


"""simple docstring"""







import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


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







  a						:		Optional[int]											=      OmegaConf.load(A_	)
  if display:
    print(yaml.dump(OmegaConf.to_container(A_	)	)	)
  return config


def 					lowercase      (    A_  ,       A_=None  ,       A_=None	)->				List[Any]:
  '''simple docstring'''







  if conf_path is None:
    a						:		str											=      "./model_checkpoints/vqgan_only.yaml"
  a						:		Union[str, Any]											=      load_config(A_  ,       display=A_	)
  a						:		Tuple											=      VQModel(**config.model.params	)
  if ckpt_path is None:
    a						:		List[str]											=      "./model_checkpoints/vqgan_only.pt"
  a						:		Tuple											=      torch.load(A_  ,       map_location=A_	)
  if ".ckpt" in ckpt_path:
    a						:		Optional[int]											=      sd["state_dict"]
  model.load_state_dict(A_  ,       strict=A_	)
  model.to(A_	)
  del sd
  return model


def 					lowercase      (    A_  ,       A_	)->				List[Any]:
  '''simple docstring'''







  a							,		a							,		a						:		Optional[int]											=      model.encode(A_	)
  print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''	)
  a						:		Union[str, Any]											=      model.decode(A_	)
  return xrec


def 					lowercase      (    A_  ,       A_=False	)->				Tuple:
  '''simple docstring'''







  a							,		a						:		Optional[Any]											=      string.rsplit("."  ,       1	)
  if reload:
    a						:		str											=      importlib.import_module(A_	)
    importlib.reload(A_	)
  return getattr(importlib.import_module(A_  ,       package=A_	)  ,       cls	)


def 					lowercase      (    A_	)->				Union[str, Any]:
  '''simple docstring'''







  if "target" not in config:
    raise KeyError("Expected key `target` to instantiate."	)
  return get_obj_from_str(config["target"]	)(**config.get("params"  ,       {}	)	)


def 					lowercase      (    A_  ,       A_  ,       A_=True  ,       A_=True	)->				Any:
  '''simple docstring'''







  a						:		Optional[int]											=      instantiate_from_config(A_	)
  if sd is not None:
    model.load_state_dict(A_	)
  if gpu:
    model.cuda()
  if eval_mode:
    model.eval()
  return {"model": model}







def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_	)->				Optional[Any]:
  '''simple docstring'''







  if ckpt:
    a						:		int											=      torch.load(A_  ,       map_location="cpu"	)
    a						:		Any											=      pl_sd["global_step"]
    print(F'''loaded model from global step {global_step}.'''	)
  else:
    a						:		Tuple											=      {"state_dict": None}
    a						:		int											=      None
  a						:		List[str]											=      load_model_from_config(config.model  ,       pl_sd["state_dict"]  ,       gpu=A_  ,       eval_mode=A_	)["model"]
  return model, global_step










"""simple docstring"""







import argparse
import json

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

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation


def 					lowercase      (    A_	)->				Optional[Any]:
  '''simple docstring'''







  a						:		Optional[Any]											=      384
  if "tiny" in model_name:
    a						:		List[str]											=      [3, 3, 9, 3]
    a						:		Optional[Any]											=      [96, 192, 384, 768]
  if "small" in model_name:
    a						:		Tuple											=      [3, 3, 27, 3]
    a						:		str											=      [96, 192, 384, 768]
  if "base" in model_name:
    a						:		Union[str, Any]											=      [3, 3, 27, 3]
    a						:		Dict											=      [128, 256, 512, 1_024]
    a						:		Any											=      512
  if "large" in model_name:
    a						:		Optional[Any]											=      [3, 3, 27, 3]
    a						:		str											=      [192, 384, 768, 1_536]
    a						:		Dict											=      768
  if "xlarge" in model_name:
    a						:		str											=      [3, 3, 27, 3]
    a						:		List[Any]											=      [256, 512, 1_024, 2_048]
    a						:		List[Any]											=      1_024

  # set label information
  a						:		int											=      150
  a						:		str											=      "huggingface/label-files"
  a						:		Tuple											=      "ade20k-id2label.json"
  a						:		Dict											=      json.load(open(hf_hub_download(A_  ,       A_  ,       repo_type="dataset"	)  ,       "r"	)	)
  a						:		int											=      {int(A_	): v for k, v in idalabel.items()}
  a						:		List[Any]											=      {v: k for k, v in idalabel.items()}

  a						:		Optional[int]											=      ConvNextConfig(
      depths=A_  ,       hidden_sizes=A_  ,       out_features=["stage1", "stage2", "stage3", "stage4"]	)
  a						:		Tuple											=      UperNetConfig(
      backbone_config=A_  ,       auxiliary_in_channels=A_  ,       num_labels=A_  ,       idalabel=A_  ,       labelaid=A_  ,       )

  return config


def 					lowercase      (    A_	)->				Tuple:
  '''simple docstring'''







  a						:		int											=      []

  # fmt: off
  # stem
  rename_keys.append(("backbone.downsample_layers.0.0.weight", "backbone.embeddings.patch_embeddings.weight")	)
  rename_keys.append(("backbone.downsample_layers.0.0.bias", "backbone.embeddings.patch_embeddings.bias")	)
  rename_keys.append(("backbone.downsample_layers.0.1.weight", "backbone.embeddings.layernorm.weight")	)
  rename_keys.append(("backbone.downsample_layers.0.1.bias", "backbone.embeddings.layernorm.bias")	)
  # stages
  for i in range(len(config.backbone_config.depths	)	):
    for j in range(config.backbone_config.depths[i]	):
      rename_keys.append((F'''backbone.stages.{i}.{j}.gamma''', F'''backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter''')	)
      rename_keys.append((F'''backbone.stages.{i}.{j}.depthwise_conv.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.dwconv.weight''')	)
      rename_keys.append((F'''backbone.stages.{i}.{j}.depthwise_conv.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.dwconv.bias''')	)
      rename_keys.append((F'''backbone.stages.{i}.{j}.norm.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.layernorm.weight''')	)
      rename_keys.append((F'''backbone.stages.{i}.{j}.norm.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.layernorm.bias''')	)
      rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv1.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight''')	)
      rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv1.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias''')	)
      rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv2.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight''')	)
      rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv2.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias''')	)
    if i > 0:
      rename_keys.append((F'''backbone.downsample_layers.{i}.0.weight''', F'''backbone.encoder.stages.{i}.downsampling_layer.0.weight''')	)
      rename_keys.append((F'''backbone.downsample_layers.{i}.0.bias''', F'''backbone.encoder.stages.{i}.downsampling_layer.0.bias''')	)
      rename_keys.append((F'''backbone.downsample_layers.{i}.1.weight''', F'''backbone.encoder.stages.{i}.downsampling_layer.1.weight''')	)
      rename_keys.append((F'''backbone.downsample_layers.{i}.1.bias''', F'''backbone.encoder.stages.{i}.downsampling_layer.1.bias''')	)

    rename_keys.append((F'''backbone.norm{i}.weight''', F'''backbone.hidden_states_norms.stage{i+1}.weight''')	)
    rename_keys.append((F'''backbone.norm{i}.bias''', F'''backbone.hidden_states_norms.stage{i+1}.bias''')	)

  # decode head
  rename_keys.extend(
      [
          ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
          ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
          ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
          ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
      ]	)
  # fmt: on

  return rename_keys


def 					lowercase      (    A_  ,       A_  ,       A_	)->				str:
  '''simple docstring'''







  a						:		str											=      dct.pop(A_	)
  a						:		str											=      val


def 					lowercase      (    A_  ,       A_  ,       A_	)->				int:
  '''simple docstring'''







  a						:		Any											=      {
      "upernet-convnext-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth",
      "upernet-convnext-small": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth",
      "upernet-convnext-base": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth",
      "upernet-convnext-large": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth",
      "upernet-convnext-xlarge": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth",
  }
  a						:		Tuple											=      model_name_to_url[model_name]
  a						:		int											=      torch.hub.load_state_dict_from_url(A_  ,       map_location="cpu"	)["state_dict"]

  a						:		Optional[Any]											=      get_upernet_config(A_	)
  a						:		int											=      UperNetForSemanticSegmentation(A_	)
  model.eval()

  # replace "bn" => "batch_norm"
  for key in state_dict.copy().keys():
    a						:		Tuple											=      state_dict.pop(A_	)
    if "bn" in key:
      a						:		Optional[int]											=      key.replace("bn"  ,       "batch_norm"	)
    a						:		Any											=      val

  # rename keys
  a						:		Any											=      create_rename_keys(A_	)
  for src, dest in rename_keys:
    rename_key(A_  ,       A_  ,       A_	)

  model.load_state_dict(A_	)

  # verify on image
  a						:		Dict											=      "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
  a						:		Any											=      Image.open(requests.get(A_  ,       stream=A_	).raw	).convert("RGB"	)

  a						:		Union[str, Any]											=      SegformerImageProcessor()
  a						:		Dict											=      processor(A_  ,       return_tensors="pt"	).pixel_values

  with torch.no_grad():
    a						:		Any											=      model(A_	)

  if model_name == "upernet-convnext-tiny":
    a						:		List[str]											=      torch.tensor(
        [[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]]	)
  elif model_name == "upernet-convnext-small":
    a						:		int											=      torch.tensor(
        [[-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.7_6_3_8, -8.7_6_3_8, -8.6_2_4_0]]	)
  elif model_name == "upernet-convnext-base":
    a						:		Union[str, Any]											=      torch.tensor(
        [[-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.7_6_6_9, -8.7_6_6_9, -8.6_0_2_1]]	)
  elif model_name == "upernet-convnext-large":
    a						:		Union[str, Any]											=      torch.tensor(
        [[-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_3_1_0, -8.6_3_1_0, -8.5_9_6_4]]	)
  elif model_name == "upernet-convnext-xlarge":
    a						:		str											=      torch.tensor(
        [[-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_3_7_9, -8.4_3_7_9, -8.3_4_1_2]]	)
  print("Logits:"  ,       outputs.logits[0, 0, :3, :3]	)
  assert torch.allclose(outputs.logits[0, 0, :3, :3]  ,       A_  ,       atol=1e-4	)
  print("Looks ok!"	)

  if pytorch_dump_folder_path is not None:
    print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''	)
    model.save_pretrained(A_	)
    print(F'''Saving processor to {pytorch_dump_folder_path}'''	)
    processor.save_pretrained(A_	)

  if push_to_hub:
    print(F'''Pushing model and processor for {model_name} to hub'''	)
    model.push_to_hub(F'''openmmlab/{model_name}'''	)
    processor.push_to_hub(F'''openmmlab/{model_name}'''	)


if __name__ == "__main__":
 __lowercase    =				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--model_name""",
     default="""upernet-convnext-tiny""",
     type=str,
     choices=[f'''upernet-convnext-{size}''' for size in ["""tiny""", """small""", """base""", """large""", """xlarge"""]],
     help="""Name of the ConvNext UperNet 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."""
 )
 parser.add_argument(
     """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
 )

 __lowercase    =				parser.parse_args()
 convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






