




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  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__=False			,		)			->							Any:





     '''simple docstring'''


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






@torch.no_grad()
def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__ = False   )			->							Optional[int]:





     '''simple docstring'''


     lowercase :       Optional[int]       =			torch.floataa if fpaa else torch.floataa
     if fpaa and torch.cuda.is_available():
          lowercase :       Tuple       =			'''cuda'''
     elif fpaa and not torch.cuda.is_available():
          raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''   )
     else:
          lowercase :       Optional[Any]       =			'''cpu'''
     lowercase :       Tuple       =			Path(__magic_name__   )

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





import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowerCAmelCase_						=			{
    'facebook/maskformer-swin-base-ade': (
        'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowerCAmelCase_						=			logging.get_logger(__name__)
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      int			=      '''maskformer'''
  _UpperCamelCase       :      Union[str, Any]			=      {'''hidden_size''': '''mask_feature_size'''}
  _UpperCamelCase       :      Dict			=      ['''resnet''', '''swin''']
  _UpperCamelCase       :      Optional[int]			=      ['''detr''']






  def __init__(  self		:     Any				,						_A		:     int = 256				,						_A		:     int = 256				,						_A		:     float = 0.1				,						_A		:     bool = False				,						_A		:     Optional[Dict] = None				,						_A		:     Optional[Dict] = None				,						_A		:     float = 0.02				,						_A		:     float = 1.0				,						_A		:     float = 1.0				,						_A		:     float = 1.0				,						_A		:     float = 20.0				,						_A		:     Optional[bool] = None				,						**_A		:     str				,						)		->   str:






       """simple docstring"""







       if backbone_config is None:
            # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
            lowercase :       List[str]       =			SwinConfig(
                image_size=384				,						in_channels=3				,						patch_size=4				,						embed_dim=128				,						depths=[2, 2, 18, 2]				,						num_heads=[4, 8, 16, 32]				,						window_size=12				,						drop_path_rate=0.3				,						out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']				,						)

       if isinstance(_A				,						_A				):
            lowercase :       Optional[int]       =			backbone_config.pop('''model_type'''				)
            lowercase :       List[str]       =			CONFIG_MAPPING[backbone_model_type]
            lowercase :       Union[str, Any]       =			config_class.from_dict(_A				)

       # verify that the backbone is supported
       if backbone_config.model_type not in self.backbones_supported:
            logger.warning_once(
                f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
                f"""Supported model types: {','.join(self.backbones_supported				)}"""				)

       if decoder_config is None:
            # fall back to https://huggingface.co/facebook/detr-resnet-50
            lowercase :       Any       =			DetrConfig()
       else:
            # verify that the decoder is supported
            lowercase :       Union[str, Any]       =			(
                decoder_config.pop('''model_type'''				) if isinstance(_A				,						_A				) else decoder_config.model_type
            )
            if decoder_type not in self.decoders_supported:
                 raise ValueError(
                     f"""Transformer Decoder {decoder_type} not supported, please use one of"""
                     f""" {','.join(self.decoders_supported				)}"""				)
            if isinstance(_A				,						_A				):
                 lowercase :       str       =			CONFIG_MAPPING[decoder_type]
                 lowercase :       Dict       =			config_class.from_dict(_A				)

       lowercase :       Tuple       =			backbone_config
       lowercase :       List[Any]       =			decoder_config
       # main feature dimension for the model
       lowercase :       Optional[int]       =			fpn_feature_size
       lowercase :       List[Any]       =			mask_feature_size
       # initializer
       lowercase :       Union[str, Any]       =			init_std
       lowercase :       Tuple       =			init_xavier_std
       # Hungarian matcher && loss
       lowercase :       List[str]       =			cross_entropy_weight
       lowercase :       int       =			dice_weight
       lowercase :       List[Any]       =			mask_weight
       lowercase :       Tuple       =			use_auxiliary_loss
       lowercase :       Tuple       =			no_object_weight
       lowercase :       int       =			output_auxiliary_logits

       lowercase :       List[Any]       =			self.decoder_config.encoder_attention_heads
       lowercase :       List[Any]       =			self.decoder_config.num_hidden_layers
       super().__init__(**_A				)






  @classmethod
  def  __a      (  cls		:     Optional[int]				,						_A		:     PretrainedConfig				,						_A		:     PretrainedConfig				,						**_A		:     str				)		->   Any:






       """simple docstring"""







       return cls(
           backbone_config=_A				,						decoder_config=_A				,						**_A				,						)






  def  __a      (  self		:     Optional[int]				)		->   Dict[str, any]:






       """simple docstring"""







       lowercase :       str       =			copy.deepcopy(self.__dict__				)
       lowercase :       Optional[Any]       =			self.backbone_config.to_dict()
       lowercase :       List[Any]       =			self.decoder_config.to_dict()
       lowercase :       Optional[int]       =			self.__class__.model_type
       return output