

"""simple docstring"""





import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_				):
	lowerCAmelCase__			:		Tuple						       =			UNetaDModel(**A_				)
	lowerCAmelCase__			:		str						       =			checkpoint['''all_modules.0.W'''].data
	lowerCAmelCase__			:		Dict						       =			checkpoint['''all_modules.0.W'''].data
	lowerCAmelCase__			:		Tuple						       =			checkpoint['''all_modules.1.weight'''].data
	lowerCAmelCase__			:		int						       =			checkpoint['''all_modules.1.bias'''].data

	lowerCAmelCase__			:		Dict						       =			checkpoint['''all_modules.2.weight'''].data
	lowerCAmelCase__			:		Tuple						       =			checkpoint['''all_modules.2.bias'''].data

	lowerCAmelCase__			:		Optional[int]						       =			checkpoint['''all_modules.3.weight'''].data
	lowerCAmelCase__			:		Optional[Any]						       =			checkpoint['''all_modules.3.bias'''].data

	lowerCAmelCase__			:		str						       =			checkpoint[list(checkpoint.keys()				)[-4]].data
	lowerCAmelCase__			:		Optional[Any]						       =			checkpoint[list(checkpoint.keys()				)[-3]].data
	lowerCAmelCase__			:		List[str]						       =			checkpoint[list(checkpoint.keys()				)[-2]].data
	lowerCAmelCase__			:		str						       =			checkpoint[list(checkpoint.keys()				)[-1]].data

	lowerCAmelCase__			:		List[str]						       =			4

	def set_attention_weights(A_		,  A_		,  A_				):
		lowerCAmelCase__			:		Optional[Any]						       =			old_checkpoint[f'all_modules.{index}.NIN_0.W'].data.T
		lowerCAmelCase__			:		Union[str, Any]						       =			old_checkpoint[f'all_modules.{index}.NIN_1.W'].data.T
		lowerCAmelCase__			:		List[Any]						       =			old_checkpoint[f'all_modules.{index}.NIN_2.W'].data.T

		lowerCAmelCase__			:		Optional[Any]						       =			old_checkpoint[f'all_modules.{index}.NIN_0.b'].data
		lowerCAmelCase__			:		int						       =			old_checkpoint[f'all_modules.{index}.NIN_1.b'].data
		lowerCAmelCase__			:		Any						       =			old_checkpoint[f'all_modules.{index}.NIN_2.b'].data

		lowerCAmelCase__			:		Tuple						       =			old_checkpoint[f'all_modules.{index}.NIN_3.W'].data.T
		lowerCAmelCase__			:		Dict						       =			old_checkpoint[f'all_modules.{index}.NIN_3.b'].data

		lowerCAmelCase__			:		Dict						       =			old_checkpoint[f'all_modules.{index}.GroupNorm_0.weight'].data
		lowerCAmelCase__			:		Optional[Any]						       =			old_checkpoint[f'all_modules.{index}.GroupNorm_0.bias'].data

	def set_resnet_weights(A_		,  A_		,  A_				):
		lowerCAmelCase__			:		List[str]						       =			old_checkpoint[f'all_modules.{index}.Conv_0.weight'].data
		lowerCAmelCase__			:		int						       =			old_checkpoint[f'all_modules.{index}.Conv_0.bias'].data
		lowerCAmelCase__			:		List[Any]						       =			old_checkpoint[f'all_modules.{index}.GroupNorm_0.weight'].data
		lowerCAmelCase__			:		Optional[int]						       =			old_checkpoint[f'all_modules.{index}.GroupNorm_0.bias'].data

		lowerCAmelCase__			:		List[Any]						       =			old_checkpoint[f'all_modules.{index}.Conv_1.weight'].data
		lowerCAmelCase__			:		int						       =			old_checkpoint[f'all_modules.{index}.Conv_1.bias'].data
		lowerCAmelCase__			:		str						       =			old_checkpoint[f'all_modules.{index}.GroupNorm_1.weight'].data
		lowerCAmelCase__			:		List[str]						       =			old_checkpoint[f'all_modules.{index}.GroupNorm_1.bias'].data

		lowerCAmelCase__			:		List[str]						       =			old_checkpoint[f'all_modules.{index}.Dense_0.weight'].data
		lowerCAmelCase__			:		Tuple						       =			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__			:		List[Any]						       =			old_checkpoint[f'all_modules.{index}.Conv_2.weight'].data
			lowerCAmelCase__			:		List[Any]						       =			old_checkpoint[f'all_modules.{index}.Conv_2.bias'].data

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

		if hasattr(A_		,  '''downsamplers'''				) and block.downsamplers is not None:
			set_resnet_weights(block.resnet_down		,  A_		,  A_				)
			module_index += 1
			lowerCAmelCase__			:		Dict						       =			checkpoint[f'all_modules.{module_index}.Conv_0.weight'].data
			lowerCAmelCase__			:		str						       =			checkpoint[f'all_modules.{module_index}.Conv_0.bias'].data
			module_index += 1

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

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

		if hasattr(A_		,  '''resnet_up'''				) and block.resnet_up is not None:
			lowerCAmelCase__			:		Tuple						       =			checkpoint[f'all_modules.{module_index}.weight'].data
			lowerCAmelCase__			:		int						       =			checkpoint[f'all_modules.{module_index}.bias'].data
			module_index += 1
			lowerCAmelCase__			:		Tuple						       =			checkpoint[f'all_modules.{module_index}.weight'].data
			lowerCAmelCase__			:		Tuple						       =			checkpoint[f'all_modules.{module_index}.bias'].data
			module_index += 1
			set_resnet_weights(block.resnet_up		,  A_		,  A_				)
			module_index += 1

	lowerCAmelCase__			:		int						       =			checkpoint[f'all_modules.{module_index}.weight'].data
	lowerCAmelCase__			:		Dict						       =			checkpoint[f'all_modules.{module_index}.bias'].data
	module_index += 1
	lowerCAmelCase__			:		Any						       =			checkpoint[f'all_modules.{module_index}.weight'].data
	lowerCAmelCase__			:		Optional[int]						       =			checkpoint[f'all_modules.{module_index}.bias'].data

	return new_model_architecture.state_dict()


if __name__ == "__main__":
					__UpperCamelCase						:   Union[str, Any]							   =	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.''',
					)

					__UpperCamelCase						:   str							   =	parser.parse_args()

					__UpperCamelCase						:   int							   =	torch.load(args.checkpoint_path, map_location='''cpu''')

					with open(args.config_file) as f:
										__UpperCamelCase						:   List[Any]							   =	json.loads(f.read())

					__UpperCamelCase						:   Tuple							   =	convert_ncsnpp_checkpoint(
					    checkpoint,
					    config,
					)

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

					__UpperCamelCase						:   Optional[Any]							   =	UNetaDModel(**config)
					model.load_state_dict(converted_checkpoint)

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

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







"""simple docstring"""





def 						__SCREAMING_SNAKE_CASE						(   A_				):
	lowerCAmelCase__			:		List[Any]						       =			53_81
	for x in s:
		lowerCAmelCase__			:		Tuple						       =			((hash_value << 5) + hash_value) + ord(A_				)
	return hash_value & 0Xf_f_f_f_f_f_f_f




