




'''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  __magic_name__						(  __UpperCAmelCase    ,  unittest.TestCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Union[str, Any]	      =					"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"


				def     _A							(							self:   List[str]		,					_lowerCamelCase:   Optional[Any]=0		):
											SCREAMING_SNAKE_CASE_   	=    np.random.RandomState(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    {
											    '''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     _A							(							self:   int		):
											SCREAMING_SNAKE_CASE_   	=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint		,					provider='''CPUExecutionProvider'''		)
											pipe.set_progress_bar_config(disable=_lowerCamelCase		)

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

											assert image.shape == (1, 1_28, 1_28, 3)
											SCREAMING_SNAKE_CASE_   	=    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     _A							(							self:   Any		):
											SCREAMING_SNAKE_CASE_   	=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint		,					provider='''CPUExecutionProvider'''		)
											SCREAMING_SNAKE_CASE_   	=    PNDMScheduler.from_config(pipe.scheduler.config		,					skip_prk_steps=_lowerCamelCase		)
											pipe.set_progress_bar_config(disable=_lowerCamelCase		)

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

											assert image.shape == (1, 1_28, 1_28, 3)
											SCREAMING_SNAKE_CASE_   	=    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     _A							(							self:   List[str]		):
											SCREAMING_SNAKE_CASE_   	=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint		,					provider='''CPUExecutionProvider'''		)
											SCREAMING_SNAKE_CASE_   	=    LMSDiscreteScheduler.from_config(pipe.scheduler.config		)
											pipe.set_progress_bar_config(disable=_lowerCamelCase		)

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

											assert image.shape == (1, 1_28, 1_28, 3)
											SCREAMING_SNAKE_CASE_   	=    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     _A							(							self:   Any		):
											SCREAMING_SNAKE_CASE_   	=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint		,					provider='''CPUExecutionProvider'''		)
											SCREAMING_SNAKE_CASE_   	=    EulerDiscreteScheduler.from_config(pipe.scheduler.config		)
											pipe.set_progress_bar_config(disable=_lowerCamelCase		)

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

											assert image.shape == (1, 1_28, 1_28, 3)
											SCREAMING_SNAKE_CASE_   	=    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     _A							(							self:   Union[str, Any]		):
											SCREAMING_SNAKE_CASE_   	=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint		,					provider='''CPUExecutionProvider'''		)
											SCREAMING_SNAKE_CASE_   	=    EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config		)
											pipe.set_progress_bar_config(disable=_lowerCamelCase		)

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

											assert image.shape == (1, 1_28, 1_28, 3)
											SCREAMING_SNAKE_CASE_   	=    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     _A							(							self:   str		):
											SCREAMING_SNAKE_CASE_   	=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint		,					provider='''CPUExecutionProvider'''		)
											SCREAMING_SNAKE_CASE_   	=    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config		)
											pipe.set_progress_bar_config(disable=_lowerCamelCase		)

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

											assert image.shape == (1, 1_28, 1_28, 3)
											SCREAMING_SNAKE_CASE_   	=    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     _A							(							self:   List[str]		):
											SCREAMING_SNAKE_CASE_   	=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint		,					provider='''CPUExecutionProvider'''		)
											pipe.set_progress_bar_config(disable=_lowerCamelCase		)

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

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

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

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

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

											SCREAMING_SNAKE_CASE_   	=    prompt_embeds

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

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





				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint		,					provider='''CPUExecutionProvider'''		)
											pipe.set_progress_bar_config(disable=_lowerCamelCase		)

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

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

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

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

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

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    embeds

											# forward
											SCREAMING_SNAKE_CASE_   	=    pipe(**_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    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  __magic_name__						(  unittest.TestCase):
				'''simple docstring'''







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


				@property
				def     _A							(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    ort.SessionOptions()
											SCREAMING_SNAKE_CASE_   	=    False
											return options


				def     _A							(							self:   List[Any]		):
											# using the PNDM scheduler by default
											SCREAMING_SNAKE_CASE_   	=    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		)

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

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

											assert image.shape == (1, 5_12, 5_12, 3)
											SCREAMING_SNAKE_CASE_   	=    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     _A							(							self:   List[str]		):
											SCREAMING_SNAKE_CASE_   	=    DDIMScheduler.from_pretrained(
											    '''runwayml/stable-diffusion-v1-5'''		,					subfolder='''scheduler'''		,					revision='''onnx'''		)
											SCREAMING_SNAKE_CASE_   	=    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		)

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

											assert image.shape == (1, 5_12, 5_12, 3)
											SCREAMING_SNAKE_CASE_   	=    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     _A							(							self:   Any		):
											SCREAMING_SNAKE_CASE_   	=    LMSDiscreteScheduler.from_pretrained(
											    '''runwayml/stable-diffusion-v1-5'''		,					subfolder='''scheduler'''		,					revision='''onnx'''		)
											SCREAMING_SNAKE_CASE_   	=    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		)

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

											assert image.shape == (1, 5_12, 5_12, 3)
											SCREAMING_SNAKE_CASE_   	=    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     _A							(							self:   List[str]		):
											SCREAMING_SNAKE_CASE_   	=    0

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

											SCREAMING_SNAKE_CASE_   	=    False

											SCREAMING_SNAKE_CASE_   	=    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		)

											SCREAMING_SNAKE_CASE_   	=    '''Andromeda galaxy in a bottle'''

											SCREAMING_SNAKE_CASE_   	=    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     _A							(							self:   str		):
											SCREAMING_SNAKE_CASE_   	=    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

											SCREAMING_SNAKE_CASE_   	=    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		)
																		SCREAMING_SNAKE_CASE_   	=    OnnxStableDiffusionPipeline.from_pretrained(_lowerCamelCase		)

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








import requests

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    {'''Content-Type''': '''application/json'''}
							SCREAMING_SNAKE_CASE_   	=    requests.post(_lowerCAmelCase  ,    json={'''text''': message_body}  ,    headers=_lowerCAmelCase     )
							if response.status_code != 2_0_0:
														SCREAMING_SNAKE_CASE_   	=    (
														    '''Request to slack returned an error '''
														    F"{response.status_code}, the response is:\n{response.text}"
														)
														raise ValueError(_lowerCAmelCase     )


if __name__ == "__main__":
			# Set the slack url to the one provided by Slack when you create the webhook at
			# https://my.slack.com/services/new/incoming-webhook/
			send_slack_message("""<YOUR MESSAGE BODY>""", """<SLACK CHANNEL URL>""")


