
     snake_case_ :    Optional[Any]     			=						image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     snake_case_ :    Tuple     			=						np.array([0.7_511, 0.3_642, 0.4_553, 0.6_236, 0.5_797, 0.5_013, 0.4_343, 0.5_611, 0.4_831])

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





 def        snake_case__		(    self):
     snake_case_ :    List[str]     			=						"cpu"  # ensure determinism for the device-dependent torch.Generator
     snake_case_ :    Union[str, Any]     			=						self.get_dummy_components()
     snake_case_ :    Tuple     			=						StableDiffusionInstructPixaPixPipeline(**lowercase_)
     snake_case_ :    Tuple     			=						sd_pipe.to(lowercase_)
     sd_pipe.set_progress_bar_config(disable=lowercase_)

     snake_case_ :    List[Any]     			=						self.get_dummy_inputs(lowercase_)
     snake_case_ :    str     			=						[inputs["prompt"]] * 2

     snake_case_ :    Optional[Any]     			=						np.array(inputs["image"]).astype(np.floataa) / 255.0
     snake_case_ :    List[str]     			=						torch.from_numpy(lowercase_).unsqueeze(0).to(lowercase_)
     snake_case_ :    Any     			=						image / 2 + 0.5
     snake_case_ :    str     			=						image.permute(0 ,       3 ,       1 ,       2)
     snake_case_ :    Dict     			=						image.repeat(2 ,       1 ,       1 ,       1)

     snake_case_ :    List[str]     			=						sd_pipe(**lowercase_).images
     snake_case_ :    Dict     			=						image[-1, -3:, -3:, -1]

     assert image.shape == (2, 32, 32, 3)
     snake_case_ :    List[Any]     			=						np.array([0.5_812, 0.5_748, 0.5_222, 0.5_908, 0.5_695, 0.7_174, 0.6_804, 0.5_523, 0.5_579])

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





 def        snake_case__		(    self):
     snake_case_ :    List[str]     			=						"cpu"  # ensure determinism for the device-dependent torch.Generator
     snake_case_ :    Tuple     			=						self.get_dummy_components()
     snake_case_ :    Tuple     			=						EulerAncestralDiscreteScheduler(
         beta_start=0.00_085 ,       beta_end=0.012 ,       beta_schedule="scaled_linear")
     snake_case_ :    Dict     			=						StableDiffusionInstructPixaPixPipeline(**lowercase_)
     snake_case_ :    Union[str, Any]     			=						sd_pipe.to(lowercase_)
     sd_pipe.set_progress_bar_config(disable=lowercase_)

     snake_case_ :    Tuple     			=						self.get_dummy_inputs(lowercase_)
     snake_case_ :    Optional[int]     			=						sd_pipe(**lowercase_).images
     snake_case_ :    Union[str, Any]     			=						image[0, -3:, -3:, -1]

     snake_case_ :    Optional[Any]     			=						[round(lowercase_ ,       4) for x in image_slice.flatten().tolist()]
     print(",".join([str(lowercase_) for x in slice]))

     assert image.shape == (1, 32, 32, 3)
     snake_case_ :    Tuple     			=						np.array([0.7_417, 0.3_842, 0.4_732, 0.5_776, 0.5_891, 0.5_139, 0.4_052, 0.5_673, 0.4_986])

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





 def        snake_case__		(    self):
     super().test_inference_batch_single_identical(expected_max_diff=3E-3)





 def        snake_case__		(    self):
     snake_case_ :    Optional[Any]     			=						self.get_dummy_components()
     snake_case_ :    Union[str, Any]     			=						StableDiffusionInstructPixaPixPipeline(**lowercase_)
     snake_case_ :    List[str]     			=						VaeImageProcessor(do_resize=lowercase_ ,       do_normalize=lowercase_)
     snake_case_ :    List[Any]     			=						pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)

     snake_case_ :    str     			=						pipe(**self.get_dummy_inputs_by_type(lowercase_ ,       input_image_type="pt"))[0]

     snake_case_ :    str     			=						components["vae"]
     snake_case_ :    str     			=						self.get_dummy_inputs_by_type(lowercase_ ,       input_image_type="pt")

     for image_param in self.image_latents_params:
         if image_param in inputs.keys():
             snake_case_ :    str     			=						vae.encode(inputs[image_param]).latent_dist.mode()

     snake_case_ :    List[str]     			=						pipe(**lowercase_)[0]

     snake_case_ :    Any     			=						np.abs(out - out_latents_inputs).max()
     self.assertLess(lowercase_ ,       1E-4 ,       "passing latents as image input generate different result from passing image")






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





 def        snake_case__		(    self):
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()





 def        snake_case__		(    self ,       lowercase_=0):
     snake_case_ :    int     			=						torch.manual_seed(lowercase_)
     snake_case_ :    Union[str, Any]     			=						load_image(
         "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg")
     snake_case_ :    Tuple     			=						{
         "prompt": "turn him into a cyborg",
         "image": image,
         "generator": generator,
         "num_inference_steps": 3,
         "guidance_scale": 7.5,
         "image_guidance_scale": 1.0,
         "output_type": "numpy",
     }
     return inputs





 def        snake_case__		(    self):
     snake_case_ :    str     			=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
         "timbrooks/instruct-pix2pix" ,       safety_checker=lowercase_)
     pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)
     pipe.enable_attention_slicing()

     snake_case_ :    int     			=						self.get_inputs()
     snake_case_ :    Dict     			=						pipe(**lowercase_).images
     snake_case_ :    str     			=						image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_12, 5_12, 3)
     snake_case_ :    Dict     			=						np.array([0.5_902, 0.6_015, 0.6_027, 0.5_983, 0.6_092, 0.6_061, 0.5_765, 0.5_785, 0.5_555])

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





 def        snake_case__		(    self):
     snake_case_ :    int     			=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
         "timbrooks/instruct-pix2pix" ,       safety_checker=lowercase_)
     snake_case_ :    Dict     			=						LMSDiscreteScheduler.from_config(pipe.scheduler.config)
     pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)
     pipe.enable_attention_slicing()

     snake_case_ :    int     			=						self.get_inputs()
     snake_case_ :    Dict     			=						pipe(**lowercase_).images
     snake_case_ :    str     			=						image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_12, 5_12, 3)
     snake_case_ :    str     			=						np.array([0.6_578, 0.6_817, 0.6_972, 0.6_761, 0.6_856, 0.6_916, 0.6_428, 0.6_516, 0.6_301])

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





 def        snake_case__		(    self):
     snake_case_ :    Optional[int]     			=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
         "timbrooks/instruct-pix2pix" ,       safety_checker=lowercase_)
     snake_case_ :    List[Any]     			=						DDIMScheduler.from_config(pipe.scheduler.config)
     pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)
     pipe.enable_attention_slicing()

     snake_case_ :    str     			=						self.get_inputs()
     snake_case_ :    Optional[Any]     			=						pipe(**lowercase_).images
     snake_case_ :    Dict     			=						image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_12, 5_12, 3)
     snake_case_ :    List[str]     			=						np.array([0.3_828, 0.3_834, 0.3_818, 0.3_792, 0.3_865, 0.3_752, 0.3_792, 0.3_847, 0.3_753])

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





 def        snake_case__		(    self):
     snake_case_ :    Union[str, Any]     			=						0

     def callback_fn(lowercase_ ,       lowercase_ ,       lowercase_) -> None:
         snake_case_ :    Optional[Any]     			=						True
         nonlocal number_of_steps
         number_of_steps += 1
         if step == 1:
             snake_case_ :    Optional[Any]     			=						latents.detach().cpu().numpy()
             assert latents.shape == (1, 4, 64, 64)
             snake_case_ :    Optional[int]     			=						latents[0, -3:, -3:, -1]
             snake_case_ :    Optional[int]     			=						np.array([-0.2_463, -0.4_644, -0.9_756, 1.5_176, 1.4_414, 0.7_866, 0.9_897, 0.8_521, 0.7_983])

             assert np.abs(latents_slice.flatten() - expected_slice).max() < 5E-2
         elif step == 2:
             snake_case_ :    List[str]     			=						latents.detach().cpu().numpy()
             assert latents.shape == (1, 4, 64, 64)
             snake_case_ :    Union[str, Any]     			=						latents[0, -3:, -3:, -1]
             snake_case_ :    Any     			=						np.array([-0.2_644, -0.4_626, -0.9_653, 1.5_176, 1.4_551, 0.7_686, 0.9_805, 0.8_452, 0.8_115])

             assert np.abs(latents_slice.flatten() - expected_slice).max() < 5E-2

     snake_case_ :    Optional[int]     			=						False

     snake_case_ :    Union[str, Any]     			=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
         "timbrooks/instruct-pix2pix" ,       safety_checker=lowercase_ ,       torch_dtype=torch.floataa)
     snake_case_ :    Dict     			=						pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)
     pipe.enable_attention_slicing()

     snake_case_ :    Any     			=						self.get_inputs()
     pipe(**lowercase_ ,       callback=lowercase_ ,       callback_steps=1)
     assert callback_fn.has_been_called
     assert number_of_steps == 3





 def        snake_case__		(    self):
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     snake_case_ :    int     			=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
         "timbrooks/instruct-pix2pix" ,       safety_checker=lowercase_ ,       torch_dtype=torch.floataa)
     snake_case_ :    str     			=						pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)
     pipe.enable_attention_slicing(1)
     pipe.enable_sequential_cpu_offload()

     snake_case_ :    Dict     			=						self.get_inputs()
     snake_case_ :    int     			=						pipe(**lowercase_)

     snake_case_ :    Dict     			=						torch.cuda.max_memory_allocated()
     # make sure that less than 2.2 GB is allocated
     assert mem_bytes < 2.2 * 10**9





 def        snake_case__		(    self):
     snake_case_ :    List[Any]     			=						self.get_inputs()
     # resize to resolution that is divisible by 8 but not 16 or 32
     snake_case_ :    Union[str, Any]     			=						inputs["image"].resize((5_04, 5_04))

     snake_case_ :    str     			=						"timbrooks/instruct-pix2pix"
     snake_case_ :    Any     			=						StableDiffusionInstructPixaPixPipeline.from_pretrained(
         lowercase_ ,       safety_checker=lowercase_ ,       )
     pipe.to(lowercase_)
     pipe.set_progress_bar_config(disable=lowercase_)
     pipe.enable_attention_slicing()

     snake_case_ :    Any     			=						pipe(**lowercase_)
     snake_case_ :    int     			=						output.images[0]

     snake_case_ :    Tuple     			=						image[2_55:2_58, 3_83:3_86, -1]

     assert image.shape == (5_04, 5_04, 3)
     snake_case_ :    Optional[int]     			=						np.array([0.2_726, 0.2_529, 0.2_664, 0.2_655, 0.2_641, 0.2_642, 0.2_591, 0.2_649, 0.2_590])

     assert np.abs(image_slice.flatten() - expected_slice).max() < 5E-3




