



"""simple docstring"""





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  lowercase__  (       SCREAMING_SNAKE_CASE							,   unittest.TestCase ):







       '''simple docstring'''


       UpperCamelCase		=	'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''



       def 	lowercase__							(				self     :    Optional[Any]						,							_UpperCAmelCase     :    Tuple=0       )					->		Dict:


             '''simple docstring'''




             UpperCAmelCase_     					=				floats_tensor((1, 3, 128, 128)						,							rng=random.Random(_UpperCAmelCase       )       )
             UpperCAmelCase_     					=				np.random.RandomState(_UpperCAmelCase       )
             UpperCAmelCase_     					=				{
                 "prompt": "A painting of a squirrel eating a burger",
                 "image": image,
                 "generator": generator,
                 "num_inference_steps": 3,
                 "strength": 0.75,
                 "guidance_scale": 7.5,
                 "output_type": "numpy",
             }
             return inputs



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


             '''simple docstring'''




             UpperCAmelCase_     					=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,							provider="CPUExecutionProvider"       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

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

             assert image.shape == (1, 128, 128, 3)
             UpperCAmelCase_     					=				np.array([0.6_9643, 0.5_8484, 0.5_0314, 0.5_8760, 0.5_5368, 0.5_9643, 0.5_1529, 0.4_1217, 0.4_9087]       )
             assert np.abs(image_slice - expected_slice       ).max() < 1e-1



       def 	lowercase__							(				self     :    Dict       )					->		str:


             '''simple docstring'''




             UpperCAmelCase_     					=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,							provider="CPUExecutionProvider"       )
             UpperCAmelCase_     					=				PNDMScheduler.from_config(pipe.scheduler.config						,							skip_prk_steps=_UpperCAmelCase       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

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

             assert image.shape == (1, 128, 128, 3)
             UpperCAmelCase_     					=				np.array([0.6_1737, 0.5_4642, 0.5_3183, 0.5_4465, 0.5_2742, 0.6_0525, 0.4_9969, 0.4_0655, 0.4_8154]       )

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



       def 	lowercase__							(				self     :    Dict       )					->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_     					=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,							provider="CPUExecutionProvider"       )
             UpperCAmelCase_     					=				LMSDiscreteScheduler.from_config(pipe.scheduler.config       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

             # warmup pass to apply optimizations
             UpperCAmelCase_     					=				pipe(**self.get_dummy_inputs()       )

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

             assert image.shape == (1, 128, 128, 3)
             UpperCAmelCase_     					=				np.array([0.5_2761, 0.5_9977, 0.4_9033, 0.4_9619, 0.5_4282, 0.5_0311, 0.4_7600, 0.4_0918, 0.4_5203]       )

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



       def 	lowercase__							(				self     :    List[Any]       )					->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_     					=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,							provider="CPUExecutionProvider"       )
             UpperCAmelCase_     					=				EulerDiscreteScheduler.from_config(pipe.scheduler.config       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

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

             assert image.shape == (1, 128, 128, 3)
             UpperCAmelCase_     					=				np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304]       )

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



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


             '''simple docstring'''




             UpperCAmelCase_     					=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,							provider="CPUExecutionProvider"       )
             UpperCAmelCase_     					=				EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

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

             assert image.shape == (1, 128, 128, 3)
             UpperCAmelCase_     					=				np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304]       )

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







       def 	lowercase__							(				self     :    List[str]       )					->		Any:


             '''simple docstring'''




             UpperCAmelCase_     					=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,							provider="CPUExecutionProvider"       )
             UpperCAmelCase_     					=				DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
             pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

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

             assert image.shape == (1, 128, 128, 3)
             UpperCAmelCase_     					=				np.array([0.6_5331, 0.5_8277, 0.4_8204, 0.5_6059, 0.5_3665, 0.5_6235, 0.5_0969, 0.4_0009, 0.4_6552]       )

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



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







       '''simple docstring'''


       @property
       def 	lowercase__							(				self     :    Any       )					->		Union[str, Any]:


             '''simple docstring'''




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



       @property
       def 	lowercase__							(				self     :    Dict       )					->		Optional[Any]:


             '''simple docstring'''




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



       def 	lowercase__							(				self     :    Dict       )					->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_     					=				load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/img2img/sketch-mountains-input.jpg"       )
             UpperCAmelCase_     					=				init_image.resize((768, 512)       )
             # using the PNDM scheduler by default
             UpperCAmelCase_     					=				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       )

             UpperCAmelCase_     					=				"A fantasy landscape, trending on artstation"

             UpperCAmelCase_     					=				np.random.RandomState(0       )
             UpperCAmelCase_     					=				pipe(
                 prompt=_UpperCAmelCase						,							image=_UpperCAmelCase						,							strength=0.75						,							guidance_scale=7.5						,							num_inference_steps=10						,							generator=_UpperCAmelCase						,							output_type="np"						,							)
             UpperCAmelCase_     					=				output.images
             UpperCAmelCase_     					=				images[0, 255:258, 383:386, -1]

             assert images.shape == (1, 512, 768, 3)
             UpperCAmelCase_     					=				np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]       )
             # 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     :    Union[str, Any]       )					->		str:


             '''simple docstring'''




             UpperCAmelCase_     					=				load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/img2img/sketch-mountains-input.jpg"       )
             UpperCAmelCase_     					=				init_image.resize((768, 512)       )
             UpperCAmelCase_     					=				LMSDiscreteScheduler.from_pretrained(
                 "runwayml/stable-diffusion-v1-5"						,							subfolder="scheduler"						,							revision="onnx"       )
             UpperCAmelCase_     					=				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       )

             UpperCAmelCase_     					=				"A fantasy landscape, trending on artstation"

             UpperCAmelCase_     					=				np.random.RandomState(0       )
             UpperCAmelCase_     					=				pipe(
                 prompt=_UpperCAmelCase						,							image=_UpperCAmelCase						,							strength=0.75						,							guidance_scale=7.5						,							num_inference_steps=20						,							generator=_UpperCAmelCase						,							output_type="np"						,							)
             UpperCAmelCase_     					=				output.images
             UpperCAmelCase_     					=				images[0, 255:258, 383:386, -1]

             assert images.shape == (1, 512, 768, 3)
             UpperCAmelCase_     					=				np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]       )
             # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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









from graphs.minimum_spanning_tree_kruskal import kruskal

def 	SCREAMING_SNAKE_CASE_							(	)  ->     Tuple:
   _A      =       9
   _A      =       [
       [0, 1, 4],
       [0, 7, 8],
       [1, 2, 8],
       [7, 8, 7],
       [7, 6, 1],
       [2, 8, 2],
       [8, 6, 6],
       [2, 3, 7],
       [2, 5, 4],
       [6, 5, 2],
       [3, 5, 14],
       [3, 4, 9],
       [5, 4, 10],
       [1, 7, 11],
   ]

   _A      =       kruskal(_snake_case	,   _snake_case				)

   _A      =       [
       [7, 6, 1],
       [2, 8, 2],
       [6, 5, 2],
       [0, 1, 4],
       [2, 5, 4],
       [2, 3, 7],
       [0, 7, 8],
       [3, 4, 9],
   ]

   assert sorted(_snake_case				) == sorted(_snake_case				)



