
"""simple docstring"""


from typing import Literal

__A 		= 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase )       ->				str:
	_lowerCAmelCase			=LETTERS if mode == """decrypt""" else key
	_lowerCAmelCase			=key if mode == """decrypt""" else LETTERS
	_lowerCAmelCase			=""""""
	# loop through each symbol in the message
	for symbol in message:
		if symbol.upper() in chars_a:
			# encrypt/decrypt the symbol
			_lowerCAmelCase			=chars_a.find(symbol.upper() )
			if symbol.isupper():
				translated += chars_b[sym_index].upper()
			else:
				translated += chars_b[sym_index].lower()
		else:
			# symbol is not in LETTERS, just add it
			translated += symbol
	return translated



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase )       ->				str:
	return translate_message(__UpperCamelCase					,				__UpperCamelCase					,				"""encrypt""" )



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase )       ->				str:
	return translate_message(__UpperCamelCase					,				__UpperCamelCase					,				"""decrypt""" )



def 					_lowerCamelCase()       ->				None:
	_lowerCAmelCase			="""Hello World"""
	_lowerCAmelCase			="""QWERTYUIOPASDFGHJKLZXCVBNM"""
	_lowerCAmelCase			="""decrypt"""  # set to 'encrypt' or 'decrypt'

	if mode == "encrypt":
		_lowerCAmelCase			=encrypt_message(__UpperCamelCase					,				__UpperCamelCase )
	elif mode == "decrypt":
		_lowerCAmelCase			=decrypt_message(__UpperCamelCase					,				__UpperCamelCase )
	print(F'''Using the key {key}, the {mode}ed message is: {translated}''' )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()





"""simple docstring"""


import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase )       ->				Dict:
	_lowerCAmelCase			=UNetaDModel(**__UpperCamelCase )
	_lowerCAmelCase			=checkpoint["""all_modules.0.W"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.0.W"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.1.weight"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.1.bias"""].data

	_lowerCAmelCase			=checkpoint["""all_modules.2.weight"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.2.bias"""].data

	_lowerCAmelCase			=checkpoint["""all_modules.3.weight"""].data
	_lowerCAmelCase			=checkpoint["""all_modules.3.bias"""].data

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

	_lowerCAmelCase			=4

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

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

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

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

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

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

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

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

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

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

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

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

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



