






import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


__UpperCamelCase       :		Optional[int]							=    version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")
def   a_			(   _A   , _A   , _A   , _A   , _A   , _A   , _A   , _A=False   , )      ->      Optional[int]:







				"""simple docstring"""
				output_path.parent.mkdir(parents=_A   , exist_ok=_A	)
				# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
				# so we check the torch version for backwards compatibility
				if is_torch_less_than_1_11:
								export(
								    _A   , _A   , f=output_path.as_posix()   , input_names=_A   , output_names=_A   , dynamic_axes=_A   , do_constant_folding=_A   , use_external_data_format=_A   , enable_onnx_checker=_A   , opset_version=_A   , )
				else:
								export(
								    _A   , _A   , f=output_path.as_posix()   , input_names=_A   , output_names=_A   , dynamic_axes=_A   , do_constant_folding=_A   , opset_version=_A   , )
@torch.no_grad()
def   a_			(   _A   , _A   , _A   , _A = False	)      ->      List[str]:







				"""simple docstring"""
				snake_case__						=  torch.floataa if fpaa else torch.floataa
				if fpaa and torch.cuda.is_available():
								snake_case__						=  "cuda"
				elif fpaa and not torch.cuda.is_available():
								raise ValueError('`float16` model export is only supported on GPUs with CUDA'	)
				else:
								snake_case__						=  "cpu"
				snake_case__						=  Path(_A	)

				# VAE DECODER
				snake_case__						=  AutoencoderKL.from_pretrained(model_path + '/vae'	)
				snake_case__						=  vae_decoder.config.latent_channels
				# forward only through the decoder part
				snake_case__						=  vae_decoder.decode
				onnx_export(
				    _A   , model_args=(
				        torch.randn(1   , _A   , 25   , 25	).to(device=_A   , dtype=_A	),
				        False,
				    )   , output_path=output_path / 'vae_decoder' / 'model.onnx'   , ordered_input_names=['latent_sample', 'return_dict']   , output_names=['sample']   , dynamic_axes={
				        'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
				    }   , opset=_A   , )
				del vae_decoder


if __name__ == "__main__":
							__UpperCamelCase       :		List[str]							=    argparse.ArgumentParser()

							parser.add_argument(
							    """--model_path""",
							    type=str,
							    required=True,
							    help="""Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).""",
							)

							parser.add_argument("""--output_path""", type=str, required=True, help="""Path to the output model.""")
							parser.add_argument(
							    """--opset""",
							    default=14,
							    type=int,
							    help="""The version of the ONNX operator set to use.""",
							)
							parser.add_argument("""--fp16""", action="""store_true""", default=False, help="""Export the models in `float16` mode""")

							__UpperCamelCase       :		Tuple							=    parser.parse_args()
							print(args.output_path)
							convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
							print("""SD: Done: ONNX""")








"""simple docstring"""



__snake_case       =       """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""







def     __lowerCAmelCase		(		)      ->		None:


    """simple docstring"""





    snake_case       : str    =       input("Enter message: "					)
    snake_case       : Tuple    =       input("Enter key [alphanumeric]: "					)
    snake_case       : Union[str, Any]    =       input("Encrypt/Decrypt [e/d]: "					)

    if mode.lower().startswith("e"					):
        snake_case       : str    =       "encrypt"
        snake_case       : Optional[int]    =       encrypt_message(lowercase		,				lowercase					)
    elif mode.lower().startswith("d"					):
        snake_case       : List[Any]    =       "decrypt"
        snake_case       : Tuple    =       decrypt_message(lowercase		,				lowercase					)

    print(F'\n{mode.title()}ed message:'					)
    print(lowercase					)







def     __lowerCAmelCase		(		lowercase    :    str		,				lowercase    :    str					)      ->		str:


    """simple docstring"""





    return translate_message(lowercase		,				lowercase		,				"encrypt"					)







def     __lowerCAmelCase		(		lowercase    :    str		,				lowercase    :    str					)      ->		str:


    """simple docstring"""





    return translate_message(lowercase		,				lowercase		,				"decrypt"					)







def     __lowerCAmelCase		(		lowercase    :    str		,				lowercase    :    str		,				lowercase    :    str					)      ->		str:


    """simple docstring"""





    snake_case       : List[Any]    =       []
    snake_case       : List[str]    =       0
    snake_case       : List[Any]    =       key.upper()

    for symbol in message:
        snake_case       : Optional[int]    =       LETTERS.find(symbol.upper()					)
        if num != -1:
            if mode == "encrypt":
                num += LETTERS.find(key[key_index]					)
            elif mode == "decrypt":
                num -= LETTERS.find(key[key_index]					)

            num %= len(lowercase					)

            if symbol.isupper():
                translated.append(LETTERS[num]					)
            elif symbol.islower():
                translated.append(LETTERS[num].lower()					)

            key_index += 1
            if key_index == len(lowercase					):
                snake_case       : List[str]    =       0
        else:
            translated.append(lowercase					)
    return "".join(lowercase					)


if __name__ == "__main__":
  main()





