


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






@nightly
@require_onnxruntime
@require_torch_gpu
class _lowerCamelCase(		unittest.TestCase  ):
       @property
       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           return (
               "CUDAExecutionProvider",
               {
                   "gpu_mem_limit": "15000000000",  # 15GB
                   "arena_extend_strategy": "kSameAsRequested",
               },
           )
       @property
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						ort.SessionOptions()
           _lowercase			:				Any 						=						False
           return options
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

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

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

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

           assert images.shape == (1, 5_12, 5_12, 3)
           _lowercase			:				Union[str, Any] 						=						np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4])

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




       def      UpperCamelCase	(   self)			->  Dict:




           """simple docstring"""

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

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

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

           assert images.shape == (1, 5_12, 5_12, 3)
           _lowercase			:				Optional[int] 						=						np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5])

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




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	list[int]		,   SCREAMING_SNAKE_CASE_	:	int       )  ->     bool:
						_lowercase					       =					len(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					[[False] * (required_sum + 1) for _ in range(arr_len + 1       )]

						# for each arr value, a sum of zero(0) can be formed by not taking any element
						# hence True/1
						for i in range(arr_len + 1       ):
												_lowercase					       =					True

						# sum is not zero and set is empty then false
						for i in range(1		,   required_sum + 1       ):
												_lowercase					       =					False

						for i in range(1		,   arr_len + 1       ):
												for j in range(1		,   required_sum + 1       ):
																		if arr[i - 1] > j:
																								_lowercase					       =					subset[i - 1][j]
																		if arr[i - 1] <= j:
																								_lowercase					       =					subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

						return subset[arr_len][required_sum]


if __name__ == "__main__":
		import doctest

		doctest.testmod()