



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__    (							lowerCamelCase__   ,       unittest.TestCase):




		'''simple docstring'''



		snake_case_			="""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""



		def 	lowerCAmelCase__   (self	,__lowerCamelCase=0				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  np.random.RandomState(__lowerCamelCase				)
						lowerCAmelCase__  :       Union[str, Any]				=  {
						    '''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 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	,provider='''CPUExecutionProvider'''				)
						pipe.set_progress_bar_config(disable=__lowerCamelCase				)

						lowerCAmelCase__  :       Union[str, Any]				=  self.get_dummy_inputs()
						lowerCAmelCase__  :       Optional[int]				=  pipe(**__lowerCamelCase				).images
						lowerCAmelCase__  :       Union[str, Any]				=  image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_28, 1_28, 3)
						lowerCAmelCase__  :       Union[str, Any]				=  np.array([0.6_5072, 0.5_8492, 0.4_8219, 0.5_5521, 0.5_3180, 0.5_5939, 0.5_0697, 0.3_9800, 0.4_6455]				)

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



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

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	,provider='''CPUExecutionProvider'''				)
						lowerCAmelCase__  :       Optional[Any]				=  PNDMScheduler.from_config(pipe.scheduler.config	,skip_prk_steps=__lowerCamelCase				)
						pipe.set_progress_bar_config(disable=__lowerCamelCase				)

						lowerCAmelCase__  :       Union[str, Any]				=  self.get_dummy_inputs()
						lowerCAmelCase__  :       List[str]				=  pipe(**__lowerCamelCase				).images
						lowerCAmelCase__  :       int				=  image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_28, 1_28, 3)
						lowerCAmelCase__  :       Any				=  np.array([0.6_5863, 0.5_9425, 0.4_9326, 0.5_6313, 0.5_3875, 0.5_6627, 0.5_1065, 0.3_9777, 0.4_6330]				)

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



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

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	,provider='''CPUExecutionProvider'''				)
						lowerCAmelCase__  :       int				=  LMSDiscreteScheduler.from_config(pipe.scheduler.config				)
						pipe.set_progress_bar_config(disable=__lowerCamelCase				)

						lowerCAmelCase__  :       Dict				=  self.get_dummy_inputs()
						lowerCAmelCase__  :       List[str]				=  pipe(**__lowerCamelCase				).images
						lowerCAmelCase__  :       Any				=  image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_28, 1_28, 3)
						lowerCAmelCase__  :       Dict				=  np.array([0.5_3755, 0.6_0786, 0.4_7402, 0.4_9488, 0.5_1869, 0.4_9819, 0.4_7985, 0.3_8957, 0.4_4279]				)

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



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

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	,provider='''CPUExecutionProvider'''				)
						lowerCAmelCase__  :       List[str]				=  EulerDiscreteScheduler.from_config(pipe.scheduler.config				)
						pipe.set_progress_bar_config(disable=__lowerCamelCase				)

						lowerCAmelCase__  :       List[str]				=  self.get_dummy_inputs()
						lowerCAmelCase__  :       Any				=  pipe(**__lowerCamelCase				).images
						lowerCAmelCase__  :       Optional[Any]				=  image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_28, 1_28, 3)
						lowerCAmelCase__  :       str				=  np.array([0.5_3755, 0.6_0786, 0.4_7402, 0.4_9488, 0.5_1869, 0.4_9819, 0.4_7985, 0.3_8957, 0.4_4279]				)

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



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

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	,provider='''CPUExecutionProvider'''				)
						lowerCAmelCase__  :       Optional[Any]				=  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config				)
						pipe.set_progress_bar_config(disable=__lowerCamelCase				)

						lowerCAmelCase__  :       int				=  self.get_dummy_inputs()
						lowerCAmelCase__  :       Tuple				=  pipe(**__lowerCamelCase				).images
						lowerCAmelCase__  :       str				=  image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_28, 1_28, 3)
						lowerCAmelCase__  :       Any				=  np.array([0.5_3817, 0.6_0812, 0.4_7384, 0.4_9530, 0.5_1894, 0.4_9814, 0.4_7984, 0.3_8958, 0.4_4271]				)

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



		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	,provider='''CPUExecutionProvider'''				)
						lowerCAmelCase__  :       Dict				=  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config				)
						pipe.set_progress_bar_config(disable=__lowerCamelCase				)

						lowerCAmelCase__  :       Dict				=  self.get_dummy_inputs()
						lowerCAmelCase__  :       Tuple				=  pipe(**__lowerCamelCase				).images
						lowerCAmelCase__  :       Any				=  image[0, -3:, -3:, -1]

						assert image.shape == (1, 1_28, 1_28, 3)
						lowerCAmelCase__  :       Dict				=  np.array([0.5_3895, 0.6_0808, 0.4_7933, 0.4_9608, 0.5_1886, 0.4_9950, 0.4_8053, 0.3_8957, 0.4_4200]				)

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



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	,provider='''CPUExecutionProvider'''				)
						pipe.set_progress_bar_config(disable=__lowerCamelCase				)

						lowerCAmelCase__  :       Union[str, Any]				=  self.get_dummy_inputs()
						lowerCAmelCase__  :       Dict				=  3 * [inputs['''prompt''']]

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

						lowerCAmelCase__  :       Dict				=  self.get_dummy_inputs()
						lowerCAmelCase__  :       Dict				=  3 * [inputs.pop('''prompt'''				)]

						lowerCAmelCase__  :       Any				=  pipe.tokenizer(
						    __lowerCamelCase	,padding='''max_length'''	,max_length=pipe.tokenizer.model_max_length	,truncation=__lowerCamelCase	,return_tensors='''np'''	,)
						lowerCAmelCase__  :       Dict				=  text_inputs['''input_ids''']

						lowerCAmelCase__  :       Dict				=  pipe.text_encoder(input_ids=text_inputs.astype(np.intaa				)				)[0]

						lowerCAmelCase__  :       Dict				=  prompt_embeds

						# forward
						lowerCAmelCase__  :       str				=  pipe(**__lowerCamelCase				)
						lowerCAmelCase__  :       Union[str, Any]				=  output.images[0, -3:, -3:, -1]

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



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

						"""simple docstring"""






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

						lowerCAmelCase__  :       Tuple				=  self.get_dummy_inputs()
						lowerCAmelCase__  :       Optional[Any]				=  3 * ['''this is a negative prompt''']
						lowerCAmelCase__  :       int				=  negative_prompt
						lowerCAmelCase__  :       Union[str, Any]				=  3 * [inputs['''prompt''']]

						# forward
						lowerCAmelCase__  :       Union[str, Any]				=  pipe(**__lowerCamelCase				)
						lowerCAmelCase__  :       str				=  output.images[0, -3:, -3:, -1]

						lowerCAmelCase__  :       Union[str, Any]				=  self.get_dummy_inputs()
						lowerCAmelCase__  :       Tuple				=  3 * [inputs.pop('''prompt'''				)]

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

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

						lowerCAmelCase__		,	lowerCAmelCase__  :       Optional[Any]				=  embeds

						# forward
						lowerCAmelCase__  :       List[Any]				=  pipe(**__lowerCamelCase				)
						lowerCAmelCase__  :       Dict				=  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 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






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



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

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  ort.SessionOptions()
						lowerCAmelCase__  :       List[str]				=  False
						return options



		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  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				)

						lowerCAmelCase__  :       Optional[int]				=  '''A painting of a squirrel eating a burger'''
						np.random.seed(0				)
						lowerCAmelCase__  :       Union[str, Any]				=  sd_pipe([prompt]	,guidance_scale=6.0	,num_inference_steps=10	,output_type='''np'''				)
						lowerCAmelCase__  :       str				=  output.images

						lowerCAmelCase__  :       Tuple				=  image[0, -3:, -3:, -1]

						assert image.shape == (1, 5_12, 5_12, 3)
						lowerCAmelCase__  :       Union[str, Any]				=  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 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  DDIMScheduler.from_pretrained(
						    '''runwayml/stable-diffusion-v1-5'''	,subfolder='''scheduler'''	,revision='''onnx'''				)
						lowerCAmelCase__  :       List[str]				=  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				)

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

						assert image.shape == (1, 5_12, 5_12, 3)
						lowerCAmelCase__  :       Union[str, Any]				=  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 	lowerCAmelCase__   (self				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  LMSDiscreteScheduler.from_pretrained(
						    '''runwayml/stable-diffusion-v1-5'''	,subfolder='''scheduler'''	,revision='''onnx'''				)
						lowerCAmelCase__  :       int				=  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				)

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

						assert image.shape == (1, 5_12, 5_12, 3)
						lowerCAmelCase__  :       Dict				=  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 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  0

						def test_callback_fn(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				) -> None:
										lowerCAmelCase__  :       List[Any]				=  True
										nonlocal number_of_steps
										number_of_steps += 1
										if step == 0:
														assert latents.shape == (1, 4, 64, 64)
														lowerCAmelCase__  :       Optional[Any]				=  latents[0, -3:, -3:, -1]
														lowerCAmelCase__  :       Tuple				=  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)
														lowerCAmelCase__  :       Union[str, Any]				=  latents[0, -3:, -3:, -1]
														lowerCAmelCase__  :       List[Any]				=  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

						lowerCAmelCase__  :       Union[str, Any]				=  False

						lowerCAmelCase__  :       List[str]				=  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				)

						lowerCAmelCase__  :       List[str]				=  '''Andromeda galaxy in a bottle'''

						lowerCAmelCase__  :       List[Any]				=  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 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  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

						lowerCAmelCase__  :       int				=  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				)
										lowerCAmelCase__  :       Dict				=  OnnxStableDiffusionPipeline.from_pretrained(__lowerCamelCase				)

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






from collections.abc import Callable

import numpy as np






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Callable ,lowerCamelCase_	:	float ,lowerCamelCase_	:	float ,lowerCamelCase_	:	float ,lowerCamelCase_	:	float):





				'''simple docstring'''

				lowerCAmelCase__  :       Dict				=  int(np.ceil((x_end - xa) / step_size))
				lowerCAmelCase__  :       Any				=  np.zeros((n + 1,))
				lowerCAmelCase__  :       Union[str, Any]				=  ya
				lowerCAmelCase__  :       str				=  xa

				for k in range(lowerCamelCase_):
								lowerCAmelCase__  :       Tuple				=  y[k] + step_size * ode_func(lowerCamelCase_ ,y[k])
								lowerCAmelCase__  :       str				=  y[k] + (
								    (step_size / 2) * (ode_func(lowerCamelCase_ ,y[k]) + ode_func(x + step_size ,lowerCamelCase_))
								)
								x += step_size

				return y


if __name__ == "__main__":
							import doctest

							doctest.testmod()

