

from typing import TYPE_CHECKING

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


lowercase_       		=  {
    "configuration_xlm_roberta": [
        "XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "XLMRobertaConfig",
        "XLMRobertaOnnxConfig",
    ],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase_       		=  ["XLMRobertaTokenizer"]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase_       		=  ["XLMRobertaTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase_       		=  [
         "XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
         "XLMRobertaForCausalLM",
         "XLMRobertaForMaskedLM",
         "XLMRobertaForMultipleChoice",
         "XLMRobertaForQuestionAnswering",
         "XLMRobertaForSequenceClassification",
         "XLMRobertaForTokenClassification",
         "XLMRobertaModel",
         "XLMRobertaPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase_       		=  [
         "TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFXLMRobertaForCausalLM",
         "TFXLMRobertaForMaskedLM",
         "TFXLMRobertaForMultipleChoice",
         "TFXLMRobertaForQuestionAnswering",
         "TFXLMRobertaForSequenceClassification",
         "TFXLMRobertaForTokenClassification",
         "TFXLMRobertaModel",
         "TFXLMRobertaPreTrainedModel",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase_       		=  [
         "FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
         "FlaxXLMRobertaForMaskedLM",
         "FlaxXLMRobertaForCausalLM",
         "FlaxXLMRobertaForMultipleChoice",
         "FlaxXLMRobertaForQuestionAnswering",
         "FlaxXLMRobertaForSequenceClassification",
         "FlaxXLMRobertaForTokenClassification",
         "FlaxXLMRobertaModel",
         "FlaxXLMRobertaPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_xlm_roberta import (
         XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
         XLMRobertaConfig,
         XLMRobertaOnnxConfig,
     )

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xlm_roberta import XLMRobertaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xlm_roberta import (
              XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              XLMRobertaForCausalLM,
              XLMRobertaForMaskedLM,
              XLMRobertaForMultipleChoice,
              XLMRobertaForQuestionAnswering,
              XLMRobertaForSequenceClassification,
              XLMRobertaForTokenClassification,
              XLMRobertaModel,
              XLMRobertaPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_xlm_roberta import (
              TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFXLMRobertaForCausalLM,
              TFXLMRobertaForMaskedLM,
              TFXLMRobertaForMultipleChoice,
              TFXLMRobertaForQuestionAnswering,
              TFXLMRobertaForSequenceClassification,
              TFXLMRobertaForTokenClassification,
              TFXLMRobertaModel,
              TFXLMRobertaPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_xlm_roberta import (
              FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
              FlaxXLMRobertaForCausalLM,
              FlaxXLMRobertaForMaskedLM,
              FlaxXLMRobertaForMultipleChoice,
              FlaxXLMRobertaForQuestionAnswering,
              FlaxXLMRobertaForSequenceClassification,
              FlaxXLMRobertaForTokenClassification,
              FlaxXLMRobertaModel,
              FlaxXLMRobertaPreTrainedModel,
          )

else:
     import sys

     lowercase_       		=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


lowercase_       		=  version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Dict    ,			SCREAMING_SNAKE_CASE__			:	tuple    ,			SCREAMING_SNAKE_CASE__			:	Path    ,			SCREAMING_SNAKE_CASE__			:	List[Any]    ,			SCREAMING_SNAKE_CASE__			:	Any    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]=False    ,			)							->      Union[str, Any]:





    '''simple docstring'''






    output_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__    ,			exist_ok=SCREAMING_SNAKE_CASE__      )
    # 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(
            SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			f=output_path.as_posix()    ,			input_names=SCREAMING_SNAKE_CASE__    ,			output_names=SCREAMING_SNAKE_CASE__    ,			dynamic_axes=SCREAMING_SNAKE_CASE__    ,			do_constant_folding=SCREAMING_SNAKE_CASE__    ,			use_external_data_format=SCREAMING_SNAKE_CASE__    ,			enable_onnx_checker=SCREAMING_SNAKE_CASE__    ,			opset_version=SCREAMING_SNAKE_CASE__    ,			)
    else:
        export(
            SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			f=output_path.as_posix()    ,			input_names=SCREAMING_SNAKE_CASE__    ,			output_names=SCREAMING_SNAKE_CASE__    ,			dynamic_axes=SCREAMING_SNAKE_CASE__    ,			do_constant_folding=SCREAMING_SNAKE_CASE__    ,			opset_version=SCREAMING_SNAKE_CASE__    ,			)
@torch.no_grad()
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	bool = False      )							->      Tuple:





    '''simple docstring'''






    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(SCREAMING_SNAKE_CASE__      )

    # 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(
        SCREAMING_SNAKE_CASE__    ,			model_args=(
            torch.randn(1    ,			SCREAMING_SNAKE_CASE__    ,			25    ,			25      ).to(device=SCREAMING_SNAKE_CASE__    ,			dtype=SCREAMING_SNAKE_CASE__      ),
            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=SCREAMING_SNAKE_CASE__    ,			)
    del vae_decoder


if __name__ == "__main__":
     lowercase_       		=  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")

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

