
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 				UpperCAmelCase (    UpperCAmelCase_     ,     unittest.TestCase    ):




	'''simple docstring'''





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






	def 							UpperCamelCase_    (			self   :					Dict				,A   :					str=0					):
					__A       =					np.random.RandomState(_snake_case					)
					__A       =					{
					    "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 							UpperCamelCase_    (			self   :					List[str]					):
					__A       =					OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,provider="CPUExecutionProvider"					)
					pipe.set_progress_bar_config(disable=_snake_case					)

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

					assert image.shape == (1, 1_28, 1_28, 3)
					__A       =					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 							UpperCamelCase_    (			self   :					Dict					):
					__A       =					OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,provider="CPUExecutionProvider"					)
					__A       =					PNDMScheduler.from_config(pipe.scheduler.config				,skip_prk_steps=_snake_case					)
					pipe.set_progress_bar_config(disable=_snake_case					)

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

					assert image.shape == (1, 1_28, 1_28, 3)
					__A       =					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 							UpperCamelCase_    (			self   :					List[Any]					):
					__A       =					OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,provider="CPUExecutionProvider"					)
					__A       =					LMSDiscreteScheduler.from_config(pipe.scheduler.config					)
					pipe.set_progress_bar_config(disable=_snake_case					)

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

					assert image.shape == (1, 1_28, 1_28, 3)
					__A       =					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 							UpperCamelCase_    (			self   :					List[str]					):
					__A       =					OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,provider="CPUExecutionProvider"					)
					__A       =					EulerDiscreteScheduler.from_config(pipe.scheduler.config					)
					pipe.set_progress_bar_config(disable=_snake_case					)

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

					assert image.shape == (1, 1_28, 1_28, 3)
					__A       =					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 							UpperCamelCase_    (			self   :					Tuple					):
					__A       =					OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,provider="CPUExecutionProvider"					)
					__A       =					EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config					)
					pipe.set_progress_bar_config(disable=_snake_case					)

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

					assert image.shape == (1, 1_28, 1_28, 3)
					__A       =					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 							UpperCamelCase_    (			self   :					List[str]					):
					__A       =					OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,provider="CPUExecutionProvider"					)
					__A       =					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
					pipe.set_progress_bar_config(disable=_snake_case					)

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

					assert image.shape == (1, 1_28, 1_28, 3)
					__A       =					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 							UpperCamelCase_    (			self   :					Dict					):
					__A       =					OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,provider="CPUExecutionProvider"					)
					pipe.set_progress_bar_config(disable=_snake_case					)

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

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

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

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

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

					__A       =					prompt_embeds

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

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







	def 							UpperCamelCase_    (			self   :					List[Any]					):
					__A       =					OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,provider="CPUExecutionProvider"					)
					pipe.set_progress_bar_config(disable=_snake_case					)

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

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

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

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

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

					__A     ,				__A       =					embeds

					# forward
					__A       =					pipe(**_snake_case					)
					__A       =					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 				UpperCAmelCase (    unittest.TestCase    ):




	'''simple docstring'''





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






	@property
	def 							UpperCamelCase_    (			self   :					Optional[Any]					):
					__A       =					ort.SessionOptions()
					__A       =					False
					return options






	def 							UpperCamelCase_    (			self   :					Dict					):
					__A       =					OnnxStableDiffusionPipeline.from_pretrained(
					    "CompVis/stable-diffusion-v1-4"				,revision="onnx"				,safety_checker=_snake_case				,feature_extractor=_snake_case				,provider=self.gpu_provider				,sess_options=self.gpu_options				,)
					sd_pipe.set_progress_bar_config(disable=_snake_case					)

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

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

					assert image.shape == (1, 5_12, 5_12, 3)
					__A       =					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 							UpperCamelCase_    (			self   :					Dict					):
					__A       =					DDIMScheduler.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"				,subfolder="scheduler"				,revision="onnx"					)
					__A       =					OnnxStableDiffusionPipeline.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"				,revision="onnx"				,scheduler=_snake_case				,safety_checker=_snake_case				,feature_extractor=_snake_case				,provider=self.gpu_provider				,sess_options=self.gpu_options				,)
					sd_pipe.set_progress_bar_config(disable=_snake_case					)

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

					assert image.shape == (1, 5_12, 5_12, 3)
					__A       =					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 							UpperCamelCase_    (			self   :					List[str]					):
					__A       =					LMSDiscreteScheduler.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"				,subfolder="scheduler"				,revision="onnx"					)
					__A       =					OnnxStableDiffusionPipeline.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"				,revision="onnx"				,scheduler=_snake_case				,safety_checker=_snake_case				,feature_extractor=_snake_case				,provider=self.gpu_provider				,sess_options=self.gpu_options				,)
					sd_pipe.set_progress_bar_config(disable=_snake_case					)

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

					assert image.shape == (1, 5_12, 5_12, 3)
					__A       =					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 							UpperCamelCase_    (			self   :					Tuple					):
					__A       =					0

					def test_callback_fn(A   :					int				,A   :					int				,A   :					np.ndarray					) -> None:
									__A       =					True
									nonlocal number_of_steps
									number_of_steps += 1
									if step == 0:
													assert latents.shape == (1, 4, 64, 64)
													__A       =					latents[0, -3:, -3:, -1]
													__A       =					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, 64, 64)
													__A       =					latents[0, -3:, -3:, -1]
													__A       =					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

					__A       =					False

					__A       =					OnnxStableDiffusionPipeline.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"				,revision="onnx"				,safety_checker=_snake_case				,feature_extractor=_snake_case				,provider=self.gpu_provider				,sess_options=self.gpu_options				,)
					pipe.set_progress_bar_config(disable=_snake_case					)

					__A       =					"Andromeda galaxy in a bottle"

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







	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					__A       =					OnnxStableDiffusionPipeline.from_pretrained(
					    "runwayml/stable-diffusion-v1-5"				,revision="onnx"				,safety_checker=_snake_case				,feature_extractor=_snake_case				,provider=self.gpu_provider				,sess_options=self.gpu_options				,)
					assert isinstance(_snake_case				,_snake_case					)
					assert pipe.safety_checker is None

					__A       =					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(_snake_case					)
									__A       =					OnnxStableDiffusionPipeline.from_pretrained(_snake_case					)

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





def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:

			if exponent == 1:
						return base
			if exponent % 2 == 0:
						A__	  =     _modexpt(__UpperCamelCase			,						exponent // 2			,						__UpperCamelCase       ) % modulo_value
						return (x * x) % modulo_value
			else:
						return (base * _modexpt(__UpperCamelCase			,						exponent - 1			,						__UpperCamelCase       )) % modulo_value


def A    (							__UpperCamelCase = 1_777			,						__UpperCamelCase = 1_855			,						__UpperCamelCase = 8       )    ->   int:
			A__	  =     base
			for _ in range(1			,						__UpperCamelCase       ):
						A__	  =     _modexpt(__UpperCamelCase			,						__UpperCamelCase			,						10**digits       )

			return result


if __name__ == "__main__":
							print(f'{solution() = }')




