

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


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

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

	# TEXT ENCODER
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				pipeline.text_encoder.config.max_position_embeddings
	SCREAMING_SNAKE_CASE      :					Tuple    =				pipeline.text_encoder.config.hidden_size
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				pipeline.tokenizer(
	    "A sample prompt"							,	padding="max_length"							,	max_length=pipeline.tokenizer.model_max_length							,	truncation=_a							,	return_tensors="pt"							,	)
	onnx_export(
	    pipeline.text_encoder							,	model_args=(text_input.input_ids.to(device=_a							,	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=_a							,	)
	del pipeline.text_encoder

	# UNET
	SCREAMING_SNAKE_CASE      :					Dict    =				pipeline.unet.config.in_channels
	SCREAMING_SNAKE_CASE      :					str    =				pipeline.unet.config.sample_size
	SCREAMING_SNAKE_CASE      :					List[str]    =				output_path / "unet" / "model.onnx"
	onnx_export(
	    pipeline.unet							,	model_args=(
	        torch.randn(2							,	_a							,	_a							,	_a).to(device=_a							,	dtype=_a),
	        torch.randn(2).to(device=_a							,	dtype=_a),
	        torch.randn(2							,	_a							,	_a).to(device=_a							,	dtype=_a),
	        False,
	    )							,	output_path=_a							,	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=_a							,	use_external_data_format=_a							,	)
	SCREAMING_SNAKE_CASE      :					List[Any]    =				str(unet_path.absolute().as_posix())
	SCREAMING_SNAKE_CASE      :					Tuple    =				os.path.dirname(_a)
	SCREAMING_SNAKE_CASE      :					List[Any]    =				onnx.load(_a)
	# clean up existing tensor files
	shutil.rmtree(_a)
	os.mkdir(_a)
	# collate external tensor files into one
	onnx.save_model(
	    _a							,	_a							,	save_as_external_data=_a							,	all_tensors_to_one_file=_a							,	location="weights.pb"							,	convert_attribute=_a							,	)
	del pipeline.unet

	# VAE ENCODER
	SCREAMING_SNAKE_CASE      :					Any    =				pipeline.vae
	SCREAMING_SNAKE_CASE      :					Tuple    =				vae_encoder.config.in_channels
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				vae_encoder.config.sample_size
	# need to get the raw tensor output (sample) from the encoder
	SCREAMING_SNAKE_CASE      :					str    =				lambda _a							,	_a: vae_encoder.encode(_a							,	_a)[0].sample()
	onnx_export(
	    _a							,	model_args=(
	        torch.randn(1							,	_a							,	_a							,	_a).to(device=_a							,	dtype=_a),
	        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=_a							,	)

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

	# SAFETY CHECKER
	if pipeline.safety_checker is not None:
		SCREAMING_SNAKE_CASE      :					int    =				pipeline.safety_checker
		SCREAMING_SNAKE_CASE      :					List[str]    =				safety_checker.config.vision_config.num_channels
		SCREAMING_SNAKE_CASE      :					List[Any]    =				safety_checker.config.vision_config.image_size
		SCREAMING_SNAKE_CASE      :					List[str]    =				safety_checker.forward_onnx
		onnx_export(
		    pipeline.safety_checker							,	model_args=(
		        torch.randn(
		            1							,	_a							,	_a							,	_a							,	).to(device=_a							,	dtype=_a),
		        torch.randn(1							,	_a							,	_a							,	_a).to(device=_a							,	dtype=_a),
		    )							,	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=_a							,	)
		del pipeline.safety_checker
		SCREAMING_SNAKE_CASE      :					Optional[int]    =				OnnxRuntimeModel.from_pretrained(output_path / "safety_checker")
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				pipeline.feature_extractor
	else:
		SCREAMING_SNAKE_CASE      :					Tuple    =				None
		SCREAMING_SNAKE_CASE      :					Optional[int]    =				None

	SCREAMING_SNAKE_CASE      :					Any    =				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=_a							,	feature_extractor=_a							,	requires_safety_checker=safety_checker is not None							,	)

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

	del pipeline
	del onnx_pipeline
	SCREAMING_SNAKE_CASE      :					Any    =				OnnxStableDiffusionPipeline.from_pretrained(_a							,	provider="CPUExecutionProvider")
	print("ONNX pipeline is loadable")


if __name__ == "__main__":
			a_     =			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')

			a_     =			parser.parse_args()

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



# Lint as: python3
import itertools
import os
import re


lowerCamelCase											=       re.compile(R'([A-Z]+)([A-Z][a-z])')
lowerCamelCase											=       re.compile(R'([a-z\d])([A-Z])')

lowerCamelCase											=       re.compile(R'(?<!_)_(?!_)')
lowerCamelCase											=       re.compile(R'(_{2,})')

lowerCamelCase											=       R'^\w+(\.\w+)*$'

lowerCamelCase											=       R'<>:/\|?*'




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Tuple     ):
       '''simple docstring'''





       _lowerCamelCase       :		List[str]		=_uppercase_uppercase_re.sub(r'\1_\2'		,			SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		List[Any]		=_lowercase_uppercase_re.sub(r'\1_\2'		,			SCREAMING_SNAKE_CASE__     )
       return name.lower()




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Union[str, Any]     ):
       '''simple docstring'''





       _lowerCamelCase       :		Tuple		=_single_underscore_re.split(SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		Tuple		=[_multiple_underscores_re.split(SCREAMING_SNAKE_CASE__     ) for n in name]
       return "".join(n.capitalize() for n in itertools.chain.from_iterable(SCREAMING_SNAKE_CASE__     ) if n != ''     )




def 	a_      (    SCREAMING_SNAKE_CASE__					:      List[Any]     ):
       '''simple docstring'''





       if os.path.basename(SCREAMING_SNAKE_CASE__     ) != name:
              raise ValueError(F'''Should be a dataset name, not a path: {name}'''     )
       return camelcase_to_snakecase(SCREAMING_SNAKE_CASE__     )




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Union[str, Any]		,			SCREAMING_SNAKE_CASE__					:      Optional[int]     ):
       '''simple docstring'''





       if os.path.basename(SCREAMING_SNAKE_CASE__     ) != name:
              raise ValueError(F'''Should be a dataset name, not a path: {name}'''     )
       if not re.match(_split_re		,			SCREAMING_SNAKE_CASE__     ):
              raise ValueError(F'''Split name should match \'{_split_re}\'\' but got \'{split}\'.'''     )
       return F'''{filename_prefix_for_name(SCREAMING_SNAKE_CASE__     )}-{split}'''




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Optional[int]		,			SCREAMING_SNAKE_CASE__					:      List[Any]		,			SCREAMING_SNAKE_CASE__					:      Optional[Any]		,			SCREAMING_SNAKE_CASE__					:      int=None     ):
       '''simple docstring'''





       _lowerCamelCase       :		Union[str, Any]		=filename_prefix_for_split(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       if filetype_suffix:
              prefix += F'''.{filetype_suffix}'''
       _lowerCamelCase       :		Optional[Any]		=os.path.join(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       return F'''{filepath}*'''




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Tuple		,			SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      str		,			SCREAMING_SNAKE_CASE__					:      List[str]=None		,			SCREAMING_SNAKE_CASE__					:      Optional[Any]=None     ):
       '''simple docstring'''





       _lowerCamelCase       :		Dict		=filename_prefix_for_split(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		Union[str, Any]		=os.path.join(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )

       if shard_lengths:
              _lowerCamelCase       :		Union[str, Any]		=len(SCREAMING_SNAKE_CASE__     )
              _lowerCamelCase       :		Optional[int]		=[F'''{prefix}-{shard_id:05d}-of-{num_shards:05d}''' for shard_id in range(SCREAMING_SNAKE_CASE__     )]
              if filetype_suffix:
                     _lowerCamelCase       :		List[Any]		=[filename + F'''.{filetype_suffix}''' for filename in filenames]
              return filenames
       else:
              _lowerCamelCase       :		Any		=prefix
              if filetype_suffix:
                     filename += F'''.{filetype_suffix}'''
              return [filename]


