






"""simple docstring"""







import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


_SCREAMING_SNAKE_CASE	       =      version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=False							,    )  ->     Union[str, Any]:







							"""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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = False )  ->     List[Any]:







							"""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__":
		_SCREAMING_SNAKE_CASE	       =      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""")

		_SCREAMING_SNAKE_CASE	       =      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 collections.abc import Sequence
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE = None )  ->     int:







							"""simple docstring"""


							if nums is None or not nums:
														raise ValueError("Input sequence should not be empty" )

							__snake_case     			=       nums[0]
							for i in range(1							,    len(SCREAMING_SNAKE_CASE ) ):
														__snake_case     			=       nums[i]
														__snake_case     			=       max(SCREAMING_SNAKE_CASE							,    ans + num							,    SCREAMING_SNAKE_CASE )

							return ans


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		# Try on a sample input from the user
		_SCREAMING_SNAKE_CASE	       =      int(input("""Enter number of elements : """).strip())
		_SCREAMING_SNAKE_CASE	       =      list(map(int, input("""\nEnter the numbers : """).strip().split()))[:n]
		print(max_subsequence_sum(array))
