




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__					(      __UpperCAmelCase				,       unittest.TestCase   ):
						_a							:					Optional[int]      =					"""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""






						def        __SCREAMING_SNAKE_CASE(     self	,    _A=0    ):




											"""simple docstring"""



											__lowerCAmelCase            = np.random.RandomState(lowerCAmelCase_    )
											__lowerCAmelCase            = {
											    "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        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	,    provider="CPUExecutionProvider"    )
											pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

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

											assert image.shape == (1, 1_2_8, 1_2_8, 3)
											__lowerCAmelCase            = np.array([0.6_50_72, 0.5_84_92, 0.4_82_19, 0.5_55_21, 0.5_31_80, 0.5_59_39, 0.5_06_97, 0.3_98_00, 0.4_64_55]    )

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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	,    provider="CPUExecutionProvider"    )
											__lowerCAmelCase            = PNDMScheduler.from_config(pipe.scheduler.config	,    skip_prk_steps=lowerCAmelCase_    )
											pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

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

											assert image.shape == (1, 1_2_8, 1_2_8, 3)
											__lowerCAmelCase            = np.array([0.6_58_63, 0.5_94_25, 0.4_93_26, 0.5_63_13, 0.5_38_75, 0.5_66_27, 0.5_10_65, 0.3_97_77, 0.4_63_30]    )

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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	,    provider="CPUExecutionProvider"    )
											__lowerCAmelCase            = LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
											pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

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

											assert image.shape == (1, 1_2_8, 1_2_8, 3)
											__lowerCAmelCase            = np.array([0.5_37_55, 0.6_07_86, 0.4_74_02, 0.4_94_88, 0.5_18_69, 0.4_98_19, 0.4_79_85, 0.3_89_57, 0.4_42_79]    )

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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	,    provider="CPUExecutionProvider"    )
											__lowerCAmelCase            = EulerDiscreteScheduler.from_config(pipe.scheduler.config    )
											pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

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

											assert image.shape == (1, 1_2_8, 1_2_8, 3)
											__lowerCAmelCase            = np.array([0.5_37_55, 0.6_07_86, 0.4_74_02, 0.4_94_88, 0.5_18_69, 0.4_98_19, 0.4_79_85, 0.3_89_57, 0.4_42_79]    )

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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	,    provider="CPUExecutionProvider"    )
											__lowerCAmelCase            = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config    )
											pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

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

											assert image.shape == (1, 1_2_8, 1_2_8, 3)
											__lowerCAmelCase            = np.array([0.5_38_17, 0.6_08_12, 0.4_73_84, 0.4_95_30, 0.5_18_94, 0.4_98_14, 0.4_79_84, 0.3_89_58, 0.4_42_71]    )

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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	,    provider="CPUExecutionProvider"    )
											__lowerCAmelCase            = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
											pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

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

											assert image.shape == (1, 1_2_8, 1_2_8, 3)
											__lowerCAmelCase            = np.array([0.5_38_95, 0.6_08_08, 0.4_79_33, 0.4_96_08, 0.5_18_86, 0.4_99_50, 0.4_80_53, 0.3_89_57, 0.4_42_00]    )

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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	,    provider="CPUExecutionProvider"    )
											pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

											__lowerCAmelCase            = self.get_dummy_inputs()
											__lowerCAmelCase            = 3 * [inputs["prompt"]]

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

											__lowerCAmelCase            = self.get_dummy_inputs()
											__lowerCAmelCase            = 3 * [inputs.pop("prompt"    )]

											__lowerCAmelCase            = pipe.tokenizer(
											    lowerCAmelCase_	,    padding="max_length"	,    max_length=pipe.tokenizer.model_max_length	,    truncation=lowerCAmelCase_	,    return_tensors="np"	,    )
											__lowerCAmelCase            = text_inputs["input_ids"]

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

											__lowerCAmelCase            = prompt_embeds

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

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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint	,    provider="CPUExecutionProvider"    )
											pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

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

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

											__lowerCAmelCase            = self.get_dummy_inputs()
											__lowerCAmelCase            = 3 * [inputs.pop("prompt"    )]

											__lowerCAmelCase            = []
											for p in [prompt, negative_prompt]:
																__lowerCAmelCase            = pipe.tokenizer(
																    lowerCAmelCase_	,    padding="max_length"	,    max_length=pipe.tokenizer.model_max_length	,    truncation=lowerCAmelCase_	,    return_tensors="np"	,    )
																__lowerCAmelCase            = text_inputs["input_ids"]

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

											__lowerCAmelCase     ,							__lowerCAmelCase            = embeds

											# forward
											__lowerCAmelCase            = pipe(**lowerCAmelCase_    )
											__lowerCAmelCase            = 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        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



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






						@property
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = 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            = "A painting of a squirrel eating a burger"
											np.random.seed(0    )
											__lowerCAmelCase            = sd_pipe([prompt]	,    guidance_scale=6.0	,    num_inference_steps=1_0	,    output_type="np"    )
											__lowerCAmelCase            = output.images

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

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											__lowerCAmelCase            = np.array([0.04_52, 0.03_90, 0.00_87, 0.03_50, 0.06_17, 0.03_64, 0.05_44, 0.05_23, 0.07_20]    )

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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = DDIMScheduler.from_pretrained(
											    "runwayml/stable-diffusion-v1-5"	,    subfolder="scheduler"	,    revision="onnx"    )
											__lowerCAmelCase            = 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            = "open neural network exchange"
											__lowerCAmelCase            = np.random.RandomState(0    )
											__lowerCAmelCase            = sd_pipe([prompt]	,    guidance_scale=7.5	,    num_inference_steps=1_0	,    generator=lowerCAmelCase_	,    output_type="np"    )
											__lowerCAmelCase            = output.images
											__lowerCAmelCase            = image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											__lowerCAmelCase            = np.array([0.28_67, 0.19_74, 0.14_81, 0.72_94, 0.72_51, 0.66_67, 0.41_94, 0.56_42, 0.64_86]    )

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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = LMSDiscreteScheduler.from_pretrained(
											    "runwayml/stable-diffusion-v1-5"	,    subfolder="scheduler"	,    revision="onnx"    )
											__lowerCAmelCase            = 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            = "open neural network exchange"
											__lowerCAmelCase            = np.random.RandomState(0    )
											__lowerCAmelCase            = sd_pipe([prompt]	,    guidance_scale=7.5	,    num_inference_steps=1_0	,    generator=lowerCAmelCase_	,    output_type="np"    )
											__lowerCAmelCase            = output.images
											__lowerCAmelCase            = image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											__lowerCAmelCase            = np.array([0.23_06, 0.19_59, 0.15_93, 0.65_49, 0.63_94, 0.54_08, 0.50_65, 0.60_10, 0.61_61]    )

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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = 0

											def test_callback_fn(_A	,    _A	,    _A    ) -> None:
																__lowerCAmelCase            = True
																nonlocal number_of_steps
																number_of_steps += 1
																if step == 0:
																					assert latents.shape == (1, 4, 6_4, 6_4)
																					__lowerCAmelCase            = latents[0, -3:, -3:, -1]
																					__lowerCAmelCase            = np.array(
																					    [-0.67_72, -0.38_35, -1.24_56, 0.19_05, -1.09_74, 0.69_67, -1.93_53, 0.01_78, 1.01_67]    )

																					assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 1E-3
																elif step == 5:
																					assert latents.shape == (1, 4, 6_4, 6_4)
																					__lowerCAmelCase            = latents[0, -3:, -3:, -1]
																					__lowerCAmelCase            = np.array(
																					    [-0.33_51, 0.22_41, -0.18_37, -0.23_25, -0.65_77, 0.33_93, -0.02_41, 0.58_99, 1.38_75]    )

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

											__lowerCAmelCase            = False

											__lowerCAmelCase            = 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            = "Andromeda galaxy in a bottle"

											__lowerCAmelCase            = 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        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = 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            = 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            = OnnxStableDiffusionPipeline.from_pretrained(lowerCAmelCase_    )

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










import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
UpperCamelCase__					    =    """."""


if __name__ == "__main__":
					UpperCamelCase__					    =    os.path.join(REPO_PATH, """utils/documentation_tests.txt""")
					UpperCamelCase__					    =    []
					UpperCamelCase__					    =    []
					with open(doctest_file_path) as fp:
										for line in fp:
															UpperCamelCase__					    =    line.strip()
															UpperCamelCase__					    =    os.path.join(REPO_PATH, line)
															if not (os.path.isfile(path) or os.path.isdir(path)):
																				non_existent_paths.append(line)
															all_paths.append(path)
					if len(non_existent_paths) > 0:
										UpperCamelCase__					    =    """\n""".join(non_existent_paths)
										raise ValueError(f'''`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}''')
					if all_paths != sorted(all_paths):
										raise ValueError("""Files in `utils/documentation_tests.txt` are not in alphabetical order.""")




