




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 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_=False					,     )       ->	str:

    """simple docstring"""


    output_path.parent.mkdir(parents=lowercase_					,     exist_ok=lowercase_      )
    # 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(
            lowercase_					,     lowercase_					,     f=output_path.as_posix()					,     input_names=lowercase_					,     output_names=lowercase_					,     dynamic_axes=lowercase_					,     do_constant_folding=lowercase_					,     use_external_data_format=lowercase_					,     enable_onnx_checker=lowercase_					,     opset_version=lowercase_					,     )
    else:
        export(
            lowercase_					,     lowercase_					,     f=output_path.as_posix()					,     input_names=lowercase_					,     output_names=lowercase_					,     dynamic_axes=lowercase_					,     do_constant_folding=lowercase_					,     opset_version=lowercase_					,     )




@torch.no_grad()
def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_ = False      )       ->	Union[str, Any]:

    """simple docstring"""


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

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

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

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

    # VAE DECODER
    __UpperCamelCase  		=						pipeline.vae
    __UpperCamelCase  		=						vae_decoder.config.latent_channels
    __UpperCamelCase  		=						vae_decoder.config.out_channels
    # forward only through the decoder part
    __UpperCamelCase  		=						vae_encoder.decode
    onnx_export(
        lowercase_					,     model_args=(
            torch.randn(1					,     lowercase_					,     lowercase_					,     lowercase_      ).to(device=lowercase_					,     dtype=lowercase_      ),
            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=lowercase_					,     )
    del pipeline.vae

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

    __UpperCamelCase  		=						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=lowercase_					,     feature_extractor=lowercase_					,     requires_safety_checker=safety_checker is not None					,     )

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

    del pipeline
    del onnx_pipeline
    __UpperCamelCase  		=						OnnxStableDiffusionPipeline.from_pretrained(lowercase_					,     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)












from abc import ABC, abstractmethod
from argparse import ArgumentParser

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      @staticmethod
      @abstractmethod
      def       snake_case	(     snake_case					: ArgumentParser				):
          raise NotImplementedError()







      @abstractmethod
      def       snake_case	(     self					: Optional[Any]				):
          raise NotImplementedError()






