


"""simple docstring"""







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 				UpperCamelCase_			(				unittest.TestCase):




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




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




	def 						UpperCAmelCase_   (				self    :       Dict     ) ->			Dict:
						__SCREAMING_SNAKE_CASE		     =       load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/in_paint/overture-creations-5sI6fQgYIuo.png"     )
						__SCREAMING_SNAKE_CASE		     =       load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"     )
						__SCREAMING_SNAKE_CASE		     =       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
						__SCREAMING_SNAKE_CASE		     =       OnnxStableDiffusionInpaintPipelineLegacy.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		     =       "A red cat sitting on a park bench"

						__SCREAMING_SNAKE_CASE		     =       np.random.RandomState(0     )
						__SCREAMING_SNAKE_CASE		     =       pipe(
						    prompt=UpperCAmelCase__    ,    image=UpperCAmelCase__    ,    mask_image=UpperCAmelCase__    ,    strength=0.75    ,    guidance_scale=7.5    ,    num_inference_steps=1_5    ,    generator=UpperCAmelCase__    ,    output_type="np"    ,    )

						__SCREAMING_SNAKE_CASE		     =       output.images[0]

						assert image.shape == (5_1_2, 5_1_2, 3)
						assert np.abs(expected_image - image     ).max() < 1E-2




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


a__       :							List[str]        =  {}


try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__       :							int        =  ['''MLukeTokenizer''']

if TYPE_CHECKING:
		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_mluke import MLukeTokenizer


else:
		import sys

		a__       :							Optional[Any]        =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
