


'''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 datetime import datetime as dt
import os

from github import Github


__snake_case							      =     [
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''feature request''',
    '''new model''',
    '''wip''',
]


def 			a      (			)  ->	List[str]:



					'''simple docstring'''






					UpperCamelCase__   :List[str]												= Github(os.environ['''GITHUB_TOKEN''']   )
					UpperCamelCase__   :Tuple												= g.get_repo('''huggingface/transformers'''   )
					UpperCamelCase__   :Union[str, Any]												= repo.get_issues(state='''open'''   )

					for issue in open_issues:
										UpperCamelCase__   :List[Any]												= sorted([comment for comment in issue.get_comments()] ,					key=lambda __a   : i.created_at ,					reverse=__a   )
										UpperCamelCase__   :List[Any]												= comments[0] if len(__a   ) > 0 else None
										if (
										    last_comment is not None
										    and last_comment.user.login == "github-actions[bot]"
										    and (dt.utcnow() - issue.updated_at).days > 7
										    and (dt.utcnow() - issue.created_at).days >= 30
										    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()   )
										):
															# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
															issue.edit(state='''closed'''   )
										elif (
										    (dt.utcnow() - issue.updated_at).days > 23
										    and (dt.utcnow() - issue.created_at).days >= 30
										    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()   )
										):
															# print(f"Would add stale comment to {issue.number}")
															issue.create_comment(
															    '''This issue has been automatically marked as stale because it has not had '''
															    '''recent activity. If you think this still needs to be addressed '''
															    '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
															    '''[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) '''
															    '''are likely to be ignored.'''   )


if __name__ == "__main__":
		main()