


"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


__SCREAMING_SNAKE_CASE							:			Tuple						=			logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:			Tuple						=			{
    'sail/poolformer_s12': 'https://huggingface.co/sail/poolformer_s12/resolve/main/config.json',
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}





class    __A   (lowerCamelCase__):





							'''simple docstring'''

							__lowercase:						str												=			'poolformer'



							def __init__( self							:					Optional[Any]						,				UpperCAmelCase_							:					int=3						,				UpperCAmelCase_							:					int=16						,				UpperCAmelCase_							:					int=16						,				UpperCAmelCase_							:					str=3						,				UpperCAmelCase_							:					Dict=4.0						,				UpperCAmelCase_							:					str=[2, 2, 6, 2]						,				UpperCAmelCase_							:					List[Any]=[64, 128, 320, 512]						,				UpperCAmelCase_							:					Union[str, Any]=[7, 3, 3, 3]						,				UpperCAmelCase_							:					Tuple=[4, 2, 2, 2]						,				UpperCAmelCase_							:					Any=[2, 1, 1, 1]						,				UpperCAmelCase_							:					Dict=4						,				UpperCAmelCase_							:					int=0.0						,				UpperCAmelCase_							:					str="gelu"						,				UpperCAmelCase_							:					int=True						,				UpperCAmelCase_							:					Dict=1E-5						,				UpperCAmelCase_							:					Dict=0.02						,				**UpperCAmelCase_							:					List[str]						,				)    ->str:



														"""simple docstring"""



														snake_case_							=						num_channels
														snake_case_							=						patch_size
														snake_case_							=						stride
														snake_case_							=						padding
														snake_case_							=						pool_size
														snake_case_							=						hidden_sizes
														snake_case_							=						mlp_ratio
														snake_case_							=						depths
														snake_case_							=						patch_sizes
														snake_case_							=						strides
														snake_case_							=						num_encoder_blocks
														snake_case_							=						drop_path_rate
														snake_case_							=						hidden_act
														snake_case_							=						use_layer_scale
														snake_case_							=						layer_scale_init_value
														snake_case_							=						initializer_range
														super().__init__(**lowercase__				)





class    __A   (lowerCamelCase__):





							'''simple docstring'''

							__lowercase:						Optional[Any]												=			version.parse("""1.11""")



							@property
							def 						lowerCAmelCase				( self							:					List[str]				)    ->Any:



														"""simple docstring"""



														return OrderedDict(
														    [
														        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
														    ]				)



							@property
							def 						lowerCAmelCase				( self							:					int				)    ->List[str]:



														"""simple docstring"""



														return 2E-3






'''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__     ,				A__     ,				A__     ,				A__     ,				A__     ,				A__     ,				A__     ,				A__=False     ,				):






 """simple docstring"""



 output_path.parent.mkdir(parents=A__     ,				exist_ok=A__      )
 # 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(
      A__     ,				A__     ,				f=output_path.as_posix()     ,				input_names=A__     ,				output_names=A__     ,				dynamic_axes=A__     ,				do_constant_folding=A__     ,				use_external_data_format=A__     ,				enable_onnx_checker=A__     ,				opset_version=A__     ,				)
 else:
  export(
      A__     ,				A__     ,				f=output_path.as_posix()     ,				input_names=A__     ,				output_names=A__     ,				dynamic_axes=A__     ,				do_constant_folding=A__     ,				opset_version=A__     ,				)


@torch.no_grad()
def 		_A     (    A__     ,				A__     ,				A__     ,				A__ = False      ):






 """simple docstring"""



 __lowercase							  = torch.floataa if fpaa else torch.floataa
 if fpaa and torch.cuda.is_available():
  __lowercase							  = '''cuda'''
 elif fpaa and not torch.cuda.is_available():
  raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''      )
 else:
  __lowercase							  = '''cpu'''
 __lowercase							  = Path(A__      )

 # VAE DECODER
 __lowercase							  = AutoencoderKL.from_pretrained(model_path + '''/vae'''      )
 __lowercase							  = vae_decoder.config.latent_channels
 # forward only through the decoder part
 __lowercase							  = vae_decoder.decode
 onnx_export(
     A__     ,				model_args=(
         torch.randn(1     ,				A__     ,				25     ,				25      ).to(device=A__     ,				dtype=A__      ),
         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=A__     ,				)
 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''')

