

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

						# TEXT ENCODER
						snake_case_  =      pipeline.text_encoder.config.max_position_embeddings
						snake_case_  =      pipeline.text_encoder.config.hidden_size
						snake_case_  =      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
						snake_case_  =      pipeline.unet.config.in_channels
						snake_case_  =      pipeline.unet.config.sample_size
						snake_case_  =      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__    ,						)
						snake_case_  =      str(unet_path.absolute().as_posix()			)
						snake_case_  =      os.path.dirname(SCREAMING_SNAKE_CASE__			)
						snake_case_  =      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
						snake_case_  =      pipeline.vae
						snake_case_  =      vae_encoder.config.in_channels
						snake_case_  =      vae_encoder.config.sample_size
						# need to get the raw tensor output (sample) from the encoder
						snake_case_  =      lambda SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			: 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
						snake_case_  =      pipeline.vae
						snake_case_  =      vae_decoder.config.latent_channels
						snake_case_  =      vae_decoder.config.out_channels
						# forward only through the decoder part
						snake_case_  =      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:
												snake_case_  =      pipeline.safety_checker
												snake_case_  =      safety_checker.config.vision_config.num_channels
												snake_case_  =      safety_checker.config.vision_config.image_size
												snake_case_  =      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
												snake_case_  =      OnnxRuntimeModel.from_pretrained(output_path / '''safety_checker'''			)
												snake_case_  =      pipeline.feature_extractor
						else:
												snake_case_  =      None
												snake_case_  =      None

						snake_case_  =      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
						snake_case_  =      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"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__				:str			   =	logging.get_logger(__name__)

UpperCamelCase__				:Optional[int]			   =	{
    """microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
    """microsoft/markuplm-large""": """https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json""",
}






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							"markuplm"
		def __init__( self					,				SCREAMING_SNAKE_CASE__=3_05_22					,				SCREAMING_SNAKE_CASE__=7_68					,				SCREAMING_SNAKE_CASE__=12					,				SCREAMING_SNAKE_CASE__=12					,				SCREAMING_SNAKE_CASE__=30_72					,				SCREAMING_SNAKE_CASE__="gelu"					,				SCREAMING_SNAKE_CASE__=0.1					,				SCREAMING_SNAKE_CASE__=0.1					,				SCREAMING_SNAKE_CASE__=5_12					,				SCREAMING_SNAKE_CASE__=2					,				SCREAMING_SNAKE_CASE__=0.0_2					,				SCREAMING_SNAKE_CASE__=1E-12					,				SCREAMING_SNAKE_CASE__=0					,				SCREAMING_SNAKE_CASE__=0					,				SCREAMING_SNAKE_CASE__=2					,				SCREAMING_SNAKE_CASE__=2_56					,				SCREAMING_SNAKE_CASE__=10_24					,				SCREAMING_SNAKE_CASE__=2_16					,				SCREAMING_SNAKE_CASE__=10_01					,				SCREAMING_SNAKE_CASE__=32					,				SCREAMING_SNAKE_CASE__=50					,				SCREAMING_SNAKE_CASE__="absolute"					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=None					,				**SCREAMING_SNAKE_CASE__					,				)      ->	Optional[Any]:

					"""simple docstring"""



					super().__init__(
					    pad_token_id=SCREAMING_SNAKE_CASE__					,				bos_token_id=SCREAMING_SNAKE_CASE__					,				eos_token_id=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__					,				)
					_UpperCamelCase					:Any					     =	vocab_size
					_UpperCamelCase					:Union[str, Any]					     =	hidden_size
					_UpperCamelCase					:List[Any]					     =	num_hidden_layers
					_UpperCamelCase					:int					     =	num_attention_heads
					_UpperCamelCase					:Tuple					     =	hidden_act
					_UpperCamelCase					:str					     =	intermediate_size
					_UpperCamelCase					:Optional[int]					     =	hidden_dropout_prob
					_UpperCamelCase					:Any					     =	attention_probs_dropout_prob
					_UpperCamelCase					:Union[str, Any]					     =	max_position_embeddings
					_UpperCamelCase					:Dict					     =	type_vocab_size
					_UpperCamelCase					:int					     =	initializer_range
					_UpperCamelCase					:Optional[int]					     =	layer_norm_eps
					_UpperCamelCase					:Any					     =	position_embedding_type
					_UpperCamelCase					:Any					     =	use_cache
					_UpperCamelCase					:List[str]					     =	classifier_dropout
					# additional properties
					_UpperCamelCase					:Union[str, Any]					     =	max_depth
					_UpperCamelCase					:Union[str, Any]					     =	max_xpath_tag_unit_embeddings
					_UpperCamelCase					:Optional[Any]					     =	max_xpath_subs_unit_embeddings
					_UpperCamelCase					:int					     =	tag_pad_id
					_UpperCamelCase					:str					     =	subs_pad_id
					_UpperCamelCase					:List[str]					     =	xpath_unit_hidden_size

