


"""simple docstring"""







import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


lowerCamelCase_   :   int									=  version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")
def _A						(						lowercase       ,    lowercase       ,    lowercase       ,    lowercase       ,    lowercase       ,    lowercase       ,    lowercase       ,    lowercase=False       ,    ):







    """simple docstring"""
    output_path.parent.mkdir(parents=lowercase       ,    exist_ok=lowercase		)
    # 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(
            lowercase       ,    lowercase       ,    f=output_path.as_posix()       ,    input_names=lowercase       ,    output_names=lowercase       ,    dynamic_axes=lowercase       ,    do_constant_folding=lowercase       ,    use_external_data_format=lowercase       ,    enable_onnx_checker=lowercase       ,    opset_version=lowercase       ,    )
    else:
        export(
            lowercase       ,    lowercase       ,    f=output_path.as_posix()       ,    input_names=lowercase       ,    output_names=lowercase       ,    dynamic_axes=lowercase       ,    do_constant_folding=lowercase       ,    opset_version=lowercase       ,    )
@torch.no_grad()
def _A						(						lowercase       ,    lowercase       ,    lowercase       ,    lowercase = False		):







    """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(lowercase		)

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


if __name__ == "__main__":
       lowerCamelCase_   :   List[Any]									=  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=1_4,
           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""")
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowercase							 =					logging.get_logger(__name__)

__lowercase							 =					{
    '''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json''',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						List[str]						=      """deformable_detr"""
    a__	:						Union[str, Any]						=      {
        """hidden_size""": """d_model""",
        """num_attention_heads""": """encoder_attention_heads""",
    }







    def __init__(	self , __lowercase=True , __lowercase=None , __lowercase=3 , __lowercase=300 , __lowercase=1_024 , __lowercase=6 , __lowercase=1_024 , __lowercase=8 , __lowercase=6 , __lowercase=1_024 , __lowercase=8 , __lowercase=0.0 , __lowercase=True , __lowercase="relu" , __lowercase=256 , __lowercase=0.1 , __lowercase=0.0 , __lowercase=0.0 , __lowercase=0.02 , __lowercase=1.0 , __lowercase=True , __lowercase=False , __lowercase="sine" , __lowercase="resnet50" , __lowercase=True , __lowercase=False , __lowercase=4 , __lowercase=4 , __lowercase=4 , __lowercase=False , __lowercase=300 , __lowercase=False , __lowercase=1 , __lowercase=5 , __lowercase=2 , __lowercase=1 , __lowercase=1 , __lowercase=5 , __lowercase=2 , __lowercase=0.1 , __lowercase=0.25 , __lowercase=False , **__lowercase , )       ->	int:
        if backbone_config is not None and use_timm_backbone:
            raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''')

        if not use_timm_backbone:
            if backbone_config is None:
                logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''')
                __UpperCamelCase     :str			  =		CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''])
            elif isinstance(__lowercase , __lowercase):
                __UpperCamelCase     :str			  =		backbone_config.get('''model_type''')
                __UpperCamelCase     :Tuple			  =		CONFIG_MAPPING[backbone_model_type]
                __UpperCamelCase     :Any			  =		config_class.from_dict(__lowercase)
        __UpperCamelCase     :int			  =		use_timm_backbone
        __UpperCamelCase     :Dict			  =		backbone_config
        __UpperCamelCase     :Any			  =		num_channels
        __UpperCamelCase     :Optional[int]			  =		num_queries
        __UpperCamelCase     :Any			  =		max_position_embeddings
        __UpperCamelCase     :str			  =		d_model
        __UpperCamelCase     :Tuple			  =		encoder_ffn_dim
        __UpperCamelCase     :Union[str, Any]			  =		encoder_layers
        __UpperCamelCase     :List[Any]			  =		encoder_attention_heads
        __UpperCamelCase     :Any			  =		decoder_ffn_dim
        __UpperCamelCase     :List[str]			  =		decoder_layers
        __UpperCamelCase     :int			  =		decoder_attention_heads
        __UpperCamelCase     :str			  =		dropout
        __UpperCamelCase     :Any			  =		attention_dropout
        __UpperCamelCase     :int			  =		activation_dropout
        __UpperCamelCase     :List[Any]			  =		activation_function
        __UpperCamelCase     :List[Any]			  =		init_std
        __UpperCamelCase     :List[Any]			  =		init_xavier_std
        __UpperCamelCase     :int			  =		encoder_layerdrop
        __UpperCamelCase     :str			  =		auxiliary_loss
        __UpperCamelCase     :Optional[Any]			  =		position_embedding_type
        __UpperCamelCase     :Union[str, Any]			  =		backbone
        __UpperCamelCase     :Any			  =		use_pretrained_backbone
        __UpperCamelCase     :str			  =		dilation
        # deformable attributes
        __UpperCamelCase     :Optional[Any]			  =		num_feature_levels
        __UpperCamelCase     :str			  =		encoder_n_points
        __UpperCamelCase     :int			  =		decoder_n_points
        __UpperCamelCase     :Union[str, Any]			  =		two_stage
        __UpperCamelCase     :Optional[Any]			  =		two_stage_num_proposals
        __UpperCamelCase     :Dict			  =		with_box_refine
        if two_stage is True and with_box_refine is False:
            raise ValueError('''If two_stage is True, with_box_refine must be True.''')
        # Hungarian matcher
        __UpperCamelCase     :Optional[int]			  =		class_cost
        __UpperCamelCase     :List[Any]			  =		bbox_cost
        __UpperCamelCase     :str			  =		giou_cost
        # Loss coefficients
        __UpperCamelCase     :Tuple			  =		mask_loss_coefficient
        __UpperCamelCase     :Tuple			  =		dice_loss_coefficient
        __UpperCamelCase     :int			  =		bbox_loss_coefficient
        __UpperCamelCase     :Any			  =		giou_loss_coefficient
        __UpperCamelCase     :Dict			  =		eos_coefficient
        __UpperCamelCase     :Optional[Any]			  =		focal_alpha
        __UpperCamelCase     :Optional[Any]			  =		disable_custom_kernels
        super().__init__(is_encoder_decoder=__lowercase , **__lowercase)







    @property
    def 			UpperCamelCase__   (	self)       ->	int:
        return self.encoder_attention_heads







    @property
    def 			UpperCamelCase__   (	self)       ->	int:
        return self.d_model







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase     :Dict			  =		copy.deepcopy(self.__dict__)
        if self.backbone_config is not None:
            __UpperCamelCase     :Tuple			  =		self.backbone_config.to_dict()
        __UpperCamelCase     :List[Any]			  =		self.__class__.model_type
        return output





