
"""simple docstring"""


from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
)


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_pt_objects import *  # noqa F403
else:
			from .consistency_models import ConsistencyModelPipeline
			from .dance_diffusion import DanceDiffusionPipeline
			from .ddim import DDIMPipeline
			from .ddpm import DDPMPipeline
			from .dit import DiTPipeline
			from .latent_diffusion import LDMSuperResolutionPipeline
			from .latent_diffusion_uncond import LDMPipeline
			from .pipeline_utils import AudioPipelineOutput, DiffusionPipeline, ImagePipelineOutput
			from .pndm import PNDMPipeline
			from .repaint import RePaintPipeline
			from .score_sde_ve import ScoreSdeVePipeline
			from .stochastic_karras_ve import KarrasVePipeline

try:
			if not (is_torch_available() and is_librosa_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
			from .audio_diffusion import AudioDiffusionPipeline, Mel

try:
			if not (is_torch_available() and is_transformers_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
			from .alt_diffusion import AltDiffusionImgaImgPipeline, AltDiffusionPipeline
			from .audioldm import AudioLDMPipeline
			from .controlnet import (
			    StableDiffusionControlNetImgaImgPipeline,
			    StableDiffusionControlNetInpaintPipeline,
			    StableDiffusionControlNetPipeline,
			)
			from .deepfloyd_if import (
			    IFImgaImgPipeline,
			    IFImgaImgSuperResolutionPipeline,
			    IFInpaintingPipeline,
			    IFInpaintingSuperResolutionPipeline,
			    IFPipeline,
			    IFSuperResolutionPipeline,
			)
			from .kandinsky import (
			    KandinskyImgaImgPipeline,
			    KandinskyInpaintPipeline,
			    KandinskyPipeline,
			    KandinskyPriorPipeline,
			)
			from .kandinskya_a import (
			    KandinskyVaaControlnetImgaImgPipeline,
			    KandinskyVaaControlnetPipeline,
			    KandinskyVaaImgaImgPipeline,
			    KandinskyVaaInpaintPipeline,
			    KandinskyVaaPipeline,
			    KandinskyVaaPriorEmbaEmbPipeline,
			    KandinskyVaaPriorPipeline,
			)
			from .latent_diffusion import LDMTextToImagePipeline
			from .paint_by_example import PaintByExamplePipeline
			from .semantic_stable_diffusion import SemanticStableDiffusionPipeline
			from .shap_e import ShapEImgaImgPipeline, ShapEPipeline
			from .stable_diffusion import (
			    CycleDiffusionPipeline,
			    StableDiffusionAttendAndExcitePipeline,
			    StableDiffusionDepthaImgPipeline,
			    StableDiffusionDiffEditPipeline,
			    StableDiffusionImageVariationPipeline,
			    StableDiffusionImgaImgPipeline,
			    StableDiffusionInpaintPipeline,
			    StableDiffusionInpaintPipelineLegacy,
			    StableDiffusionInstructPixaPixPipeline,
			    StableDiffusionLatentUpscalePipeline,
			    StableDiffusionLDMaDPipeline,
			    StableDiffusionModelEditingPipeline,
			    StableDiffusionPanoramaPipeline,
			    StableDiffusionParadigmsPipeline,
			    StableDiffusionPipeline,
			    StableDiffusionPixaPixZeroPipeline,
			    StableDiffusionSAGPipeline,
			    StableDiffusionUpscalePipeline,
			    StableUnCLIPImgaImgPipeline,
			    StableUnCLIPPipeline,
			)
			from .stable_diffusion_safe import StableDiffusionPipelineSafe
			from .text_to_video_synthesis import TextToVideoSDPipeline, TextToVideoZeroPipeline, VideoToVideoSDPipeline
			from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline
			from .unidiffuser import ImageTextPipelineOutput, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder
			from .versatile_diffusion import (
			    VersatileDiffusionDualGuidedPipeline,
			    VersatileDiffusionImageVariationPipeline,
			    VersatileDiffusionPipeline,
			    VersatileDiffusionTextToImagePipeline,
			)
			from .vq_diffusion import VQDiffusionPipeline


try:
			if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
			from .stable_diffusion_xl import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
			if not is_onnx_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_onnx_objects import *  # noqa F403
else:
			from .onnx_utils import OnnxRuntimeModel

try:
			if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
			from .stable_diffusion import (
			    OnnxStableDiffusionImgaImgPipeline,
			    OnnxStableDiffusionInpaintPipeline,
			    OnnxStableDiffusionInpaintPipelineLegacy,
			    OnnxStableDiffusionPipeline,
			    OnnxStableDiffusionUpscalePipeline,
			    StableDiffusionOnnxPipeline,
			)

try:
			if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
			from .stable_diffusion import StableDiffusionKDiffusionPipeline

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_flax_objects import *  # noqa F403
else:
			from .pipeline_flax_utils import FlaxDiffusionPipeline


try:
			if not (is_flax_available() and is_transformers_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
			from .controlnet import FlaxStableDiffusionControlNetPipeline
			from .stable_diffusion import (
			    FlaxStableDiffusionImgaImgPipeline,
			    FlaxStableDiffusionInpaintPipeline,
			    FlaxStableDiffusionPipeline,
			)
try:
			if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
			from .spectrogram_diffusion import MidiProcessor, SpectrogramDiffusionPipeline





"""simple docstring"""


import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase )       ->				Dict:
	_lowerCAmelCase			=UNetaDModel(**__UpperCamelCase )
	_lowerCAmelCase			=checkpoint["""all_modules.0.W"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.0.W"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.1.weight"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.1.bias"""].data

	_lowerCAmelCase			=checkpoint["""all_modules.2.weight"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.2.bias"""].data

	_lowerCAmelCase			=checkpoint["""all_modules.3.weight"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.3.bias"""].data

	_lowerCAmelCase			=checkpoint[list(checkpoint.keys() )[-4]].data
	_lowerCAmelCase			=checkpoint[list(checkpoint.keys() )[-3]].data
	_lowerCAmelCase			=checkpoint[list(checkpoint.keys() )[-2]].data
	_lowerCAmelCase			=checkpoint[list(checkpoint.keys() )[-1]].data

	_lowerCAmelCase			=4

	def set_attention_weights(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase ):
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_0.W'''].data.T
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_1.W'''].data.T
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_2.W'''].data.T

		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_0.b'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_1.b'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_2.b'''].data

		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_3.W'''].data.T
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.NIN_3.b'''].data

		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

	def set_resnet_weights(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase ):
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_0.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_0.bias'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_1.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_1.bias'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_1.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.GroupNorm_1.bias'''].data

		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Dense_0.weight'''].data
		_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Dense_0.bias'''].data

		if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
			_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_2.weight'''].data
			_lowerCAmelCase			=old_checkpoint[F'''all_modules.{index}.Conv_2.bias'''].data

	for i, block in enumerate(new_model_architecture.downsample_blocks ):
		_lowerCAmelCase			=hasattr(__UpperCamelCase					,				"""attentions""" )
		for j in range(len(block.resnets ) ):
			set_resnet_weights(block.resnets[j]					,				__UpperCamelCase					,				__UpperCamelCase )
			module_index += 1
			if has_attentions:
				set_attention_weights(block.attentions[j]					,				__UpperCamelCase					,				__UpperCamelCase )
				module_index += 1

		if hasattr(__UpperCamelCase					,				"""downsamplers""" ) and block.downsamplers is not None:
			set_resnet_weights(block.resnet_down					,				__UpperCamelCase					,				__UpperCamelCase )
			module_index += 1
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.Conv_0.weight'''].data
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.Conv_0.bias'''].data
			module_index += 1

	set_resnet_weights(new_model_architecture.mid_block.resnets[0]					,				__UpperCamelCase					,				__UpperCamelCase )
	module_index += 1
	set_attention_weights(new_model_architecture.mid_block.attentions[0]					,				__UpperCamelCase					,				__UpperCamelCase )
	module_index += 1
	set_resnet_weights(new_model_architecture.mid_block.resnets[1]					,				__UpperCamelCase					,				__UpperCamelCase )
	module_index += 1

	for i, block in enumerate(new_model_architecture.up_blocks ):
		_lowerCAmelCase			=hasattr(__UpperCamelCase					,				"""attentions""" )
		for j in range(len(block.resnets ) ):
			set_resnet_weights(block.resnets[j]					,				__UpperCamelCase					,				__UpperCamelCase )
			module_index += 1
		if has_attentions:
			set_attention_weights(
			    block.attentions[0]					,				__UpperCamelCase					,				__UpperCamelCase )  # why can there only be a single attention layer for up?
			module_index += 1

		if hasattr(__UpperCamelCase					,				"""resnet_up""" ) and block.resnet_up is not None:
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.weight'''].data
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.bias'''].data
			module_index += 1
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.weight'''].data
			_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.bias'''].data
			module_index += 1
			set_resnet_weights(block.resnet_up					,				__UpperCamelCase					,				__UpperCamelCase )
			module_index += 1

	_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.weight'''].data
	_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.bias'''].data
	module_index += 1
	_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.weight'''].data
	_lowerCAmelCase			=checkpoint[F'''all_modules.{module_index}.bias'''].data

	return new_model_architecture.state_dict()


if __name__ == "__main__":
			__A 		= argparse.ArgumentParser()

			parser.add_argument(
			    '--checkpoint_path',
			    default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin',
			    type=str,
			    required=False,
			    help='Path to the checkpoint to convert.',
			)

			parser.add_argument(
			    '--config_file',
			    default='/Users/arthurzucker/Work/diffusers/ArthurZ/config.json',
			    type=str,
			    required=False,
			    help='The config json file corresponding to the architecture.',
			)

			parser.add_argument(
			    '--dump_path',
			    default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt',
			    type=str,
			    required=False,
			    help='Path to the output model.',
			)

			__A 		= parser.parse_args()

			__A 		= torch.load(args.checkpoint_path, map_location='cpu')

			with open(args.config_file) as f:
						__A 		= json.loads(f.read())

			__A 		= convert_ncsnpp_checkpoint(
			    checkpoint,
			    config,
			)

			if "sde" in config:
						del config["sde"]

			__A 		= UNetaDModel(**config)
			model.load_state_dict(converted_checkpoint)

			try:
						__A 		= ScoreSdeVeScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

						__A 		= ScoreSdeVePipeline(unet=model, scheduler=scheduler)
						pipe.save_pretrained(args.dump_path)
			except:  # noqa: E722
						model.save_pretrained(args.dump_path)



