



'''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 	lowerCamelCase_							(					snake_case_    ,				unittest.TestCase			):
						# FIXME: add fast tests
						pass


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







						@property
						def 					__lowercase    (  self   :      str				):

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







						@property
						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			ort.SessionOptions()
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			False
													return options







						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
													    '''/in_paint/overture-creations-5sI6fQgYIuo.png'''				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
													    '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			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__				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			'''A red cat sitting on a park bench'''

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			np.random.RandomState(0				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			pipe(
													    prompt=lowerCAmelCase__				,    image=lowerCAmelCase__				,    mask_image=lowerCAmelCase__				,    guidance_scale=7.5				,    num_inference_steps=10				,    generator=lowerCAmelCase__				,    output_type='''np'''				,    )
													SCREAMING_SNAKE_CASE					:      List[str]     					=			output.images
													SCREAMING_SNAKE_CASE					:      List[str]     					=			images[0, 2_55:2_58, 2_55:2_58, -1]

													assert images.shape == (1, 5_12, 5_12, 3)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			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 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
													    '''/in_paint/overture-creations-5sI6fQgYIuo.png'''				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
													    '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			LMSDiscreteScheduler.from_pretrained(
													    '''runwayml/stable-diffusion-inpainting'''				,    subfolder='''scheduler'''				,    revision='''onnx'''				)
													SCREAMING_SNAKE_CASE					:      str     					=			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__				)

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			'''A red cat sitting on a park bench'''

													SCREAMING_SNAKE_CASE					:      Dict     					=			np.random.RandomState(0				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			pipe(
													    prompt=lowerCAmelCase__				,    image=lowerCAmelCase__				,    mask_image=lowerCAmelCase__				,    guidance_scale=7.5				,    num_inference_steps=20				,    generator=lowerCAmelCase__				,    output_type='''np'''				,    )
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			output.images
													SCREAMING_SNAKE_CASE					:      str     					=			images[0, 2_55:2_58, 2_55:2_58, -1]

													assert images.shape == (1, 5_12, 5_12, 3)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			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 bisect import bisect
from itertools import accumulate
def 					UpperCAmelCase   (						A  :				Tuple					,		A  :				Optional[Any]					,		A  :				Dict					,		A  :				Any				):
							SCREAMING_SNAKE_CASE					:      List[str]     					=			sorted(zip(A					,		A				)					,		key=lambda A				: x[0] / x[1]					,		reverse=A				)
							SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Dict     					=			[i[0] for i in r], [i[1] for i in r]
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			list(accumulate(A				)				)
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			bisect(A					,		A				)
							return (
							    0
							    if k == 0
							    else sum(vl[:k]				) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
							    if k != n
							    else sum(vl[:k]				)
							)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




