

from __future__ import annotations
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	list[list[int]]      )							->      bool:





    '''simple docstring'''






    A__          =   len(SCREAMING_SNAKE_CASE__      )
    # We need to create solution object to save path.
    A__          =   [[0 for _ in range(SCREAMING_SNAKE_CASE__      )] for _ in range(SCREAMING_SNAKE_CASE__      )]
    A__          =   run_maze(SCREAMING_SNAKE_CASE__    ,			0    ,			0    ,			SCREAMING_SNAKE_CASE__      )
    if solved:
        print('\n'.join(str(SCREAMING_SNAKE_CASE__      ) for row in solutions      )      )
    else:
        print('No solution exists!'      )
    return solved
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	list[list[int]]    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	list[list[int]]      )							->      bool:





    '''simple docstring'''






    A__          =   len(SCREAMING_SNAKE_CASE__      )
    # Final check point.
    if i == j == (size - 1):
        A__          =   1
        return True

    A__          =   (not i < 0) and (not j < 0)  # Check lower bounds
    A__          =   (i < size) and (j < size)  # Check upper bounds

    if lower_flag and upper_flag:
        # check for already visited and block points.
        A__          =   (not solutions[i][j]) and (not maze[i][j])
        if block_flag:
            # check visited
            A__          =   1

            # check for directions
            if (
                run_maze(SCREAMING_SNAKE_CASE__    ,			i + 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
                or run_maze(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			j + 1    ,			SCREAMING_SNAKE_CASE__      )
                or run_maze(SCREAMING_SNAKE_CASE__    ,			i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
                or run_maze(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			j - 1    ,			SCREAMING_SNAKE_CASE__      )
            ):
                return True

            A__          =   0
            return False
    return False


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")

