


"""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


__lowercase    =				version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_  ,       A_  ,       A_  ,       A_  ,       A_=False  ,       )->				Optional[int]:
  '''simple docstring'''







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


@torch.no_grad()
def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_ = False	)->				List[str]:
  '''simple docstring'''







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

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

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

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

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

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

  a						:		Dict											=      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=A_  ,       feature_extractor=A_  ,       requires_safety_checker=safety_checker is not None  ,       )

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

  del pipeline
  del onnx_pipeline
  a						:		int											=      OnnxStableDiffusionPipeline.from_pretrained(A_  ,       provider="CPUExecutionProvider"	)
  print("ONNX pipeline is loadable"	)


if __name__ == "__main__":
 __lowercase    =				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""")

 __lowercase    =				parser.parse_args()

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










"""simple docstring"""







import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
 import torch






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							str     =	"""char"""
    UpperCAmelCase	:							Optional[Any]     =	"""bpe"""
    UpperCAmelCase	:							Optional[Any]     =	"""wp"""






__lowercase    =				(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							Optional[Any]     =	["""image_processor""", """char_tokenizer"""]
    UpperCAmelCase	:							Optional[Any]     =	"""ViTImageProcessor"""
    UpperCAmelCase	:							List[Any]     =	"""MgpstrTokenizer"""






    def __init__( self    :   List[Any]    ,			__UpperCAmelCase    :   Union[str, Any]=None    ,			__UpperCAmelCase    :   Dict=None    ,			**__UpperCAmelCase    :   str):
      a						:		Tuple											=      None
      if "feature_extractor" in kwargs:
        warnings.warn(
            "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
            " instead."    ,			__UpperCAmelCase    ,			)
        a						:		List[str]											=      kwargs.pop("feature_extractor")

      a						:		Dict											=      image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
        raise ValueError("You need to specify an `image_processor`.")
      if tokenizer is None:
        raise ValueError("You need to specify a `tokenizer`.")

      a						:		Union[str, Any]											=      tokenizer
      a						:		int											=      AutoTokenizer.from_pretrained("gpt2")
      a						:		str											=      AutoTokenizer.from_pretrained("bert-base-uncased")

      super().__init__(__UpperCAmelCase    ,			__UpperCAmelCase)






    def __call__( self    :   str    ,			__UpperCAmelCase    :   Dict=None    ,			__UpperCAmelCase    :   Tuple=None    ,			__UpperCAmelCase    :   Union[str, Any]=None    ,			**__UpperCAmelCase    :   int):
      if images is None and text is None:
        raise ValueError("You need to specify either an `images` or `text` input to process.")

      if images is not None:
        a						:		List[str]											=      self.image_processor(__UpperCAmelCase    ,			return_tensors=__UpperCAmelCase    ,			**__UpperCAmelCase)
      if text is not None:
        a						:		Optional[Any]											=      self.char_tokenizer(__UpperCAmelCase    ,			return_tensors=__UpperCAmelCase    ,			**__UpperCAmelCase)

      if text is None:
        return inputs
      elif images is None:
        return encodings
      else:
        a						:		Any											=      encodings["input_ids"]
        return inputs






    def      __snake_case							( self    :   List[Any]    ,			__UpperCAmelCase    :   List[str]):
      a							,		a							,		a						:		Tuple											=      sequences
      a						:		Optional[int]											=      char_preds.size(0)

      a							,		a						:		Dict											=      self._decode_helper(__UpperCAmelCase    ,			"char")
      a							,		a						:		Dict											=      self._decode_helper(__UpperCAmelCase    ,			"bpe")
      a							,		a						:		Union[str, Any]											=      self._decode_helper(__UpperCAmelCase    ,			"wp")

      a						:		Any											=      []
      a						:		Union[str, Any]											=      []
      for i in range(__UpperCAmelCase):
        a						:		Any											=      [char_scores[i], bpe_scores[i], wp_scores[i]]
        a						:		Optional[Any]											=      [char_strs[i], bpe_strs[i], wp_strs[i]]
        a						:		List[str]											=      scores.index(max(__UpperCAmelCase))
        final_strs.append(strs[max_score_index])
        final_scores.append(scores[max_score_index])

      a						:		Dict											=      {}
      a						:		List[str]											=      final_strs
      a						:		str											=      final_scores
      a						:		int											=      char_strs
      a						:		int											=      bpe_strs
      a						:		Tuple											=      wp_strs
      return out






    def      __snake_case							( self    :   Any    ,			__UpperCAmelCase    :   List[str]    ,			__UpperCAmelCase    :   List[str]):
      if format == DecodeType.CHARACTER:
        a						:		int											=      self.char_decode
        a						:		int											=      1
        a						:		Dict											=      "[s]"
      elif format == DecodeType.BPE:
        a						:		List[str]											=      self.bpe_decode
        a						:		List[str]											=      2
        a						:		int											=      "#"
      elif format == DecodeType.WORDPIECE:
        a						:		Union[str, Any]											=      self.wp_decode
        a						:		List[str]											=      102
        a						:		int											=      "[SEP]"
      else:
        raise ValueError(f'''Format {format} is not supported.''')

      a							,		a						:		str											=      [], []
      a						:		Optional[int]											=      pred_logits.size(0)
      a						:		List[str]											=      pred_logits.size(1)
      a							,		a						:		Tuple											=      pred_logits.topk(1    ,			dim=-1    ,			largest=__UpperCAmelCase    ,			sorted=__UpperCAmelCase)
      a						:		List[str]											=      preds_index.view(-1    ,			__UpperCAmelCase)[:, 1:]
      a						:		Any											=      decoder(__UpperCAmelCase)
      a							,		a						:		Union[str, Any]											=      torch.nn.functional.softmax(__UpperCAmelCase    ,			dim=2).max(dim=2)
      a						:		Union[str, Any]											=      preds_max_prob[:, 1:]

      for index in range(__UpperCAmelCase):
        a						:		str											=      preds_str[index].find(__UpperCAmelCase)
        a						:		Optional[Any]											=      preds_str[index][:pred_eos]
        a						:		Optional[int]											=      preds_index[index].cpu().tolist()
        a						:		Optional[int]											=      pred_index.index(__UpperCAmelCase) if eos_token in pred_index else -1
        a						:		List[str]											=      preds_max_prob[index][: pred_eos_index + 1]
        a						:		int											=      pred_max_prob.cumprod(dim=0)[-1] if pred_max_prob.nelement() != 0 else 0.0
        dec_strs.append(__UpperCAmelCase)
        conf_scores.append(__UpperCAmelCase)

      return dec_strs, conf_scores






    def      __snake_case							( self    :   Optional[int]    ,			__UpperCAmelCase    :   Any):
      a						:		Dict											=      [seq.replace(" "    ,			"") for seq in self.char_tokenizer.batch_decode(__UpperCAmelCase)]
      return decode_strs






    def      __snake_case							( self    :   Optional[int]    ,			__UpperCAmelCase    :   List[str]):
      return self.bpe_tokenizer.batch_decode(__UpperCAmelCase)






    def      __snake_case							( self    :   Union[str, Any]    ,			__UpperCAmelCase    :   int):
      a						:		Any											=      [seq.replace(" "    ,			"") for seq in self.wp_tokenizer.batch_decode(__UpperCAmelCase)]
      return decode_strs






