


'''simple docstring'''




import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


__snake_case    						=version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")





def 	a_					( lowerCamelCase	:     Dict	, lowerCamelCase	:     tuple	, lowerCamelCase	:     Path	, lowerCamelCase	:     Dict	, lowerCamelCase	:     List[str]	, lowerCamelCase	:     Dict	, lowerCamelCase	:     Optional[Any]	, lowerCamelCase	:     Optional[int]=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	:     str	, lowerCamelCase	:     str	, lowerCamelCase	:     int	, lowerCamelCase	:     bool = False				):
   lowerCAmelCase     =			torch.floataa if fpaa else torch.floataa
   if fpaa and torch.cuda.is_available():
      lowerCAmelCase     =			'cuda'
   elif fpaa and not torch.cuda.is_available():
      raise ValueError('`float16` model export is only supported on GPUs with CUDA'				)
   else:
      lowerCAmelCase     =			'cpu'
   lowerCAmelCase     =			Path(lowerCamelCase				)

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

       __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 __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge

@total_ordering
class 		UpperCAmelCase_      (  __lowercase		):





     def __lt__( self       :      Optional[int]    ,      UpperCAmelCase__       :      List[str]    )				->  List[Any]:
        return self[-1] < other[-1]





     def __eq__( self       :      str    ,      UpperCAmelCase__       :      List[str]    )				->  Tuple:
        return self[-1] == other[-1]





def 	a_					( lowerCamelCase	:     list				):
   lowerCAmelCase     =			[]
   # sort into stacks
   for element in collection:
      lowerCAmelCase     =			Stack([element]				)
      lowerCAmelCase     =			bisect_left(lowerCamelCase	, lowerCamelCase				)
      if i != len(lowerCamelCase				):
         stacks[i].append(lowerCamelCase				)
      else:
         stacks.append(lowerCamelCase				)

    # use a heap-based merge to merge stack efficiently
   lowerCAmelCase     =			merge(*(reversed(lowerCamelCase				) for stack in stacks)				)
   return collection


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





