




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


SCREAMING_SNAKE_CASE__       :      Dict  =   version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')

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

       # TEXT ENCODER
       lowerCamelCase   :      Dict			 =					pipeline.text_encoder.config.max_position_embeddings
       lowerCamelCase   :      Dict			 =					pipeline.text_encoder.config.hidden_size
       lowerCamelCase   :      Dict			 =					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
       lowerCamelCase   :      int			 =					pipeline.unet.config.in_channels
       lowerCamelCase   :      Any			 =					pipeline.unet.config.sample_size
       lowerCamelCase   :      List[str]			 =					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 ,)
       lowerCamelCase   :      List[str]			 =					str(unet_path.absolute().as_posix()    )
       lowerCamelCase   :      Optional[int]			 =					os.path.dirname(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      str			 =					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
       lowerCamelCase   :      int			 =					pipeline.vae
       lowerCamelCase   :      Optional[Any]			 =					vae_encoder.config.in_channels
       lowerCamelCase   :      int			 =					vae_encoder.config.sample_size
       # need to get the raw tensor output (sample) from the encoder
       lowerCamelCase   :      str			 =					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
       lowerCamelCase   :      int			 =					pipeline.vae
       lowerCamelCase   :      Optional[int]			 =					vae_decoder.config.latent_channels
       lowerCamelCase   :      str			 =					vae_decoder.config.out_channels
       # forward only through the decoder part
       lowerCamelCase   :      str			 =					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:
              lowerCamelCase   :      int			 =					pipeline.safety_checker
              lowerCamelCase   :      str			 =					safety_checker.config.vision_config.num_channels
              lowerCamelCase   :      Tuple			 =					safety_checker.config.vision_config.image_size
              lowerCamelCase   :      int			 =					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
              lowerCamelCase   :      List[Any]			 =					OnnxRuntimeModel.from_pretrained(output_path / "safety_checker"    )
              lowerCamelCase   :      Optional[Any]			 =					pipeline.feature_extractor
       else:
              lowerCamelCase   :      List[Any]			 =					None
              lowerCamelCase   :      Optional[int]			 =					None

       lowerCamelCase   :      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=_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
       lowerCamelCase   :      List[str]			 =					OnnxStableDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE ,provider="CPUExecutionProvider"    )
       print("ONNX pipeline is loadable"    )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      int  =   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')

       SCREAMING_SNAKE_CASE__       :      Any  =   parser.parse_args()

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












import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property




@unittest.skipUnless(os.path.exists(lowerCAmelCase__     )    ,				"""Tatoeba directory does not exist."""     )
class 		UpperCamelCase__	(unittest.TestCase     ):

    '''simple docstring'''



    @cached_property
    def     _lowercase     (    self     )     ->      int:
           lowerCamelCase   :      str			 =					tempfile.mkdtemp()
           return TatoebaConverter(save_dir=UpperCamelCase__     )







    @slow
    def     _lowercase     (    self     )     ->      List[Any]:
           self.resolver.convert_models(["heb-eng"]     )







    @slow
    def     _lowercase     (    self     )     ->      Tuple:
           lowerCamelCase ,      lowerCamelCase   :      Dict			 =					self.resolver.write_model_card("opus-mt-he-en"     ,  dry_run=UpperCamelCase__     )
           assert mmeta["long_pair"] == "heb-eng"






