
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__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	# FIXME: add fast tests
	pass





@nightly
@require_onnxruntime
@require_torch_gpu
class 						lowercase__  (						unittest.TestCase  ):
	@property
	def      A_				(			self							:   int							):
						return (
						    "CUDAExecutionProvider",
						    {
						        "gpu_mem_limit": "15000000000",  # 15GB
						        "arena_extend_strategy": "kSameAsRequested",
						    },
						)
	@property
	def      A_				(			self							:   Union[str, Any]							):
						SCREAMING_SNAKE_CASE__													= ort.SessionOptions()
						SCREAMING_SNAKE_CASE__													= False
						return options
	def      A_				(			self							:   Optional[Any]							):
						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__													= OnnxStableDiffusionInpaintPipeline.from_pretrained(
						    'runwayml/stable-diffusion-inpainting'       ,   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_       ,   guidance_scale=7.5       ,   num_inference_steps=10       ,   generator=UpperCAmelCase_       ,   output_type='np'       ,   )
						SCREAMING_SNAKE_CASE__													= output.images
						SCREAMING_SNAKE_CASE__													= images[0, 255:258, 255:258, -1]

						assert images.shape == (1, 512, 512, 3)
						SCREAMING_SNAKE_CASE__													= 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							:   List[Any]							):
						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__													= LMSDiscreteScheduler.from_pretrained(
						    'runwayml/stable-diffusion-inpainting'       ,   subfolder='scheduler'       ,   revision='onnx'							)
						SCREAMING_SNAKE_CASE__													= OnnxStableDiffusionInpaintPipeline.from_pretrained(
						    'runwayml/stable-diffusion-inpainting'       ,   revision='onnx'       ,   scheduler=UpperCAmelCase_       ,   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_       ,   guidance_scale=7.5       ,   num_inference_steps=20       ,   generator=UpperCAmelCase_       ,   output_type='np'       ,   )
						SCREAMING_SNAKE_CASE__													= output.images
						SCREAMING_SNAKE_CASE__													= images[0, 255:258, 255:258, -1]

						assert images.shape == (1, 512, 512, 3)
						SCREAMING_SNAKE_CASE__													= 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







import os
from math import logaa






def 							_lowercase							(     UpperCamelCase_ = "base_exp.txt"		)		->		int:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= 0
					SCREAMING_SNAKE_CASE__													= 0
					for i, line in enumerate(open(os.path.join(os.path.dirname(UpperCamelCase_		)	,	UpperCamelCase_		)		)		):
										SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= list(map(UpperCamelCase_	,	line.split(','		)		)		)
										if x * logaa(UpperCamelCase_		) > largest:
															SCREAMING_SNAKE_CASE__													= x * logaa(UpperCamelCase_		)
															SCREAMING_SNAKE_CASE__													= i + 1
					return result


if __name__ == "__main__":
				print(solution())





