


'''simple docstring'''
import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
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     (							A__     ,		unittest.TestCase ):

		"""simple docstring"""

		pass



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

		"""simple docstring"""

		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[Any]							 =			ort.SessionOptions()
							UpperCamelCase__       :Optional[Any]							 =			False
							return options




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :List[str]							 =			load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/in_paint/overture-creations-5sI6fQgYIuo.png'''							)
							UpperCamelCase__       :int							 =			load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''							)
							UpperCamelCase__       :List[Any]							 =			OnnxStableDiffusionInpaintPipeline.from_pretrained(
							    '''runwayml/stable-diffusion-inpainting'''   ,       revision='''onnx'''   ,       safety_checker=_a   ,       feature_extractor=_a   ,       provider=self.gpu_provider   ,       sess_options=self.gpu_options   ,       )
							pipe.set_progress_bar_config(disable=_a							)

							UpperCamelCase__       :List[Any]							 =			'''A red cat sitting on a park bench'''

							UpperCamelCase__       :Any							 =			np.random.RandomState(0							)
							UpperCamelCase__       :List[str]							 =			pipe(
							    prompt=_a   ,       image=_a   ,       mask_image=_a   ,       guidance_scale=7.5   ,       num_inference_steps=10   ,       generator=_a   ,       output_type='''np'''   ,       )
							UpperCamelCase__       :Dict							 =			output.images
							UpperCamelCase__       :str							 =			images[0, 255:258, 255:258, -1]

							assert images.shape == (1, 512, 512, 3)
							UpperCamelCase__       :Dict							 =			np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]							)

							assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[Any]							 =			load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/in_paint/overture-creations-5sI6fQgYIuo.png'''							)
							UpperCamelCase__       :List[str]							 =			load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''							)
							UpperCamelCase__       :Any							 =			LMSDiscreteScheduler.from_pretrained(
							    '''runwayml/stable-diffusion-inpainting'''   ,       subfolder='''scheduler'''   ,       revision='''onnx'''							)
							UpperCamelCase__       :Dict							 =			OnnxStableDiffusionInpaintPipeline.from_pretrained(
							    '''runwayml/stable-diffusion-inpainting'''   ,       revision='''onnx'''   ,       scheduler=_a   ,       safety_checker=_a   ,       feature_extractor=_a   ,       provider=self.gpu_provider   ,       sess_options=self.gpu_options   ,       )
							pipe.set_progress_bar_config(disable=_a							)

							UpperCamelCase__       :Tuple							 =			'''A red cat sitting on a park bench'''

							UpperCamelCase__       :Optional[Any]							 =			np.random.RandomState(0							)
							UpperCamelCase__       :List[Any]							 =			pipe(
							    prompt=_a   ,       image=_a   ,       mask_image=_a   ,       guidance_scale=7.5   ,       num_inference_steps=20   ,       generator=_a   ,       output_type='''np'''   ,       )
							UpperCamelCase__       :List[Any]							 =			output.images
							UpperCamelCase__       :str							 =			images[0, 255:258, 255:258, -1]

							assert images.shape == (1, 512, 512, 3)
							UpperCamelCase__       :List[Any]							 =			np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]							)

							assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3



'''simple docstring'''
from argparse import ArgumentParser

from .env import EnvironmentCommand


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



					'''simple docstring'''






					UpperCamelCase__       :Union[str, Any]							 =			ArgumentParser('''Diffusers CLI tool'''			, usage='''diffusers-cli <command> [<args>]'''       )
					UpperCamelCase__       :Union[str, Any]							 =			parser.add_subparsers(help='''diffusers-cli command helpers'''       )

					# Register commands
					EnvironmentCommand.register_subcommand(__a       )

					# Let's go
					UpperCamelCase__       :Optional[int]							 =			parser.parse_args()

					if not hasattr(__a			, '''func'''       ):
										parser.print_help()
										exit(1       )

					# Run
					UpperCamelCase__       :Optional[int]							 =			args.func(__a       )
					service.run()


if __name__ == "__main__":
		main()