


"""simple docstring"""


import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


__lowerCamelCase      :Tuple	    =		version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')




def   snake_case		(     UpperCamelCase__       :						Dict			,				UpperCamelCase__       :						tuple			,				UpperCamelCase__       :						Path			,				UpperCamelCase__       :						Optional[int]			,				UpperCamelCase__       :						Optional[int]			,				UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						Tuple			,				UpperCamelCase__       :						Dict=False			,				)   ->      Union[str, Any]:
      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   snake_case		(     UpperCamelCase__       :						str			,				UpperCamelCase__       :						str			,				UpperCamelCase__       :						int			,				UpperCamelCase__       :						bool = False    )   ->      Dict:
      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__			,				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__":
    __lowerCamelCase      :List[str]	    =		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      :int	    =		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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase      :Tuple	    =		logging.get_logger(__name__)

__lowerCamelCase      :Any	    =		{
    'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					Tuple													='''glpn'''

     def __init__(	self:						Dict     ,		__a:						List[str]=3     ,		__a:						Optional[int]=4     ,		__a:						Dict=[2, 2, 2, 2]     ,		__a:						str=[8, 4, 2, 1]     ,		__a:						Optional[int]=[32, 64, 160, 256]     ,		__a:						Dict=[7, 3, 3, 3]     ,		__a:						Dict=[4, 2, 2, 2]     ,		__a:						Optional[Any]=[1, 2, 5, 8]     ,		__a:						Tuple=[4, 4, 4, 4]     ,		__a:						int="gelu"     ,		__a:						Union[str, Any]=0.0     ,		__a:						str=0.0     ,		__a:						Union[str, Any]=0.02     ,		__a:						str=0.1     ,		__a:						Union[str, Any]=1e-6     ,		__a:						Any=64     ,		__a:						Dict=10     ,		__a:						Union[str, Any]=-1     ,		**__a:						Optional[Any]     ,		)->    Dict:
           super().__init__(**__a	)

           lowerCamelCase					:					Dict        =	num_channels
           lowerCamelCase					:					Any        =	num_encoder_blocks
           lowerCamelCase					:					Dict        =	depths
           lowerCamelCase					:					List[str]        =	sr_ratios
           lowerCamelCase					:					Dict        =	hidden_sizes
           lowerCamelCase					:					Tuple        =	patch_sizes
           lowerCamelCase					:					Optional[int]        =	strides
           lowerCamelCase					:					Optional[Any]        =	mlp_ratios
           lowerCamelCase					:					Union[str, Any]        =	num_attention_heads
           lowerCamelCase					:					List[str]        =	hidden_act
           lowerCamelCase					:					Any        =	hidden_dropout_prob
           lowerCamelCase					:					Optional[int]        =	attention_probs_dropout_prob
           lowerCamelCase					:					List[Any]        =	initializer_range
           lowerCamelCase					:					Dict        =	drop_path_rate
           lowerCamelCase					:					Any        =	layer_norm_eps
           lowerCamelCase					:					Optional[Any]        =	decoder_hidden_size
           lowerCamelCase					:					Tuple        =	max_depth
           lowerCamelCase					:					Optional[Any]        =	head_in_index




