


import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel







def  _lowerCamelCase(			lowercase__     ,				lowercase__			)		->			List[str]:

		'''simple docstring'''







		__lowercase=    UNetaDModel(**lowercase__			)
		__lowercase=    checkpoint['all_modules.0.W'].data
		__lowercase=    checkpoint['all_modules.0.W'].data
		__lowercase=    checkpoint['all_modules.1.weight'].data
		__lowercase=    checkpoint['all_modules.1.bias'].data

		__lowercase=    checkpoint['all_modules.2.weight'].data
		__lowercase=    checkpoint['all_modules.2.bias'].data

		__lowercase=    checkpoint['all_modules.3.weight'].data
		__lowercase=    checkpoint['all_modules.3.bias'].data

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

		__lowercase=    4

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

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

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

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

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

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

				__lowercase=    old_checkpoint[F'all_modules.{index}.Dense_0.weight'].data
				__lowercase=    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:
						__lowercase=    old_checkpoint[F'all_modules.{index}.Conv_2.weight'].data
						__lowercase=    old_checkpoint[F'all_modules.{index}.Conv_2.bias'].data

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

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

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

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

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

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

		return new_model_architecture.state_dict()


if __name__ == "__main__":
	lowerCAmelCase					 =	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.''',
	)

	lowerCAmelCase					 =	parser.parse_args()

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

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

	lowerCAmelCase					 =	convert_ncsnpp_checkpoint(
	    checkpoint,
	    config,
	)

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

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

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

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










from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available





@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class    A					(     A_				):
							UpperCamelCase_     :							Union[List[PIL.Image.Image], np.ndarray]
							UpperCamelCase_     :							Optional[List[bool]]


if is_transformers_available() and is_torch_available():
	from .modeling_roberta_series import RobertaSeriesModelWithTransformation
	from .pipeline_alt_diffusion import AltDiffusionPipeline
	from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline






