


"""simple docstring"""







def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				0

					# if input_string is "aba" than new_input_string become "a|b|a"
					__SCREAMING_SNAKE_CASE						   =				""
					__SCREAMING_SNAKE_CASE						   =				""

					# append each character + "|" in new_string for range(0, length-1)
					for i in input_string[: len(SCREAMING_SNAKE_CASE__   ) - 1]:
										new_input_string += i + "|"
					# append last character
					new_input_string += input_string[-1]

					# we will store the starting and ending of previous furthest ending palindromic
					# substring
					__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				0, 0

					# length[i] shows the length of palindromic substring with center i
					__SCREAMING_SNAKE_CASE						   =				[1 for i in range(len(SCREAMING_SNAKE_CASE__   )   )]

					# for each character in new_string find corresponding palindromic string
					__SCREAMING_SNAKE_CASE						   =				0
					for j in range(len(SCREAMING_SNAKE_CASE__   )   ):
										__SCREAMING_SNAKE_CASE						   =				1 if j > r else min(length[l + r - j] // 2  ,						r - j + 1   )
										while (
										    j - k >= 0
										    and j + k < len(SCREAMING_SNAKE_CASE__   )
										    and new_input_string[k + j] == new_input_string[j - k]
										):
															k += 1

										__SCREAMING_SNAKE_CASE						   =				2 * k - 1

										# does this string is ending after the previously explored end (that is r) ?
										# if yes the update the new r to the last index of this
										if j + k - 1 > r:
															__SCREAMING_SNAKE_CASE						   =				j - k + 1  # noqa: E741
															__SCREAMING_SNAKE_CASE						   =				j + k - 1

										# update max_length and start position
										if max_length < length[j]:
															__SCREAMING_SNAKE_CASE						   =				length[j]
															__SCREAMING_SNAKE_CASE						   =				j

    # create that string
					__SCREAMING_SNAKE_CASE						   =				new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
					for i in s:
										if i != "|":
															output_string += i

					return output_string


if __name__ == "__main__":
		import doctest

		doctest.testmod()



import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


lowercase_       		=  version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Dict    ,			SCREAMING_SNAKE_CASE__			:	tuple    ,			SCREAMING_SNAKE_CASE__			:	Path    ,			SCREAMING_SNAKE_CASE__			:	List[Any]    ,			SCREAMING_SNAKE_CASE__			:	Any    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]=False    ,			)							->      Union[str, Any]:





    '''simple docstring'''






    output_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__    ,			exist_ok=SCREAMING_SNAKE_CASE__      )
    # 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(
            SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			f=output_path.as_posix()    ,			input_names=SCREAMING_SNAKE_CASE__    ,			output_names=SCREAMING_SNAKE_CASE__    ,			dynamic_axes=SCREAMING_SNAKE_CASE__    ,			do_constant_folding=SCREAMING_SNAKE_CASE__    ,			use_external_data_format=SCREAMING_SNAKE_CASE__    ,			enable_onnx_checker=SCREAMING_SNAKE_CASE__    ,			opset_version=SCREAMING_SNAKE_CASE__    ,			)
    else:
        export(
            SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			f=output_path.as_posix()    ,			input_names=SCREAMING_SNAKE_CASE__    ,			output_names=SCREAMING_SNAKE_CASE__    ,			dynamic_axes=SCREAMING_SNAKE_CASE__    ,			do_constant_folding=SCREAMING_SNAKE_CASE__    ,			opset_version=SCREAMING_SNAKE_CASE__    ,			)
@torch.no_grad()
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	bool = False      )							->      Tuple:





    '''simple docstring'''






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

    # VAE DECODER
    A__          =   AutoencoderKL.from_pretrained(model_path + '/vae'      )
    A__          =   vae_decoder.config.latent_channels
    # forward only through the decoder part
    A__          =   vae_decoder.decode
    onnx_export(
        SCREAMING_SNAKE_CASE__    ,			model_args=(
            torch.randn(1    ,			SCREAMING_SNAKE_CASE__    ,			25    ,			25      ).to(device=SCREAMING_SNAKE_CASE__    ,			dtype=SCREAMING_SNAKE_CASE__      ),
            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=SCREAMING_SNAKE_CASE__    ,			)
    del vae_decoder


if __name__ == "__main__":
     lowercase_       		=  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")

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

