




import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


__a							     =					version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase=False					,		):

     '''simple docstring'''


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







@torch.no_grad()
def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase = False ):

     '''simple docstring'''


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

     # VAE DECODER
     UpperCAmelCase_     :				Optional[Any]  =     AutoencoderKL.from_pretrained(model_path + '''/vae''' )
     UpperCAmelCase_     :				Union[str, Any]  =     vae_decoder.config.latent_channels
     # forward only through the decoder part
     UpperCAmelCase_     :				Optional[Any]  =     vae_decoder.decode
     onnx_export(
         _lowercase					,		model_args=(
             torch.randn(1					,		_lowercase					,		25					,		25 ).to(device=_lowercase					,		dtype=_lowercase ),
             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=_lowercase					,		)
     del vae_decoder


if __name__ == "__main__":
     __a							     =					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')

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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__a							     =					{
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a							     =					[
         'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
         'EncodecModel',
         'EncodecPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_encodec import (
         ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
         EncodecConfig,
     )
     from .feature_extraction_encodec import EncodecFeatureExtractor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_encodec import (
              ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
              EncodecModel,
              EncodecPreTrainedModel,
          )

else:
     import sys

     __a							     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)