





'''simple docstring'''

import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, 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      :	Dict									=       'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'





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

            '''simple docstring'''




            _UpperCamelCase							       =			np.random.RandomState(lowerCAmelCase__	)
            _UpperCamelCase							       =			{
                '''prompt''': '''A painting of a squirrel eating a burger''',
                '''generator''': generator,
                '''num_inference_steps''': 2,
                '''guidance_scale''': 7.5,
                '''output_type''': '''numpy''',
            }
            return inputs





       def snake_case__		(				self  :						Any	)       ->	Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionPipeline.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]

            assert image.shape == (1, 128, 128, 3)
            _UpperCamelCase							       =			np.array([0.65072, 0.58492, 0.48219, 0.55521, 0.53180, 0.55939, 0.50697, 0.39800, 0.46455]	)

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2





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

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionPipeline.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([0.65863, 0.59425, 0.49326, 0.56313, 0.53875, 0.56627, 0.51065, 0.39777, 0.46330]	)

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2





       def snake_case__		(				self  :						List[str]	)       ->	List[str]:

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,					provider='''CPUExecutionProvider'''	)
            _UpperCamelCase							       =			LMSDiscreteScheduler.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([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279]	)

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2





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

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionPipeline.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([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279]	)

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2





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

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionPipeline.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([0.53817, 0.60812, 0.47384, 0.49530, 0.51894, 0.49814, 0.47984, 0.38958, 0.44271]	)

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2





       def snake_case__		(				self  :						List[str]	)       ->	Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionPipeline.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([0.53895, 0.60808, 0.47933, 0.49608, 0.51886, 0.49950, 0.48053, 0.38957, 0.44200]	)

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2





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

            '''simple docstring'''




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

            _UpperCamelCase							       =			self.get_dummy_inputs()
            _UpperCamelCase							       =			3 * [inputs['''prompt''']]

            # forward
            _UpperCamelCase							       =			pipe(**lowerCAmelCase__	)
            _UpperCamelCase							       =			output.images[0, -3:, -3:, -1]

            _UpperCamelCase							       =			self.get_dummy_inputs()
            _UpperCamelCase							       =			3 * [inputs.pop('''prompt'''	)]

            _UpperCamelCase							       =			pipe.tokenizer(
                lowerCAmelCase__			,					padding='''max_length'''			,					max_length=pipe.tokenizer.model_max_length			,					truncation=lowerCAmelCase__			,					return_tensors='''np'''			,					)
            _UpperCamelCase							       =			text_inputs['''input_ids''']

            _UpperCamelCase							       =			pipe.text_encoder(input_ids=text_inputs.astype(np.intaa	)	)[0]

            _UpperCamelCase							       =			prompt_embeds

            # forward
            _UpperCamelCase							       =			pipe(**lowerCAmelCase__	)
            _UpperCamelCase							       =			output.images[0, -3:, -3:, -1]

            assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()	).max() < 1e-4







       def snake_case__		(				self  :						Tuple	)       ->	int:

            '''simple docstring'''




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

            _UpperCamelCase							       =			self.get_dummy_inputs()
            _UpperCamelCase							       =			3 * ['''this is a negative prompt''']
            _UpperCamelCase							       =			negative_prompt
            _UpperCamelCase							       =			3 * [inputs['''prompt''']]

            # forward
            _UpperCamelCase							       =			pipe(**lowerCAmelCase__	)
            _UpperCamelCase							       =			output.images[0, -3:, -3:, -1]

            _UpperCamelCase							       =			self.get_dummy_inputs()
            _UpperCamelCase							       =			3 * [inputs.pop('''prompt'''	)]

            _UpperCamelCase							       =			[]
            for p in [prompt, negative_prompt]:
                 _UpperCamelCase							       =			pipe.tokenizer(
                     lowerCAmelCase__			,					padding='''max_length'''			,					max_length=pipe.tokenizer.model_max_length			,					truncation=lowerCAmelCase__			,					return_tensors='''np'''			,					)
                 _UpperCamelCase							       =			text_inputs['''input_ids''']

                 embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa	)	)[0]	)

            _UpperCamelCase       ,							_UpperCamelCase							       =			embeds

            # forward
            _UpperCamelCase							       =			pipe(**lowerCAmelCase__	)
            _UpperCamelCase							       =			output.images[0, -3:, -3:, -1]

            assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()	).max() < 1e-4

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






       """simple docstring"""





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

            '''simple docstring'''




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





       @property
       def snake_case__		(				self  :						Optional[Any]	)       ->	Union[str, Any]:

            '''simple docstring'''




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





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

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionPipeline.from_pretrained(
                '''CompVis/stable-diffusion-v1-4'''			,					revision='''onnx'''			,					safety_checker=lowerCAmelCase__			,					feature_extractor=lowerCAmelCase__			,					provider=self.gpu_provider			,					sess_options=self.gpu_options			,					)
            sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

            _UpperCamelCase							       =			'''A painting of a squirrel eating a burger'''
            np.random.seed(0	)
            _UpperCamelCase							       =			sd_pipe([prompt]			,					guidance_scale=6.0			,					num_inference_steps=10			,					output_type='''np'''	)
            _UpperCamelCase							       =			output.images

            _UpperCamelCase							       =			image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            _UpperCamelCase							       =			np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720]	)

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





       def snake_case__		(				self  :						str	)       ->	Dict:

            '''simple docstring'''




            _UpperCamelCase							       =			DDIMScheduler.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''			,					subfolder='''scheduler'''			,					revision='''onnx'''	)
            _UpperCamelCase							       =			OnnxStableDiffusionPipeline.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			,					)
            sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

            _UpperCamelCase							       =			'''open neural network exchange'''
            _UpperCamelCase							       =			np.random.RandomState(0	)
            _UpperCamelCase							       =			sd_pipe([prompt]			,					guidance_scale=7.5			,					num_inference_steps=10			,					generator=lowerCAmelCase__			,					output_type='''np'''	)
            _UpperCamelCase							       =			output.images
            _UpperCamelCase							       =			image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            _UpperCamelCase							       =			np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486]	)

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





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

            '''simple docstring'''




            _UpperCamelCase							       =			LMSDiscreteScheduler.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''			,					subfolder='''scheduler'''			,					revision='''onnx'''	)
            _UpperCamelCase							       =			OnnxStableDiffusionPipeline.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			,					)
            sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

            _UpperCamelCase							       =			'''open neural network exchange'''
            _UpperCamelCase							       =			np.random.RandomState(0	)
            _UpperCamelCase							       =			sd_pipe([prompt]			,					guidance_scale=7.5			,					num_inference_steps=10			,					generator=lowerCAmelCase__			,					output_type='''np'''	)
            _UpperCamelCase							       =			output.images
            _UpperCamelCase							       =			image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            _UpperCamelCase							       =			np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161]	)

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





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

            '''simple docstring'''




            _UpperCamelCase							       =			0

            def test_callback_fn(lowerCAmelCase__  :						int			,					lowerCAmelCase__  :						int			,					lowerCAmelCase__  :						np.ndarray	) -> None:
                 _UpperCamelCase							       =			True
                 nonlocal number_of_steps
                 number_of_steps += 1
                 if step == 0:
                      assert latents.shape == (1, 4, 64, 64)
                      _UpperCamelCase							       =			latents[0, -3:, -3:, -1]
                      _UpperCamelCase							       =			np.array(
                          [-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167]	)

                      assert np.abs(latents_slice.flatten() - expected_slice	).max() < 1e-3
                 elif step == 5:
                      assert latents.shape == (1, 4, 64, 64)
                      _UpperCamelCase							       =			latents[0, -3:, -3:, -1]
                      _UpperCamelCase							       =			np.array(
                          [-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875]	)

                      assert np.abs(latents_slice.flatten() - expected_slice	).max() < 1e-3

            _UpperCamelCase							       =			False

            _UpperCamelCase							       =			OnnxStableDiffusionPipeline.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''			,					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							       =			'''Andromeda galaxy in a bottle'''

            _UpperCamelCase							       =			np.random.RandomState(0	)
            pipe(
                prompt=lowerCAmelCase__			,					num_inference_steps=5			,					guidance_scale=7.5			,					generator=lowerCAmelCase__			,					callback=lowerCAmelCase__			,					callback_steps=1			,					)
            assert test_callback_fn.has_been_called
            assert number_of_steps == 6







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

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionPipeline.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''			,					revision='''onnx'''			,					safety_checker=lowerCAmelCase__			,					feature_extractor=lowerCAmelCase__			,					provider=self.gpu_provider			,					sess_options=self.gpu_options			,					)
            assert isinstance(lowerCAmelCase__			,					lowerCAmelCase__	)
            assert pipe.safety_checker is None

            _UpperCamelCase							       =			pipe('''example prompt'''			,					num_inference_steps=2	).images[0]
            assert image is not None

            # check that there's no error when saving a pipeline with one of the models being None
            with tempfile.TemporaryDirectory() as tmpdirname:
                 pipe.save_pretrained(lowerCAmelCase__	)
                 _UpperCamelCase							       =			OnnxStableDiffusionPipeline.from_pretrained(lowerCAmelCase__	)

            # sanity check that the pipeline still works
            assert pipe.safety_checker is None
            _UpperCamelCase							       =			pipe('''example prompt'''			,					num_inference_steps=2	).images[0]
            assert image is not None










import numpy as np
def      A__       (    SCREAMING_SNAKE_CASE_   :      np.ndarray				,	SCREAMING_SNAKE_CASE_   :      float						)  ->  np.ndarray:






   """simple docstring"""







   return np.where(vector > 0				,	SCREAMING_SNAKE_CASE_				,	(alpha * (np.exp(SCREAMING_SNAKE_CASE_						) - 1))						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()