






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


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


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Union[str, Any]					,							__lowerCAmelCase							:					tuple					,							__lowerCAmelCase							:					Path					,							__lowerCAmelCase							:					int					,							__lowerCAmelCase							:					int					,							__lowerCAmelCase							:					Union[str, Any]					,							__lowerCAmelCase							:					Any					,							__lowerCAmelCase							:					Dict=False					,							)     ->  List[str]:
   output_path.parent.mkdir(parents=__lowerCAmelCase					,							exist_ok=__lowerCAmelCase							)
   # 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(
          __lowerCAmelCase					,							__lowerCAmelCase					,							f=output_path.as_posix()					,							input_names=__lowerCAmelCase					,							output_names=__lowerCAmelCase					,							dynamic_axes=__lowerCAmelCase					,							do_constant_folding=__lowerCAmelCase					,							use_external_data_format=__lowerCAmelCase					,							enable_onnx_checker=__lowerCAmelCase					,							opset_version=__lowerCAmelCase					,							)
   else:
      export(
          __lowerCAmelCase					,							__lowerCAmelCase					,							f=output_path.as_posix()					,							input_names=__lowerCAmelCase					,							output_names=__lowerCAmelCase					,							dynamic_axes=__lowerCAmelCase					,							do_constant_folding=__lowerCAmelCase					,							opset_version=__lowerCAmelCase					,							)


@torch.no_grad()
def 				__lowerCamelCase     (							__lowerCAmelCase							:					str					,							__lowerCAmelCase							:					str					,							__lowerCAmelCase							:					int					,							__lowerCAmelCase							:					bool = False							)     ->  Dict:
   __UpperCamelCase	:			str					=					torch.floataa if fpaa else torch.floataa
   if fpaa and torch.cuda.is_available():
      __UpperCamelCase	:			List[str]					=					"""cuda"""
   elif fpaa and not torch.cuda.is_available():
      raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""							)
   else:
      __UpperCamelCase	:			Optional[Any]					=					"""cpu"""
   __UpperCamelCase	:			Tuple					=					StableDiffusionPipeline.from_pretrained(__lowerCAmelCase					,							torch_dtype=__lowerCAmelCase							).to(__lowerCAmelCase							)
   __UpperCamelCase	:			List[Any]					=					Path(__lowerCAmelCase							)

   # TEXT ENCODER
   __UpperCamelCase	:			Dict					=					pipeline.text_encoder.config.max_position_embeddings
   __UpperCamelCase	:			List[str]					=					pipeline.text_encoder.config.hidden_size
   __UpperCamelCase	:			str					=					pipeline.tokenizer(
       """A sample prompt"""					,							padding="""max_length"""					,							max_length=pipeline.tokenizer.model_max_length					,							truncation=__lowerCAmelCase					,							return_tensors="""pt"""					,							)
   onnx_export(
       pipeline.text_encoder					,							model_args=(text_input.input_ids.to(device=__lowerCAmelCase					,							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=__lowerCAmelCase					,							)
   del pipeline.text_encoder

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

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

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

   # SAFETY CHECKER
   if pipeline.safety_checker is not None:
      __UpperCamelCase	:			Union[str, Any]					=					pipeline.safety_checker
      __UpperCamelCase	:			Optional[Any]					=					safety_checker.config.vision_config.num_channels
      __UpperCamelCase	:			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					,							__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase					,							).to(device=__lowerCAmelCase					,							dtype=__lowerCAmelCase							),
              torch.randn(1					,							__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase							).to(device=__lowerCAmelCase					,							dtype=__lowerCAmelCase							),
          )					,							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=__lowerCAmelCase					,							)
      del pipeline.safety_checker
      __UpperCamelCase	:			Any					=					OnnxRuntimeModel.from_pretrained(output_path / """safety_checker"""							)
      __UpperCamelCase	:			Union[str, Any]					=					pipeline.feature_extractor
   else:
      __UpperCamelCase	:			Optional[Any]					=					None
      __UpperCamelCase	:			Union[str, Any]					=					None

   __UpperCamelCase	:			str					=					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=__lowerCAmelCase					,							feature_extractor=__lowerCAmelCase					,							requires_safety_checker=safety_checker is not None					,							)

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

   del pipeline
   del onnx_pipeline
   __UpperCamelCase	:			Optional[int]					=					OnnxStableDiffusionPipeline.from_pretrained(__lowerCAmelCase					,							provider="""CPUExecutionProvider"""							)
   print("""ONNX pipeline is loadable"""							)


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

       UpperCamelCase    							=      parser.parse_args()

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











from collections.abc import Iterable
from typing import Generic, TypeVar

UpperCamelCase    							=      TypeVar('_T')







class 		_A						( Generic[_T] ):

  def __init__(    self						:	int   ,				lowerCamelCase__						:	Iterable[_T] | None = None			):
     """simple docstring"""

     __UpperCamelCase	:			list[_T]					=					list(iterable or []			)
     __UpperCamelCase	:			list[_T]					=					[]

  def __len__(    self						:	str			):
     """simple docstring"""

     return len(self._stacka			) + len(self._stacka			)

  def __repr__(    self						:	Dict			):
     """simple docstring"""

     return f'Queue({tuple(self._stacka[::-1] + self._stacka			)})'

  def    a					(    self						:	Union[str, Any]   ,				lowerCamelCase__						:	_T			):
     """simple docstring"""

     self._stacka.append(lowerCamelCase__			)

  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     __UpperCamelCase	:			Any					=					self._stacka.pop
     __UpperCamelCase	:			int					=					self._stacka.append

     if not self._stacka:
        while self._stacka:
           stacka_append(stacka_pop()			)

     if not self._stacka:
        raise IndexError("""Queue is empty"""			)
     return self._stacka.pop()


if __name__ == "__main__":
       from doctest import testmod

       testmod()



