


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


snake_case    :   int	    =					version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def     __lowerCamelCase						(			UpperCAmelCase_						:		Tuple			,  UpperCAmelCase_						:		tuple			,  UpperCAmelCase_						:		Path			,  UpperCAmelCase_						:		str			,  UpperCAmelCase_						:		str			,  UpperCAmelCase_						:		Optional[Any]			,  UpperCAmelCase_						:		Union[str, Any]			,  UpperCAmelCase_						:		int=False			,  ):




      """simple docstring"""





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




      """simple docstring"""





      a						:Optional[int]										=	torch.floataa if fpaa else torch.floataa
      if fpaa and torch.cuda.is_available():
            a						:Optional[int]										=	'''cuda'''
      elif fpaa and not torch.cuda.is_available():
            raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''						)
      else:
            a						:List[Any]										=	'''cpu'''
      a						:Optional[Any]										=	StableDiffusionPipeline.from_pretrained(UpperCAmelCase_			,  torch_dtype=UpperCAmelCase_						).to(UpperCAmelCase_						)
      a						:Union[str, Any]										=	Path(UpperCAmelCase_						)

      # TEXT ENCODER
      a						:Optional[int]										=	pipeline.text_encoder.config.max_position_embeddings
      a						:Optional[Any]										=	pipeline.text_encoder.config.hidden_size
      a						:Tuple										=	pipeline.tokenizer(
          '''A sample prompt'''			,  padding='''max_length'''			,  max_length=pipeline.tokenizer.model_max_length			,  truncation=UpperCAmelCase_			,  return_tensors='''pt'''			,  )
      onnx_export(
          pipeline.text_encoder			,  model_args=(text_input.input_ids.to(device=UpperCAmelCase_			,  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=UpperCAmelCase_			,  )
      del pipeline.text_encoder

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

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

      # VAE DECODER
      a						:str										=	pipeline.vae
      a						:str										=	vae_decoder.config.latent_channels
      a						:Any										=	vae_decoder.config.out_channels
      # forward only through the decoder part
      a						:Tuple										=	vae_encoder.decode
      onnx_export(
          UpperCAmelCase_			,  model_args=(
              torch.randn(1			,  UpperCAmelCase_			,  UpperCAmelCase_			,  UpperCAmelCase_						).to(device=UpperCAmelCase_			,  dtype=UpperCAmelCase_						),
              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=UpperCAmelCase_			,  )
      del pipeline.vae

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

      a						:Union[str, 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=UpperCAmelCase_			,  feature_extractor=UpperCAmelCase_			,  requires_safety_checker=safety_checker is not None			,  )

      onnx_pipeline.save_pretrained(UpperCAmelCase_						)
      print('''ONNX pipeline saved to'''			,  UpperCAmelCase_						)

      del pipeline
      del onnx_pipeline
      a						:Tuple										=	OnnxStableDiffusionPipeline.from_pretrained(UpperCAmelCase_			,  provider='''CPUExecutionProvider'''						)
      print('''ONNX pipeline is loadable'''						)


if __name__ == "__main__":
     snake_case    :   str	    =					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''')

     snake_case    :   Optional[int]	    =					parser.parse_args()

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









from ...configuration_utils import PretrainedConfig







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	'bert-generation'




 def __init__(		self ,  _lowerCamelCase=5_0358 ,  _lowerCamelCase=1024 ,  _lowerCamelCase=24 ,  _lowerCamelCase=16 ,  _lowerCamelCase=4096 ,  _lowerCamelCase="gelu" ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=512 ,  _lowerCamelCase=0.02 ,  _lowerCamelCase=1e-12 ,  _lowerCamelCase=0 ,  _lowerCamelCase=2 ,  _lowerCamelCase=1 ,  _lowerCamelCase="absolute" ,  _lowerCamelCase=True ,  **_lowerCamelCase ,  ):
       super().__init__(pad_token_id=_lowerCamelCase ,  bos_token_id=_lowerCamelCase ,  eos_token_id=_lowerCamelCase ,  **_lowerCamelCase   )

       a						:Optional[int]										=	vocab_size
       a						:Tuple										=	hidden_size
       a						:Any										=	num_hidden_layers
       a						:Any										=	num_attention_heads
       a						:List[Any]										=	hidden_act
       a						:Tuple										=	intermediate_size
       a						:Any										=	hidden_dropout_prob
       a						:int										=	attention_probs_dropout_prob
       a						:Dict										=	max_position_embeddings
       a						:int										=	initializer_range
       a						:Union[str, Any]										=	layer_norm_eps
       a						:str										=	position_embedding_type
       a						:int										=	use_cache





