
"""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 				A__ (			_lowerCamelCase ,  unittest.TestCase):
		A_		: Tuple            =			'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE=0				):
			__lowerCAmelCase    :		Union[str, Any]		    =   np.random.RandomState(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		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				):
			__lowerCAmelCase    :		str		    =   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,	provider='CPUExecutionProvider'				)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE				)

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

			assert image.shape == (1, 1_28, 1_28, 3)
			__lowerCAmelCase    :		str		    =   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				):
			__lowerCAmelCase    :		Optional[int]		    =   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,	provider='CPUExecutionProvider'				)
			__lowerCAmelCase    :		str		    =   PNDMScheduler.from_config(pipe.scheduler.config ,	skip_prk_steps=_SCREAMING_SNAKE_CASE				)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Any		    =   self.get_dummy_inputs()
			__lowerCAmelCase    :		str		    =   pipe(**_SCREAMING_SNAKE_CASE				).images
			__lowerCAmelCase    :		Tuple		    =   image[0, -3:, -3:, -1]

			assert image.shape == (1, 1_28, 1_28, 3)
			__lowerCAmelCase    :		Union[str, 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				):
			__lowerCAmelCase    :		int		    =   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,	provider='CPUExecutionProvider'				)
			__lowerCAmelCase    :		int		    =   LMSDiscreteScheduler.from_config(pipe.scheduler.config				)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Tuple		    =   self.get_dummy_inputs()
			__lowerCAmelCase    :		str		    =   pipe(**_SCREAMING_SNAKE_CASE				).images
			__lowerCAmelCase    :		List[str]		    =   image[0, -3:, -3:, -1]

			assert image.shape == (1, 1_28, 1_28, 3)
			__lowerCAmelCase    :		Optional[int]		    =   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				):
			__lowerCAmelCase    :		Dict		    =   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,	provider='CPUExecutionProvider'				)
			__lowerCAmelCase    :		Any		    =   EulerDiscreteScheduler.from_config(pipe.scheduler.config				)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		str		    =   self.get_dummy_inputs()
			__lowerCAmelCase    :		Union[str, Any]		    =   pipe(**_SCREAMING_SNAKE_CASE				).images
			__lowerCAmelCase    :		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				):
			__lowerCAmelCase    :		Optional[int]		    =   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,	provider='CPUExecutionProvider'				)
			__lowerCAmelCase    :		int		    =   EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config				)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE				)

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

			assert image.shape == (1, 1_28, 1_28, 3)
			__lowerCAmelCase    :		Optional[int]		    =   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				):
			__lowerCAmelCase    :		Union[str, Any]		    =   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,	provider='CPUExecutionProvider'				)
			__lowerCAmelCase    :		int		    =   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config				)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		List[Any]		    =   self.get_dummy_inputs()
			__lowerCAmelCase    :		Optional[Any]		    =   pipe(**_SCREAMING_SNAKE_CASE				).images
			__lowerCAmelCase    :		Dict		    =   image[0, -3:, -3:, -1]

			assert image.shape == (1, 1_28, 1_28, 3)
			__lowerCAmelCase    :		List[Any]		    =   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				):
			__lowerCAmelCase    :		Optional[Any]		    =   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,	provider='CPUExecutionProvider'				)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Dict		    =   self.get_dummy_inputs()
			__lowerCAmelCase    :		Any		    =   3 * [inputs['prompt']]

			# forward
			__lowerCAmelCase    :		Any		    =   pipe(**_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Any		    =   output.images[0, -3:, -3:, -1]

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

			__lowerCAmelCase    :		str		    =   pipe.tokenizer(
			    _SCREAMING_SNAKE_CASE ,	padding='max_length' ,	max_length=pipe.tokenizer.model_max_length ,	truncation=_SCREAMING_SNAKE_CASE ,	return_tensors='np' ,	)
			__lowerCAmelCase    :		List[Any]		    =   text_inputs['input_ids']

			__lowerCAmelCase    :		List[Any]		    =   pipe.text_encoder(input_ids=text_inputs.astype(np.intaa				)				)[0]

			__lowerCAmelCase    :		Union[str, Any]		    =   prompt_embeds

			# forward
			__lowerCAmelCase    :		List[Any]		    =   pipe(**_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[str]		    =   output.images[0, -3:, -3:, -1]

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







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[Any]		    =   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,	provider='CPUExecutionProvider'				)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		List[Any]		    =   self.get_dummy_inputs()
			__lowerCAmelCase    :		str		    =   3 * ['this is a negative prompt']
			__lowerCAmelCase    :		Dict		    =   negative_prompt
			__lowerCAmelCase    :		Any		    =   3 * [inputs['prompt']]

			# forward
			__lowerCAmelCase    :		List[str]		    =   pipe(**_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Dict		    =   output.images[0, -3:, -3:, -1]

			__lowerCAmelCase    :		List[Any]		    =   self.get_dummy_inputs()
			__lowerCAmelCase    :		List[str]		    =   3 * [inputs.pop('prompt'				)]

			__lowerCAmelCase    :		List[str]		    =   []
			for p in [prompt, negative_prompt]:
				__lowerCAmelCase    :		int		    =   pipe.tokenizer(
				    _SCREAMING_SNAKE_CASE ,	padding='max_length' ,	max_length=pipe.tokenizer.model_max_length ,	truncation=_SCREAMING_SNAKE_CASE ,	return_tensors='np' ,	)
				__lowerCAmelCase    :		List[str]		    =   text_inputs['input_ids']

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

			__lowerCAmelCase    ,     __lowerCAmelCase    :		Any		    =   embeds

			# forward
			__lowerCAmelCase    :		Union[str, Any]		    =   pipe(**_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[str]		    =   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 				A__ (			unittest.TestCase):

		@property
		def 							__lowerCamelCase							(   self				):
			return (
			    "CUDAExecutionProvider",
			    {
			        "gpu_mem_limit": "15000000000",  # 15GB
			        "arena_extend_strategy": "kSameAsRequested",
			    },
			)

		@property
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   ort.SessionOptions()
			__lowerCAmelCase    :		List[str]		    =   False
			return options

		def 							__lowerCamelCase							(   self				):
			# using the PNDM scheduler by default
			__lowerCAmelCase    :		Optional[int]		    =   OnnxStableDiffusionPipeline.from_pretrained(
			    'CompVis/stable-diffusion-v1-4' ,	revision='onnx' ,	safety_checker=_SCREAMING_SNAKE_CASE ,	feature_extractor=_SCREAMING_SNAKE_CASE ,	provider=self.gpu_provider ,	sess_options=self.gpu_options ,	)
			sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE				)

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

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

			assert image.shape == (1, 5_12, 5_12, 3)
			__lowerCAmelCase    :		Optional[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				):
			__lowerCAmelCase    :		int		    =   DDIMScheduler.from_pretrained(
			    'runwayml/stable-diffusion-v1-5' ,	subfolder='scheduler' ,	revision='onnx'				)
			__lowerCAmelCase    :		int		    =   OnnxStableDiffusionPipeline.from_pretrained(
			    'runwayml/stable-diffusion-v1-5' ,	revision='onnx' ,	scheduler=_SCREAMING_SNAKE_CASE ,	safety_checker=_SCREAMING_SNAKE_CASE ,	feature_extractor=_SCREAMING_SNAKE_CASE ,	provider=self.gpu_provider ,	sess_options=self.gpu_options ,	)
			sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE				)

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

			assert image.shape == (1, 5_12, 5_12, 3)
			__lowerCAmelCase    :		Tuple		    =   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				):
			__lowerCAmelCase    :		str		    =   LMSDiscreteScheduler.from_pretrained(
			    'runwayml/stable-diffusion-v1-5' ,	subfolder='scheduler' ,	revision='onnx'				)
			__lowerCAmelCase    :		str		    =   OnnxStableDiffusionPipeline.from_pretrained(
			    'runwayml/stable-diffusion-v1-5' ,	revision='onnx' ,	scheduler=_SCREAMING_SNAKE_CASE ,	safety_checker=_SCREAMING_SNAKE_CASE ,	feature_extractor=_SCREAMING_SNAKE_CASE ,	provider=self.gpu_provider ,	sess_options=self.gpu_options ,	)
			sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE				)

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

			assert image.shape == (1, 5_12, 5_12, 3)
			__lowerCAmelCase    :		Any		    =   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				):
			__lowerCAmelCase    :		Dict		    =   0

			def test_callback_fn(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) -> None:
				__lowerCAmelCase    :		List[str]		    =   True
				nonlocal number_of_steps
				number_of_steps += 1
				if step == 0:
					assert latents.shape == (1, 4, 64, 64)
					__lowerCAmelCase    :		Optional[int]		    =   latents[0, -3:, -3:, -1]
					__lowerCAmelCase    :		Dict		    =   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[str]		    =   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    :		Optional[int]		    =   False

			__lowerCAmelCase    :		str		    =   OnnxStableDiffusionPipeline.from_pretrained(
			    'runwayml/stable-diffusion-v1-5' ,	revision='onnx' ,	safety_checker=_SCREAMING_SNAKE_CASE ,	feature_extractor=_SCREAMING_SNAKE_CASE ,	provider=self.gpu_provider ,	sess_options=self.gpu_options ,	)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		int		    =   'Andromeda galaxy in a bottle'

			__lowerCAmelCase    :		List[Any]		    =   np.random.RandomState(0				)
			pipe(
			    prompt=_SCREAMING_SNAKE_CASE ,	num_inference_steps=5 ,	guidance_scale=7.5 ,	generator=_SCREAMING_SNAKE_CASE ,	callback=_SCREAMING_SNAKE_CASE ,	callback_steps=1 ,	)
			assert test_callback_fn.has_been_called
			assert number_of_steps == 6







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Union[str, Any]		    =   OnnxStableDiffusionPipeline.from_pretrained(
			    'runwayml/stable-diffusion-v1-5' ,	revision='onnx' ,	safety_checker=_SCREAMING_SNAKE_CASE ,	feature_extractor=_SCREAMING_SNAKE_CASE ,	provider=self.gpu_provider ,	sess_options=self.gpu_options ,	)
			assert isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			assert pipe.safety_checker is None

			__lowerCAmelCase    :		Optional[Any]		    =   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(_SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase    :		Optional[Any]		    =   OnnxStableDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE				)

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

"""simple docstring"""







def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		List[str]		    =   len(_UpperCamelCase	)
	__lowerCAmelCase    :		Tuple		    =   [[0] * n for i in range(_UpperCamelCase	)]
	for i in range(_UpperCamelCase	):
		__lowerCAmelCase    :		Any		    =   y_points[i]

	for i in range(2 ,						_UpperCamelCase	):
		for j in range(_UpperCamelCase ,						_UpperCamelCase	):
			__lowerCAmelCase    :		int		    =   (
			    (xa - x_points[j - i + 1]) * q[j][i - 1]
			    - (xa - x_points[j]) * q[j - 1][i - 1]
			) / (x_points[j] - x_points[j - i + 1])

	return [q[n - 1][n - 1], q]


if __name__ == "__main__":
							import doctest

							doctest.testmod()