


'''simple docstring'''


import argparse
import os
import shutil
from pathlib import Path

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

from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline


lowerCamelCase__					=   version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase=False					,       ):
		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        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase = False      ):
		_UpperCAmelCase						:				Dict	    =					torch.floataa if fpaa else torch.floataa
		if fpaa and torch.cuda.is_available():
				_UpperCAmelCase						:				Any	    =					"cuda"
		elif fpaa and not torch.cuda.is_available():
				raise ValueError("`float16` model export is only supported on GPUs with CUDA"      )
		else:
				_UpperCAmelCase						:				List[str]	    =					"cpu"
		_UpperCAmelCase						:				Dict	    =					StableDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE					,       torch_dtype=__SCREAMING_SNAKE_CASE      ).to(__SCREAMING_SNAKE_CASE      )
		_UpperCAmelCase						:				Dict	    =					Path(__SCREAMING_SNAKE_CASE      )

		# TEXT ENCODER
		_UpperCAmelCase						:				Optional[int]	    =					pipeline.text_encoder.config.max_position_embeddings
		_UpperCAmelCase						:				Union[str, Any]	    =					pipeline.text_encoder.config.hidden_size
		_UpperCAmelCase						:				Dict	    =					pipeline.tokenizer(
		    "A sample prompt"					,       padding="max_length"					,       max_length=pipeline.tokenizer.model_max_length					,       truncation=__SCREAMING_SNAKE_CASE					,       return_tensors="pt"					,       )
		onnx_export(
		    pipeline.text_encoder					,       model_args=(text_input.input_ids.to(device=__SCREAMING_SNAKE_CASE					,       dtype=torch.intaa      ))					,       output_path=output_path / "text_encoder" / "model.onnx"					,       ordered_input_names=["input_ids"]					,       output_names=["last_hidden_state", "pooler_output"]					,       dynamic_axes={
		        "input_ids": {0: "batch", 1: "sequence"},
		    }					,       opset=__SCREAMING_SNAKE_CASE					,       )
		del pipeline.text_encoder

		# UNET
		_UpperCAmelCase						:				List[Any]	    =					pipeline.unet.config.in_channels
		_UpperCAmelCase						:				Any	    =					pipeline.unet.config.sample_size
		_UpperCAmelCase						:				Dict	    =					output_path / "unet" / "model.onnx"
		onnx_export(
		    pipeline.unet					,       model_args=(
		        torch.randn(2					,       __SCREAMING_SNAKE_CASE					,       __SCREAMING_SNAKE_CASE					,       __SCREAMING_SNAKE_CASE      ).to(device=__SCREAMING_SNAKE_CASE					,       dtype=__SCREAMING_SNAKE_CASE      ),
		        torch.randn(2      ).to(device=__SCREAMING_SNAKE_CASE					,       dtype=__SCREAMING_SNAKE_CASE      ),
		        torch.randn(2					,       __SCREAMING_SNAKE_CASE					,       __SCREAMING_SNAKE_CASE      ).to(device=__SCREAMING_SNAKE_CASE					,       dtype=__SCREAMING_SNAKE_CASE      ),
		        False,
		    )					,       output_path=__SCREAMING_SNAKE_CASE					,       ordered_input_names=["sample", "timestep", "encoder_hidden_states", "return_dict"]					,       output_names=["out_sample"]					,       dynamic_axes={
		        "sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
		        "timestep": {0: "batch"},
		        "encoder_hidden_states": {0: "batch", 1: "sequence"},
		    }					,       opset=__SCREAMING_SNAKE_CASE					,       use_external_data_format=__SCREAMING_SNAKE_CASE					,       )
		_UpperCAmelCase						:				Any	    =					str(unet_path.absolute().as_posix()      )
		_UpperCAmelCase						:				str	    =					os.path.dirname(__SCREAMING_SNAKE_CASE      )
		_UpperCAmelCase						:				int	    =					onnx.load(__SCREAMING_SNAKE_CASE      )
		# clean up existing tensor files
		shutil.rmtree(__SCREAMING_SNAKE_CASE      )
		os.mkdir(__SCREAMING_SNAKE_CASE      )
		# collate external tensor files into one
		onnx.save_model(
		    __SCREAMING_SNAKE_CASE					,       __SCREAMING_SNAKE_CASE					,       save_as_external_data=__SCREAMING_SNAKE_CASE					,       all_tensors_to_one_file=__SCREAMING_SNAKE_CASE					,       location="weights.pb"					,       convert_attribute=__SCREAMING_SNAKE_CASE					,       )
		del pipeline.unet

		# VAE ENCODER
		_UpperCAmelCase						:				str	    =					pipeline.vae
		_UpperCAmelCase						:				str	    =					vae_encoder.config.in_channels
		_UpperCAmelCase						:				str	    =					vae_encoder.config.sample_size
		# need to get the raw tensor output (sample) from the encoder
		_UpperCAmelCase						:				Union[str, Any]	    =					lambda __lowerCAmelCase					,       __lowerCAmelCase      : vae_encoder.encode(__SCREAMING_SNAKE_CASE					,       __SCREAMING_SNAKE_CASE      )[0].sample()
		onnx_export(
		    __SCREAMING_SNAKE_CASE					,       model_args=(
		        torch.randn(1					,       __SCREAMING_SNAKE_CASE					,       __SCREAMING_SNAKE_CASE					,       __SCREAMING_SNAKE_CASE      ).to(device=__SCREAMING_SNAKE_CASE					,       dtype=__SCREAMING_SNAKE_CASE      ),
		        False,
		    )					,       output_path=output_path / "vae_encoder" / "model.onnx"					,       ordered_input_names=["sample", "return_dict"]					,       output_names=["latent_sample"]					,       dynamic_axes={
		        "sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
		    }					,       opset=__SCREAMING_SNAKE_CASE					,       )

		# VAE DECODER
		_UpperCAmelCase						:				List[str]	    =					pipeline.vae
		_UpperCAmelCase						:				Optional[int]	    =					vae_decoder.config.latent_channels
		_UpperCAmelCase						:				List[str]	    =					vae_decoder.config.out_channels
		# forward only through the decoder part
		_UpperCAmelCase						:				int	    =					vae_encoder.decode
		onnx_export(
		    __SCREAMING_SNAKE_CASE					,       model_args=(
		        torch.randn(1					,       __SCREAMING_SNAKE_CASE					,       __SCREAMING_SNAKE_CASE					,       __SCREAMING_SNAKE_CASE      ).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 pipeline.vae

		# SAFETY CHECKER
		if pipeline.safety_checker is not None:
				_UpperCAmelCase						:				List[Any]	    =					pipeline.safety_checker
				_UpperCAmelCase						:				str	    =					safety_checker.config.vision_config.num_channels
				_UpperCAmelCase						:				Optional[int]	    =					safety_checker.config.vision_config.image_size
				_UpperCAmelCase						:				Union[str, Any]	    =					safety_checker.forward_onnx
				onnx_export(
				    pipeline.safety_checker					,       model_args=(
				        torch.randn(
				            1					,       __SCREAMING_SNAKE_CASE					,       __SCREAMING_SNAKE_CASE					,       __SCREAMING_SNAKE_CASE					,       ).to(device=__SCREAMING_SNAKE_CASE					,       dtype=__SCREAMING_SNAKE_CASE      ),
				        torch.randn(1					,       __SCREAMING_SNAKE_CASE					,       __SCREAMING_SNAKE_CASE					,       __SCREAMING_SNAKE_CASE      ).to(device=__SCREAMING_SNAKE_CASE					,       dtype=__SCREAMING_SNAKE_CASE      ),
				    )					,       output_path=output_path / "safety_checker" / "model.onnx"					,       ordered_input_names=["clip_input", "images"]					,       output_names=["out_images", "has_nsfw_concepts"]					,       dynamic_axes={
				        "clip_input": {0: "batch", 1: "channels", 2: "height", 3: "width"},
				        "images": {0: "batch", 1: "height", 2: "width", 3: "channels"},
				    }					,       opset=__SCREAMING_SNAKE_CASE					,       )
				del pipeline.safety_checker
				_UpperCAmelCase						:				Union[str, Any]	    =					OnnxRuntimeModel.from_pretrained(output_path / "safety_checker"      )
				_UpperCAmelCase						:				Union[str, Any]	    =					pipeline.feature_extractor
		else:
				_UpperCAmelCase						:				Optional[int]	    =					None
				_UpperCAmelCase						:				Any	    =					None

		_UpperCAmelCase						:				Optional[int]	    =					OnnxStableDiffusionPipeline(
		    vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_encoder"      )					,       vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_decoder"      )					,       text_encoder=OnnxRuntimeModel.from_pretrained(output_path / "text_encoder"      )					,       tokenizer=pipeline.tokenizer					,       unet=OnnxRuntimeModel.from_pretrained(output_path / "unet"      )					,       scheduler=pipeline.scheduler					,       safety_checker=__SCREAMING_SNAKE_CASE					,       feature_extractor=__SCREAMING_SNAKE_CASE					,       requires_safety_checker=safety_checker is not None					,       )

		onnx_pipeline.save_pretrained(__SCREAMING_SNAKE_CASE      )
		print("ONNX pipeline saved to"					,       __SCREAMING_SNAKE_CASE      )

		del pipeline
		del onnx_pipeline
		_UpperCAmelCase						:				List[Any]	    =					OnnxStableDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE					,       provider="CPUExecutionProvider"      )
		print("ONNX pipeline is loadable"      )


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()

						convert_models(args.model_path, args.output_path, args.opset, args.fpaa)






"""simple docstring"""


import copy

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


__A						   =			logging.get_logger(__name__)

__A						   =			{
    "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     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      str         =  """deformable_detr"""
				SCREAMING_SNAKE_CASE_   :      int         =  {
				    """hidden_size""": """d_model""",
				    """num_attention_heads""": """encoder_attention_heads""",
				}

				def __init__(	self							:				int     ,  UpperCamelCase__							:				int=True     ,  UpperCamelCase__							:				str=None     ,  UpperCamelCase__							:				int=3     ,  UpperCamelCase__							:				Dict=3_0_0     ,  UpperCamelCase__							:				Optional[int]=1_0_2_4     ,  UpperCamelCase__							:				int=6     ,  UpperCamelCase__							:				List[Any]=1_0_2_4     ,  UpperCamelCase__							:				List[Any]=8     ,  UpperCamelCase__							:				str=6     ,  UpperCamelCase__							:				str=1_0_2_4     ,  UpperCamelCase__							:				Union[str, Any]=8     ,  UpperCamelCase__							:				Dict=0.0     ,  UpperCamelCase__							:				str=True     ,  UpperCamelCase__							:				List[Any]="relu"     ,  UpperCamelCase__							:				Tuple=2_5_6     ,  UpperCamelCase__							:				Tuple=0.1     ,  UpperCamelCase__							:				Optional[int]=0.0     ,  UpperCamelCase__							:				Dict=0.0     ,  UpperCamelCase__							:				Optional[Any]=0.02     ,  UpperCamelCase__							:				Dict=1.0     ,  UpperCamelCase__							:				Dict=True     ,  UpperCamelCase__							:				Optional[Any]=False     ,  UpperCamelCase__							:				List[str]="sine"     ,  UpperCamelCase__							:				Any="resnet50"     ,  UpperCamelCase__							:				List[Any]=True     ,  UpperCamelCase__							:				Any=False     ,  UpperCamelCase__							:				Optional[int]=4     ,  UpperCamelCase__							:				List[str]=4     ,  UpperCamelCase__							:				Tuple=4     ,  UpperCamelCase__							:				List[str]=False     ,  UpperCamelCase__							:				Optional[int]=3_0_0     ,  UpperCamelCase__							:				int=False     ,  UpperCamelCase__							:				List[Any]=1     ,  UpperCamelCase__							:				Optional[Any]=5     ,  UpperCamelCase__							:				List[Any]=2     ,  UpperCamelCase__							:				str=1     ,  UpperCamelCase__							:				int=1     ,  UpperCamelCase__							:				Dict=5     ,  UpperCamelCase__							:				Tuple=2     ,  UpperCamelCase__							:				Optional[Any]=0.1     ,  UpperCamelCase__							:				Union[str, Any]=0.25     ,  UpperCamelCase__							:				List[Any]=False     ,  **UpperCamelCase__							:				Dict     ,  )->   Optional[int]:



						'''simple docstring'''


						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:       List[Any]          =			CONFIG_MAPPING["resnet"](out_features=["stage4"])
								elif isinstance(UpperCamelCase__     ,  UpperCamelCase__):
										__lowerCAmelCase:       int          =			backbone_config.get("model_type")
										__lowerCAmelCase:       List[str]          =			CONFIG_MAPPING[backbone_model_type]
										__lowerCAmelCase:       Any          =			config_class.from_dict(UpperCamelCase__)
						__lowerCAmelCase:       int          =			use_timm_backbone
						__lowerCAmelCase:       Any          =			backbone_config
						__lowerCAmelCase:       Tuple          =			num_channels
						__lowerCAmelCase:       str          =			num_queries
						__lowerCAmelCase:       List[str]          =			max_position_embeddings
						__lowerCAmelCase:       List[Any]          =			d_model
						__lowerCAmelCase:       Union[str, Any]          =			encoder_ffn_dim
						__lowerCAmelCase:       Tuple          =			encoder_layers
						__lowerCAmelCase:       List[str]          =			encoder_attention_heads
						__lowerCAmelCase:       Any          =			decoder_ffn_dim
						__lowerCAmelCase:       Union[str, Any]          =			decoder_layers
						__lowerCAmelCase:       List[Any]          =			decoder_attention_heads
						__lowerCAmelCase:       List[Any]          =			dropout
						__lowerCAmelCase:       Optional[Any]          =			attention_dropout
						__lowerCAmelCase:       Union[str, Any]          =			activation_dropout
						__lowerCAmelCase:       Union[str, Any]          =			activation_function
						__lowerCAmelCase:       Dict          =			init_std
						__lowerCAmelCase:       int          =			init_xavier_std
						__lowerCAmelCase:       str          =			encoder_layerdrop
						__lowerCAmelCase:       Union[str, Any]          =			auxiliary_loss
						__lowerCAmelCase:       List[Any]          =			position_embedding_type
						__lowerCAmelCase:       str          =			backbone
						__lowerCAmelCase:       Tuple          =			use_pretrained_backbone
						__lowerCAmelCase:       int          =			dilation
						# deformable attributes
						__lowerCAmelCase:       Union[str, Any]          =			num_feature_levels
						__lowerCAmelCase:       Optional[Any]          =			encoder_n_points
						__lowerCAmelCase:       Dict          =			decoder_n_points
						__lowerCAmelCase:       Optional[Any]          =			two_stage
						__lowerCAmelCase:       Tuple          =			two_stage_num_proposals
						__lowerCAmelCase:       int          =			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
						__lowerCAmelCase:       str          =			class_cost
						__lowerCAmelCase:       List[str]          =			bbox_cost
						__lowerCAmelCase:       List[str]          =			giou_cost
						# Loss coefficients
						__lowerCAmelCase:       Tuple          =			mask_loss_coefficient
						__lowerCAmelCase:       int          =			dice_loss_coefficient
						__lowerCAmelCase:       Any          =			bbox_loss_coefficient
						__lowerCAmelCase:       str          =			giou_loss_coefficient
						__lowerCAmelCase:       int          =			eos_coefficient
						__lowerCAmelCase:       Tuple          =			focal_alpha
						__lowerCAmelCase:       Optional[Any]          =			disable_custom_kernels
						super().__init__(is_encoder_decoder=UpperCamelCase__     ,  **UpperCamelCase__)

				@property
				def 							lowercase_     (	self							:				List[Any])->   int:



						'''simple docstring'''


						return self.encoder_attention_heads

				@property
				def 							lowercase_     (	self							:				Optional[Any])->   int:



						'''simple docstring'''


						return self.d_model




				def 							lowercase_     (	self							:				Union[str, Any])->   List[str]:



						'''simple docstring'''


						__lowerCAmelCase:       Tuple          =			copy.deepcopy(self.__dict__)
						if self.backbone_config is not None:
								__lowerCAmelCase:       str          =			self.backbone_config.to_dict()
						__lowerCAmelCase:       Tuple          =			self.__class__.model_type
						return output


