




import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


__a      :       Optional[Any]				  =   version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[str]			,				__lowercase   :			tuple			,				__lowercase   :			Path			,				__lowercase   :			Optional[int]			,				__lowercase   :			List[str]			,				__lowercase   :			Union[str, Any]			,				__lowercase   :			Optional[Any]			,				__lowercase   :			str=False			,				)						->      List[Any]:





    """simple docstring"""

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



@torch.no_grad()
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str			,				__lowercase   :			str			,				__lowercase   :			int			,				__lowercase   :			bool = False    )						->      str:





    """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(__lowercase    )

    # 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(
        __lowercase			,				model_args=(
            torch.randn(1			,				__lowercase			,				2_5			,				2_5    ).to(device=__lowercase			,				dtype=__lowercase    ),
            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=__lowercase			,				)
    del vae_decoder


if __name__ == "__main__":
  __a      :       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")

  __a      :       Tuple				  =   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

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str			,				__lowercase   :			float | Decimal			,				__lowercase   :			float = 1_0**-1_0    )						->      float:





    """simple docstring"""

    __A         =	a
    while True:
        __A         =	Decimal(__lowercase    ) - (
            Decimal(eval(__lowercase    )    ) / Decimal(eval(str(diff(__lowercase    )    )    )    )  # noqa: S307
        )
        # This number dictates the accuracy of the answer
        if abs(eval(__lowercase    )    ) < precision:  # noqa: S307
            return float(__lowercase    )


# Let's Execute
if __name__ == "__main__":
  # Find root of trigonometric function
  # Find value of pi
  print(f"""The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}""")
  # Find root of polynomial
  print(f"""The root of x**2 - 5*x + 2 = 0 is {newton_raphson('x**2 - 5*x + 2', 0.4)}""")
  # Find Square Root of 5
  print(f"""The root of log(x) - 1 = 0 is {newton_raphson('log(x) - 1', 2)}""")
  # Exponential Roots
  print(f"""The root of exp(x) - 1 = 0 is {newton_raphson('exp(x) - 1', 0)}""")




