



import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    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       _SCREAMING_SNAKE_CASE	(UpperCamelCase			,    unittest.TestCase ):
     lowerCAmelCase					=			"""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""





     def 	__snake_case    (       self :							str     ,  UpperCamelCase :							int=0				)->Dict:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	floats_tensor((1, 3, 1_2_8, 1_2_8)     ,  rng=random.Random(UpperCamelCase				)				)
      __SCREAMING_SNAKE_CASE       :    Dict       =	np.random.RandomState(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	{
          "prompt": "A painting of a squirrel eating a burger",
          "image": image,
          "generator": generator,
          "num_inference_steps": 3,
          "strength": 0.7_5,
          "guidance_scale": 7.5,
          "output_type": "numpy",
      }
      return inputs





     def 	__snake_case    (       self :							Optional[Any]				)->Dict:
      __SCREAMING_SNAKE_CASE       :    Dict       =	OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,  provider="CPUExecutionProvider"				)
      pipe.set_progress_bar_config(disable=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    int       =	self.get_dummy_inputs()
      __SCREAMING_SNAKE_CASE       :    str       =	pipe(**UpperCamelCase				).images
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	image[0, -3:, -3:, -1].flatten()

      assert image.shape == (1, 1_2_8, 1_2_8, 3)
      __SCREAMING_SNAKE_CASE       :    Dict       =	np.array([0.6_9_6_4_3, 0.5_8_4_8_4, 0.5_0_3_1_4, 0.5_8_7_6_0, 0.5_5_3_6_8, 0.5_9_6_4_3, 0.5_1_5_2_9, 0.4_1_2_1_7, 0.4_9_0_8_7]				)
      assert np.abs(image_slice - expected_slice				).max() < 1E-1





     def 	__snake_case    (       self :							str				)->List[Any]:
      __SCREAMING_SNAKE_CASE       :    Any       =	OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,  provider="CPUExecutionProvider"				)
      __SCREAMING_SNAKE_CASE       :    Any       =	PNDMScheduler.from_config(pipe.scheduler.config     ,  skip_prk_steps=UpperCamelCase				)
      pipe.set_progress_bar_config(disable=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    int       =	self.get_dummy_inputs()
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	pipe(**UpperCamelCase				).images
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	image[0, -3:, -3:, -1]

      assert image.shape == (1, 1_2_8, 1_2_8, 3)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	np.array([0.6_1_7_3_7, 0.5_4_6_4_2, 0.5_3_1_8_3, 0.5_4_4_6_5, 0.5_2_7_4_2, 0.6_0_5_2_5, 0.4_9_9_6_9, 0.4_0_6_5_5, 0.4_8_1_5_4]				)

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





     def 	__snake_case    (       self :							List[str]				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,  provider="CPUExecutionProvider"				)
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	LMSDiscreteScheduler.from_config(pipe.scheduler.config				)
      pipe.set_progress_bar_config(disable=UpperCamelCase				)

      # warmup pass to apply optimizations
      __SCREAMING_SNAKE_CASE       :    str       =	pipe(**self.get_dummy_inputs()				)

      __SCREAMING_SNAKE_CASE       :    Dict       =	self.get_dummy_inputs()
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	pipe(**UpperCamelCase				).images
      __SCREAMING_SNAKE_CASE       :    int       =	image[0, -3:, -3:, -1]

      assert image.shape == (1, 1_2_8, 1_2_8, 3)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	np.array([0.5_2_7_6_1, 0.5_9_9_7_7, 0.4_9_0_3_3, 0.4_9_6_1_9, 0.5_4_2_8_2, 0.5_0_3_1_1, 0.4_7_6_0_0, 0.4_0_9_1_8, 0.4_5_2_0_3]				)

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





     def 	__snake_case    (       self :							Union[str, Any]				)->Dict:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,  provider="CPUExecutionProvider"				)
      __SCREAMING_SNAKE_CASE       :    Any       =	EulerDiscreteScheduler.from_config(pipe.scheduler.config				)
      pipe.set_progress_bar_config(disable=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    str       =	self.get_dummy_inputs()
      __SCREAMING_SNAKE_CASE       :    Tuple       =	pipe(**UpperCamelCase				).images
      __SCREAMING_SNAKE_CASE       :    Tuple       =	image[0, -3:, -3:, -1]

      assert image.shape == (1, 1_2_8, 1_2_8, 3)
      __SCREAMING_SNAKE_CASE       :    Dict       =	np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]				)

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





     def 	__snake_case    (       self :							Dict				)->Tuple:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,  provider="CPUExecutionProvider"				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config				)
      pipe.set_progress_bar_config(disable=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.get_dummy_inputs()
      __SCREAMING_SNAKE_CASE       :    Dict       =	pipe(**UpperCamelCase				).images
      __SCREAMING_SNAKE_CASE       :    Any       =	image[0, -3:, -3:, -1]

      assert image.shape == (1, 1_2_8, 1_2_8, 3)
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]				)

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






     def 	__snake_case    (       self :							Optional[int]				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint     ,  provider="CPUExecutionProvider"				)
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config				)
      pipe.set_progress_bar_config(disable=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.get_dummy_inputs()
      __SCREAMING_SNAKE_CASE       :    Any       =	pipe(**UpperCamelCase				).images
      __SCREAMING_SNAKE_CASE       :    Any       =	image[0, -3:, -3:, -1]

      assert image.shape == (1, 1_2_8, 1_2_8, 3)
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	np.array([0.6_5_3_3_1, 0.5_8_2_7_7, 0.4_8_2_0_4, 0.5_6_0_5_9, 0.5_3_6_6_5, 0.5_6_2_3_5, 0.5_0_9_6_9, 0.4_0_0_0_9, 0.4_6_5_5_2]				)

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






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





     @property
     def 	__snake_case    (       self :							Optional[int]				)->Dict:
      return (
          "CUDAExecutionProvider",
          {
              "gpu_mem_limit": "15000000000",  # 15GB
              "arena_extend_strategy": "kSameAsRequested",
          },
      )





     @property
     def 	__snake_case    (       self :							List[Any]				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	ort.SessionOptions()
      __SCREAMING_SNAKE_CASE       :    List[str]       =	False
      return options





     def 	__snake_case    (       self :							Optional[int]				)->str:
      __SCREAMING_SNAKE_CASE       :    Tuple       =	load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/img2img/sketch-mountains-input.jpg"				)
      __SCREAMING_SNAKE_CASE       :    List[str]       =	init_image.resize((7_6_8, 5_1_2)				)
      # using the PNDM scheduler by default
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	OnnxStableDiffusionImgaImgPipeline.from_pretrained(
          "CompVis/stable-diffusion-v1-4"     ,  revision="onnx"     ,  safety_checker=UpperCamelCase     ,  feature_extractor=UpperCamelCase     ,  provider=self.gpu_provider     ,  sess_options=self.gpu_options     ,  )
      pipe.set_progress_bar_config(disable=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    List[str]       =	"A fantasy landscape, trending on artstation"

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	np.random.RandomState(0				)
      __SCREAMING_SNAKE_CASE       :    Dict       =	pipe(
          prompt=UpperCamelCase     ,  image=UpperCamelCase     ,  strength=0.7_5     ,  guidance_scale=7.5     ,  num_inference_steps=1_0     ,  generator=UpperCamelCase     ,  output_type="np"     ,  )
      __SCREAMING_SNAKE_CASE       :    List[str]       =	output.images
      __SCREAMING_SNAKE_CASE       :    Tuple       =	images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

      assert images.shape == (1, 5_1_2, 7_6_8, 3)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	np.array([0.4_9_0_9, 0.5_0_5_9, 0.5_3_7_2, 0.4_6_2_3, 0.4_8_7_6, 0.5_0_4_9, 0.4_8_2_0, 0.4_9_5_6, 0.5_0_1_9]				)
      # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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






     def 	__snake_case    (       self :							Union[str, Any]				)->Dict:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/img2img/sketch-mountains-input.jpg"				)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	init_image.resize((7_6_8, 5_1_2)				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	LMSDiscreteScheduler.from_pretrained(
          "runwayml/stable-diffusion-v1-5"     ,  subfolder="scheduler"     ,  revision="onnx"				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	OnnxStableDiffusionImgaImgPipeline.from_pretrained(
          "runwayml/stable-diffusion-v1-5"     ,  revision="onnx"     ,  scheduler=UpperCamelCase     ,  safety_checker=UpperCamelCase     ,  feature_extractor=UpperCamelCase     ,  provider=self.gpu_provider     ,  sess_options=self.gpu_options     ,  )
      pipe.set_progress_bar_config(disable=UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    str       =	"A fantasy landscape, trending on artstation"

      __SCREAMING_SNAKE_CASE       :    Tuple       =	np.random.RandomState(0				)
      __SCREAMING_SNAKE_CASE       :    str       =	pipe(
          prompt=UpperCamelCase     ,  image=UpperCamelCase     ,  strength=0.7_5     ,  guidance_scale=7.5     ,  num_inference_steps=2_0     ,  generator=UpperCamelCase     ,  output_type="np"     ,  )
      __SCREAMING_SNAKE_CASE       :    Tuple       =	output.images
      __SCREAMING_SNAKE_CASE       :    int       =	images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

      assert images.shape == (1, 5_1_2, 7_6_8, 3)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	np.array([0.8_0_4_3, 0.9_2_6, 0.9_5_8_1, 0.8_1_1_9, 0.8_9_5_4, 0.9_1_3, 0.7_2_0_9, 0.7_4_6_3, 0.7_4_3_1]				)
      # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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











from diffusers.utils.testing_utils import require_onnxruntime

@require_onnxruntime
class       _SCREAMING_SNAKE_CASE	:
     pass






