


"""simple docstring"""







import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
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 				__A    (	_SCREAMING_SNAKE_CASE,   unittest.TestCase      ):






	"""simple docstring"""




	__lowerCAmelCase			 =  "ssube/stable-diffusion-x4-upscaler-onnx"


	def 				SCREAMING_SNAKE_CASE							(		self			,   __A=0						) ->					Dict:
					a					=floats_tensor((1, 3, 128, 128)			,   rng=random.Random(__A						)						)
					a					=torch.manual_seed(__A						)
					a					={
					    '''prompt''': '''A painting of a squirrel eating a burger''',
					    '''image''': image,
					    '''generator''': generator,
					    '''num_inference_steps''': 3,
					    '''guidance_scale''': 7.5,
					    '''output_type''': '''numpy''',
					}
					return inputs


	def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
					a					=OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			,   provider='''CPUExecutionProvider'''						)
					pipe.set_progress_bar_config(disable=__A						)

					a					=self.get_dummy_inputs()
					a					=pipe(**__A						).images
					a					=image[0, -3:, -3:, -1].flatten()

					# started as 128, should now be 512
					assert image.shape == (1, 512, 512, 3)
					a					=np.array(
					    [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]						)
					assert np.abs(image_slice - expected_slice						).max() < 1E-1


	def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
					a					=OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			,   provider='''CPUExecutionProvider'''						)
					a					=PNDMScheduler.from_config(pipe.scheduler.config			,   skip_prk_steps=__A						)
					pipe.set_progress_bar_config(disable=__A						)

					a					=self.get_dummy_inputs()
					a					=pipe(**__A						).images
					a					=image[0, -3:, -3:, -1]

					assert image.shape == (1, 512, 512, 3)
					a					=np.array(
					    [0.6_898_892, 0.59_240_556, 0.52_499_527, 0.58_866_215, 0.52_258_235, 0.52_572_715, 0.62_414_473, 0.6_174_387, 0.6_214_964]						)
					assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-1


	def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
					a					=OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			,   provider='''CPUExecutionProvider'''						)
					a					=DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
					pipe.set_progress_bar_config(disable=__A						)

					a					=self.get_dummy_inputs()
					a					=pipe(**__A						).images
					a					=image[0, -3:, -3:, -1]

					assert image.shape == (1, 512, 512, 3)
					a					=np.array(
					    [0.7_659_278, 0.76_437_664, 0.75_579_107, 0.7_691_116, 0.77_666_986, 0.7_727_672, 0.7_758_664, 0.7_812_226, 0.76_942_515]						)

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


	def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
					a					=OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			,   provider='''CPUExecutionProvider'''						)
					a					=EulerDiscreteScheduler.from_config(pipe.scheduler.config						)
					pipe.set_progress_bar_config(disable=__A						)

					a					=self.get_dummy_inputs()
					a					=pipe(**__A						).images
					a					=image[0, -3:, -3:, -1]

					assert image.shape == (1, 512, 512, 3)
					a					=np.array(
					    [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]						)
					assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-1





	def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
					a					=OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			,   provider='''CPUExecutionProvider'''						)
					a					=EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config						)
					pipe.set_progress_bar_config(disable=__A						)

					a					=self.get_dummy_inputs()
					a					=pipe(**__A						).images
					a					=image[0, -3:, -3:, -1]

					assert image.shape == (1, 512, 512, 3)
					a					=np.array(
					    [0.77_424_496, 0.773_601, 0.7_645_288, 0.7_769_598, 0.7_772_739, 0.7_738_688, 0.78_187_233, 0.77_879_584, 0.767_043]						)

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






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






	"""simple docstring"""




	@property
	def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
					return (
					    "CUDAExecutionProvider",
					    {
					        "gpu_mem_limit": "15000000000",  # 15GB
					        "arena_extend_strategy": "kSameAsRequested",
					    },
					)


	@property
	def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
					a					=ort.SessionOptions()
					a					=False
					return options


	def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
					a					=load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/img2img/sketch-mountains-input.jpg'''						)
					a					=init_image.resize((128, 128)						)
					# using the PNDM scheduler by default
					a					=OnnxStableDiffusionUpscalePipeline.from_pretrained(
					    '''ssube/stable-diffusion-x4-upscaler-onnx'''			,   provider=self.gpu_provider			,   sess_options=self.gpu_options			,   )
					pipe.set_progress_bar_config(disable=__A						)

					a					='''A fantasy landscape, trending on artstation'''

					a					=torch.manual_seed(0						)
					a					=pipe(
					    prompt=__A			,   image=__A			,   guidance_scale=7.5			,   num_inference_steps=10			,   generator=__A			,   output_type='''np'''			,   )
					a					=output.images
					a					=images[0, 255:258, 383:386, -1]

					assert images.shape == (1, 512, 512, 3)
					a					=np.array([0.4_883, 0.4_947, 0.4_980, 0.4_975, 0.4_982, 0.4_980, 0.5_000, 0.5_006, 0.4_972]						)
					# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

					assert np.abs(image_slice.flatten() - expected_slice						).max() < 2E-2





	def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
					a					=load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/img2img/sketch-mountains-input.jpg'''						)
					a					=init_image.resize((128, 128)						)
					a					=LMSDiscreteScheduler.from_pretrained(
					    '''ssube/stable-diffusion-x4-upscaler-onnx'''			,   subfolder='''scheduler'''						)
					a					=OnnxStableDiffusionUpscalePipeline.from_pretrained(
					    '''ssube/stable-diffusion-x4-upscaler-onnx'''			,   scheduler=__A			,   provider=self.gpu_provider			,   sess_options=self.gpu_options			,   )
					pipe.set_progress_bar_config(disable=__A						)

					a					='''A fantasy landscape, trending on artstation'''

					a					=torch.manual_seed(0						)
					a					=pipe(
					    prompt=__A			,   image=__A			,   guidance_scale=7.5			,   num_inference_steps=20			,   generator=__A			,   output_type='''np'''			,   )
					a					=output.images
					a					=images[0, 255:258, 383:386, -1]

					assert images.shape == (1, 512, 512, 3)
					a					=np.array(
					    [0.50_173_753, 0.50_223_356, 0.502_039, 0.50_233_036, 0.5_023_725, 0.5_022_601, 0.5_018_758, 0.50_234_085, 0.50_241_566]						)
					# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

					assert np.abs(image_slice.flatten() - expected_slice						).max() < 2E-2



"""simple docstring"""







from __future__ import annotations
def _A						(						lowercase	,   lowercase	,   lowercase	,   ):







    """simple docstring"""
    if (electron_conc, hole_conc, intrinsic_conc).count(0		) != 1:
        raise ValueError('''You cannot supply more or less than 2 values'''		)
    elif electron_conc < 0:
        raise ValueError('''Electron concentration cannot be negative in a semiconductor'''		)
    elif hole_conc < 0:
        raise ValueError('''Hole concentration cannot be negative in a semiconductor'''		)
    elif intrinsic_conc < 0:
        raise ValueError(
            '''Intrinsic concentration cannot be negative in a semiconductor'''		)
    elif electron_conc == 0:
        return (
            "electron_conc",
            intrinsic_conc**2 / hole_conc,
        )
    elif hole_conc == 0:
        return (
            "hole_conc",
            intrinsic_conc**2 / electron_conc,
        )
    elif intrinsic_conc == 0:
        return (
            "intrinsic_conc",
            (electron_conc * hole_conc) ** 0.5,
        )
    else:
        return (-1, -1)


if __name__ == "__main__":
       import doctest

       doctest.testmod()