

import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
			import onnxruntime as ort






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




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




	@property
	def        lowerCAmelCase_       (     self						:     Tuple							):
		__A :			List[str]		    =			ort.SessionOptions()
		__A :			List[str]		    =			False
		return options




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			Optional[Any]		    =			load_image(
		    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
		    """/in_paint/overture-creations-5sI6fQgYIuo.png"""							)
		__A :			Dict		    =			load_image(
		    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
		    """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""							)
		__A :			List[str]		    =			load_numpy(
		    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
		    """/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"""							)

		# using the PNDM scheduler by default
		__A :			int		    =			OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
		    """CompVis/stable-diffusion-v1-4"""					,      revision="""onnx"""					,      safety_checker=__A					,      feature_extractor=__A					,      provider=self.gpu_provider					,      sess_options=self.gpu_options					,      )
		pipe.set_progress_bar_config(disable=__A							)

		__A :			List[Any]		    =			"""A red cat sitting on a park bench"""

		__A :			Dict		    =			np.random.RandomState(0							)
		__A :			str		    =			pipe(
		    prompt=__A					,      image=__A					,      mask_image=__A					,      strength=0.7_5					,      guidance_scale=7.5					,      num_inference_steps=15					,      generator=__A					,      output_type="""np"""					,      )

		__A :			List[Any]		    =			output.images[0]

		assert image.shape == (512, 512, 3)
		assert np.abs(expected_image - image							).max() < 1e-2













"""simple docstring"""





from argparse import ArgumentParser

from .env import EnvironmentCommand



def   a    (     )    ->							Tuple:
						__magic_name__:	Optional[int]							=       ArgumentParser("""Diffusers CLI tool"""      ,   usage="""diffusers-cli <command> [<args>]"""  )
						__magic_name__:	Any							=       parser.add_subparsers(help="""diffusers-cli command helpers"""  )

						# Register commands
						EnvironmentCommand.register_subcommand(__UpperCAmelCase  )

						# Let's go
						__magic_name__:	int							=       parser.parse_args()

						if not hasattr(__UpperCAmelCase      ,   """func"""  ):
												parser.print_help()
												exit(1  )

						# Run
						__magic_name__:	Optional[Any]							=       args.func(__UpperCAmelCase  )
						service.run()


if __name__ == "__main__":
							main()



