


import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							int      =       UNetaDModel(**lowerCAmelCase_)
	lowerCamelCase_			:							Tuple      =       checkpoint["all_modules.0.W"].data
	lowerCamelCase_			:							Dict      =       checkpoint["all_modules.0.W"].data
	lowerCamelCase_			:							int      =       checkpoint["all_modules.1.weight"].data
	lowerCamelCase_			:							Optional[int]      =       checkpoint["all_modules.1.bias"].data

	lowerCamelCase_			:							Tuple      =       checkpoint["all_modules.2.weight"].data
	lowerCamelCase_			:							Tuple      =       checkpoint["all_modules.2.bias"].data

	lowerCamelCase_			:							List[str]      =       checkpoint["all_modules.3.weight"].data
	lowerCamelCase_			:							Tuple      =       checkpoint["all_modules.3.bias"].data

	lowerCamelCase_			:							List[str]      =       checkpoint[list(checkpoint.keys())[-4]].data
	lowerCamelCase_			:							List[Any]      =       checkpoint[list(checkpoint.keys())[-3]].data
	lowerCamelCase_			:							Tuple      =       checkpoint[list(checkpoint.keys())[-2]].data
	lowerCamelCase_			:							Dict      =       checkpoint[list(checkpoint.keys())[-1]].data

	lowerCamelCase_			:							Any      =       4

	def set_attention_weights(lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_):
		lowerCamelCase_			:							Optional[Any]      =       old_checkpoint[F"""all_modules.{index}.NIN_0.W"""].data.T
		lowerCamelCase_			:							Tuple      =       old_checkpoint[F"""all_modules.{index}.NIN_1.W"""].data.T
		lowerCamelCase_			:							Union[str, Any]      =       old_checkpoint[F"""all_modules.{index}.NIN_2.W"""].data.T

		lowerCamelCase_			:							Any      =       old_checkpoint[F"""all_modules.{index}.NIN_0.b"""].data
		lowerCamelCase_			:							Any      =       old_checkpoint[F"""all_modules.{index}.NIN_1.b"""].data
		lowerCamelCase_			:							List[Any]      =       old_checkpoint[F"""all_modules.{index}.NIN_2.b"""].data

		lowerCamelCase_			:							Union[str, Any]      =       old_checkpoint[F"""all_modules.{index}.NIN_3.W"""].data.T
		lowerCamelCase_			:							List[str]      =       old_checkpoint[F"""all_modules.{index}.NIN_3.b"""].data

		lowerCamelCase_			:							Any      =       old_checkpoint[F"""all_modules.{index}.GroupNorm_0.weight"""].data
		lowerCamelCase_			:							Union[str, Any]      =       old_checkpoint[F"""all_modules.{index}.GroupNorm_0.bias"""].data

	def set_resnet_weights(lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_):
		lowerCamelCase_			:							Optional[int]      =       old_checkpoint[F"""all_modules.{index}.Conv_0.weight"""].data
		lowerCamelCase_			:							Any      =       old_checkpoint[F"""all_modules.{index}.Conv_0.bias"""].data
		lowerCamelCase_			:							Union[str, Any]      =       old_checkpoint[F"""all_modules.{index}.GroupNorm_0.weight"""].data
		lowerCamelCase_			:							str      =       old_checkpoint[F"""all_modules.{index}.GroupNorm_0.bias"""].data

		lowerCamelCase_			:							List[Any]      =       old_checkpoint[F"""all_modules.{index}.Conv_1.weight"""].data
		lowerCamelCase_			:							List[str]      =       old_checkpoint[F"""all_modules.{index}.Conv_1.bias"""].data
		lowerCamelCase_			:							str      =       old_checkpoint[F"""all_modules.{index}.GroupNorm_1.weight"""].data
		lowerCamelCase_			:							Tuple      =       old_checkpoint[F"""all_modules.{index}.GroupNorm_1.bias"""].data

		lowerCamelCase_			:							Optional[Any]      =       old_checkpoint[F"""all_modules.{index}.Dense_0.weight"""].data
		lowerCamelCase_			:							List[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:
			lowerCamelCase_			:							Optional[Any]      =       old_checkpoint[F"""all_modules.{index}.Conv_2.weight"""].data
			lowerCamelCase_			:							Optional[int]      =       old_checkpoint[F"""all_modules.{index}.Conv_2.bias"""].data

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

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

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

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

		if hasattr(lowerCAmelCase_      ,					"resnet_up") and block.resnet_up is not None:
			lowerCamelCase_			:							Any      =       checkpoint[F"""all_modules.{module_index}.weight"""].data
			lowerCamelCase_			:							Tuple      =       checkpoint[F"""all_modules.{module_index}.bias"""].data
			module_index += 1
			lowerCamelCase_			:							List[str]      =       checkpoint[F"""all_modules.{module_index}.weight"""].data
			lowerCamelCase_			:							Optional[int]      =       checkpoint[F"""all_modules.{module_index}.bias"""].data
			module_index += 1
			set_resnet_weights(block.resnet_up      ,					lowerCAmelCase_      ,					lowerCAmelCase_)
			module_index += 1

	lowerCamelCase_			:							str      =       checkpoint[F"""all_modules.{module_index}.weight"""].data
	lowerCamelCase_			:							Union[str, Any]      =       checkpoint[F"""all_modules.{module_index}.bias"""].data
	module_index += 1
	lowerCamelCase_			:							List[Any]      =       checkpoint[F"""all_modules.{module_index}.weight"""].data
	lowerCamelCase_			:							str      =       checkpoint[F"""all_modules.{module_index}.bias"""].data

	return new_model_architecture.state_dict()


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

							__magic_name__       					=      parser.parse_args()

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

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

							__magic_name__       					=      convert_ncsnpp_checkpoint(
							    checkpoint,
							    config,
							)

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

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

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

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





__magic_name__       					=      {
    '''0''': 1,
    '''1''': 1,
    '''2''': 2,
    '''3''': 6,
    '''4''': 2_4,
    '''5''': 1_2_0,
    '''6''': 7_2_0,
    '''7''': 5_0_4_0,
    '''8''': 4_0_3_2_0,
    '''9''': 3_6_2_8_8_0,
}

__magic_name__       					=      {1_4_5: 1_4_5}

__magic_name__       					=      {
    1_4_5: 0,
    1_6_9: 3,
    3_6_3_0_1: 3,
    1_4_5_4: 3,
    8_7_1: 2,
    4_5_3_6_1: 2,
    8_7_2: 2,
}







def     __magic_name__  (      lowerCAmelCase_):
	'''simple docstring'''


	if n in CACHE_SUM_DIGIT_FACTORIALS:
		return CACHE_SUM_DIGIT_FACTORIALS[n]
	lowerCamelCase_			:							List[str]      =       sum(DIGIT_FACTORIALS[let] for let in str(lowerCAmelCase_))
	lowerCamelCase_			:							List[str]      =       ret
	return ret







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_ = None):
	'''simple docstring'''


	lowerCamelCase_			:							List[str]      =       previous or set()
	if n in CHAIN_LENGTH_CACHE:
		return CHAIN_LENGTH_CACHE[n]
	lowerCamelCase_			:							str      =       sum_digit_factorials(lowerCAmelCase_)
	if next_number in previous:
		lowerCamelCase_			:							List[str]      =       0
		return 0
	else:
		previous.add(lowerCAmelCase_)
		lowerCamelCase_			:							List[Any]      =       1 + chain_length(lowerCAmelCase_      ,					lowerCAmelCase_)
		lowerCamelCase_			:							Optional[int]      =       ret
		return ret







def     __magic_name__  (      lowerCAmelCase_ = 60      ,					lowerCAmelCase_ = 100_0000):
	'''simple docstring'''


	return sum(1 for i in range(1      ,					lowerCAmelCase_) if chain_length(lowerCAmelCase_) == num_terms)


if __name__ == "__main__":
							print(f'''{solution() = }''')

