





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)





import math
import os

import cva
import numpy as np

__magic_name__       					=      2_55.0







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


	lowerCamelCase_			:							List[Any]      =       np.mean((original - contrast) ** 2)
	if mse == 0:
		return 100

	return 20 * math.logaa(PIXEL_MAX / math.sqrt(lowerCAmelCase_))







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


	lowerCamelCase_			:							List[str]      =       os.path.dirname(os.path.realpath(lowerCAmelCase_))
	# Loading images (original image and compressed image)
	lowerCamelCase_			:							Optional[int]      =       cva.imread(os.path.join(lowerCAmelCase_      ,					"image_data/original_image.png"))
	lowerCamelCase_			:							List[str]      =       cva.imread(os.path.join(lowerCAmelCase_      ,					"image_data/compressed_image.png")      ,					1)

	lowerCamelCase_			:							str      =       cva.imread(os.path.join(lowerCAmelCase_      ,					"image_data/PSNR-example-base.png"))
	lowerCamelCase_			:							int      =       cva.imread(
	    os.path.join(lowerCAmelCase_      ,					"image_data/PSNR-example-comp-10.jpg")      ,					1)

	# Value expected: 29.73dB
	print("-- First Test --")
	print(F"""PSNR value is {peak_signal_to_noise_ratio(lowerCAmelCase_      ,					lowerCAmelCase_)} dB""")

	# # Value expected: 31.53dB (Wikipedia Example)
	print("\n-- Second Test --")
	print(F"""PSNR value is {peak_signal_to_noise_ratio(lowerCAmelCase_      ,					lowerCAmelCase_)} dB""")


if __name__ == "__main__":
							main()

