
"""simple docstring"""





import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


_a		       =							version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')







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







@torch.no_grad()
def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase = False  ):
   UpperCAmelCase_					:					Optional[Any]			     =    torch.floataa if fpaa else torch.floataa
   if fpaa and torch.cuda.is_available():
      UpperCAmelCase_					:					Optional[Any]			     =    "cuda"
   elif fpaa and not torch.cuda.is_available():
      raise ValueError("`float16` model export is only supported on GPUs with CUDA"  )
   else:
      UpperCAmelCase_					:					Any			     =    "cpu"
   UpperCAmelCase_					:					Optional[Any]			     =    Path(__lowerCamelCase  )

   # VAE DECODER
   UpperCAmelCase_					:					List[Any]			     =    AutoencoderKL.from_pretrained(model_path + "/vae"  )
   UpperCAmelCase_					:					List[str]			     =    vae_decoder.config.latent_channels
   # forward only through the decoder part
   UpperCAmelCase_					:					List[Any]			     =    vae_decoder.decode
   onnx_export(
       __lowerCamelCase,		model_args=(
           torch.randn(1,		__lowerCamelCase,		25,		25  ).to(device=__lowerCamelCase,		dtype=__lowerCamelCase  ),
           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=__lowerCamelCase,		)
   del vae_decoder


if __name__ == "__main__":
    _a		       =							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')

    _a		       =							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"""




from __future__ import annotations






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    list[int]    ,       _lowercase						:    int					) ->int:






	'''simple docstring'''






	if len(_lowercase					) < k or k < 0:
		raise ValueError("Invalid Input"					)
	a      :		Optional[Any]	=				sum(array[:k]					)
	for i in range(len(_lowercase					) - k					):
		a      :		Optional[Any]	=				current_sum - array[i] + array[i + k]
		a      :		Union[str, Any]	=				max(_lowercase    ,       _lowercase					)
	return max_sum


if __name__ == "__main__":
		from doctest import testmod
		from random import randint

		testmod()
		a						: Any     	=			[randint(-1000, 1000) for i in range(100)]
		a						: List[str]     	=			randint(0, 110)
		print(F'''The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}''')


