


"""simple docstring"""
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 				_snake_case      (   __snake_case							:      str						, __snake_case							:      tuple						, __snake_case							:      Path						, __snake_case							:      Tuple						, __snake_case							:      List[str]						, __snake_case							:      List[str]						, __snake_case							:      List[Any]						, __snake_case							:      Optional[Any]=False						, ):




   """simple docstring"""







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

@torch.no_grad()
def 				_snake_case      (   __snake_case							:      str						, __snake_case							:      str						, __snake_case							:      int						, __snake_case							:      bool = False   ):




   """simple docstring"""







   _lowerCamelCase	:					List[str]   =		torch.floataa if fpaa else torch.floataa
   if fpaa and torch.cuda.is_available():
      _lowerCamelCase	:					Optional[Any]   =		"""cuda"""
   elif fpaa and not torch.cuda.is_available():
      raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""   )
   else:
      _lowerCamelCase	:					Tuple   =		"""cpu"""
   _lowerCamelCase	:					Optional[int]   =		Path(__snake_case   )

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

 UpperCAmelCase				=						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"""
import random

def 				_snake_case      (   __snake_case							:      List[Any]						, __snake_case							:      List[Any]						, __snake_case							:      int   ):




   """simple docstring"""







   _lowerCamelCase	:					List[str]   =		a[left_index]
   _lowerCamelCase	:					Dict   =		left_index + 1
   for j in range(left_index + 1						, __snake_case   ):
      if a[j] < pivot:
         _lowerCamelCase   ,    _lowerCamelCase	:					List[str]   =		a[i], a[j]
         i += 1
   _lowerCamelCase   ,    _lowerCamelCase	:					Optional[int]   =		a[i - 1], a[left_index]
   return i - 1

def 				_snake_case      (   __snake_case							:      Tuple						, __snake_case							:      List[str]						, __snake_case							:      List[str]   ):




   """simple docstring"""







   if left < right:
      _lowerCamelCase	:					Any   =		random.randint(__snake_case						, right - 1   )
      _lowerCamelCase   ,    _lowerCamelCase	:					Optional[Any]   =		(
          a[left],
          a[pivot],
      )  # switches the pivot with the left most bound
      _lowerCamelCase	:					List[str]   =		partition(__snake_case						, __snake_case						, __snake_case   )
      quick_sort_random(
          __snake_case						, __snake_case						, __snake_case   )  # recursive quicksort to the left of the pivot point
      quick_sort_random(
          __snake_case						, pivot_index + 1						, __snake_case   )  # recursive quicksort to the right of the pivot point

def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Union[str, Any]   =		input("""Enter numbers separated by a comma:\n"""   ).strip()
   _lowerCamelCase	:					int   =		[int(__snake_case   ) for item in user_input.split(""","""   )]

   quick_sort_random(__snake_case						, 0						, len(__snake_case   )   )

   print(__snake_case   )


if __name__ == "__main__":
 main()

