



import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


__snake_case		:Union[str, Any]   	=  version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase=False     ,				):
    output_path.parent.mkdir(parents=_UpperCAmelCase     ,				exist_ok=_UpperCAmelCase       )
    # 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(
            _UpperCAmelCase     ,				_UpperCAmelCase     ,				f=output_path.as_posix()     ,				input_names=_UpperCAmelCase     ,				output_names=_UpperCAmelCase     ,				dynamic_axes=_UpperCAmelCase     ,				do_constant_folding=_UpperCAmelCase     ,				use_external_data_format=_UpperCAmelCase     ,				enable_onnx_checker=_UpperCAmelCase     ,				opset_version=_UpperCAmelCase     ,				)
    else:
        export(
            _UpperCAmelCase     ,				_UpperCAmelCase     ,				f=output_path.as_posix()     ,				input_names=_UpperCAmelCase     ,				output_names=_UpperCAmelCase     ,				dynamic_axes=_UpperCAmelCase     ,				do_constant_folding=_UpperCAmelCase     ,				opset_version=_UpperCAmelCase     ,				)





@torch.no_grad()
def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase = False       ):
    __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(_UpperCAmelCase       )

    # 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(
        _UpperCAmelCase     ,				model_args=(
            torch.randn(1     ,				_UpperCAmelCase     ,				25     ,				25       ).to(device=_UpperCAmelCase     ,				dtype=_UpperCAmelCase       ),
            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=_UpperCAmelCase     ,				)
    del vae_decoder


if __name__ == "__main__":
 __snake_case		:List[Any]   	=  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''')

 __snake_case		:str   	=  parser.parse_args()
 print(args.output_path)
 convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
 print('''SD: Done: ONNX''')










from __future__ import annotations

__snake_case		:Optional[Any]   	=  []





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    for i in range(len(_UpperCAmelCase       )       ):
        if board[row][i] == 1:
            return False
    for i in range(len(_UpperCAmelCase       )       ):
        if board[i][column] == 1:
            return False
    for i, j in zip(range(_UpperCAmelCase     ,				-1     ,				-1       )     ,				range(_UpperCAmelCase     ,				-1     ,				-1       )       ):
        if board[i][j] == 1:
            return False
    for i, j in zip(range(_UpperCAmelCase     ,				-1     ,				-1       )     ,				range(_UpperCAmelCase     ,				len(_UpperCAmelCase       )       )       ):
        if board[i][j] == 1:
            return False
    return True





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    if row >= len(_UpperCAmelCase       ):
        solution.append(_UpperCAmelCase       )
        printboard(_UpperCAmelCase       )
        print()
        return True
    for i in range(len(_UpperCAmelCase       )       ):
        if is_safe(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
            __a		 =       1
            solve(_UpperCAmelCase     ,				row + 1       )
            __a		 =       0
    return False





def   __snake_case			(    _UpperCAmelCase       ):
    for i in range(len(_UpperCAmelCase       )       ):
        for j in range(len(_UpperCAmelCase       )       ):
            if board[i][j] == 1:
                print('''Q'''     ,				end=''' '''       )
            else:
                print('''.'''     ,				end=''' '''       )
        print()


# n=int(input("The no. of queens"))
__snake_case		:Optional[Any]   	=  8
__snake_case		:Tuple   	=  [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print('''The total no. of solutions are :''', len(solution))





