






import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							List[str]					,  snake_case_ :							Any					,  snake_case_ :							Dict       ):
	snake_case__			:				int       =  OmegaConf.load(snake_case_       )
	snake_case__			:				List[Any]       =  torch.load(snake_case_					,  map_location="cpu"       )["model"]
	snake_case__			:				str       =  list(state_dict.keys()       )

	# extract state_dict for VQVAE
	snake_case__			:				Union[str, Any]       =  {}
	snake_case__			:				Dict       =  "first_stage_model."
	for key in keys:
		if key.startswith(snake_case_       ):
			snake_case__			:				List[str]       =  state_dict[key]

    # extract state_dict for UNetLDM
	snake_case__			:				str       =  {}
	snake_case__			:				Any       =  "model.diffusion_model."
	for key in keys:
		if key.startswith(snake_case_       ):
			snake_case__			:				List[Any]       =  state_dict[key]

	snake_case__			:				List[Any]       =  config.model.params.first_stage_config.params
	snake_case__			:				List[Any]       =  config.model.params.unet_config.params

	snake_case__			:				Any       =  VQModel(**snake_case_       ).eval()
	vqvae.load_state_dict(snake_case_       )

	snake_case__			:				Optional[Any]       =  UNetLDMModel(**snake_case_       ).eval()
	unet.load_state_dict(snake_case_       )

	snake_case__			:				List[Any]       =  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=snake_case_					,  )

	snake_case__			:				str       =  LDMPipeline(snake_case_					,  snake_case_					,  snake_case_       )
	pipeline.save_pretrained(snake_case_       )


if __name__ == "__main__":
	__lowerCamelCase :      Union[str, Any]										=				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)
	__lowerCamelCase :      List[Any]										=				parser.parse_args()

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











import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_			,       UpperCamelCase_			,       UpperCamelCase_			,       unittest.TestCase						):





					"""simple docstring"""






					a_     =		StableDiffusionInstructPixaPixPipeline
					a_     =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
					a_     =		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					a_     =		IMAGE_TO_IMAGE_IMAGE_PARAMS
					a_     =		IMAGE_TO_IMAGE_IMAGE_PARAMS



					def 	_lowercase					( self					:  List[str]	):
						torch.manual_seed(0	)
						snake_case__			:				Any       =  UNetaDConditionModel(
						    block_out_channels=(3_2, 6_4)							,		layers_per_block=2							,		sample_size=3_2							,		in_channels=8							,		out_channels=4							,		down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")							,		up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")							,		cross_attention_dim=3_2							,		)
						snake_case__			:				int       =  PNDMScheduler(skip_prk_steps=__A	)
						torch.manual_seed(0	)
						snake_case__			:				Union[str, Any]       =  AutoencoderKL(
						    block_out_channels=[3_2, 6_4]							,		in_channels=3							,		out_channels=3							,		down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]							,		up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]							,		latent_channels=4							,		)
						torch.manual_seed(0	)
						snake_case__			:				int       =  CLIPTextConfig(
						    bos_token_id=0							,		eos_token_id=2							,		hidden_size=3_2							,		intermediate_size=3_7							,		layer_norm_eps=1e-0_5							,		num_attention_heads=4							,		num_hidden_layers=5							,		pad_token_id=1							,		vocab_size=1_0_0_0							,		)
						snake_case__			:				Union[str, Any]       =  CLIPTextModel(__A	)
						snake_case__			:				List[Any]       =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

						snake_case__			:				str       =  {
						    "unet": unet,
						    "scheduler": scheduler,
						    "vae": vae,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "safety_checker": None,
						    "feature_extractor": None,
						}
						return components



					def 	_lowercase					( self					:  List[Any]							,		__A					:  int							,		__A					:  Any=0	):
						snake_case__			:				Optional[int]       =  floats_tensor((1, 3, 3_2, 3_2)							,		rng=random.Random(__A	)	).to(__A	)
						snake_case__			:				int       =  image.cpu().permute(0							,		2							,		3							,		1	)[0]
						snake_case__			:				Union[str, Any]       =  Image.fromarray(np.uinta(__A	)	).convert("RGB"	)
						if str(__A	).startswith("mps"	):
							snake_case__			:				List[Any]       =  torch.manual_seed(__A	)
						else:
							snake_case__			:				Optional[int]       =  torch.Generator(device=__A	).manual_seed(__A	)
						snake_case__			:				Optional[int]       =  {
						    "prompt": "A painting of a squirrel eating a burger",
						    "image": image,
						    "generator": generator,
						    "num_inference_steps": 2,
						    "guidance_scale": 6.0,
						    "image_guidance_scale": 1,
						    "output_type": "numpy",
						}
						return inputs



					def 	_lowercase					( self					:  int	):
						snake_case__			:				Tuple       =  "cpu"  # ensure determinism for the device-dependent torch.Generator
						snake_case__			:				int       =  self.get_dummy_components()
						snake_case__			:				List[Any]       =  StableDiffusionInstructPixaPixPipeline(**__A	)
						snake_case__			:				List[Any]       =  sd_pipe.to(__A	)
						sd_pipe.set_progress_bar_config(disable=__A	)

						snake_case__			:				Tuple       =  self.get_dummy_inputs(__A	)
						snake_case__			:				List[str]       =  sd_pipe(**__A	).images
						snake_case__			:				str       =  image[0, -3:, -3:, -1]
						assert image.shape == (1, 3_2, 3_2, 3)
						snake_case__			:				List[Any]       =  np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5]	)

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



					def 	_lowercase					( self					:  Union[str, Any]	):
						snake_case__			:				Dict       =  "cpu"  # ensure determinism for the device-dependent torch.Generator
						snake_case__			:				List[Any]       =  self.get_dummy_components()
						snake_case__			:				List[Any]       =  StableDiffusionInstructPixaPixPipeline(**__A	)
						snake_case__			:				str       =  sd_pipe.to(__A	)
						sd_pipe.set_progress_bar_config(disable=__A	)

						snake_case__			:				str       =  self.get_dummy_inputs(__A	)
						snake_case__			:				List[Any]       =  "french fries"
						snake_case__			:				str       =  sd_pipe(**__A							,		negative_prompt=__A	)
						snake_case__			:				Any       =  output.images
						snake_case__			:				Any       =  image[0, -3:, -3:, -1]

						assert image.shape == (1, 3_2, 3_2, 3)
						snake_case__			:				Union[str, Any]       =  np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1]	)

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



					def 	_lowercase					( self					:  Optional[int]	):
						snake_case__			:				Tuple       =  "cpu"  # ensure determinism for the device-dependent torch.Generator
						snake_case__			:				List[Any]       =  self.get_dummy_components()
						snake_case__			:				str       =  StableDiffusionInstructPixaPixPipeline(**__A	)
						snake_case__			:				List[str]       =  sd_pipe.to(__A	)
						sd_pipe.set_progress_bar_config(disable=__A	)

						snake_case__			:				Any       =  self.get_dummy_inputs(__A	)
						snake_case__			:				Tuple       =  [inputs["prompt"]] * 2

						snake_case__			:				Any       =  np.array(inputs["image"]	).astype(np.floataa	) / 2_5_5.0
						snake_case__			:				List[str]       =  torch.from_numpy(__A	).unsqueeze(0	).to(__A	)
						snake_case__			:				Union[str, Any]       =  image / 2 + 0.5
						snake_case__			:				str       =  image.permute(0							,		3							,		1							,		2	)
						snake_case__			:				int       =  image.repeat(2							,		1							,		1							,		1	)

						snake_case__			:				str       =  sd_pipe(**__A	).images
						snake_case__			:				Any       =  image[-1, -3:, -3:, -1]

						assert image.shape == (2, 3_2, 3_2, 3)
						snake_case__			:				int       =  np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9]	)

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



					def 	_lowercase					( self					:  Union[str, Any]	):
						snake_case__			:				Union[str, Any]       =  "cpu"  # ensure determinism for the device-dependent torch.Generator
						snake_case__			:				int       =  self.get_dummy_components()
						snake_case__			:				Dict       =  EulerAncestralDiscreteScheduler(
						    beta_start=0.0_0_0_8_5							,		beta_end=0.0_1_2							,		beta_schedule="scaled_linear"	)
						snake_case__			:				Tuple       =  StableDiffusionInstructPixaPixPipeline(**__A	)
						snake_case__			:				str       =  sd_pipe.to(__A	)
						sd_pipe.set_progress_bar_config(disable=__A	)

						snake_case__			:				str       =  self.get_dummy_inputs(__A	)
						snake_case__			:				Optional[Any]       =  sd_pipe(**__A	).images
						snake_case__			:				Dict       =  image[0, -3:, -3:, -1]

						snake_case__			:				Union[str, Any]       =  [round(__A							,		4	) for x in image_slice.flatten().tolist()]
						print(",".join([str(__A	) for x in slice]	)	)

						assert image.shape == (1, 3_2, 3_2, 3)
						snake_case__			:				str       =  np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6]	)

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



					def 	_lowercase					( self					:  List[str]	):
						super().test_inference_batch_single_identical(expected_max_diff=3e-3	)






					def 	_lowercase					( self					:  List[Any]	):
						snake_case__			:				Tuple       =  self.get_dummy_components()
						snake_case__			:				Tuple       =  StableDiffusionInstructPixaPixPipeline(**__A	)
						snake_case__			:				int       =  VaeImageProcessor(do_resize=__A							,		do_normalize=__A	)
						snake_case__			:				Any       =  pipe.to(__A	)
						pipe.set_progress_bar_config(disable=__A	)

						snake_case__			:				Dict       =  pipe(**self.get_dummy_inputs_by_type(__A							,		input_image_type="pt"	)	)[0]

						snake_case__			:				int       =  components["vae"]
						snake_case__			:				Union[str, Any]       =  self.get_dummy_inputs_by_type(__A							,		input_image_type="pt"	)

						for image_param in self.image_latents_params:
							if image_param in inputs.keys():
								snake_case__			:				Optional[int]       =  vae.encode(inputs[image_param]	).latent_dist.mode()

						snake_case__			:				str       =  pipe(**__A	)[0]

						snake_case__			:				Dict       =  np.abs(out - out_latents_inputs	).max()
						self.assertLess(__A							,		1e-4							,		"passing latents as image input generate different result from passing image"	)



@slow
@require_torch_gpu
class 							SCREAMING_SNAKE_CASE__						(    unittest.TestCase						):





					"""simple docstring"""






					def 	_lowercase					( self					:  Optional[int]	):
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



					def 	_lowercase					( self					:  str							,		__A					:  Dict=0	):
						snake_case__			:				Optional[int]       =  torch.manual_seed(__A	)
						snake_case__			:				Tuple       =  load_image(
						    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"	)
						snake_case__			:				Optional[Any]       =  {
						    "prompt": "turn him into a cyborg",
						    "image": image,
						    "generator": generator,
						    "num_inference_steps": 3,
						    "guidance_scale": 7.5,
						    "image_guidance_scale": 1.0,
						    "output_type": "numpy",
						}
						return inputs



					def 	_lowercase					( self					:  int	):
						snake_case__			:				Union[str, Any]       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    "timbrooks/instruct-pix2pix"							,		safety_checker=__A	)
						pipe.to(__A	)
						pipe.set_progress_bar_config(disable=__A	)
						pipe.enable_attention_slicing()

						snake_case__			:				Union[str, Any]       =  self.get_inputs()
						snake_case__			:				Union[str, Any]       =  pipe(**__A	).images
						snake_case__			:				Dict       =  image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						snake_case__			:				Any       =  np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5]	)

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



					def 	_lowercase					( self					:  str	):
						snake_case__			:				int       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    "timbrooks/instruct-pix2pix"							,		safety_checker=__A	)
						snake_case__			:				Dict       =  LMSDiscreteScheduler.from_config(pipe.scheduler.config	)
						pipe.to(__A	)
						pipe.set_progress_bar_config(disable=__A	)
						pipe.enable_attention_slicing()

						snake_case__			:				List[str]       =  self.get_inputs()
						snake_case__			:				Any       =  pipe(**__A	).images
						snake_case__			:				Union[str, Any]       =  image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						snake_case__			:				Optional[Any]       =  np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1]	)

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



					def 	_lowercase					( self					:  Dict	):
						snake_case__			:				List[str]       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    "timbrooks/instruct-pix2pix"							,		safety_checker=__A	)
						snake_case__			:				List[str]       =  DDIMScheduler.from_config(pipe.scheduler.config	)
						pipe.to(__A	)
						pipe.set_progress_bar_config(disable=__A	)
						pipe.enable_attention_slicing()

						snake_case__			:				int       =  self.get_inputs()
						snake_case__			:				Union[str, Any]       =  pipe(**__A	).images
						snake_case__			:				Tuple       =  image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						snake_case__			:				Union[str, Any]       =  np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3]	)

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



					def 	_lowercase					( self					:  List[Any]	):
						snake_case__			:				Optional[Any]       =  0

						def callback_fn(__A					:  int							,		__A					:  int							,		__A					:  torch.FloatTensor	) -> None:
							snake_case__			:				Union[str, Any]       =  True
							nonlocal number_of_steps
							number_of_steps += 1
							if step == 1:
								snake_case__			:				Optional[Any]       =  latents.detach().cpu().numpy()
								assert latents.shape == (1, 4, 6_4, 6_4)
								snake_case__			:				int       =  latents[0, -3:, -3:, -1]
								snake_case__			:				Optional[int]       =  np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3]	)

								assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5e-2
							elif step == 2:
								snake_case__			:				int       =  latents.detach().cpu().numpy()
								assert latents.shape == (1, 4, 6_4, 6_4)
								snake_case__			:				Any       =  latents[0, -3:, -3:, -1]
								snake_case__			:				Dict       =  np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5]	)

								assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5e-2

						snake_case__			:				Any       =  False

						snake_case__			:				Union[str, Any]       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    "timbrooks/instruct-pix2pix"							,		safety_checker=__A							,		torch_dtype=torch.floataa	)
						snake_case__			:				int       =  pipe.to(__A	)
						pipe.set_progress_bar_config(disable=__A	)
						pipe.enable_attention_slicing()

						snake_case__			:				Optional[Any]       =  self.get_inputs()
						pipe(**__A							,		callback=__A							,		callback_steps=1	)
						assert callback_fn.has_been_called
						assert number_of_steps == 3



					def 	_lowercase					( self					:  List[Any]	):
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

						snake_case__			:				Dict       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    "timbrooks/instruct-pix2pix"							,		safety_checker=__A							,		torch_dtype=torch.floataa	)
						snake_case__			:				Tuple       =  pipe.to(__A	)
						pipe.set_progress_bar_config(disable=__A	)
						pipe.enable_attention_slicing(1	)
						pipe.enable_sequential_cpu_offload()

						snake_case__			:				Dict       =  self.get_inputs()
						snake_case__			:				List[Any]       =  pipe(**__A	)

						snake_case__			:				List[str]       =  torch.cuda.max_memory_allocated()
						# make sure that less than 2.2 GB is allocated
						assert mem_bytes < 2.2 * 1_0**9






					def 	_lowercase					( self					:  Tuple	):
						snake_case__			:				int       =  self.get_inputs()
						# resize to resolution that is divisible by 8 but not 16 or 32
						snake_case__			:				Union[str, Any]       =  inputs["image"].resize((5_0_4, 5_0_4)	)

						snake_case__			:				Optional[Any]       =  "timbrooks/instruct-pix2pix"
						snake_case__			:				Union[str, Any]       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    __A							,		safety_checker=__A							,		)
						pipe.to(__A	)
						pipe.set_progress_bar_config(disable=__A	)
						pipe.enable_attention_slicing()

						snake_case__			:				Union[str, Any]       =  pipe(**__A	)
						snake_case__			:				Tuple       =  output.images[0]

						snake_case__			:				List[Any]       =  image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

						assert image.shape == (5_0_4, 5_0_4, 3)
						snake_case__			:				int       =  np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0]	)

						assert np.abs(image_slice.flatten() - expected_slice	).max() < 5e-3



