

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 						__UpperCamelCase    (					lowercase     ,     unittest.TestCase						):
	# FIXME: add fast tests
	pass


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




	@property
	def       __A					(		self	:						Any							):


		'''simple docstring'''







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




	@property
	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







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




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_       				=			load_image(
		    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
		    "/in_paint/overture-creations-5sI6fQgYIuo.png"							)
		UpperCAmelCase_       				=			load_image(
		    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
		    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"							)
		UpperCAmelCase_       				=			OnnxStableDiffusionInpaintPipeline.from_pretrained(
		    "runwayml/stable-diffusion-inpainting"		,	revision="onnx"		,	safety_checker=lowerCAmelCase		,	feature_extractor=lowerCAmelCase		,	provider=self.gpu_provider		,	sess_options=self.gpu_options		,	)
		pipe.set_progress_bar_config(disable=lowerCAmelCase							)

		UpperCAmelCase_       				=			"A red cat sitting on a park bench"

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

		assert images.shape == (1, 512, 512, 3)
		UpperCAmelCase_       				=			np.array([0.2_514, 0.3_007, 0.3_517, 0.1_790, 0.2_382, 0.3_167, 0.1_944, 0.2_273, 0.2_464]							)

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




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			load_image(
		    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
		    "/in_paint/overture-creations-5sI6fQgYIuo.png"							)
		UpperCAmelCase_       				=			load_image(
		    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
		    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"							)
		UpperCAmelCase_       				=			LMSDiscreteScheduler.from_pretrained(
		    "runwayml/stable-diffusion-inpainting"		,	subfolder="scheduler"		,	revision="onnx"							)
		UpperCAmelCase_       				=			OnnxStableDiffusionInpaintPipeline.from_pretrained(
		    "runwayml/stable-diffusion-inpainting"		,	revision="onnx"		,	scheduler=lowerCAmelCase		,	safety_checker=lowerCAmelCase		,	feature_extractor=lowerCAmelCase		,	provider=self.gpu_provider		,	sess_options=self.gpu_options		,	)
		pipe.set_progress_bar_config(disable=lowerCAmelCase							)

		UpperCAmelCase_       				=			"A red cat sitting on a park bench"

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

		assert images.shape == (1, 512, 512, 3)
		UpperCAmelCase_       				=			np.array([0.0_086, 0.0_077, 0.0_083, 0.0_093, 0.0_107, 0.0_139, 0.0_094, 0.0_097, 0.0_125]							)

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


from collections.abc import Sequence



def       __lowerCAmelCase      (						A       ,     A = False    ):
	if not arr:
		return 0

	UpperCAmelCase_       				=			0 if allow_empty_subarrays else float("-inf"    )
	UpperCAmelCase_       				=			0.0
	for num in arr:
		UpperCAmelCase_       				=			max(0 if allow_empty_subarrays else num       ,     curr_sum + num    )
		UpperCAmelCase_       				=			max(A       ,     A    )

	return max_sum


if __name__ == "__main__":
				from doctest import testmod

				testmod()

				_a:    Any 							=			[-2, 1, -3, 4, -1, 2, 1, -5, 4]
				print(F'{max_subarray_sum(nums) = }')