



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 						lowercase    (							SCREAMING_SNAKE_CASE_      ,    unittest.TestCase):
						'''simple docstring'''


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







						def       lowerCamelCase_				(  self    :				str       ,	snake_case    :				Dict=0	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				floats_tensor((1, 3, 128, 128)       ,	rng=random.Random(snake_case	)	)
											SCREAMING_SNAKE_CASE							:     Dict						=				np.random.RandomState(snake_case	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				{
											    'prompt': 'A painting of a squirrel eating a burger',
											    'image': image,
											    'generator': generator,
											    'num_inference_steps': 3,
											    'strength': 0.75,
											    'guidance_scale': 7.5,
											    'output_type': 'numpy',
											}
											return inputs







						def       lowerCamelCase_				(  self    :				Optional[int]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,	provider='CPUExecutionProvider'	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     List[Any]						=				self.get_dummy_inputs()
											SCREAMING_SNAKE_CASE							:     Any						=				pipe(**snake_case	).images
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 128, 128, 3)
											SCREAMING_SNAKE_CASE							:     str						=				np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087]	)
											assert np.abs(image_slice - expected_slice	).max() < 1E-1







						def       lowerCamelCase_				(  self    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,	provider='CPUExecutionProvider'	)
											SCREAMING_SNAKE_CASE							:     int						=				PNDMScheduler.from_config(pipe.scheduler.config       ,	skip_prk_steps=snake_case	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     Tuple						=				self.get_dummy_inputs()
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				pipe(**snake_case	).images
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				image[0, -3:, -3:, -1]

											assert image.shape == (1, 128, 128, 3)
											SCREAMING_SNAKE_CASE							:     List[str]						=				np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154]	)

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







						def       lowerCamelCase_				(  self    :				str	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     str						=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,	provider='CPUExecutionProvider'	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				LMSDiscreteScheduler.from_config(pipe.scheduler.config	)
											pipe.set_progress_bar_config(disable=snake_case	)

											# warmup pass to apply optimizations
											SCREAMING_SNAKE_CASE							:     int						=				pipe(**self.get_dummy_inputs()	)

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

											assert image.shape == (1, 128, 128, 3)
											SCREAMING_SNAKE_CASE							:     str						=				np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203]	)

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







						def       lowerCamelCase_				(  self    :				List[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,	provider='CPUExecutionProvider'	)
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				EulerDiscreteScheduler.from_config(pipe.scheduler.config	)
											pipe.set_progress_bar_config(disable=snake_case	)

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

											assert image.shape == (1, 128, 128, 3)
											SCREAMING_SNAKE_CASE							:     Any						=				np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]	)

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







						def       lowerCamelCase_				(  self    :				int	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,	provider='CPUExecutionProvider'	)
											SCREAMING_SNAKE_CASE							:     List[str]						=				EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     Tuple						=				self.get_dummy_inputs()
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				pipe(**snake_case	).images
											SCREAMING_SNAKE_CASE							:     Any						=				image[0, -3:, -3:, -1]

											assert image.shape == (1, 128, 128, 3)
											SCREAMING_SNAKE_CASE							:     str						=				np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]	)

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







						def       lowerCamelCase_				(  self    :				List[str]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Any						=				OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint       ,	provider='CPUExecutionProvider'	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     List[Any]						=				self.get_dummy_inputs()
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				pipe(**snake_case	).images
											SCREAMING_SNAKE_CASE							:     int						=				image[0, -3:, -3:, -1]

											assert image.shape == (1, 128, 128, 3)
											SCREAMING_SNAKE_CASE							:     List[str]						=				np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552]	)

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


@nightly
@require_onnxruntime
@require_torch_gpu
class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						@property
						def       lowerCamelCase_				(  self    :				Dict	):



											'''simple docstring'''


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







						@property
						def       lowerCamelCase_				(  self    :				Dict	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Any						=				ort.SessionOptions()
											SCREAMING_SNAKE_CASE							:     Any						=				False
											return options







						def       lowerCamelCase_				(  self    :				str	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/img2img/sketch-mountains-input.jpg'	)
											SCREAMING_SNAKE_CASE							:     Tuple						=				init_image.resize((768, 512)	)
											# using the PNDM scheduler by default
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				OnnxStableDiffusionImgaImgPipeline.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       ,	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				'A fantasy landscape, trending on artstation'

											SCREAMING_SNAKE_CASE							:     Dict						=				np.random.RandomState(0	)
											SCREAMING_SNAKE_CASE							:     int						=				pipe(
											    prompt=snake_case       ,	image=snake_case       ,	strength=0.75       ,	guidance_scale=7.5       ,	num_inference_steps=10       ,	generator=snake_case       ,	output_type='np'       ,	)
											SCREAMING_SNAKE_CASE							:     Dict						=				output.images
											SCREAMING_SNAKE_CASE							:     Dict						=				images[0, 255:258, 383:386, -1]

											assert images.shape == (1, 512, 768, 3)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]	)
											# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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







						def       lowerCamelCase_				(  self    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     int						=				load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/img2img/sketch-mountains-input.jpg'	)
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				init_image.resize((768, 512)	)
											SCREAMING_SNAKE_CASE							:     str						=				LMSDiscreteScheduler.from_pretrained(
											    'runwayml/stable-diffusion-v1-5'       ,	subfolder='scheduler'       ,	revision='onnx'	)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				OnnxStableDiffusionImgaImgPipeline.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       ,	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     List[Any]						=				'A fantasy landscape, trending on artstation'

											SCREAMING_SNAKE_CASE							:     Dict						=				np.random.RandomState(0	)
											SCREAMING_SNAKE_CASE							:     str						=				pipe(
											    prompt=snake_case       ,	image=snake_case       ,	strength=0.75       ,	guidance_scale=7.5       ,	num_inference_steps=20       ,	generator=snake_case       ,	output_type='np'       ,	)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				output.images
											SCREAMING_SNAKE_CASE							:     Any						=				images[0, 255:258, 383:386, -1]

											assert images.shape == (1, 512, 768, 3)
											SCREAMING_SNAKE_CASE							:     str						=				np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]	)
											# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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




import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     str:
					# load base model
					SCREAMING_SNAKE_CASE							:     Dict						=				StableDiffusionPipeline.from_pretrained(__lowerCAmelCase      ,	torch_dtype=torch.floataa   )

					# load LoRA weight from .safetensors
					SCREAMING_SNAKE_CASE							:     Any						=				load_file(__lowerCAmelCase   )

					SCREAMING_SNAKE_CASE							:     Optional[int]						=				[]

					# directly update weight in diffusers model
					for key in state_dict:
										# it is suggested to print out the key, it usually will be something like below
										# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

										# as we have set the alpha beforehand, so just skip
										if ".alpha" in key or key in visited:
															continue

										if "text" in key:
															SCREAMING_SNAKE_CASE							:     Dict						=				key.split('.'   )[0].split(LORA_PREFIX_TEXT_ENCODER + '_'   )[-1].split('_'   )
															SCREAMING_SNAKE_CASE							:     Tuple						=				pipeline.text_encoder
										else:
															SCREAMING_SNAKE_CASE							:     Any						=				key.split('.'   )[0].split(LORA_PREFIX_UNET + '_'   )[-1].split('_'   )
															SCREAMING_SNAKE_CASE							:     Dict						=				pipeline.unet

										# find the target layer
										SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				layer_infos.pop(0   )
										while len(__lowerCAmelCase   ) > -1:
															try:
																				SCREAMING_SNAKE_CASE							:     List[Any]						=				curr_layer.__getattr__(__lowerCAmelCase   )
																				if len(__lowerCAmelCase   ) > 0:
																									SCREAMING_SNAKE_CASE							:     Optional[int]						=				layer_infos.pop(0   )
																				elif len(__lowerCAmelCase   ) == 0:
																									break
															except Exception:
																				if len(__lowerCAmelCase   ) > 0:
																									temp_name += "_" + layer_infos.pop(0   )
																				else:
																									SCREAMING_SNAKE_CASE							:     List[str]						=				layer_infos.pop(0   )

										SCREAMING_SNAKE_CASE							:     str						=				[]
										if "lora_down" in key:
															pair_keys.append(key.replace('lora_down'      ,	'lora_up'   )   )
															pair_keys.append(__lowerCAmelCase   )
										else:
															pair_keys.append(__lowerCAmelCase   )
															pair_keys.append(key.replace('lora_up'      ,	'lora_down'   )   )

										# update weight
										if len(state_dict[pair_keys[0]].shape   ) == 4:
															SCREAMING_SNAKE_CASE							:     Any						=				state_dict[pair_keys[0]].squeeze(3   ).squeeze(2   ).to(torch.floataa   )
															SCREAMING_SNAKE_CASE							:     str						=				state_dict[pair_keys[1]].squeeze(3   ).squeeze(2   ).to(torch.floataa   )
															curr_layer.weight.data += alpha * torch.mm(__lowerCAmelCase      ,	__lowerCAmelCase   ).unsqueeze(2   ).unsqueeze(3   )
										else:
															SCREAMING_SNAKE_CASE							:     List[Any]						=				state_dict[pair_keys[0]].to(torch.floataa   )
															SCREAMING_SNAKE_CASE							:     Optional[int]						=				state_dict[pair_keys[1]].to(torch.floataa   )
															curr_layer.weight.data += alpha * torch.mm(__lowerCAmelCase      ,	__lowerCAmelCase   )

										# update visited list
										for item in pair_keys:
															visited.append(__lowerCAmelCase   )

					return pipeline


if __name__ == "__main__":
			_lowerCamelCase    :		Tuple		      =    argparse.ArgumentParser()

			parser.add_argument(
			    """--base_model_path""", default=None, type=str, required=True, help="""Path to the base model in diffusers format."""
			)
			parser.add_argument(
			    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
			)
			parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
			parser.add_argument(
			    """--lora_prefix_unet""", default="""lora_unet""", type=str, help="""The prefix of UNet weight in safetensors"""
			)
			parser.add_argument(
			    """--lora_prefix_text_encoder""",
			    default="""lora_te""",
			    type=str,
			    help="""The prefix of text encoder weight in safetensors""",
			)
			parser.add_argument("""--alpha""", default=0.75, type=float, help="""The merging ratio in W = W0 + alpha * deltaW""")
			parser.add_argument(
			    """--to_safetensors""", action="""store_true""", help="""Whether to store pipeline in safetensors format or not."""
			)
			parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")

			_lowerCamelCase    :		List[Any]		      =    parser.parse_args()

			_lowerCamelCase    :		List[str]		      =    args.base_model_path
			_lowerCamelCase    :		str		      =    args.checkpoint_path
			_lowerCamelCase    :		Dict		      =    args.dump_path
			_lowerCamelCase    :		Any		      =    args.lora_prefix_unet
			_lowerCamelCase    :		List[Any]		      =    args.lora_prefix_text_encoder
			_lowerCamelCase    :		List[Any]		      =    args.alpha

			_lowerCamelCase    :		Optional[Any]		      =    convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

			_lowerCamelCase    :		Optional[Any]		      =    pipe.to(args.device)
			pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)