import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


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




def        lowerCAmelCase_			(					__A,  __A,  __A,  __A,  __A,  __A,  __A,  __A=False,  )					->    Optional[int]:







      '''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        lowerCAmelCase_			(					__A,  __A,  __A,  __A = False							)					->    Optional[Any]:







      '''simple docstring'''


      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(__A							)

      # 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(
          __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__":
      UpperCamelCase__    							= 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=1_4,
          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')

      UpperCamelCase__    							= 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




def        lowerCAmelCase_			(					__A							)					->    list[int]:







      '''simple docstring'''


      if len(__A							) == 0:
            return array

      UpperCAmelCase__		,				UpperCAmelCase__								=  min(__A							), max(__A							)

      # Compute the variables
      UpperCAmelCase__								=  _max - _min + 1
      UpperCAmelCase__		,				UpperCAmelCase__								=  [0] * holes_range, [0] * holes_range

      # Make the sorting.
      for i in array:
            UpperCAmelCase__								=  i - _min
            UpperCAmelCase__								=  i
            holes_repeat[index] += 1

      # Makes the array back by replacing the numbers.
      UpperCAmelCase__								=  0
      for i in range(__A							):
            while holes_repeat[i] > 0:
                  UpperCAmelCase__								=  holes[i]
                  index += 1
                  holes_repeat[i] -= 1

    # Returns the sorted array.
      return array


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      UpperCamelCase__    							= input('Enter numbers separated by comma:\n')
      UpperCamelCase__    							= [int(x) for x in user_input.split(',')]
      print(pigeon_sort(unsorted))



