





'''simple docstring'''

import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    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 							__lowerCAmelCase	(       __magic_name__		,		unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	Optional[Any]									=       'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'





       def snake_case__		(				self  :						Dict			,					lowerCAmelCase__  :						str=0	)       ->	Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			floats_tensor((1, 3, 128, 128)			,					rng=random.Random(lowerCAmelCase__	)	)
            _UpperCamelCase							       =			np.random.RandomState(lowerCAmelCase__	)
            _UpperCamelCase							       =			{
                '''prompt''': '''A painting of a squirrel eating a burger''',
                '''image''': image,
                '''generator''': generator,
                '''num_inference_steps''': 3,
                '''strength''': 0.75,
                '''guidance_scale''': 7.5,
                '''output_type''': '''numpy''',
            }
            return inputs





       def snake_case__		(				self  :						str	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,					provider='''CPUExecutionProvider'''	)
            pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

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

            assert image.shape == (1, 128, 128, 3)
            _UpperCamelCase							       =			np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087]	)
            assert np.abs(image_slice - expected_slice	).max() < 1e-1





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

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,					provider='''CPUExecutionProvider'''	)
            _UpperCamelCase							       =			PNDMScheduler.from_config(pipe.scheduler.config			,					skip_prk_steps=lowerCAmelCase__	)
            pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

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

            assert image.shape == (1, 128, 128, 3)
            _UpperCamelCase							       =			np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154]	)

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





       def snake_case__		(				self  :						Any	)       ->	Optional[int]:

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,					provider='''CPUExecutionProvider'''	)
            _UpperCamelCase							       =			LMSDiscreteScheduler.from_config(pipe.scheduler.config	)
            pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

            # warmup pass to apply optimizations
            _UpperCamelCase							       =			pipe(**self.get_dummy_inputs()	)

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

            assert image.shape == (1, 128, 128, 3)
            _UpperCamelCase							       =			np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203]	)

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





       def snake_case__		(				self  :						str	)       ->	Optional[int]:

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,					provider='''CPUExecutionProvider'''	)
            _UpperCamelCase							       =			EulerDiscreteScheduler.from_config(pipe.scheduler.config	)
            pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

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

            assert image.shape == (1, 128, 128, 3)
            _UpperCamelCase							       =			np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]	)

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





       def snake_case__		(				self  :						Tuple	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,					provider='''CPUExecutionProvider'''	)
            _UpperCamelCase							       =			EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config	)
            pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

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

            assert image.shape == (1, 128, 128, 3)
            _UpperCamelCase							       =			np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]	)

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







       def snake_case__		(				self  :						List[Any]	)       ->	Dict:

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,					provider='''CPUExecutionProvider'''	)
            _UpperCamelCase							       =			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
            pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

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

            assert image.shape == (1, 128, 128, 3)
            _UpperCamelCase							       =			np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552]	)

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

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






       """simple docstring"""





       @property
       def snake_case__		(				self  :						List[Any]	)       ->	Dict:

            '''simple docstring'''




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





       @property
       def snake_case__		(				self  :						List[Any]	)       ->	Optional[Any]:

            '''simple docstring'''




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





       def snake_case__		(				self  :						str	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			load_image(
                '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                '''/img2img/sketch-mountains-input.jpg'''	)
            _UpperCamelCase							       =			init_image.resize((768, 512)	)
            # using the PNDM scheduler by default
            _UpperCamelCase							       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(
                '''CompVis/stable-diffusion-v1-4'''			,					revision='''onnx'''			,					safety_checker=lowerCAmelCase__			,					feature_extractor=lowerCAmelCase__			,					provider=self.gpu_provider			,					sess_options=self.gpu_options			,					)
            pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

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

            _UpperCamelCase							       =			np.random.RandomState(0	)
            _UpperCamelCase							       =			pipe(
                prompt=lowerCAmelCase__			,					image=lowerCAmelCase__			,					strength=0.75			,					guidance_scale=7.5			,					num_inference_steps=10			,					generator=lowerCAmelCase__			,					output_type='''np'''			,					)
            _UpperCamelCase							       =			output.images
            _UpperCamelCase							       =			images[0, 255:258, 383:386, -1]

            assert images.shape == (1, 512, 768, 3)
            _UpperCamelCase							       =			np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]	)
            # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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







       def snake_case__		(				self  :						Any	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			load_image(
                '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                '''/img2img/sketch-mountains-input.jpg'''	)
            _UpperCamelCase							       =			init_image.resize((768, 512)	)
            _UpperCamelCase							       =			LMSDiscreteScheduler.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''			,					subfolder='''scheduler'''			,					revision='''onnx'''	)
            _UpperCamelCase							       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''			,					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__	)

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

            _UpperCamelCase							       =			np.random.RandomState(0	)
            _UpperCamelCase							       =			pipe(
                prompt=lowerCAmelCase__			,					image=lowerCAmelCase__			,					strength=0.75			,					guidance_scale=7.5			,					num_inference_steps=20			,					generator=lowerCAmelCase__			,					output_type='''np'''			,					)
            _UpperCamelCase							       =			output.images
            _UpperCamelCase							       =			images[0, 255:258, 383:386, -1]

            assert images.shape == (1, 512, 768, 3)
            _UpperCamelCase							       =			np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]	)
            # 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'''

def    a__							(							lowercase   :	int,   lowercase   :	int,   lowercase   :	int )						->						float:





     """simple docstring"""

     _UpperCamelCase							       =			(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
     # formula for sum of series
     return total







def    a__							(							)						->						Tuple:





     """simple docstring"""

     print(sum_of_series(1,   1,   10 ) )


if __name__ == "__main__":
   import doctest

   doctest.testmod()



