"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 			UpperCamelCase__(	__A						,  unittest.TestCase       ):
						lowerCAmelCase__     :  int  							=						KandinskyVaaImgaImgPipeline
						lowerCAmelCase__     :  Optional[Any]  							=						["image_embeds", "negative_image_embeds", "image"]
						lowerCAmelCase__     :  int  							=						[
						    "image_embeds",
						    "negative_image_embeds",
						    "image",
						]
						lowerCAmelCase__     :  List[str]  							=						[
						    "generator",
						    "height",
						    "width",
						    "strength",
						    "guidance_scale",
						    "num_inference_steps",
						    "return_dict",
						    "guidance_scale",
						    "num_images_per_prompt",
						    "output_type",
						    "return_dict",
						]
						lowerCAmelCase__     :  List[Any]  							=						False





						@property
						def  snake_case__		(				self   )    -> Dict:
													return 32





						@property
						def  snake_case__		(				self   )    -> str:
													return 32





						@property
						def  snake_case__		(				self   )    -> Optional[Any]:
													return self.time_input_dim





						@property
						def  snake_case__		(				self   )    -> Optional[int]:
													return self.time_input_dim * 4





						@property
						def  snake_case__		(				self   )    -> Optional[int]:
													return 1_00





						@property
						def  snake_case__		(				self   )    -> Optional[int]:
													torch.manual_seed(0   )

													A__		   =							{
													    'in_channels': 4,
													    # Out channels is double in channels because predicts mean and variance
													    'out_channels': 8,
													    'addition_embed_type': 'image',
													    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
													    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
													    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
													    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
													    'layers_per_block': 1,
													    'encoder_hid_dim': self.text_embedder_hidden_size,
													    'encoder_hid_dim_type': 'image_proj',
													    'cross_attention_dim': self.cross_attention_dim,
													    'attention_head_dim': 4,
													    'resnet_time_scale_shift': 'scale_shift',
													    'class_embed_type': None,
													}

													A__		   =							UNetaDConditionModel(**UpperCAmelCase__   )
													return model





						@property
						def  snake_case__		(				self   )    -> Dict:
													return {
													    "block_out_channels": [32, 64],
													    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
													    "in_channels": 3,
													    "latent_channels": 4,
													    "layers_per_block": 1,
													    "norm_num_groups": 8,
													    "norm_type": "spatial",
													    "num_vq_embeddings": 12,
													    "out_channels": 3,
													    "up_block_types": [
													        "AttnUpDecoderBlock2D",
													        "UpDecoderBlock2D",
													    ],
													    "vq_embed_dim": 4,
													}





						@property
						def  snake_case__		(				self   )    -> Dict:
													torch.manual_seed(0   )
													A__		   =							VQModel(**self.dummy_movq_kwargs   )
													return model





						def  snake_case__		(				self   )    -> int:
													A__		   =							self.dummy_unet
													A__		   =							self.dummy_movq

													A__		   =							{
													    'num_train_timesteps': 10_00,
													    'beta_schedule': 'linear',
													    'beta_start': 0.0_0_0_8_5,
													    'beta_end': 0.0_1_2,
													    'clip_sample': False,
													    'set_alpha_to_one': False,
													    'steps_offset': 0,
													    'prediction_type': 'epsilon',
													    'thresholding': False,
													}

													A__		   =							DDIMScheduler(**UpperCAmelCase__   )

													A__		   =							{
													    'unet': unet,
													    'scheduler': scheduler,
													    'movq': movq,
													}

													return components





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase=0   )    -> Any:
													A__		   =							floats_tensor((1, self.text_embedder_hidden_size)						,rng=random.Random(UpperCAmelCase__   )   ).to(UpperCAmelCase__   )
													A__		   =							floats_tensor((1, self.text_embedder_hidden_size)						,rng=random.Random(seed + 1   )   ).to(
													    UpperCAmelCase__   )
													# create init_image
													A__		   =							floats_tensor((1, 3, 64, 64)						,rng=random.Random(UpperCAmelCase__   )   ).to(UpperCAmelCase__   )
													A__		   =							image.cpu().permute(0						,2						,3						,1   )[0]
													A__		   =							Image.fromarray(np.uinta(UpperCAmelCase__   )   ).convert('RGB'   ).resize((2_56, 2_56)   )

													if str(UpperCAmelCase__   ).startswith('mps'   ):
																				A__		   =							torch.manual_seed(UpperCAmelCase__   )
													else:
																				A__		   =							torch.Generator(device=UpperCAmelCase__   ).manual_seed(UpperCAmelCase__   )
													A__		   =							{
													    'image': init_image,
													    'image_embeds': image_embeds,
													    'negative_image_embeds': negative_image_embeds,
													    'generator': generator,
													    'height': 64,
													    'width': 64,
													    'num_inference_steps': 10,
													    'guidance_scale': 7.0,
													    'strength': 0.2,
													    'output_type': 'np',
													}
													return inputs





						def  snake_case__		(				self   )    -> str:
													A__		   =							'cpu'

													A__		   =							self.get_dummy_components()

													A__		   =							self.pipeline_class(**UpperCAmelCase__   )
													A__		   =							pipe.to(UpperCAmelCase__   )

													pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

													A__		   =							pipe(**self.get_dummy_inputs(UpperCAmelCase__   )   )
													A__		   =							output.images

													A__		   =							pipe(
													    **self.get_dummy_inputs(UpperCAmelCase__   )						,return_dict=UpperCAmelCase__						,)[0]

													A__		   =							image[0, -3:, -3:, -1]
													A__		   =							image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)

													A__		   =							np.array(
													    [0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_3]   )
													assert (
													    np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
													), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
													assert (
													    np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2
													), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






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





						def  snake_case__		(				self   )    -> str:
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()





						def  snake_case__		(				self   )    -> Optional[Any]:
													A__		   =							load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/kandinskyv22/kandinskyv22_img2img_frog.npy'   )

													A__		   =							load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'   )
													A__		   =							'A red cartoon frog, 4k'

													A__		   =							KandinskyVaaPriorPipeline.from_pretrained(
													    'kandinsky-community/kandinsky-2-2-prior'						,torch_dtype=torch.floataa   )
													pipe_prior.to(UpperCAmelCase__   )

													A__		   =							KandinskyVaaImgaImgPipeline.from_pretrained(
													    'kandinsky-community/kandinsky-2-2-decoder'						,torch_dtype=torch.floataa   )
													A__		   =							pipeline.to(UpperCAmelCase__   )

													pipeline.set_progress_bar_config(disable=UpperCAmelCase__   )

													A__		   =							torch.Generator(device='cpu'   ).manual_seed(0   )
													A__   ,     A__		   =							pipe_prior(
													    UpperCAmelCase__						,generator=UpperCAmelCase__						,num_inference_steps=5						,negative_prompt=''						,).to_tuple()

													A__		   =							pipeline(
													    image=UpperCAmelCase__						,image_embeds=UpperCAmelCase__						,negative_image_embeds=UpperCAmelCase__						,generator=UpperCAmelCase__						,num_inference_steps=1_00						,height=7_68						,width=7_68						,strength=0.2						,output_type='np'						,)

													A__		   =							output.images[0]

													assert image.shape == (7_68, 7_68, 3)

													assert_mean_pixel_difference(UpperCAmelCase__						,UpperCAmelCase__   )








"""simple docstring"""





import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
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 					_lowerCAmelCase  (	lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					str   		=	"ssube/stable-diffusion-x4-upscaler-onnx"


 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			List[str]=0	):
    __lowercase								=						floats_tensor((1, 3, 1_2_8, 1_2_8),					rng=random.Random(UpperCAmelCase__	)	)
    __lowercase								=						torch.manual_seed(UpperCAmelCase__	)
    __lowercase								=						{
        "prompt": "A painting of a squirrel eating a burger",
        "image": image,
        "generator": generator,
        "num_inference_steps": 3,
        "guidance_scale": 7.5,
        "output_type": "numpy",
    }
    return inputs


 def 					_lowercase (			self							:			Any	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						self.get_dummy_inputs()
    __lowercase								=						pipe(**UpperCAmelCase__	).images
    __lowercase								=						image[0, -3:, -3:, -1].flatten()

    # started as 128, should now be 512
    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array(
        [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]	)
    assert np.abs(image_slice - expected_slice	).max() < 1E-1


 def 					_lowercase (			self							:			Optional[Any]	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    __lowercase								=						PNDMScheduler.from_config(pipe.scheduler.config,					skip_prk_steps=UpperCAmelCase__	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						self.get_dummy_inputs()
    __lowercase								=						pipe(**UpperCAmelCase__	).images
    __lowercase								=						image[0, -3:, -3:, -1]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array(
        [0.6_898_892, 0.59_240_556, 0.52_499_527, 0.58_866_215, 0.52_258_235, 0.52_572_715, 0.62_414_473, 0.6_174_387, 0.6_214_964]	)
    assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-1


 def 					_lowercase (			self							:			int	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    __lowercase								=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						self.get_dummy_inputs()
    __lowercase								=						pipe(**UpperCAmelCase__	).images
    __lowercase								=						image[0, -3:, -3:, -1]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array(
        [0.7_659_278, 0.76_437_664, 0.75_579_107, 0.7_691_116, 0.77_666_986, 0.7_727_672, 0.7_758_664, 0.7_812_226, 0.76_942_515]	)

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


 def 					_lowercase (			self							:			str	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    __lowercase								=						EulerDiscreteScheduler.from_config(pipe.scheduler.config	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						self.get_dummy_inputs()
    __lowercase								=						pipe(**UpperCAmelCase__	).images
    __lowercase								=						image[0, -3:, -3:, -1]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array(
        [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]	)
    assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-1



 def 					_lowercase (			self							:			Any	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    __lowercase								=						EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						self.get_dummy_inputs()
    __lowercase								=						pipe(**UpperCAmelCase__	).images
    __lowercase								=						image[0, -3:, -3:, -1]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array(
        [0.77_424_496, 0.773_601, 0.7_645_288, 0.7_769_598, 0.7_772_739, 0.7_738_688, 0.78_187_233, 0.77_879_584, 0.767_043]	)

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







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







 """simple docstring"""







 @property
 def 					_lowercase (			self							:			Tuple	):
    return (
        "CUDAExecutionProvider",
        {
            "gpu_mem_limit": "15000000000",  # 15GB
            "arena_extend_strategy": "kSameAsRequested",
        },
    )


 @property
 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						ort.SessionOptions()
    __lowercase								=						False
    return options


 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/img2img/sketch-mountains-input.jpg"	)
    __lowercase								=						init_image.resize((1_2_8, 1_2_8)	)
    # using the PNDM scheduler by default
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(
        "ssube/stable-diffusion-x4-upscaler-onnx",					provider=self.gpu_provider,					sess_options=self.gpu_options,					)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						"A fantasy landscape, trending on artstation"

    __lowercase								=						torch.manual_seed(0	)
    __lowercase								=						pipe(
        prompt=UpperCAmelCase__,					image=UpperCAmelCase__,					guidance_scale=7.5,					num_inference_steps=1_0,					generator=UpperCAmelCase__,					output_type="np",					)
    __lowercase								=						output.images
    __lowercase								=						images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

    assert images.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array([0.4_883, 0.4_947, 0.4_980, 0.4_975, 0.4_982, 0.4_980, 0.5_000, 0.5_006, 0.4_972]	)
    # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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



 def 					_lowercase (			self							:			str	):
    __lowercase								=						load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/img2img/sketch-mountains-input.jpg"	)
    __lowercase								=						init_image.resize((1_2_8, 1_2_8)	)
    __lowercase								=						LMSDiscreteScheduler.from_pretrained(
        "ssube/stable-diffusion-x4-upscaler-onnx",					subfolder="scheduler"	)
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(
        "ssube/stable-diffusion-x4-upscaler-onnx",					scheduler=UpperCAmelCase__,					provider=self.gpu_provider,					sess_options=self.gpu_options,					)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						"A fantasy landscape, trending on artstation"

    __lowercase								=						torch.manual_seed(0	)
    __lowercase								=						pipe(
        prompt=UpperCAmelCase__,					image=UpperCAmelCase__,					guidance_scale=7.5,					num_inference_steps=2_0,					generator=UpperCAmelCase__,					output_type="np",					)
    __lowercase								=						output.images
    __lowercase								=						images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

    assert images.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array(
        [0.50_173_753, 0.50_223_356, 0.502_039, 0.50_233_036, 0.5_023_725, 0.5_022_601, 0.5_018_758, 0.50_234_085, 0.50_241_566]	)
    # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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




