





"""simple docstring"""





import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


__A											=						{
    """facebook/maskformer-swin-base-ade""": (
        """https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"""
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

__A											=						logging.get_logger(__name__)






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:Optional[Any]						    =   """maskformer"""
       __magic_name__	:Optional[int]						    =   {"""hidden_size""": """mask_feature_size"""}
       __magic_name__	:Tuple						    =   ["""resnet""", """swin"""]
       __magic_name__	:Optional[int]						    =   ["""detr"""]






       def __init__(       self       ,					__UpperCAmelCase = 2_5_6       ,					__UpperCAmelCase = 2_5_6       ,					__UpperCAmelCase = 0.1       ,					__UpperCAmelCase = False       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = 0.02       ,					__UpperCAmelCase = 1.0       ,					__UpperCAmelCase = 1.0       ,					__UpperCAmelCase = 1.0       ,					__UpperCAmelCase = 20.0       ,					__UpperCAmelCase = None       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         if backbone_config is None:
           # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
           lowerCAmelCase__     :Optional[int]											=				SwinConfig(
               image_size=3_8_4       ,					in_channels=3       ,					patch_size=4       ,					embed_dim=1_2_8       ,					depths=[2, 2, 1_8, 2]       ,					num_heads=[4, 8, 1_6, 3_2]       ,					window_size=1_2       ,					drop_path_rate=0.3       ,					out_features=['stage1', 'stage2', 'stage3', 'stage4']       ,					)

         if isinstance(__UpperCAmelCase       ,					__UpperCAmelCase ):
           lowerCAmelCase__     :Union[str, Any]											=				backbone_config.pop('model_type' )
           lowerCAmelCase__     :str											=				CONFIG_MAPPING[backbone_model_type]
           lowerCAmelCase__     :Tuple											=				config_class.from_dict(__UpperCAmelCase )

         # verify that the backbone is supported
         if backbone_config.model_type not in self.backbones_supported:
           logger.warning_once(
               F"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. "
               F"Supported model types: {','.join(self.backbones_supported )}" )

         if decoder_config is None:
           # fall back to https://huggingface.co/facebook/detr-resnet-50
           lowerCAmelCase__     :Dict											=				DetrConfig()
         else:
           # verify that the decoder is supported
           lowerCAmelCase__     :Dict											=				(
               decoder_config.pop('model_type' ) if isinstance(__UpperCAmelCase       ,					__UpperCAmelCase ) else decoder_config.model_type
           )
           if decoder_type not in self.decoders_supported:
             raise ValueError(
                 F"Transformer Decoder {decoder_type} not supported, please use one of"
                 F" {','.join(self.decoders_supported )}" )
           if isinstance(__UpperCAmelCase       ,					__UpperCAmelCase ):
             lowerCAmelCase__     :str											=				CONFIG_MAPPING[decoder_type]
             lowerCAmelCase__     :Union[str, Any]											=				config_class.from_dict(__UpperCAmelCase )

         lowerCAmelCase__     :Optional[int]											=				backbone_config
         lowerCAmelCase__     :int											=				decoder_config
         # main feature dimension for the model
         lowerCAmelCase__     :int											=				fpn_feature_size
         lowerCAmelCase__     :Any											=				mask_feature_size
         # initializer
         lowerCAmelCase__     :List[Any]											=				init_std
         lowerCAmelCase__     :Optional[int]											=				init_xavier_std
         # Hungarian matcher && loss
         lowerCAmelCase__     :Optional[Any]											=				cross_entropy_weight
         lowerCAmelCase__     :Any											=				dice_weight
         lowerCAmelCase__     :List[str]											=				mask_weight
         lowerCAmelCase__     :Tuple											=				use_auxiliary_loss
         lowerCAmelCase__     :Tuple											=				no_object_weight
         lowerCAmelCase__     :str											=				output_auxiliary_logits

         lowerCAmelCase__     :List[str]											=				self.decoder_config.encoder_attention_heads
         lowerCAmelCase__     :List[Any]											=				self.decoder_config.num_hidden_layers
         super().__init__(**__UpperCAmelCase )






       @classmethod
       def 	snake_case		(       cls       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					**__UpperCAmelCase ):



         '''simple docstring'''

         return cls(
             backbone_config=__UpperCAmelCase       ,					decoder_config=__UpperCAmelCase       ,					**__UpperCAmelCase       ,					)






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				copy.deepcopy(self.__dict__ )
         lowerCAmelCase__     :List[Any]											=				self.backbone_config.to_dict()
         lowerCAmelCase__     :Union[str, Any]											=				self.decoder_config.to_dict()
         lowerCAmelCase__     :int											=				self.__class__.model_type
         return output







"""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											=						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	,  )   ->Tuple:





  """simple docstring"""


  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]:





  """simple docstring"""


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

  # TEXT ENCODER
  lowerCAmelCase__     :str											=				pipeline.text_encoder.config.max_position_embeddings
  lowerCAmelCase__     :Dict											=				pipeline.text_encoder.config.hidden_size
  lowerCAmelCase__     :List[Any]											=				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__     :Optional[Any]											=				pipeline.unet.config.sample_size
  lowerCAmelCase__     :Dict											=				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[Any]											=				str(unet_path.absolute().as_posix()     )
  lowerCAmelCase__     :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__     :List[Any]											=				vae_decoder.config.latent_channels
  lowerCAmelCase__     :Optional[int]											=				vae_decoder.config.out_channels
  # forward only through the decoder part
  lowerCAmelCase__     :Any											=				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__     :Optional[int]											=				pipeline.safety_checker
    lowerCAmelCase__     :Optional[int]											=				safety_checker.config.vision_config.num_channels
    lowerCAmelCase__     :Any											=				safety_checker.config.vision_config.image_size
    lowerCAmelCase__     :List[str]											=				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__     :Union[str, Any]											=				OnnxRuntimeModel.from_pretrained(output_path / 'safety_checker'     )
    lowerCAmelCase__     :Dict											=				pipeline.feature_extractor
  else:
    lowerCAmelCase__     :Tuple											=				None
    lowerCAmelCase__     :Optional[int]											=				None

  lowerCAmelCase__     :List[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__     :Dict											=				OnnxStableDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE	,  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)
