






"""simple docstring"""







import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
	import onnxruntime as ort







class        _SCREAMING_SNAKE_CASE						(  __snake_case       , unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'




							def  lowercase		(   self:     int				,		__A:     List[Any]=0    ):

												'''simple docstring'''

												a__          =  floats_tensor((1, 3, 128, 128)				,		rng=random.Random(__A    )    )
												a__          =  np.random.RandomState(__A    )
												a__          =  {
												    '''prompt''': '''A painting of a squirrel eating a burger''',
												    '''image''': image,
												    '''generator''': generator,
												    '''num_inference_steps''': 3,
												    '''strength''': 0.7_5,
												    '''guidance_scale''': 7.5,
												    '''output_type''': '''numpy''',
												}
												return inputs




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,		provider='''CPUExecutionProvider'''    )
												pipe.set_progress_bar_config(disable=__A    )

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

												assert image.shape == (1, 128, 128, 3)
												a__          =  np.array([0.6_9_6_4_3, 0.5_8_4_8_4, 0.5_0_3_1_4, 0.5_8_7_6_0, 0.5_5_3_6_8, 0.5_9_6_4_3, 0.5_1_5_2_9, 0.4_1_2_1_7, 0.4_9_0_8_7]    )
												assert np.abs(image_slice - expected_slice    ).max() < 1e-1




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__          =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,		provider='''CPUExecutionProvider'''    )
												a__          =  PNDMScheduler.from_config(pipe.scheduler.config				,		skip_prk_steps=__A    )
												pipe.set_progress_bar_config(disable=__A    )

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

												assert image.shape == (1, 128, 128, 3)
												a__          =  np.array([0.6_1_7_3_7, 0.5_4_6_4_2, 0.5_3_1_8_3, 0.5_4_4_6_5, 0.5_2_7_4_2, 0.6_0_5_2_5, 0.4_9_9_6_9, 0.4_0_6_5_5, 0.4_8_1_5_4]    )

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




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,		provider='''CPUExecutionProvider'''    )
												a__          =  LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
												pipe.set_progress_bar_config(disable=__A    )

												# warmup pass to apply optimizations
												a__          =  pipe(**self.get_dummy_inputs()    )

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

												assert image.shape == (1, 128, 128, 3)
												a__          =  np.array([0.5_2_7_6_1, 0.5_9_9_7_7, 0.4_9_0_3_3, 0.4_9_6_1_9, 0.5_4_2_8_2, 0.5_0_3_1_1, 0.4_7_6_0_0, 0.4_0_9_1_8, 0.4_5_2_0_3]    )

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




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__          =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,		provider='''CPUExecutionProvider'''    )
												a__          =  EulerDiscreteScheduler.from_config(pipe.scheduler.config    )
												pipe.set_progress_bar_config(disable=__A    )

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

												assert image.shape == (1, 128, 128, 3)
												a__          =  np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]    )

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




							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												a__          =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,		provider='''CPUExecutionProvider'''    )
												a__          =  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config    )
												pipe.set_progress_bar_config(disable=__A    )

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

												assert image.shape == (1, 128, 128, 3)
												a__          =  np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]    )

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





							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												a__          =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,		provider='''CPUExecutionProvider'''    )
												a__          =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
												pipe.set_progress_bar_config(disable=__A    )

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

												assert image.shape == (1, 128, 128, 3)
												a__          =  np.array([0.6_5_3_3_1, 0.5_8_2_7_7, 0.4_8_2_0_4, 0.5_6_0_5_9, 0.5_3_6_6_5, 0.5_6_2_3_5, 0.5_0_9_6_9, 0.4_0_0_0_9, 0.4_6_5_5_2]    )

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







@nightly
@require_onnxruntime
@require_torch_gpu
class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							@property
							def  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

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




							@property
							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

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




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/img2img/sketch-mountains-input.jpg'''    )
												a__          =  init_image.resize((768, 512)    )
												# using the PNDM scheduler by default
												a__          =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(
												    '''CompVis/stable-diffusion-v1-4'''				,		revision='''onnx'''				,		safety_checker=__A				,		feature_extractor=__A				,		provider=self.gpu_provider				,		sess_options=self.gpu_options				,		)
												pipe.set_progress_bar_config(disable=__A    )

												a__          =  '''A fantasy landscape, trending on artstation'''

												a__          =  np.random.RandomState(0    )
												a__          =  pipe(
												    prompt=__A				,		image=__A				,		strength=0.7_5				,		guidance_scale=7.5				,		num_inference_steps=10				,		generator=__A				,		output_type='''np'''				,		)
												a__          =  output.images
												a__          =  images[0, 255:258, 383:386, -1]

												assert images.shape == (1, 512, 768, 3)
												a__          =  np.array([0.4_9_0_9, 0.5_0_5_9, 0.5_3_7_2, 0.4_6_2_3, 0.4_8_7_6, 0.5_0_4_9, 0.4_8_2_0, 0.4_9_5_6, 0.5_0_1_9]    )
												# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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





							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/img2img/sketch-mountains-input.jpg'''    )
												a__          =  init_image.resize((768, 512)    )
												a__          =  LMSDiscreteScheduler.from_pretrained(
												    '''runwayml/stable-diffusion-v1-5'''				,		subfolder='''scheduler'''				,		revision='''onnx'''    )
												a__          =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(
												    '''runwayml/stable-diffusion-v1-5'''				,		revision='''onnx'''				,		scheduler=__A				,		safety_checker=__A				,		feature_extractor=__A				,		provider=self.gpu_provider				,		sess_options=self.gpu_options				,		)
												pipe.set_progress_bar_config(disable=__A    )

												a__          =  '''A fantasy landscape, trending on artstation'''

												a__          =  np.random.RandomState(0    )
												a__          =  pipe(
												    prompt=__A				,		image=__A				,		strength=0.7_5				,		guidance_scale=7.5				,		num_inference_steps=20				,		generator=__A				,		output_type='''np'''				,		)
												a__          =  output.images
												a__          =  images[0, 255:258, 383:386, -1]

												assert images.shape == (1, 512, 768, 3)
												a__          =  np.array([0.8_0_4_3, 0.9_2_6, 0.9_5_8_1, 0.8_1_1_9, 0.8_9_5_4, 0.9_1_3, 0.7_2_0_9, 0.7_4_6_3, 0.7_4_3_1]    )
												# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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










"""simple docstring"""







import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  OmegaConf.load(lowerCamelCase_)
					a__          =  torch.load(lowerCamelCase_ ,     map_location='''cpu''')['''model''']
					a__          =  list(state_dict.keys())

					# extract state_dict for VQVAE
					a__          =  {}
					a__          =  '''first_stage_model.'''
					for key in keys:
										if key.startswith(lowerCamelCase_):
															a__          =  state_dict[key]

    # extract state_dict for UNetLDM
					a__          =  {}
					a__          =  '''model.diffusion_model.'''
					for key in keys:
										if key.startswith(lowerCamelCase_):
															a__          =  state_dict[key]

					a__          =  config.model.params.first_stage_config.params
					a__          =  config.model.params.unet_config.params

					a__          =  VQModel(**lowerCamelCase_).eval()
					vqvae.load_state_dict(lowerCamelCase_)

					a__          =  UNetLDMModel(**lowerCamelCase_).eval()
					unet.load_state_dict(lowerCamelCase_)

					a__          =  DDIMScheduler(
					    timesteps=config.model.params.timesteps ,     beta_schedule='''scaled_linear''' ,     beta_start=config.model.params.linear_start ,     beta_end=config.model.params.linear_end ,     clip_sample=lowerCamelCase_ ,     )

					a__          =  LDMPipeline(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
					pipeline.save_pretrained(lowerCamelCase_)


if __name__ == "__main__":
	__a       :	Tuple             = argparse.ArgumentParser()
	parser.add_argument('--checkpoint_path', type=str, required=True)
	parser.add_argument('--config_path', type=str, required=True)
	parser.add_argument('--output_path', type=str, required=True)
	__a       :	Optional[int]             = parser.parse_args()

	convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)


