






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

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

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
  from scipy.stats import pearsonr, spearmanr
  from sklearn.metrics import fa_score, matthews_corrcoef


__SCREAMING_SNAKE_CASE     :List[str]       =				(
    '''This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '''
    '''library. You can have a look at this example script for pointers: '''
    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''
)
def 	UpperCAmelCase_							(					__lowercase							:      Any			,     __lowercase							:      Tuple       )		->       int:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     return (preds == labels).mean()
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      str       )		->       Optional[Any]:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     _UpperCAmelCase     							=   simple_accuracy(__lowercase			,     __lowercase       )
     _UpperCAmelCase     							=   fa_score(y_true=__lowercase			,     y_pred=__lowercase       )
     return {
         "acc": acc,
         "f1": fa,
         "acc_and_f1": (acc + fa) / 2,
     }
def 	UpperCAmelCase_							(					__lowercase							:      Optional[int]			,     __lowercase							:      List[str]       )		->       List[Any]:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     _UpperCAmelCase     							=   pearsonr(__lowercase			,     __lowercase       )[0]
     _UpperCAmelCase     							=   spearmanr(__lowercase			,     __lowercase       )[0]
     return {
         "pearson": pearson_corr,
         "spearmanr": spearman_corr,
         "corr": (pearson_corr + spearman_corr) / 2,
     }
def 	UpperCAmelCase_							(					__lowercase							:      Optional[Any]			,     __lowercase							:      str			,     __lowercase							:      str       )		->       Tuple:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     assert len(__lowercase       ) == len(__lowercase       ), f'Predictions and labels have mismatched lengths {len(__lowercase       )} and {len(__lowercase       )}'
     if task_name == "cola":
          return {"mcc": matthews_corrcoef(__lowercase			,     __lowercase       )}
     elif task_name == "sst-2":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "mrpc":
          return acc_and_fa(__lowercase			,     __lowercase       )
     elif task_name == "sts-b":
          return pearson_and_spearman(__lowercase			,     __lowercase       )
     elif task_name == "qqp":
          return acc_and_fa(__lowercase			,     __lowercase       )
     elif task_name == "mnli":
          return {"mnli/acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "mnli-mm":
          return {"mnli-mm/acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "qnli":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "rte":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "wnli":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "hans":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     else:
          raise KeyError(__lowercase       )






def 	UpperCAmelCase_							(					__lowercase							:      List[Any]			,     __lowercase							:      Dict			,     __lowercase							:      str       )		->       Union[str, Any]:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     if len(__lowercase       ) != len(__lowercase       ):
          raise ValueError(f'Predictions and labels have mismatched lengths {len(__lowercase       )} and {len(__lowercase       )}'       )
     if task_name == "xnli":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     else:
          raise KeyError(__lowercase       )




