'''simple docstring'''
import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


lowercase__  :      int        =					version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")



def  __lowerCamelCase       (      _UpperCamelCase  :  Optional[int]							,    _UpperCamelCase  :  tuple							,    _UpperCamelCase  :  Path							,    _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  Optional[int]							,    _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  List[Any]							,    _UpperCamelCase  :  List[str]=False							,    ):


      '''simple docstring'''


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



@torch.no_grad()
def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  str							,    _UpperCamelCase  :  int							,    _UpperCamelCase  :  bool = False					):


      '''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(_UpperCamelCase					)

      # 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(
          _UpperCamelCase							,    model_args=(
              torch.randn(1							,    _UpperCamelCase							,    25							,    25					).to(device=_UpperCamelCase							,    dtype=_UpperCamelCase					),
              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=_UpperCamelCase							,    )
      del vae_decoder


if __name__ == "__main__":
      lowercase__  :      Union[str, 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")

      lowercase__  :      Any        =					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 typing import TypedDict







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	42
  lowerCAmelCase__										=	42






def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      if not isinstance(_UpperCamelCase							,    _UpperCamelCase					):
            raise TypeError('''The parameter s type must be str.'''					)

      return [s[i:] + s[:i] for i in range(len(_UpperCamelCase					)					)]



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      if not isinstance(_UpperCamelCase							,    _UpperCamelCase					):
            raise TypeError('''The parameter s type must be str.'''					)
      if not s:
            raise ValueError('''The parameter s must not be empty.'''					)

      UpperCAmelCase_            =  all_rotations(_UpperCamelCase					)
      rotations.sort()  # sort the list of rotations in alphabetically order
      # make a string composed of the last char of each rotation
      UpperCAmelCase_            =  {
          "bwt_string": "".join([word[-1] for word in rotations]					),
          "idx_original_string": rotations.index(_UpperCamelCase					),
      }
      return response



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      if not isinstance(_UpperCamelCase							,    _UpperCamelCase					):
            raise TypeError('''The parameter bwt_string type must be str.'''					)
      if not bwt_string:
            raise ValueError('''The parameter bwt_string must not be empty.'''					)
      try:
            UpperCAmelCase_            =  int(_UpperCamelCase					)
      except ValueError:
            raise TypeError(
                '''The parameter idx_original_string type must be int or passive'''
                ''' of cast to int.'''					)
      if idx_original_string < 0:
            raise ValueError('''The parameter idx_original_string must not be lower than 0.'''					)
      if idx_original_string >= len(_UpperCamelCase					):
            raise ValueError(
                '''The parameter idx_original_string must be lower than''' ''' len(bwt_string).'''					)

      UpperCAmelCase_            =  [''''''] * len(_UpperCamelCase					)
      for _ in range(len(_UpperCamelCase					)					):
            for i in range(len(_UpperCamelCase					)					):
                  UpperCAmelCase_            =  bwt_string[i] + ordered_rotations[i]
            ordered_rotations.sort()
      return ordered_rotations[idx_original_string]


if __name__ == "__main__":
      lowercase__  :      Optional[int]        =					"Provide a string that I will generate its BWT transform: "
      lowercase__  :      List[Any]        =					input(entry_msg).strip()
      lowercase__  :      Any        =					bwt_transform(s)
      print(
          F'''Burrows Wheeler transform for string \'{s}\' results '''
          F'''in \'{result['bwt_string']}\''''
      )
      lowercase__  :      Dict        =					reverse_bwt(result["bwt_string"], result["idx_original_string"])
      print(
          F'''Reversing Burrows Wheeler transform for entry \'{result['bwt_string']}\' '''
          F'''we get original string \'{original_string}\''''
      )





