

import copy
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
from ..auto import CONFIG_MAPPING


a		=logging.get_logger(__name__)

a		={
    """microsoft/conditional-detr-resnet-50""": (
        """https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
    ),
}






class        A_		(       SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase    :  List[str]						      =      '''conditional_detr'''
							_UpperCAmelCase    :  int						      =      ['''past_key_values''']
							_UpperCAmelCase    :  Optional[int]						      =      {
							    '''hidden_size''': '''d_model''',
							    '''num_attention_heads''': '''encoder_attention_heads''',
							}
							def __init__(		self		:			Dict   ,SCREAMING_SNAKE_CASE__		:			int=True   ,SCREAMING_SNAKE_CASE__		:			List[str]=None   ,SCREAMING_SNAKE_CASE__		:			List[str]=3   ,SCREAMING_SNAKE_CASE__		:			int=3_0_0   ,SCREAMING_SNAKE_CASE__		:			str=6   ,SCREAMING_SNAKE_CASE__		:			Dict=2_0_4_8   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=8   ,SCREAMING_SNAKE_CASE__		:			int=6   ,SCREAMING_SNAKE_CASE__		:			Optional[int]=2_0_4_8   ,SCREAMING_SNAKE_CASE__		:			List[Any]=8   ,SCREAMING_SNAKE_CASE__		:			int=0.0   ,SCREAMING_SNAKE_CASE__		:			Tuple=0.0   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=True   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]="relu"   ,SCREAMING_SNAKE_CASE__		:			List[Any]=2_5_6   ,SCREAMING_SNAKE_CASE__		:			List[Any]=0.1   ,SCREAMING_SNAKE_CASE__		:			Any=0.0   ,SCREAMING_SNAKE_CASE__		:			List[Any]=0.0   ,SCREAMING_SNAKE_CASE__		:			str=0.02   ,SCREAMING_SNAKE_CASE__		:			Tuple=1.0   ,SCREAMING_SNAKE_CASE__		:			str=False   ,SCREAMING_SNAKE_CASE__		:			Dict="sine"   ,SCREAMING_SNAKE_CASE__		:			int="resnet50"   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=True   ,SCREAMING_SNAKE_CASE__		:			str=False   ,SCREAMING_SNAKE_CASE__		:			Tuple=2   ,SCREAMING_SNAKE_CASE__		:			Optional[int]=5   ,SCREAMING_SNAKE_CASE__		:			int=2   ,SCREAMING_SNAKE_CASE__		:			List[str]=1   ,SCREAMING_SNAKE_CASE__		:			int=1   ,SCREAMING_SNAKE_CASE__		:			str=2   ,SCREAMING_SNAKE_CASE__		:			Optional[int]=5   ,SCREAMING_SNAKE_CASE__		:			int=2   ,SCREAMING_SNAKE_CASE__		:			Dict=0.25   ,**SCREAMING_SNAKE_CASE__		:			Union[str, Any]   ,):
								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.')
										__lowerCamelCase				:				str					       = CONFIG_MAPPING['resnet'](out_features=['stage4'])
									elif isinstance(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__):
										__lowerCamelCase				:				int					       = backbone_config.get('model_type')
										__lowerCamelCase				:				Optional[int]					       = CONFIG_MAPPING[backbone_model_type]
										__lowerCamelCase				:				int					       = config_class.from_dict(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				Optional[Any]					       = use_timm_backbone
								__lowerCamelCase				:				Dict					       = backbone_config
								__lowerCamelCase				:				int					       = num_channels
								__lowerCamelCase				:				Union[str, Any]					       = num_queries
								__lowerCamelCase				:				List[Any]					       = d_model
								__lowerCamelCase				:				str					       = encoder_ffn_dim
								__lowerCamelCase				:				Union[str, Any]					       = encoder_layers
								__lowerCamelCase				:				Union[str, Any]					       = encoder_attention_heads
								__lowerCamelCase				:				Union[str, Any]					       = decoder_ffn_dim
								__lowerCamelCase				:				Optional[Any]					       = decoder_layers
								__lowerCamelCase				:				int					       = decoder_attention_heads
								__lowerCamelCase				:				Optional[Any]					       = dropout
								__lowerCamelCase				:				Optional[Any]					       = attention_dropout
								__lowerCamelCase				:				Any					       = activation_dropout
								__lowerCamelCase				:				int					       = activation_function
								__lowerCamelCase				:				Dict					       = init_std
								__lowerCamelCase				:				int					       = init_xavier_std
								__lowerCamelCase				:				Any					       = encoder_layerdrop
								__lowerCamelCase				:				str					       = decoder_layerdrop
								__lowerCamelCase				:				Dict					       = encoder_layers
								__lowerCamelCase				:				List[str]					       = auxiliary_loss
								__lowerCamelCase				:				Optional[int]					       = position_embedding_type
								__lowerCamelCase				:				List[str]					       = backbone
								__lowerCamelCase				:				Dict					       = use_pretrained_backbone
								__lowerCamelCase				:				Union[str, Any]					       = dilation
								# Hungarian matcher
								__lowerCamelCase				:				Dict					       = class_cost
								__lowerCamelCase				:				Dict					       = bbox_cost
								__lowerCamelCase				:				Any					       = giou_cost
								# Loss coefficients
								__lowerCamelCase				:				List[str]					       = mask_loss_coefficient
								__lowerCamelCase				:				Optional[int]					       = dice_loss_coefficient
								__lowerCamelCase				:				Any					       = cls_loss_coefficient
								__lowerCamelCase				:				str					       = bbox_loss_coefficient
								__lowerCamelCase				:				str					       = giou_loss_coefficient
								__lowerCamelCase				:				Optional[Any]					       = focal_alpha
								super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)
							@property
							def     lowerCAmelCase     (		self		:			Union[str, Any]):
								return self.encoder_attention_heads
							@property
							def     lowerCAmelCase     (		self		:			int):
								return self.d_model


							def     lowerCAmelCase     (		self		:			str):
								__lowerCamelCase				:				Optional[int]					       = copy.deepcopy(self.__dict__)
								if self.backbone_config is not None:
									__lowerCamelCase				:				str					       = self.backbone_config.to_dict()
								__lowerCamelCase				:				Optional[int]					       = self.__class__.model_type
								return output






class        A_		(       SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase    :  int						      =      version.parse('''1.11'''				)
							@property
							def     lowerCAmelCase     (		self		:			Optional[int]):
								return OrderedDict(
								    [
								        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
								        ('pixel_mask', {0: 'batch'}),
								    ])
							@property
							def     lowerCAmelCase     (		self		:			Optional[Any]):
								return 1E-5


							@property
							def     lowerCAmelCase     (		self		:			str):
								return 1_2







import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


lowercase_       		=  version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Dict    ,			SCREAMING_SNAKE_CASE__			:	tuple    ,			SCREAMING_SNAKE_CASE__			:	Path    ,			SCREAMING_SNAKE_CASE__			:	List[Any]    ,			SCREAMING_SNAKE_CASE__			:	Any    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]=False    ,			)							->      Union[str, Any]:





    '''simple docstring'''






    output_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__    ,			exist_ok=SCREAMING_SNAKE_CASE__      )
    # 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(
            SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			f=output_path.as_posix()    ,			input_names=SCREAMING_SNAKE_CASE__    ,			output_names=SCREAMING_SNAKE_CASE__    ,			dynamic_axes=SCREAMING_SNAKE_CASE__    ,			do_constant_folding=SCREAMING_SNAKE_CASE__    ,			use_external_data_format=SCREAMING_SNAKE_CASE__    ,			enable_onnx_checker=SCREAMING_SNAKE_CASE__    ,			opset_version=SCREAMING_SNAKE_CASE__    ,			)
    else:
        export(
            SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			f=output_path.as_posix()    ,			input_names=SCREAMING_SNAKE_CASE__    ,			output_names=SCREAMING_SNAKE_CASE__    ,			dynamic_axes=SCREAMING_SNAKE_CASE__    ,			do_constant_folding=SCREAMING_SNAKE_CASE__    ,			opset_version=SCREAMING_SNAKE_CASE__    ,			)
@torch.no_grad()
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	bool = False      )							->      Tuple:





    '''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(SCREAMING_SNAKE_CASE__      )

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


if __name__ == "__main__":
     lowercase_       		=  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")

     lowercase_       		=  parser.parse_args()
     print(args.output_path)
     convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
     print("SD: Done: ONNX")

