




import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel





def 							A__   (snake_case      :		str		,  snake_case      :		Optional[Any]							)    ->	Union[str, Any]:
					__UpperCamelCase				:  Optional[Any]								=			UNetaDModel(**snake_case							)
					__UpperCamelCase				:  Dict								=			checkpoint["""all_modules.0.W"""].data
					__UpperCamelCase				:  Dict								=			checkpoint["""all_modules.0.W"""].data
					__UpperCamelCase				:  int								=			checkpoint["""all_modules.1.weight"""].data
					__UpperCamelCase				:  Tuple								=			checkpoint["""all_modules.1.bias"""].data

					__UpperCamelCase				:  int								=			checkpoint["""all_modules.2.weight"""].data
					__UpperCamelCase				:  int								=			checkpoint["""all_modules.2.bias"""].data

					__UpperCamelCase				:  Optional[int]								=			checkpoint["""all_modules.3.weight"""].data
					__UpperCamelCase				:  Tuple								=			checkpoint["""all_modules.3.bias"""].data

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

					__UpperCamelCase				:  Optional[int]								=			4

					def set_attention_weights(snake_case      :		Dict		,  snake_case      :		Optional[Any]		,  snake_case      :		str							):
										__UpperCamelCase				:  Tuple								=			old_checkpoint[F'''all_modules.{index}.NIN_0.W'''].data.T
										__UpperCamelCase				:  List[str]								=			old_checkpoint[F'''all_modules.{index}.NIN_1.W'''].data.T
										__UpperCamelCase				:  List[str]								=			old_checkpoint[F'''all_modules.{index}.NIN_2.W'''].data.T

										__UpperCamelCase				:  List[str]								=			old_checkpoint[F'''all_modules.{index}.NIN_0.b'''].data
										__UpperCamelCase				:  int								=			old_checkpoint[F'''all_modules.{index}.NIN_1.b'''].data
										__UpperCamelCase				:  Union[str, Any]								=			old_checkpoint[F'''all_modules.{index}.NIN_2.b'''].data

										__UpperCamelCase				:  Union[str, Any]								=			old_checkpoint[F'''all_modules.{index}.NIN_3.W'''].data.T
										__UpperCamelCase				:  Optional[int]								=			old_checkpoint[F'''all_modules.{index}.NIN_3.b'''].data

										__UpperCamelCase				:  Optional[Any]								=			old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
										__UpperCamelCase				:  Optional[Any]								=			old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

					def set_resnet_weights(snake_case      :		Dict		,  snake_case      :		Union[str, Any]		,  snake_case      :		Union[str, Any]							):
										__UpperCamelCase				:  int								=			old_checkpoint[F'''all_modules.{index}.Conv_0.weight'''].data
										__UpperCamelCase				:  Optional[int]								=			old_checkpoint[F'''all_modules.{index}.Conv_0.bias'''].data
										__UpperCamelCase				:  Dict								=			old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
										__UpperCamelCase				:  Optional[int]								=			old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

										__UpperCamelCase				:  Dict								=			old_checkpoint[F'''all_modules.{index}.Conv_1.weight'''].data
										__UpperCamelCase				:  Any								=			old_checkpoint[F'''all_modules.{index}.Conv_1.bias'''].data
										__UpperCamelCase				:  Dict								=			old_checkpoint[F'''all_modules.{index}.GroupNorm_1.weight'''].data
										__UpperCamelCase				:  int								=			old_checkpoint[F'''all_modules.{index}.GroupNorm_1.bias'''].data

										__UpperCamelCase				:  Union[str, Any]								=			old_checkpoint[F'''all_modules.{index}.Dense_0.weight'''].data
										__UpperCamelCase				:  str								=			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:
															__UpperCamelCase				:  str								=			old_checkpoint[F'''all_modules.{index}.Conv_2.weight'''].data
															__UpperCamelCase				:  Optional[int]								=			old_checkpoint[F'''all_modules.{index}.Conv_2.bias'''].data

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

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

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

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

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

					__UpperCamelCase				:  Optional[Any]								=			checkpoint[F'''all_modules.{module_index}.weight'''].data
					__UpperCamelCase				:  int								=			checkpoint[F'''all_modules.{module_index}.bias'''].data
					module_index += 1
					__UpperCamelCase				:  Optional[int]								=			checkpoint[F'''all_modules.{module_index}.weight'''].data
					__UpperCamelCase				:  Optional[int]								=			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)












a__		    =    {
    "kilogram": 1,
    "gram": pow(10, 3),
    "milligram": pow(10, 6),
    "metric-ton": pow(10, -3),
    "long-ton": 0.00_0984_2073,
    "short-ton": 0.00_1102_3122,
    "pound": 2.20_4624_4202,
    "stone": 0.15_7473_1728,
    "ounce": 35.2_7399_0723,
    "carrat": 5000,
    "atomic-mass-unit": 6.0_2213_6652e26,
}

a__		    =    {
    "kilogram": 1,
    "gram": pow(10, -3),
    "milligram": pow(10, -6),
    "metric-ton": pow(10, 3),
    "long-ton": 1016.0_4608,
    "short-ton": 907.184,
    "pound": 0.45_3592,
    "stone": 6.3_5029,
    "ounce": 0.028_3495,
    "carrat": 0.0002,
    "atomic-mass-unit": 1.6_6054_0199e-27,
}





def 							A__   (snake_case      :		str		,  snake_case      :		str		,  snake_case      :		float							)    ->	float:
					if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
										__UpperCamelCase				:  Any								=			(
										    F'''Invalid \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
										    F'''Supported values are: {", ".join(snake_case							)}'''
										)
										raise ValueError(snake_case							)
					return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
	import doctest

	doctest.testmod()






