



"""simple docstring"""





import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


lowerCamelCase       	=						version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__=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__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__ = False				):
      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(lowerCAmelCase__				)

      # 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(
          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__":
  lowerCamelCase       	=						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""")

  lowerCamelCase       	=						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 ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					['''image_processor''', '''tokenizer''']
       UpperCamelCase						=					'''ViTImageProcessor'''
       UpperCamelCase						=					('''CLIPTokenizer''', '''CLIPTokenizerFast''')



       def __init__(							self      :  List[Any]		,    _UpperCAmelCase      :  str=None		,    _UpperCAmelCase      :  Optional[int]=None		,    **_UpperCAmelCase      :  List[str]   )			->						Any:


             '''simple docstring'''




             UpperCAmelCase_      			=							None
             if "feature_extractor" in kwargs:
                   warnings.warn(
                       "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                       " instead."		,    _UpperCAmelCase		,    )
                   UpperCAmelCase_      			=							kwargs.pop("feature_extractor"   )

             UpperCAmelCase_      			=							image_processor if image_processor is not None else feature_extractor
             if image_processor is None:
                   raise ValueError("You need to specify an `image_processor`."   )
             if tokenizer is None:
                   raise ValueError("You need to specify a `tokenizer`."   )

             super().__init__(_UpperCAmelCase		,    _UpperCAmelCase   )



       def __call__(							self      :  Optional[Any]		,    _UpperCAmelCase      :  Any=None		,    _UpperCAmelCase      :  Optional[int]=None		,    _UpperCAmelCase      :  str=None		,    _UpperCAmelCase      :  List[Any]=None		,    **_UpperCAmelCase      :  str   )			->						Optional[int]:


             '''simple docstring'''




             if text is None and visual_prompt is None and images is None:
                   raise ValueError("You have to specify either text, visual prompt or images."   )

             if text is not None and visual_prompt is not None:
                   raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt."   )

             if text is not None:
                   UpperCAmelCase_      			=							self.tokenizer(_UpperCAmelCase		,    return_tensors=_UpperCAmelCase		,    **_UpperCAmelCase   )

             if visual_prompt is not None:
                   UpperCAmelCase_      			=							self.image_processor(_UpperCAmelCase		,    return_tensors=_UpperCAmelCase		,    **_UpperCAmelCase   )

             if images is not None:
                   UpperCAmelCase_      			=							self.image_processor(_UpperCAmelCase		,    return_tensors=_UpperCAmelCase		,    **_UpperCAmelCase   )

             if visual_prompt is not None and images is not None:
                   UpperCAmelCase_      			=							{
                       "pixel_values": image_features.pixel_values,
                       "conditional_pixel_values": prompt_features.pixel_values,
                   }
                   return encoding
             elif text is not None and images is not None:
                   UpperCAmelCase_      			=							image_features.pixel_values
                   return encoding
             elif text is not None:
                   return encoding
             elif visual_prompt is not None:
                   UpperCAmelCase_      			=							{
                       "conditional_pixel_values": prompt_features.pixel_values,
                   }
                   return encoding
             else:
                   return BatchEncoding(data=dict(**_UpperCAmelCase   )		,    tensor_type=_UpperCAmelCase   )



       def 					lowercase__    (							self      :  List[Any]		,    *_UpperCAmelCase      :  List[str]		,    **_UpperCAmelCase      :  List[Any]   )			->						Any:


             '''simple docstring'''




             return self.tokenizer.batch_decode(*_UpperCAmelCase		,    **_UpperCAmelCase   )



       def 					lowercase__    (							self      :  Dict		,    *_UpperCAmelCase      :  Tuple		,    **_UpperCAmelCase      :  List[Any]   )			->						Union[str, Any]:


             '''simple docstring'''




             return self.tokenizer.decode(*_UpperCAmelCase		,    **_UpperCAmelCase   )



       @property
       def 					lowercase__    (							self      :  int   )			->						Optional[int]:


             '''simple docstring'''




             warnings.warn(
                 "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,    _UpperCAmelCase		,    )
             return self.image_processor_class







       @property
       def 					lowercase__    (							self      :  str   )			->						List[Any]:


             '''simple docstring'''




             warnings.warn(
                 "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,    _UpperCAmelCase		,    )
             return self.image_processor




