




'''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 			_snake_case		(     _UpperCAmelCase						,						unittest.TestCase							):
   # TODO: is there an appropriate internal test set?
   lowerCAmelCase_					:				Tuple							=   "ssube/stable-diffusion-x4-upscaler-onnx"




   def        lowerCAmelCase__    ( self    ,							a__=0							)    ->    Dict:

          '''simple docstring'''




          snake_case_      =				floats_tensor((1, 3, 128, 128)    ,							rng=random.Random(lowercase_							)							)
          snake_case_      =				torch.manual_seed(lowercase_							)
          snake_case_      =				{
              """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        lowerCAmelCase__    ( self							)    ->    List[str]:

          '''simple docstring'''




          snake_case_      =				OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,							provider="CPUExecutionProvider"							)
          pipe.set_progress_bar_config(disable=lowercase_							)

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

          # started as 128, should now be 512
          assert image.shape == (1, 512, 512, 3)
          snake_case_      =				np.array(
              [0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3]							)
          assert np.abs(image_slice - expected_slice							).max() < 1e-1




   def        lowerCAmelCase__    ( self							)    ->    Union[str, Any]:

          '''simple docstring'''




          snake_case_      =				OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,							provider="CPUExecutionProvider"							)
          snake_case_      =				PNDMScheduler.from_config(pipe.scheduler.config    ,							skip_prk_steps=lowercase_							)
          pipe.set_progress_bar_config(disable=lowercase_							)

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

          assert image.shape == (1, 512, 512, 3)
          snake_case_      =				np.array(
              [0.6_8_9_8_8_9_2, 0.5_9_2_4_0_5_5_6, 0.5_2_4_9_9_5_2_7, 0.5_8_8_6_6_2_1_5, 0.5_2_2_5_8_2_3_5, 0.5_2_5_7_2_7_1_5, 0.6_2_4_1_4_4_7_3, 0.6_1_7_4_3_8_7, 0.6_2_1_4_9_6_4]							)
          assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-1




   def        lowerCAmelCase__    ( self							)    ->    Union[str, Any]:

          '''simple docstring'''




          snake_case_      =				OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,							provider="CPUExecutionProvider"							)
          snake_case_      =				DPMSolverMultistepScheduler.from_config(pipe.scheduler.config							)
          pipe.set_progress_bar_config(disable=lowercase_							)

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

          assert image.shape == (1, 512, 512, 3)
          snake_case_      =				np.array(
              [0.7_6_5_9_2_7_8, 0.7_6_4_3_7_6_6_4, 0.7_5_5_7_9_1_0_7, 0.7_6_9_1_1_1_6, 0.7_7_6_6_6_9_8_6, 0.7_7_2_7_6_7_2, 0.7_7_5_8_6_6_4, 0.7_8_1_2_2_2_6, 0.7_6_9_4_2_5_1_5]							)

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




   def        lowerCAmelCase__    ( self							)    ->    Union[str, Any]:

          '''simple docstring'''




          snake_case_      =				OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,							provider="CPUExecutionProvider"							)
          snake_case_      =				EulerDiscreteScheduler.from_config(pipe.scheduler.config							)
          pipe.set_progress_bar_config(disable=lowercase_							)

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

          assert image.shape == (1, 512, 512, 3)
          snake_case_      =				np.array(
              [0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3]							)
          assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-1




   def        lowerCAmelCase__    ( self							)    ->    List[str]:

          '''simple docstring'''




          snake_case_      =				OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,							provider="CPUExecutionProvider"							)
          snake_case_      =				EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config							)
          pipe.set_progress_bar_config(disable=lowercase_							)

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

          assert image.shape == (1, 512, 512, 3)
          snake_case_      =				np.array(
              [0.7_7_4_2_4_4_9_6, 0.7_7_3_6_0_1, 0.7_6_4_5_2_8_8, 0.7_7_6_9_5_9_8, 0.7_7_7_2_7_3_9, 0.7_7_3_8_6_8_8, 0.7_8_1_8_7_2_3_3, 0.7_7_8_7_9_5_8_4, 0.7_6_7_0_4_3]							)

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




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




   @property
   def        lowerCAmelCase__    ( self							)    ->    str:

          '''simple docstring'''




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




   @property
   def        lowerCAmelCase__    ( self							)    ->    Optional[int]:

          '''simple docstring'''




          snake_case_      =				ort.SessionOptions()
          snake_case_      =				False
          return options




   def        lowerCAmelCase__    ( self							)    ->    Optional[Any]:

          '''simple docstring'''




          snake_case_      =				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/img2img/sketch-mountains-input.jpg"							)
          snake_case_      =				init_image.resize((128, 128)							)
          # using the PNDM scheduler by default
          snake_case_      =				OnnxStableDiffusionUpscalePipeline.from_pretrained(
              "ssube/stable-diffusion-x4-upscaler-onnx"    ,							provider=self.gpu_provider    ,							sess_options=self.gpu_options    ,							)
          pipe.set_progress_bar_config(disable=lowercase_							)

          snake_case_      =				"""A fantasy landscape, trending on artstation"""

          snake_case_      =				torch.manual_seed(0							)
          snake_case_      =				pipe(
              prompt=lowercase_    ,							image=lowercase_    ,							guidance_scale=7.5    ,							num_inference_steps=10    ,							generator=lowercase_    ,							output_type="np"    ,							)
          snake_case_      =				output.images
          snake_case_      =				images[0, 255:258, 383:386, -1]

          assert images.shape == (1, 512, 512, 3)
          snake_case_      =				np.array([0.4_8_8_3, 0.4_9_4_7, 0.4_9_8_0, 0.4_9_7_5, 0.4_9_8_2, 0.4_9_8_0, 0.5_0_0_0, 0.5_0_0_6, 0.4_9_7_2]							)
          # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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




   def        lowerCAmelCase__    ( self							)    ->    Any:

          '''simple docstring'''




          snake_case_      =				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/img2img/sketch-mountains-input.jpg"							)
          snake_case_      =				init_image.resize((128, 128)							)
          snake_case_      =				LMSDiscreteScheduler.from_pretrained(
              "ssube/stable-diffusion-x4-upscaler-onnx"    ,							subfolder="scheduler"							)
          snake_case_      =				OnnxStableDiffusionUpscalePipeline.from_pretrained(
              "ssube/stable-diffusion-x4-upscaler-onnx"    ,							scheduler=lowercase_    ,							provider=self.gpu_provider    ,							sess_options=self.gpu_options    ,							)
          pipe.set_progress_bar_config(disable=lowercase_							)

          snake_case_      =				"""A fantasy landscape, trending on artstation"""

          snake_case_      =				torch.manual_seed(0							)
          snake_case_      =				pipe(
              prompt=lowercase_    ,							image=lowercase_    ,							guidance_scale=7.5    ,							num_inference_steps=20    ,							generator=lowercase_    ,							output_type="np"    ,							)
          snake_case_      =				output.images
          snake_case_      =				images[0, 255:258, 383:386, -1]

          assert images.shape == (1, 512, 512, 3)
          snake_case_      =				np.array(
              [0.5_0_1_7_3_7_5_3, 0.5_0_2_2_3_3_5_6, 0.5_0_2_0_3_9, 0.5_0_2_3_3_0_3_6, 0.5_0_2_3_7_2_5, 0.5_0_2_2_6_0_1, 0.5_0_1_8_7_5_8, 0.5_0_2_3_4_0_8_5, 0.5_0_2_4_1_5_6_6]							)
          # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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










'''simple docstring'''





import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


_SCREAMING_SNAKE_CASE							:       Any						=			False




class 			_snake_case		(     unittest.TestCase							):
			pass




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




			def        lowerCAmelCase__    ( self							)    ->    List[Any]:

										'''simple docstring'''




										snake_case_      =				VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"							)
										pipe.to(a__							)
										pipe.set_progress_bar_config(disable=a__							)

										snake_case_      =				load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"							)
										snake_case_      =				torch.manual_seed(0							)
										snake_case_      =				pipe(
										    image=a__    ,							generator=a__    ,							guidance_scale=7.5    ,							num_inference_steps=50    ,							output_type="numpy"    ,							).images

										snake_case_      =				image[0, 253:256, 253:256, -1]

										assert image.shape == (1, 512, 512, 3)
										snake_case_      =				np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5]							)

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




