


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	:		Optional[int]    =       version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')






def 			UpperCamelCase (     __magic_name__					:							Dict   ,   __magic_name__					:							tuple   ,   __magic_name__					:							Path   ,   __magic_name__					:							int   ,   __magic_name__					:							Optional[Any]   ,   __magic_name__					:							List[Any]   ,   __magic_name__					:							Optional[int]   ,   __magic_name__					:							List[Any]=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 			UpperCamelCase (     __magic_name__					:							str   ,   __magic_name__					:							str   ,   __magic_name__					:							int   ,   __magic_name__					:							bool = False					)       ->  List[str]:
		"""simple docstring"""




		lowercase__					      =		torch.floataa if fpaa else torch.floataa
		if fpaa and torch.cuda.is_available():
				lowercase__					      =		"""cuda"""
		elif fpaa and not torch.cuda.is_available():
				raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""					)
		else:
				lowercase__					      =		"""cpu"""
		lowercase__					      =		StableDiffusionPipeline.from_pretrained(__magic_name__   ,   torch_dtype=__magic_name__					).to(__magic_name__					)
		lowercase__					      =		Path(__magic_name__					)

		# TEXT ENCODER
		lowercase__					      =		pipeline.text_encoder.config.max_position_embeddings
		lowercase__					      =		pipeline.text_encoder.config.hidden_size
		lowercase__					      =		pipeline.tokenizer(
		    """A sample prompt"""   ,   padding="""max_length"""   ,   max_length=pipeline.tokenizer.model_max_length   ,   truncation=__magic_name__   ,   return_tensors="""pt"""   ,   )
		onnx_export(
		    pipeline.text_encoder   ,   model_args=(text_input.input_ids.to(device=__magic_name__   ,   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=__magic_name__   ,   )
		del pipeline.text_encoder

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

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

		# VAE DECODER
		lowercase__					      =		pipeline.vae
		lowercase__					      =		vae_decoder.config.latent_channels
		lowercase__					      =		vae_decoder.config.out_channels
		# forward only through the decoder part
		lowercase__					      =		vae_encoder.decode
		onnx_export(
		    __magic_name__   ,   model_args=(
		        torch.randn(1   ,   __magic_name__   ,   __magic_name__   ,   __magic_name__					).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 pipeline.vae

		# SAFETY CHECKER
		if pipeline.safety_checker is not None:
				lowercase__					      =		pipeline.safety_checker
				lowercase__					      =		safety_checker.config.vision_config.num_channels
				lowercase__					      =		safety_checker.config.vision_config.image_size
				lowercase__					      =		safety_checker.forward_onnx
				onnx_export(
				    pipeline.safety_checker   ,   model_args=(
				        torch.randn(
				            1   ,   __magic_name__   ,   __magic_name__   ,   __magic_name__   ,   ).to(device=__magic_name__   ,   dtype=__magic_name__					),
				        torch.randn(1   ,   __magic_name__   ,   __magic_name__   ,   __magic_name__					).to(device=__magic_name__   ,   dtype=__magic_name__					),
				    )   ,   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=__magic_name__   ,   )
				del pipeline.safety_checker
				lowercase__					      =		OnnxRuntimeModel.from_pretrained(output_path / """safety_checker"""					)
				lowercase__					      =		pipeline.feature_extractor
		else:
				lowercase__					      =		None
				lowercase__					      =		None

		lowercase__					      =		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=__magic_name__   ,   feature_extractor=__magic_name__   ,   requires_safety_checker=safety_checker is not None   ,   )

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

		del pipeline
		del onnx_pipeline
		lowercase__					      =		OnnxStableDiffusionPipeline.from_pretrained(__magic_name__   ,   provider="""CPUExecutionProvider"""					)
		print("""ONNX pipeline is loadable"""					)


if __name__ == "__main__":
	A	:		Union[str, 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')

	A	:		Any    =       parser.parse_args()

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





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A	:		Union[str, Any]    =       logging.get_logger(__name__)

A	:		int    =       {
    'andreasmadsen/efficient_mlm_m0.40': (
        'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
    ),
}



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			'''roberta-prelayernorm'''

					def __init__(self   :						Dict				, _UpperCAmelCase   :						List[Any]=5_0265				, _UpperCAmelCase   :						List[Any]=768				, _UpperCAmelCase   :						str=12				, _UpperCAmelCase   :						Any=12				, _UpperCAmelCase   :						Any=3072				, _UpperCAmelCase   :						int="gelu"				, _UpperCAmelCase   :						Tuple=0.1				, _UpperCAmelCase   :						Tuple=0.1				, _UpperCAmelCase   :						Any=512				, _UpperCAmelCase   :						Dict=2				, _UpperCAmelCase   :						int=0.02				, _UpperCAmelCase   :						List[Any]=1E-1_2				, _UpperCAmelCase   :						Tuple=1				, _UpperCAmelCase   :						int=0				, _UpperCAmelCase   :						int=2				, _UpperCAmelCase   :						Optional[Any]="absolute"				, _UpperCAmelCase   :						int=True				, _UpperCAmelCase   :						Optional[int]=None				, **_UpperCAmelCase   :						Any				, )						->       List[str]:




							"""simple docstring"""


							super().__init__(pad_token_id=_UpperCAmelCase				, bos_token_id=_UpperCAmelCase				, eos_token_id=_UpperCAmelCase				, **_UpperCAmelCase		)

							lowercase__					      =		vocab_size
							lowercase__					      =		hidden_size
							lowercase__					      =		num_hidden_layers
							lowercase__					      =		num_attention_heads
							lowercase__					      =		hidden_act
							lowercase__					      =		intermediate_size
							lowercase__					      =		hidden_dropout_prob
							lowercase__					      =		attention_probs_dropout_prob
							lowercase__					      =		max_position_embeddings
							lowercase__					      =		type_vocab_size
							lowercase__					      =		initializer_range
							lowercase__					      =		layer_norm_eps
							lowercase__					      =		position_embedding_type
							lowercase__					      =		use_cache
							lowercase__					      =		classifier_dropout






class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					@property
					def   lowerCamelCase__					(self   :						Any		)						->       Mapping[str, Mapping[int, str]]:




							"""simple docstring"""


							if self.task == "multiple-choice":
									lowercase__					      =		{0: """batch""", 1: """choice""", 2: """sequence"""}
							else:
									lowercase__					      =		{0: """batch""", 1: """sequence"""}
							return OrderedDict(
							    [
							        ("""input_ids""", dynamic_axis),
							        ("""attention_mask""", dynamic_axis),
							    ]		)

