




"""simple docstring"""





import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


lowerCamelCase      =		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_    				=							torch.floataa if fpaa else torch.floataa
      if fpaa and torch.cuda.is_available():
            UpperCAmelCase_    				=							"cuda"
      elif fpaa and not torch.cuda.is_available():
            raise ValueError("`float16` model export is only supported on GPUs with CUDA"   )
      else:
            UpperCAmelCase_    				=							"cpu"
      UpperCAmelCase_    				=							Path(lowerCamelCase__   )

      # VAE DECODER
      UpperCAmelCase_    				=							AutoencoderKL.from_pretrained(model_path + "/vae"   )
      UpperCAmelCase_    				=							vae_decoder.config.latent_channels
      # forward only through the decoder part
      UpperCAmelCase_    				=							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__":
  lowerCamelCase      =		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""")

  lowerCamelCase      =		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      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							[]
      UpperCAmelCase_				,					UpperCAmelCase_    				=							input_list[low:mid], input_list[mid : high + 1]
      while left and right:
            result.append((left if left[0] <= right[0] else right).pop(0   )   )
      UpperCAmelCase_    				=							result + left + right
      return input_list
def      a__			(   lowerCAmelCase__   ):
      if len(lowerCAmelCase__   ) <= 1:
            return input_list
      UpperCAmelCase_    				=							list(lowerCAmelCase__   )

      # iteration for two-way merging
      UpperCAmelCase_    				=							2
      while p <= len(lowerCAmelCase__   ):
            # getting low, high and middle value for merge-sort of single list
            for i in range(0			,    len(lowerCAmelCase__   )			,    lowerCAmelCase__   ):
                  UpperCAmelCase_    				=							i
                  UpperCAmelCase_    				=							i + p - 1
                  UpperCAmelCase_    				=							(low + high + 1) // 2
                  UpperCAmelCase_    				=							merge(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )
            # final merge of last two parts
            if p * 2 >= len(lowerCAmelCase__   ):
                  UpperCAmelCase_    				=							i
                  UpperCAmelCase_    				=							merge(lowerCAmelCase__			,    0			,    lowerCAmelCase__			,    len(lowerCAmelCase__   ) - 1   )
                  break
            p *= 2

      return input_list


if __name__ == "__main__":
  lowerCamelCase      =		input("""Enter numbers separated by a comma:\n""").strip()
  if user_input == "":
    lowerCamelCase      =		[]
  else:
    lowerCamelCase      =		[int(item.strip()) for item in user_input.split(""",""")]
  print(iter_merge_sort(unsorted))




