

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      a      (			A__	:   int      ,   A__	:   str      ,   A__	:   Tuple      ,   A__	:   Tuple      ,   A__	:   List[str]      ,   A__	:   Optional[int]      ,   A__	:   Any      ,   A__	:   Optional[int]=False      ,   )    ->      Optional[int]:





      """simple docstring"""



      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      a      (			A__	:   List[str]      ,   A__	:   List[Any]      ,   A__	:   List[Any]      ,   A__	:   Union[str, Any] = False     )    ->      List[Any]:





      """simple docstring"""



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

      # TEXT ENCODER
      _lowercase	       =pipeline.text_encoder.config.max_position_embeddings
      _lowercase	       =pipeline.text_encoder.config.hidden_size
      _lowercase	       =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
      _lowercase	       =pipeline.unet.config.in_channels
      _lowercase	       =pipeline.unet.config.sample_size
      _lowercase	       =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      ,   )
      _lowercase	       =str(unet_path.absolute().as_posix()     )
      _lowercase	       =os.path.dirname(_lowerCAmelCase     )
      _lowercase	       =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
      _lowercase	       =pipeline.vae
      _lowercase	       =vae_encoder.config.in_channels
      _lowercase	       =vae_encoder.config.sample_size
      # need to get the raw tensor output (sample) from the encoder
      _lowercase	       =lambda A__      ,   A__     : 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
      _lowercase	       =pipeline.vae
      _lowercase	       =vae_decoder.config.latent_channels
      _lowercase	       =vae_decoder.config.out_channels
      # forward only through the decoder part
      _lowercase	       =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:
            _lowercase	       =pipeline.safety_checker
            _lowercase	       =safety_checker.config.vision_config.num_channels
            _lowercase	       =safety_checker.config.vision_config.image_size
            _lowercase	       =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
            _lowercase	       =OnnxRuntimeModel.from_pretrained(output_path / 'safety_checker'     )
            _lowercase	       =pipeline.feature_extractor
      else:
            _lowercase	       =None
            _lowercase	       =None

      _lowercase	       =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
      _lowercase	       =OnnxStableDiffusionPipeline.from_pretrained(_lowerCAmelCase      ,   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=1_4,
      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 argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification





def 						__snake_case(				_lowerCAmelCase		)		-> List[Any]:
      snake_case__  :    Dict    						=      SwinConfig()
      snake_case__  :    Optional[Any]    						=      swin_name.split("""_"""		)

      snake_case__  :    Any    						=      name_split[1]
      snake_case__  :    List[Any]    						=      int(name_split[4]		)
      snake_case__  :    int    						=      int(name_split[3][-1]		)

      if model_size == "tiny":
            snake_case__  :    List[Any]    						=      96
            snake_case__  :    int    						=      (2, 2, 6, 2)
            snake_case__  :    int    						=      (3, 6, 12, 24)
      elif model_size == "small":
            snake_case__  :    Union[str, Any]    						=      96
            snake_case__  :    Optional[Any]    						=      (2, 2, 18, 2)
            snake_case__  :    str    						=      (3, 6, 12, 24)
      elif model_size == "base":
            snake_case__  :    Dict    						=      128
            snake_case__  :    str    						=      (2, 2, 18, 2)
            snake_case__  :    Dict    						=      (4, 8, 16, 32)
      else:
            snake_case__  :    List[str]    						=      192
            snake_case__  :    str    						=      (2, 2, 18, 2)
            snake_case__  :    List[Any]    						=      (6, 12, 24, 48)

      if "in22k" in swin_name:
            snake_case__  :    str    						=      21_841
      else:
            snake_case__  :    List[str]    						=      1_000
            snake_case__  :    int    						=      """huggingface/label-files"""
            snake_case__  :    Any    						=      """imagenet-1k-id2label.json"""
            snake_case__  :    List[Any]    						=      json.load(open(hf_hub_download(_lowerCAmelCase     ,   _lowerCAmelCase     ,   repo_type="""dataset"""		)     ,   """r"""		)		)
            snake_case__  :    Dict    						=      {int(_lowerCAmelCase		): v for k, v in idalabel.items()}
            snake_case__  :    Optional[int]    						=      idalabel
            snake_case__  :    List[Any]    						=      {v: k for k, v in idalabel.items()}

      snake_case__  :    List[Any]    						=      img_size
      snake_case__  :    Dict    						=      num_classes
      snake_case__  :    Dict    						=      embed_dim
      snake_case__  :    Optional[int]    						=      depths
      snake_case__  :    int    						=      num_heads
      snake_case__  :    Optional[int]    						=      window_size

      return config





def 						__snake_case(				_lowerCAmelCase		)		-> Dict:
      if "patch_embed.proj" in name:
            snake_case__  :    List[str]    						=      name.replace("""patch_embed.proj"""     ,   """embeddings.patch_embeddings.projection"""		)
      if "patch_embed.norm" in name:
            snake_case__  :    int    						=      name.replace("""patch_embed.norm"""     ,   """embeddings.norm"""		)
      if "layers" in name:
            snake_case__  :    str    						=      """encoder.""" + name
      if "attn.proj" in name:
            snake_case__  :    List[str]    						=      name.replace("""attn.proj"""     ,   """attention.output.dense"""		)
      if "attn" in name:
            snake_case__  :    Tuple    						=      name.replace("""attn"""     ,   """attention.self"""		)
      if "norm1" in name:
            snake_case__  :    List[str]    						=      name.replace("""norm1"""     ,   """layernorm_before"""		)
      if "norm2" in name:
            snake_case__  :    Optional[Any]    						=      name.replace("""norm2"""     ,   """layernorm_after"""		)
      if "mlp.fc1" in name:
            snake_case__  :    Union[str, Any]    						=      name.replace("""mlp.fc1"""     ,   """intermediate.dense"""		)
      if "mlp.fc2" in name:
            snake_case__  :    Dict    						=      name.replace("""mlp.fc2"""     ,   """output.dense"""		)

      if name == "norm.weight":
            snake_case__  :    Tuple    						=      """layernorm.weight"""
      if name == "norm.bias":
            snake_case__  :    Union[str, Any]    						=      """layernorm.bias"""

      if "head" in name:
            snake_case__  :    Optional[int]    						=      name.replace("""head"""     ,   """classifier"""		)
      else:
            snake_case__  :    List[str]    						=      """swin.""" + name

      return name





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Optional[Any]:
      for key in orig_state_dict.copy().keys():
            snake_case__  :    Optional[int]    						=      orig_state_dict.pop(_lowerCAmelCase		)

            if "mask" in key:
                  continue
            elif "qkv" in key:
                  snake_case__  :    Dict    						=      key.split("""."""		)
                  snake_case__  :    Optional[int]    						=      int(key_split[1]		)
                  snake_case__  :    Union[str, Any]    						=      int(key_split[3]		)
                  snake_case__  :    List[Any]    						=      model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                  if "weight" in key:
                        snake_case__  :    Optional[Any]    						=      val[:dim, :]
                        snake_case__  :    Tuple    						=      val[
                            dim : dim * 2, :
                        ]
                        snake_case__  :    Dict    						=      val[-dim:, :]
                  else:
                        snake_case__  :    Tuple    						=      val[
                            :dim
                        ]
                        snake_case__  :    int    						=      val[
                            dim : dim * 2
                        ]
                        snake_case__  :    int    						=      val[
                            -dim:
                        ]
            else:
                  snake_case__  :    Union[str, Any]    						=      val

      return orig_state_dict





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> int:
      snake_case__  :    Optional[int]    						=      timm.create_model(_lowerCAmelCase     ,   pretrained=_lowerCAmelCase		)
      timm_model.eval()

      snake_case__  :    Optional[int]    						=      get_swin_config(_lowerCAmelCase		)
      snake_case__  :    Optional[Any]    						=      SwinForImageClassification(_lowerCAmelCase		)
      model.eval()

      snake_case__  :    str    						=      convert_state_dict(timm_model.state_dict()     ,   _lowerCAmelCase		)
      model.load_state_dict(_lowerCAmelCase		)

      snake_case__  :    int    						=      """http://images.cocodataset.org/val2017/000000039769.jpg"""

      snake_case__  :    Dict    						=      AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_"""     ,   """-"""		)		)		)
      snake_case__  :    Dict    						=      Image.open(requests.get(_lowerCAmelCase     ,   stream=_lowerCAmelCase		).raw		)
      snake_case__  :    Optional[int]    						=      image_processor(images=_lowerCAmelCase     ,   return_tensors="""pt"""		)

      snake_case__  :    Optional[Any]    						=      timm_model(inputs["""pixel_values"""]		)
      snake_case__  :    str    						=      model(**_lowerCAmelCase		).logits

      assert torch.allclose(_lowerCAmelCase     ,   _lowerCAmelCase     ,   atol=1e-3		)

      print(f"Saving model {swin_name} to {pytorch_dump_folder_path}"		)
      model.save_pretrained(_lowerCAmelCase		)

      print(f"Saving image processor to {pytorch_dump_folder_path}"		)
      image_processor.save_pretrained(_lowerCAmelCase		)


if __name__ == "__main__":
       __a          =		argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--swin_name",
           default="swin_tiny_patch4_window7_224",
           type=str,
           help="Name of the Swin timm model you'd like to convert.",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
       )

       __a          =		parser.parse_args()
       convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)
