



"""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"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase       	=						logging.get_logger(__name__)

lowerCamelCase       	=						{
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}
class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					'''roberta'''



       def __init__(							self      :  int		,    _UpperCAmelCase      :  List[Any]=50265		,    _UpperCAmelCase      :  str=768		,    _UpperCAmelCase      :  List[str]=12		,    _UpperCAmelCase      :  int=12		,    _UpperCAmelCase      :  Tuple=3072		,    _UpperCAmelCase      :  Dict="gelu"		,    _UpperCAmelCase      :  int=0.1		,    _UpperCAmelCase      :  Optional[int]=0.1		,    _UpperCAmelCase      :  Optional[int]=512		,    _UpperCAmelCase      :  Optional[Any]=2		,    _UpperCAmelCase      :  Optional[int]=0.02		,    _UpperCAmelCase      :  Optional[Any]=1e-12		,    _UpperCAmelCase      :  Dict=1		,    _UpperCAmelCase      :  str=0		,    _UpperCAmelCase      :  int=2		,    _UpperCAmelCase      :  Tuple="absolute"		,    _UpperCAmelCase      :  Any=True		,    _UpperCAmelCase      :  int=None		,    **_UpperCAmelCase      :  List[str]		,    )			->						Any:


             '''simple docstring'''




             super().__init__(pad_token_id=_UpperCAmelCase		,    bos_token_id=_UpperCAmelCase		,    eos_token_id=_UpperCAmelCase		,    **_UpperCAmelCase   )

             UpperCAmelCase_      			=							vocab_size
             UpperCAmelCase_      			=							hidden_size
             UpperCAmelCase_      			=							num_hidden_layers
             UpperCAmelCase_      			=							num_attention_heads
             UpperCAmelCase_      			=							hidden_act
             UpperCAmelCase_      			=							intermediate_size
             UpperCAmelCase_      			=							hidden_dropout_prob
             UpperCAmelCase_      			=							attention_probs_dropout_prob
             UpperCAmelCase_      			=							max_position_embeddings
             UpperCAmelCase_      			=							type_vocab_size
             UpperCAmelCase_      			=							initializer_range
             UpperCAmelCase_      			=							layer_norm_eps
             UpperCAmelCase_      			=							position_embedding_type
             UpperCAmelCase_      			=							use_cache
             UpperCAmelCase_      			=							classifier_dropout



class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


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


             '''simple docstring'''




             if self.task == "multiple-choice":
                   UpperCAmelCase_      			=							{0: "batch", 1: "choice", 2: "sequence"}
             else:
                   UpperCAmelCase_      			=							{0: "batch", 1: "sequence"}
             return OrderedDict(
                 [
                     ("input_ids", dynamic_axis),
                     ("attention_mask", dynamic_axis),
                 ]   )




