

"""simple docstring"""


import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


a					:List[Any]    = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase=False  ,					)       ->				List[str]:
      output_path.parent.mkdir(parents=UpperCAmelCase__  ,					exist_ok=UpperCAmelCase__							)
      # 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(
                UpperCAmelCase__  ,					UpperCAmelCase__  ,					f=output_path.as_posix()  ,					input_names=UpperCAmelCase__  ,					output_names=UpperCAmelCase__  ,					dynamic_axes=UpperCAmelCase__  ,					do_constant_folding=UpperCAmelCase__  ,					use_external_data_format=UpperCAmelCase__  ,					enable_onnx_checker=UpperCAmelCase__  ,					opset_version=UpperCAmelCase__  ,					)
      else:
            export(
                UpperCAmelCase__  ,					UpperCAmelCase__  ,					f=output_path.as_posix()  ,					input_names=UpperCAmelCase__  ,					output_names=UpperCAmelCase__  ,					dynamic_axes=UpperCAmelCase__  ,					do_constant_folding=UpperCAmelCase__  ,					opset_version=UpperCAmelCase__  ,					)




@torch.no_grad()
def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase = False							)       ->				List[Any]:
      SCREAMING_SNAKE_CASE__   :      Dict      		=	torch.floataa if fpaa else torch.floataa
      if fpaa and torch.cuda.is_available():
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	"""cuda"""
      elif fpaa and not torch.cuda.is_available():
            raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""							)
      else:
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	"""cpu"""
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	Path(UpperCAmelCase__							)

      # VAE DECODER
      SCREAMING_SNAKE_CASE__   :      Dict      		=	AutoencoderKL.from_pretrained(model_path + """/vae"""							)
      SCREAMING_SNAKE_CASE__   :      Any      		=	vae_decoder.config.latent_channels
      # forward only through the decoder part
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	vae_decoder.decode
      onnx_export(
          UpperCAmelCase__  ,					model_args=(
              torch.randn(1  ,					UpperCAmelCase__  ,					25  ,					25							).to(device=UpperCAmelCase__  ,					dtype=UpperCAmelCase__							),
              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=UpperCAmelCase__  ,					)
      del vae_decoder


if __name__ == "__main__":
  a					:Optional[int]    = 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					:Dict    = 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 typing import TYPE_CHECKING

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


a					:Any    = {
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a					:Union[str, Any]    = [
      "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RobertaPreLayerNormForCausalLM",
      "RobertaPreLayerNormForMaskedLM",
      "RobertaPreLayerNormForMultipleChoice",
      "RobertaPreLayerNormForQuestionAnswering",
      "RobertaPreLayerNormForSequenceClassification",
      "RobertaPreLayerNormForTokenClassification",
      "RobertaPreLayerNormModel",
      "RobertaPreLayerNormPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a					:Optional[Any]    = [
      "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFRobertaPreLayerNormForCausalLM",
      "TFRobertaPreLayerNormForMaskedLM",
      "TFRobertaPreLayerNormForMultipleChoice",
      "TFRobertaPreLayerNormForQuestionAnswering",
      "TFRobertaPreLayerNormForSequenceClassification",
      "TFRobertaPreLayerNormForTokenClassification",
      "TFRobertaPreLayerNormMainLayer",
      "TFRobertaPreLayerNormModel",
      "TFRobertaPreLayerNormPreTrainedModel",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a					:List[Any]    = [
      "FlaxRobertaPreLayerNormForCausalLM",
      "FlaxRobertaPreLayerNormForMaskedLM",
      "FlaxRobertaPreLayerNormForMultipleChoice",
      "FlaxRobertaPreLayerNormForQuestionAnswering",
      "FlaxRobertaPreLayerNormForSequenceClassification",
      "FlaxRobertaPreLayerNormForTokenClassification",
      "FlaxRobertaPreLayerNormModel",
      "FlaxRobertaPreLayerNormPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_roberta_prelayernorm import (
      ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
      RobertaPreLayerNormConfig,
      RobertaPreLayerNormOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roberta_prelayernorm import (
        ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
        RobertaPreLayerNormForCausalLM,
        RobertaPreLayerNormForMaskedLM,
        RobertaPreLayerNormForMultipleChoice,
        RobertaPreLayerNormForQuestionAnswering,
        RobertaPreLayerNormForSequenceClassification,
        RobertaPreLayerNormForTokenClassification,
        RobertaPreLayerNormModel,
        RobertaPreLayerNormPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_roberta_prelayernorm import (
        TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRobertaPreLayerNormForCausalLM,
        TFRobertaPreLayerNormForMaskedLM,
        TFRobertaPreLayerNormForMultipleChoice,
        TFRobertaPreLayerNormForQuestionAnswering,
        TFRobertaPreLayerNormForSequenceClassification,
        TFRobertaPreLayerNormForTokenClassification,
        TFRobertaPreLayerNormMainLayer,
        TFRobertaPreLayerNormModel,
        TFRobertaPreLayerNormPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_roberta_prelayernorm import (
        FlaxRobertaPreLayerNormForCausalLM,
        FlaxRobertaPreLayerNormForMaskedLM,
        FlaxRobertaPreLayerNormForMultipleChoice,
        FlaxRobertaPreLayerNormForQuestionAnswering,
        FlaxRobertaPreLayerNormForSequenceClassification,
        FlaxRobertaPreLayerNormForTokenClassification,
        FlaxRobertaPreLayerNormModel,
        FlaxRobertaPreLayerNormPreTrainedModel,
    )

else:
  import sys

  a					:Optional[int]    = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




