


import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


UpperCAmelCase_				:			Union[str, Any]   =	version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Dict  ,   __A			:      Optional[int]  ,   __A			:      Any  ,   __A			:      List[str]  ,   __A			:      str  ,   __A			:      Optional[Any]  ,   __A			:      Optional[Any]  ,   __A			:      Optional[int]=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 	SCREAMING_SNAKE_CASE_						(      __A			:      List[str]  ,   __A			:      List[str]  ,   __A			:      str  ,   __A			:      Union[str, Any] = False    )     ->		List[Any]:

   """simple docstring"""

   a_ :		Tuple							=  torch.floataa if fpaa else torch.floataa
   if fpaa and torch.cuda.is_available():
      a_ :		Tuple							=  'cuda'
   elif fpaa and not torch.cuda.is_available():
      raise ValueError('`float16` model export is only supported on GPUs with CUDA'    )
   else:
      a_ :		int							=  'cpu'
   a_ :		Optional[int]							=  Path(A__    )

   # VAE DECODER
   a_ :		Optional[Any]							=  AutoencoderKL.from_pretrained(model_path + '/vae'    )
   a_ :		Dict							=  vae_decoder.config.latent_channels
   # forward only through the decoder part
   a_ :		Union[str, Any]							=  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_				:			Optional[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')

   UpperCAmelCase_				:			Tuple   =	parser.parse_args()
   print(args.output_path)
   convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
   print('SD: Done: ONNX')






import json
import sys







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[Any]  ,   __A			:      List[str]    )     ->		Tuple:

       """simple docstring"""

       with open(__A  ,   encoding='utf-8'    ) as f:
              a_ :		Union[str, Any]							=  json.load(__A    )

       a_ :		Any							=  ['<details>', '<summary>Show updated benchmarks!</summary>', ' ']

       for benchmark_name in sorted(__A    ):
              a_ :		List[str]							=  results[benchmark_name]

              a_ :		int							=  benchmark_name.split('/'    )[-1]
              output_md.append(F"""### Benchmark: {benchmark_file_name}"""    )

              a_ :		Any							=  '| metric |'
              a_ :		Optional[Any]							=  '|--------|'
              a_ :		int							=  '| new / old (diff) |'
              for metric_name in sorted(__A    ):
                     a_ :		List[Any]							=  benchmark_res[metric_name]
                     a_ :		int							=  metric_vals['new']
                     a_ :		Union[str, Any]							=  metric_vals.get('old'  ,   __A    )
                     a_ :		Optional[int]							=  metric_vals.get('diff'  ,   __A    )

                     a_ :		str							=  F""" {new_val:f}""" if isinstance(__A  ,   (int, float)    ) else 'None'

                     if old_val is not None:
                            val_str += F""" / {old_val:f}""" if isinstance(__A  ,   (int, float)    ) else "None"
                     if dif_val is not None:
                            val_str += F""" ({dif_val:f})""" if isinstance(__A  ,   (int, float)    ) else "None"

                     title += " " + metric_name + " |"
                     lines += "---|"
                     value += val_str + " |"

              output_md += [title, lines, value, " "]

       output_md.append('</details>'    )

       with open(__A  ,   'w'  ,   encoding='utf-8'    ) as f:
              f.writelines('\n'.join(__A    )    )


if __name__ == "__main__":
   UpperCAmelCase_				:			int   =	sys.argv[1]
   UpperCAmelCase_				:			Any   =	sys.argv[2]

   format_json_to_md(input_json_file, output_md_file)


