





'''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						(							a_						:  Dict      ,a_						:  tuple      ,a_						:  Path      ,a_						:  Optional[int]      ,a_						:  Optional[int]      ,a_						:  Optional[Any]      ,a_						:  Tuple      ,a_						:  Dict=False      ,):
		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      __A						(							a_						:  str      ,a_						:  str      ,a_						:  int      ,a_						:  bool = False	):
		lowerCAmelCase     :    Tuple						=   torch.floataa if fpaa else torch.floataa
		if fpaa and torch.cuda.is_available():
				lowerCAmelCase     :    str						=   """cuda"""
		elif fpaa and not torch.cuda.is_available():
				raise ValueError("`float16` model export is only supported on GPUs with CUDA"	)
		else:
				lowerCAmelCase     :    Dict						=   """cpu"""
		lowerCAmelCase     :    Optional[Any]						=   Path(UpperCamelCase__	)

		# VAE DECODER
		lowerCAmelCase     :    List[Any]						=   AutoencoderKL.from_pretrained(model_path + "/vae"	)
		lowerCAmelCase     :    Optional[int]						=   vae_decoder.config.latent_channels
		# forward only through the decoder part
		lowerCAmelCase     :    Dict						=   vae_decoder.decode
		onnx_export(
		    UpperCamelCase__      ,model_args=(
		        torch.randn(1      ,UpperCamelCase__      ,2_5      ,2_5	).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__":
					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 ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
					from .pipeline_kandinsky import KandinskyPipeline
					from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
					from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
					from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
					from .text_encoder import MultilingualCLIP



