



"""simple docstring"""






import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
def      A      (			_A,     _A							):




					"""simple docstring"""
					snake_case_						:Tuple								=						UNetaDModel(**_UpperCAmelCase							)
					snake_case_						:List[str]								=						checkpoint["all_modules.0.W"].data
					snake_case_						:Optional[Any]								=						checkpoint["all_modules.0.W"].data
					snake_case_						:Optional[Any]								=						checkpoint["all_modules.1.weight"].data
					snake_case_						:Any								=						checkpoint["all_modules.1.bias"].data

					snake_case_						:Any								=						checkpoint["all_modules.2.weight"].data
					snake_case_						:str								=						checkpoint["all_modules.2.bias"].data

					snake_case_						:Dict								=						checkpoint["all_modules.3.weight"].data
					snake_case_						:Optional[int]								=						checkpoint["all_modules.3.bias"].data

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

					snake_case_						:int								=						4

					def set_attention_weights(_A,     _A,     _A							):
										snake_case_						:Any								=						old_checkpoint[F'''all_modules.{index}.NIN_0.W'''].data.T
										snake_case_						:Optional[Any]								=						old_checkpoint[F'''all_modules.{index}.NIN_1.W'''].data.T
										snake_case_						:str								=						old_checkpoint[F'''all_modules.{index}.NIN_2.W'''].data.T

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

										snake_case_						:Any								=						old_checkpoint[F'''all_modules.{index}.NIN_3.W'''].data.T
										snake_case_						:List[str]								=						old_checkpoint[F'''all_modules.{index}.NIN_3.b'''].data

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

					def set_resnet_weights(_A,     _A,     _A							):
										snake_case_						:List[str]								=						old_checkpoint[F'''all_modules.{index}.Conv_0.weight'''].data
										snake_case_						:List[Any]								=						old_checkpoint[F'''all_modules.{index}.Conv_0.bias'''].data
										snake_case_						:Union[str, Any]								=						old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
										snake_case_						:int								=						old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

										snake_case_						:Optional[int]								=						old_checkpoint[F'''all_modules.{index}.Conv_1.weight'''].data
										snake_case_						:str								=						old_checkpoint[F'''all_modules.{index}.Conv_1.bias'''].data
										snake_case_						:Optional[Any]								=						old_checkpoint[F'''all_modules.{index}.GroupNorm_1.weight'''].data
										snake_case_						:int								=						old_checkpoint[F'''all_modules.{index}.GroupNorm_1.bias'''].data

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

					for i, block in enumerate(new_model_architecture.downsample_blocks							):
										snake_case_						:int								=						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
															snake_case_						:Optional[Any]								=						checkpoint[F'''all_modules.{module_index}.Conv_0.weight'''].data
															snake_case_						:str								=						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							):
										snake_case_						:Dict								=						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:
															snake_case_						:Tuple								=						checkpoint[F'''all_modules.{module_index}.weight'''].data
															snake_case_						:Tuple								=						checkpoint[F'''all_modules.{module_index}.bias'''].data
															module_index += 1
															snake_case_						:Tuple								=						checkpoint[F'''all_modules.{module_index}.weight'''].data
															snake_case_						:str								=						checkpoint[F'''all_modules.{module_index}.bias'''].data
															module_index += 1
															set_resnet_weights(block.resnet_up,     _UpperCAmelCase,     _UpperCAmelCase							)
															module_index += 1

					snake_case_						:Any								=						checkpoint[F'''all_modules.{module_index}.weight'''].data
					snake_case_						:Any								=						checkpoint[F'''all_modules.{module_index}.bias'''].data
					module_index += 1
					snake_case_						:str								=						checkpoint[F'''all_modules.{module_index}.weight'''].data
					snake_case_						:Union[str, Any]								=						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      :      int   					=					parser.parse_args()

				__UpperCAmelCase      :      Dict   					=					torch.load(args.checkpoint_path, map_location='cpu')

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

				__UpperCAmelCase      :      int   					=					convert_ncsnpp_checkpoint(
				    checkpoint,
				    config,
				)

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

				__UpperCAmelCase      :      Optional[int]   					=					UNetaDModel(**config)
				model.load_state_dict(converted_checkpoint)

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

								__UpperCAmelCase      :      Union[str, Any]   					=					ScoreSdeVePipeline(unet=model, scheduler=scheduler)
								pipe.save_pretrained(args.dump_path)
				except:  # noqa: E722
								model.save_pretrained(args.dump_path)










"""simple docstring"""






def      A      (			_A,     _A							):




					"""simple docstring"""
					if not isinstance(_A,     _A							):
										raise TypeError("plaintext must be a string"							)
					if not isinstance(_A,     _A							):
										raise TypeError("key must be a string"							)

					if not plaintext:
										raise ValueError("plaintext is empty"							)
					if not key:
										raise ValueError("key is empty"							)

					key += plaintext
					snake_case_						:Optional[int]								=						plaintext.lower()
					snake_case_						:Union[str, Any]								=						key.lower()
					snake_case_						:Any								=						0
					snake_case_						:str								=						0
					snake_case_						:Union[str, Any]								=						""
					while plaintext_iterator < len(_A							):
										if (
										    ord(plaintext[plaintext_iterator]							) < 97
										    or ord(plaintext[plaintext_iterator]							) > 122
										):
															ciphertext += plaintext[plaintext_iterator]
															plaintext_iterator += 1
										elif ord(key[key_iterator]							) < 97 or ord(key[key_iterator]							) > 122:
															key_iterator += 1
										else:
															ciphertext += chr(
															    (
															        (ord(plaintext[plaintext_iterator]							) - 97 + ord(key[key_iterator]							))
															        - 97
															    )
															    % 26
															    + 97							)
															key_iterator += 1
															plaintext_iterator += 1
					return ciphertext
def      A      (			_A,     _A							):




					"""simple docstring"""
					if not isinstance(_A,     _A							):
										raise TypeError("ciphertext must be a string"							)
					if not isinstance(_A,     _A							):
										raise TypeError("key must be a string"							)

					if not ciphertext:
										raise ValueError("ciphertext is empty"							)
					if not key:
										raise ValueError("key is empty"							)

					snake_case_						:Tuple								=						key.lower()
					snake_case_						:Tuple								=						0
					snake_case_						:int								=						0
					snake_case_						:int								=						""
					while ciphertext_iterator < len(_A							):
										if (
										    ord(ciphertext[ciphertext_iterator]							) < 97
										    or ord(ciphertext[ciphertext_iterator]							) > 122
										):
															plaintext += ciphertext[ciphertext_iterator]
										else:
															plaintext += chr(
															    (ord(ciphertext[ciphertext_iterator]							) - ord(key[key_iterator]							)) % 26
															    + 97							)
															key += chr(
															    (ord(ciphertext[ciphertext_iterator]							) - ord(key[key_iterator]							)) % 26
															    + 97							)
															key_iterator += 1
										ciphertext_iterator += 1
					return plaintext


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				__UpperCAmelCase      :      Dict   					=					int(input('Type 1 to encrypt or 2 to decrypt:'))
				if operation == 1:
								__UpperCAmelCase      :      List[str]   					=					input('Typeplaintext to be encrypted:\n')
								__UpperCAmelCase      :      Optional[int]   					=					input('Type the key:\n')
								print(encrypt(plaintext, key))
				elif operation == 2:
								__UpperCAmelCase      :      List[Any]   					=					input('Type the ciphertext to be decrypted:\n')
								__UpperCAmelCase      :      Dict   					=					input('Type the key:\n')
								print(decrypt(ciphertext, key))
				decrypt('jsqqs avvwo', 'coffee')

