



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 			_snake_case     (   _A      ,		unittest.TestCase):
				UpperCamelCase__			:   Any       						="""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""




				def 							A__ (  self   :  str,   __lowercase   :  int=0  ):
							lowercase__   					=			floats_tensor((1, 3, 128, 128),   rng=random.Random(__SCREAMING_SNAKE_CASE  )  )
							lowercase__   					=			np.random.RandomState(__SCREAMING_SNAKE_CASE  )
							lowercase__   					=			{
							    "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 							A__ (  self   :  Any  ):
							lowercase__   					=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,   provider="CPUExecutionProvider"  )
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )

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

							assert image.shape == (1, 128, 128, 3)
							lowercase__   					=			np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087]  )
							assert np.abs(image_slice - expected_slice  ).max() < 1e-1




				def 							A__ (  self   :  Union[str, Any]  ):
							lowercase__   					=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,   provider="CPUExecutionProvider"  )
							lowercase__   					=			PNDMScheduler.from_config(pipe.scheduler.config,   skip_prk_steps=__SCREAMING_SNAKE_CASE  )
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )

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

							assert image.shape == (1, 128, 128, 3)
							lowercase__   					=			np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154]  )

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




				def 							A__ (  self   :  Union[str, Any]  ):
							lowercase__   					=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,   provider="CPUExecutionProvider"  )
							lowercase__   					=			LMSDiscreteScheduler.from_config(pipe.scheduler.config  )
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )

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

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

							assert image.shape == (1, 128, 128, 3)
							lowercase__   					=			np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203]  )

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




				def 							A__ (  self   :  Tuple  ):
							lowercase__   					=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,   provider="CPUExecutionProvider"  )
							lowercase__   					=			EulerDiscreteScheduler.from_config(pipe.scheduler.config  )
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )

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

							assert image.shape == (1, 128, 128, 3)
							lowercase__   					=			np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]  )

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




				def 							A__ (  self   :  List[str]  ):
							lowercase__   					=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,   provider="CPUExecutionProvider"  )
							lowercase__   					=			EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config  )
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )

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

							assert image.shape == (1, 128, 128, 3)
							lowercase__   					=			np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]  )

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




				def 							A__ (  self   :  str  ):
							lowercase__   					=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,   provider="CPUExecutionProvider"  )
							lowercase__   					=			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )

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

							assert image.shape == (1, 128, 128, 3)
							lowercase__   					=			np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552]  )

							assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class 			_snake_case     (   unittest.TestCase):




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




				@property
				def 							A__ (  self   :  Any  ):
							lowercase__   					=			ort.SessionOptions()
							lowercase__   					=			False
							return options




				def 							A__ (  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((768, 512)  )
							# using the PNDM scheduler by default
							lowercase__   					=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(
							    "CompVis/stable-diffusion-v1-4",   revision="onnx",   safety_checker=__SCREAMING_SNAKE_CASE,   feature_extractor=__SCREAMING_SNAKE_CASE,   provider=self.gpu_provider,   sess_options=self.gpu_options,   )
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )

							lowercase__   					=			"A fantasy landscape, trending on artstation"

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

							assert images.shape == (1, 512, 768, 3)
							lowercase__   					=			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 							A__ (  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((768, 512)  )
							lowercase__   					=			LMSDiscreteScheduler.from_pretrained(
							    "runwayml/stable-diffusion-v1-5",   subfolder="scheduler",   revision="onnx"  )
							lowercase__   					=			OnnxStableDiffusionImgaImgPipeline.from_pretrained(
							    "runwayml/stable-diffusion-v1-5",   revision="onnx",   scheduler=__SCREAMING_SNAKE_CASE,   safety_checker=__SCREAMING_SNAKE_CASE,   feature_extractor=__SCREAMING_SNAKE_CASE,   provider=self.gpu_provider,   sess_options=self.gpu_options,   )
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE  )

							lowercase__   					=			"A fantasy landscape, trending on artstation"

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

							assert images.shape == (1, 512, 768, 3)
							lowercase__   					=			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









import argparse
import json
import subprocess



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			[]

			lowercase__   					=			(
			    f'''curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'''
			    " https://api.github.com/repos/huggingface/transformers/actions/runners"
			)
			lowercase__   					=			subprocess.run(SCREAMING_SNAKE_CASE_  ,					shell=SCREAMING_SNAKE_CASE_  ,					stdout=subprocess.PIPE					)
			lowercase__   					=			output.stdout.decode("utf-8"					)
			lowercase__   					=			json.loads(SCREAMING_SNAKE_CASE_					)

			lowercase__   					=			status["runners"]
			for runner in runners:
						if runner["name"] in target_runners:
									if runner["status"] == "offline":
												offline_runners.append(SCREAMING_SNAKE_CASE_					)

    # save the result so we can report them on Slack
			with open("offline_runners.txt"  ,					"w"					) as fp:
						fp.write(json.dumps(SCREAMING_SNAKE_CASE_					)					)

			if len(SCREAMING_SNAKE_CASE_					) > 0:
						lowercase__   					=			"\n".join([x["name"] for x in offline_runners]					)
						raise ValueError(f'''The following runners are offline:\n{failed}'''					)


if __name__ == "__main__":



							def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
										return values.split(","					)

							lowercase_						=       argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--target_runners""",
							    default=None,
							    type=list_str,
							    required=True,
							    help="""Comma-separated list of runners to check status.""",
							)

							parser.add_argument(
							    """--token""", default=None, type=str, required=True, help="""A token that has actions:read permission."""
							)
							lowercase_						=       parser.parse_args()




							get_runner_status(args.target_runners, args.token)




