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				:      Tuple   )							->		str:


									'''simple docstring'''

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

						@property
						def        __a		(	self				:      Optional[int]   )							->		str:


									'''simple docstring'''

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

						def        __a		(	self				:      List[Any]   )							->		Optional[Any]:


									'''simple docstring'''

									lowercase							=		load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/in_paint/overture-creations-5sI6fQgYIuo.png'''   )
									lowercase							=		load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''   )
									lowercase							=		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							=		'''A red cat sitting on a park bench'''

									lowercase							=		np.random.RandomState(0   )
									lowercase							=		pipe(
									    prompt=__lowerCamelCase							,							image=__lowerCamelCase							,							mask_image=__lowerCamelCase							,							guidance_scale=7.5							,							num_inference_steps=10							,							generator=__lowerCamelCase							,							output_type='''np'''							,							)
									lowercase							=		output.images
									lowercase							=		images[0, 2_55:2_58, 2_55:2_58, -1]

									assert images.shape == (1, 5_12, 5_12, 3)
									lowercase							=		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        __a		(	self				:      int   )							->		Dict:


									'''simple docstring'''

									lowercase							=		load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/in_paint/overture-creations-5sI6fQgYIuo.png'''   )
									lowercase							=		load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''   )
									lowercase							=		LMSDiscreteScheduler.from_pretrained(
									    '''runwayml/stable-diffusion-inpainting'''							,							subfolder='''scheduler'''							,							revision='''onnx'''   )
									lowercase							=		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							=		'''A red cat sitting on a park bench'''

									lowercase							=		np.random.RandomState(0   )
									lowercase							=		pipe(
									    prompt=__lowerCamelCase							,							image=__lowerCamelCase							,							mask_image=__lowerCamelCase							,							guidance_scale=7.5							,							num_inference_steps=20							,							generator=__lowerCamelCase							,							output_type='''np'''							,							)
									lowercase							=		output.images
									lowercase							=		images[0, 2_55:2_58, 2_55:2_58, -1]

									assert images.shape == (1, 5_12, 5_12, 3)
									lowercase							=		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






from __future__ import annotations

from math import pi






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       dict[str, float]:



			"""simple docstring"""







			if (inductance, frequency, reactance).count(0       ) != 1:
						raise ValueError('''One and only one argument must be 0'''       )
			if inductance < 0:
						raise ValueError('''Inductance cannot be negative'''       )
			if frequency < 0:
						raise ValueError('''Frequency cannot be negative'''       )
			if reactance < 0:
						raise ValueError('''Inductive reactance cannot be negative'''       )
			if inductance == 0:
						return {"inductance": reactance / (2 * pi * frequency)}
			elif frequency == 0:
						return {"frequency": reactance / (2 * pi * inductance)}
			elif reactance == 0:
						return {"reactance": 2 * pi * frequency * inductance}
			else:
						raise ValueError('''Exactly one argument must be 0'''       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()





