
"""simple docstring"""




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     :			List[str]  						=			version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")


def     __magic_name__      ( __snake_case							:			Optional[int]	,					__snake_case							:			tuple	,					__snake_case							:			Path	,					__snake_case							:			int	,					__snake_case							:			str	,					__snake_case							:			str	,					__snake_case							:			Union[str, Any]	,					__snake_case							:			Optional[int]=False	,					)					->							Tuple:
       output_path.parent.mkdir(parents=__snake_case	,					exist_ok=__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(
                  __snake_case	,					__snake_case	,					f=output_path.as_posix()	,					input_names=__snake_case	,					output_names=__snake_case	,					dynamic_axes=__snake_case	,					do_constant_folding=__snake_case	,					use_external_data_format=__snake_case	,					enable_onnx_checker=__snake_case	,					opset_version=__snake_case	,					)
       else:
              export(
                  __snake_case	,					__snake_case	,					f=output_path.as_posix()	,					input_names=__snake_case	,					output_names=__snake_case	,					dynamic_axes=__snake_case	,					do_constant_folding=__snake_case	,					opset_version=__snake_case	,					)


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

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

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

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

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

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

       lowercase       :							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=__snake_case	,					feature_extractor=__snake_case	,					requires_safety_checker=safety_checker is not None	,					)

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

       del pipeline
       del onnx_pipeline
       lowercase       :							Dict									=				OnnxStableDiffusionPipeline.from_pretrained(__snake_case	,					provider="CPUExecutionProvider"    )
       print("ONNX pipeline is loadable"    )


if __name__ == "__main__":
   _A     :			Dict  						=			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     :			List[str]  						=			parser.parse_args()

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




"""simple docstring"""




# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


_A     :			List[str]  						=			get_logger(__name__)

class 						a__		:
   __lowerCAmelCase				=							"""dummy_data"""
   __lowerCAmelCase				=							"""datasets"""
   __lowerCAmelCase				=							False
   def __init__(     self      ,       _a      ,       _a      ,       _a      ,       _a = None      ,       _a = False      ,       _a = True      ,       _a = None      ,       ):
          lowercase       :							int									=				0
          lowercase       :							Optional[Any]									=				dataset_name
          lowercase       :							List[str]									=				cache_dir
          lowercase       :							Union[str, Any]									=				use_local_dummy_data
          lowercase       :							str									=				config
          # download_callbacks take a single url as input
          lowercase       :							List[Callable]									=				download_callbacks or []
          # if False, it doesn't load existing files and it returns the paths of the dummy files relative
          # to the dummy_data zip file root
          lowercase       :							List[Any]									=				load_existing_dummy_data

          # TODO(PVP, QL) might need to make this more general
          lowercase       :							Tuple									=				str(_a							)
          # to be downloaded
          lowercase       :							Tuple									=				None
          lowercase       :							List[Any]									=				None
   @property
   def      __magic_name__     (     self							):
          if self._dummy_file is None:
                 lowercase       :							Optional[int]									=				self.download_dummy_data()
          return self._dummy_file
   @property
   def      __magic_name__     (     self							):
          if self.config is not None:
                 # structure is dummy / config_name / version_name
                 return os.path.join("dummy"      ,       self.config.name      ,       self.version_name							)
          # structure is dummy / version_name
          return os.path.join("dummy"      ,       self.version_name							)
   @property
   def      __magic_name__     (     self							):
          return os.path.join(self.dummy_data_folder      ,       "dummy_data.zip"							)
   def      __magic_name__     (     self							):
          lowercase       :							Optional[Any]									=				(
              self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
          )

          lowercase       :							str									=				cached_path(
              _a      ,       cache_dir=self.cache_dir      ,       extract_compressed_file=_a      ,       force_extract=_a							)

          return os.path.join(_a      ,       self.dummy_file_name							)
   @property
   def      __magic_name__     (     self							):
          return os.path.join(self.datasets_scripts_dir      ,       self.dataset_name      ,       self.dummy_zip_file							)
   @property
   def      __magic_name__     (     self							):
          if self._bucket_url is None:
                 lowercase       :							Dict									=				hf_github_url(self.dataset_name      ,       self.dummy_zip_file.replace(os.sep      ,       "/"							)							)
          return self._bucket_url
   @property
   def      __magic_name__     (     self							):
          # return full path if its a dir
          if os.path.isdir(self.dummy_file							):
                 return self.dummy_file
          # else cut off path to file -> example `xsum`.
          return "/".join(self.dummy_file.replace(os.sep      ,       "/"							).split("/"							)[:-1]							)
   def      __magic_name__     (     self      ,       _a      ,       *_a							):
          if self.load_existing_dummy_data:
                 # dummy data is downloaded and tested
                 lowercase       :							Optional[int]									=				self.dummy_file
          else:
                 # dummy data cannot be downloaded and only the path to dummy file is returned
                 lowercase       :							Optional[Any]									=				self.dummy_file_name

          # special case when data_url is a dict
          if isinstance(_a      ,       _a							):
                 return self.create_dummy_data_dict(_a      ,       _a							)
          elif isinstance(_a      ,       (list, tuple)							):
                 return self.create_dummy_data_list(_a      ,       _a							)
          else:
                 return self.create_dummy_data_single(_a      ,       _a							)
   def      __magic_name__     (     self      ,       _a      ,       *_a							):
          return self.download_and_extract(_a							)
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          return self.download_and_extract(_a							)
   def      __magic_name__     (     self      ,       _a      ,       *_a      ,       **_a							):
          return path
   def      __magic_name__     (     self							):
          return {}
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          lowercase       :							List[str]									=				{}
          for key, single_urls in data_url.items():
                 for download_callback in self.download_callbacks:
                        if isinstance(_a      ,       _a							):
                               for single_url in single_urls:
                                      download_callback(_a							)
                        else:
                               lowercase       :							Union[str, Any]									=				single_urls
                               download_callback(_a							)
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
                 if isinstance(_a      ,       _a							):
                        lowercase       :							Any									=				[os.path.join(_a      ,       urllib.parse.quote_plus(Path(_a							).name							)							) for x in single_urls]
                 else:
                        lowercase       :							int									=				single_urls
                        lowercase       :							Tuple									=				os.path.join(_a      ,       urllib.parse.quote_plus(Path(_a							).name							)							)
                 lowercase       :							List[str]									=				value

          # make sure that values are unique
          if all(isinstance(_a      ,       _a							) for i in dummy_data_dict.values()							) and len(set(dummy_data_dict.values()							)							) < len(
              dummy_data_dict.values()							):
                 # append key to value to make its name unique
                 lowercase       :							str									=				{key: value + key for key, value in dummy_data_dict.items()}

          return dummy_data_dict
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          lowercase       :							Union[str, Any]									=				[]
          # trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
          lowercase       :							Any									=				all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}"      ,       _a							)							) for url in data_url							)
          lowercase       :							List[Any]									=				all(
              url.startswith("https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed"							) for url in data_url							)
          if data_url and (is_tf_records or is_pubmed_records):
                 lowercase       :							Tuple									=				[data_url[0]] * len(_a							)
          for single_url in data_url:
                 for download_callback in self.download_callbacks:
                        download_callback(_a							)
                 # we force the name of each key to be the last file / folder name of the url path
                 # if the url has arguments, we need to encode them with urllib.parse.quote_plus
                 lowercase       :							Union[str, Any]									=				os.path.join(_a      ,       urllib.parse.quote_plus(single_url.split("/"							)[-1]							)							)
                 dummy_data_list.append(_a							)
          return dummy_data_list
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          for download_callback in self.download_callbacks:
                 download_callback(_a							)
          # we force the name of each key to be the last file / folder name of the url path
          # if the url has arguments, we need to encode them with urllib.parse.quote_plus
          lowercase       :							Tuple									=				os.path.join(_a      ,       urllib.parse.quote_plus(data_url.split("/"							)[-1]							)							)
          if os.path.exists(_a							) or not self.load_existing_dummy_data:
                 return value
          else:
                 # Backward compatibility, maybe deprecate at one point.
                 # For many datasets with single url calls to dl_manager.download_and_extract,
                 # the dummy_data.zip file is actually the zipped downloaded file
                 # while now we expected the dummy_data.zip file to be a directory containing
                 # the downloaded file.
                 return path_to_dummy_data
   def      __magic_name__     (     self							):
          pass
   def      __magic_name__     (     self							):
          pass
   def      __magic_name__     (     self      ,       _a							):
          def _iter_archive_members(_a							):
                 # this preserves the order of the members inside the ZIP archive
                 lowercase       :							Optional[int]									=				Path(self.dummy_file							).parent
                 lowercase       :							List[str]									=				path.relative_to(_a							)
                 with ZipFile(self.local_path_to_dummy_data							) as zip_file:
                        lowercase       :							List[str]									=				zip_file.namelist()
                 for member in members:
                        if member.startswith(relative_path.as_posix()							):
                               yield dummy_parent_path.joinpath(_a							)

          lowercase       :							Union[str, Any]									=				Path(_a							)
          lowercase       :							List[Any]									=				_iter_archive_members(_a							) if self.use_local_dummy_data else path.rglob("*"							)
          for file_path in file_paths:
                 if file_path.is_file() and not file_path.name.startswith((".", "__")							):
                        yield file_path.relative_to(_a							).as_posix(), file_path.open("rb"							)

   def      __magic_name__     (     self      ,       _a							):
          if not isinstance(_a      ,       _a							):
                 lowercase       :							Any									=				[paths]
          for path in paths:
                 if os.path.isfile(_a							):
                        if os.path.basename(_a							).startswith((".", "__")							):
                               return
                        yield path
                 else:
                        for dirpath, dirnames, filenames in os.walk(_a							):
                               if os.path.basename(_a							).startswith((".", "__")							):
                                      continue
                               dirnames.sort()
                               for filename in sorted(_a							):
                                      if filename.startswith((".", "__")							):
                                             continue
                                      yield os.path.join(_a      ,       _a							)


