





"""simple docstring"""




import argparse
import json
import pickle
from pathlib import Path

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

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_     :						Any						=					logging.get_logger(__name__)





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       Any										=  SwinConfig.from_pretrained(
      'microsoft/swin-tiny-patch4-window7-224' ,					out_features=['stage1', 'stage2', 'stage3', 'stage4']					)
  A_  :       Any										=  MaskFormerConfig(backbone_config=a_					)

  A_  :       Optional[int]										=  'huggingface/label-files'
  if "ade20k-full" in model_name:
    # this should be ok
    A_  :       Optional[int]										=  847
    A_  :       Union[str, Any]										=  'maskformer-ade20k-full-id2label.json'
  elif "ade" in model_name:
    # this should be ok
    A_  :       str										=  150
    A_  :       Tuple										=  'ade20k-id2label.json'
  elif "coco-stuff" in model_name:
    # this should be ok
    A_  :       Optional[Any]										=  171
    A_  :       Dict										=  'maskformer-coco-stuff-id2label.json'
  elif "coco" in model_name:
    # TODO
    A_  :       Union[str, Any]										=  133
    A_  :       List[str]										=  'coco-panoptic-id2label.json'
  elif "cityscapes" in model_name:
    # this should be ok
    A_  :       Union[str, Any]										=  19
    A_  :       Dict										=  'cityscapes-id2label.json'
  elif "vistas" in model_name:
    # this should be ok
    A_  :       Optional[int]										=  65
    A_  :       Any										=  'mapillary-vistas-id2label.json'

  A_  :       str										=  json.load(open(hf_hub_download(a_ ,					a_ ,					repo_type='dataset'					) ,					'r'					)					)
  A_  :       Union[str, Any]										=  {int(a_					): v for k, v in idalabel.items()}

  return config





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       int										=  []
  # stem
  # fmt: off
  rename_keys.append(('backbone.patch_embed.proj.weight', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight')					)
  rename_keys.append(('backbone.patch_embed.proj.bias', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias')					)
  rename_keys.append(('backbone.patch_embed.norm.weight', 'model.pixel_level_module.encoder.model.embeddings.norm.weight')					)
  rename_keys.append(('backbone.patch_embed.norm.bias', 'model.pixel_level_module.encoder.model.embeddings.norm.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.layers.{i}.blocks.{j}.norm1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")					)
      rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")					)
      rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")					)
      rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")					)
      rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")					)
      rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")					)
      rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")					)
      rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")					)
      rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")					)
      rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")					)
      rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")					)
      rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")					)

    if i < 3:
      rename_keys.append((f"""backbone.layers.{i}.downsample.reduction.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""")					)
      rename_keys.append((f"""backbone.layers.{i}.downsample.norm.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""")					)
      rename_keys.append((f"""backbone.layers.{i}.downsample.norm.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""")					)
    rename_keys.append((f"""backbone.norm{i}.weight""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""")					)
    rename_keys.append((f"""backbone.norm{i}.bias""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""")					)

  # FPN
  rename_keys.append(('sem_seg_head.layer_4.weight', 'model.pixel_level_module.decoder.fpn.stem.0.weight')					)
  rename_keys.append(('sem_seg_head.layer_4.norm.weight', 'model.pixel_level_module.decoder.fpn.stem.1.weight')					)
  rename_keys.append(('sem_seg_head.layer_4.norm.bias', 'model.pixel_level_module.decoder.fpn.stem.1.bias')					)
  for source_index, target_index in zip(range(3 ,					0 ,					-1					) ,					range(0 ,					3					)					):
    rename_keys.append((f"""sem_seg_head.adapter_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""")					)
    rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""")					)
    rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""")					)
    rename_keys.append((f"""sem_seg_head.layer_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""")					)
    rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""")					)
    rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""")					)
  rename_keys.append(('sem_seg_head.mask_features.weight', 'model.pixel_level_module.decoder.mask_projection.weight')					)
  rename_keys.append(('sem_seg_head.mask_features.bias', 'model.pixel_level_module.decoder.mask_projection.bias')					)

  # Transformer decoder
  for idx in range(config.decoder_config.decoder_layers					):
    # self-attention out projection
    rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""")					)
    rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""")					)
    # cross-attention out projection
    rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""")					)
    rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""")					)
    # MLP 1
    rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc1.weight""")					)
    rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc1.bias""")					)
    # MLP 2
    rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc2.weight""")					)
    rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc2.bias""")					)
    # layernorm 1 (self-attention layernorm)
    rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""")					)
    rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""")					)
    # layernorm 2 (cross-attention layernorm)
    rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""")					)
    rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""")					)
    # layernorm 3 (final layernorm)
    rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""")					)
    rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""")					)

  rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.weight', 'model.transformer_module.decoder.layernorm.weight')					)
  rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.bias', 'model.transformer_module.decoder.layernorm.bias')					)

  # heads on top
  rename_keys.append(('sem_seg_head.predictor.query_embed.weight', 'model.transformer_module.queries_embedder.weight')					)

  rename_keys.append(('sem_seg_head.predictor.input_proj.weight', 'model.transformer_module.input_projection.weight')					)
  rename_keys.append(('sem_seg_head.predictor.input_proj.bias', 'model.transformer_module.input_projection.bias')					)

  rename_keys.append(('sem_seg_head.predictor.class_embed.weight', 'class_predictor.weight')					)
  rename_keys.append(('sem_seg_head.predictor.class_embed.bias', 'class_predictor.bias')					)

  for i in range(3					):
    rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", f"""mask_embedder.{i}.0.weight""")					)
    rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", f"""mask_embedder.{i}.0.bias""")					)
  # fmt: on

  return rename_keys





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Optional[int]										=  dct.pop(a_					)
  A_  :       Dict										=  val





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Tuple										=  [int(backbone_config.embed_dim * 2**i					) for i in range(len(backbone_config.depths					)					)]
  for i in range(len(backbone_config.depths					)					):
    A_  :       Tuple										=  num_features[i]
    for j in range(backbone_config.depths[i]					):
      # fmt: off
      # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
      A_  :       int										=  state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""					)
      A_  :       Optional[Any]										=  state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""					)
      # next, add query, keys and values (in that order) to the state dict
      A_  :       int										=  in_proj_weight[:dim, :]
      A_  :       str										=  in_proj_bias[: dim]
      A_  :       List[str]										=  in_proj_weight[
          dim : dim * 2, :
      ]
      A_  :       Tuple										=  in_proj_bias[
          dim : dim * 2
      ]
      A_  :       List[Any]										=  in_proj_weight[
          -dim :, :
      ]
      A_  :       Union[str, Any]										=  in_proj_bias[-dim :]
      # fmt: on





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       str										=  config.decoder_config.hidden_size
  for idx in range(config.decoder_config.decoder_layers					):
    # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
    A_  :       Union[str, Any]										=  state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""					)
    A_  :       Any										=  state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""					)
    # next, add query, keys and values (in that order) to the state dict
    A_  :       Union[str, Any]										=  in_proj_weight[: hidden_size, :]
    A_  :       List[Any]										=  in_proj_bias[:config.hidden_size]
    A_  :       List[Any]										=  in_proj_weight[hidden_size : hidden_size * 2, :]
    A_  :       Optional[Any]										=  in_proj_bias[hidden_size : hidden_size * 2]
    A_  :       List[Any]										=  in_proj_weight[-hidden_size :, :]
    A_  :       Tuple										=  in_proj_bias[-hidden_size :]
    # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
    A_  :       int										=  state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""					)
    A_  :       List[Any]										=  state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""					)
    # next, add query, keys and values (in that order) to the state dict
    A_  :       int										=  in_proj_weight[: hidden_size, :]
    A_  :       Tuple										=  in_proj_bias[:config.hidden_size]
    A_  :       Optional[int]										=  in_proj_weight[hidden_size : hidden_size * 2, :]
    A_  :       List[str]										=  in_proj_bias[hidden_size : hidden_size * 2]
    A_  :       List[str]										=  in_proj_weight[-hidden_size :, :]
    A_  :       Optional[Any]										=  in_proj_bias[-hidden_size :]
  # fmt: on





def 						UpperCAmelCase__		(     ):






  """simple docstring"""





  A_  :       int										=  'http://images.cocodataset.org/val2017/000000039769.jpg'
  A_  :       Dict										=  Image.open(requests.get(a_ ,					stream=a_					).raw					)
  return im





@torch.no_grad()
def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase = False					):






  """simple docstring"""





  A_  :       Optional[Any]										=  get_maskformer_config(a_					)

  # load original state_dict
  with open(a_ ,					'rb'					) as f:
    A_  :       Dict										=  pickle.load(a_					)
  A_  :       List[Any]										=  data['model']

  # for name, param in state_dict.items():
  #     print(name, param.shape)

  # rename keys
  A_  :       int										=  create_rename_keys(a_					)
  for src, dest in rename_keys:
    rename_key(a_ ,					a_ ,					a_					)
  read_in_swin_q_k_v(a_ ,					config.backbone_config					)
  read_in_decoder_q_k_v(a_ ,					a_					)

  # update to torch tensors
  for key, value in state_dict.items():
    A_  :       Any										=  torch.from_numpy(a_					)

  # load 🤗 model
  A_  :       Optional[Any]										=  MaskFormerForInstanceSegmentation(a_					)
  model.eval()

  for name, param in model.named_parameters():
    print(a_ ,					param.shape					)

  A_						,       A_  :       List[str]										=  model.load_state_dict(a_ ,					strict=a_					)
  assert missing_keys == [
      "model.pixel_level_module.encoder.model.layernorm.weight",
      "model.pixel_level_module.encoder.model.layernorm.bias",
  ]
  assert len(a_					) == 0, f"""Unexpected keys: {unexpected_keys}"""

  # verify results
  A_  :       int										=  prepare_img()
  if "vistas" in model_name:
    A_  :       str										=  65
  elif "cityscapes" in model_name:
    A_  :       Optional[Any]										=  65535
  else:
    A_  :       List[Any]										=  255
  A_  :       Optional[int]										=  True if 'ade' in model_name else False
  A_  :       int										=  MaskFormerImageProcessor(ignore_index=a_ ,					reduce_labels=a_					)

  A_  :       Dict										=  image_processor(a_ ,					return_tensors='pt'					)

  A_  :       Dict										=  model(**a_					)

  print('Logits:' ,					outputs.class_queries_logits[0, :3, :3]					)

  if model_name == "maskformer-swin-tiny-ade":
    A_  :       Optional[Any]										=  torch.tensor(
        [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]]					)
  assert torch.allclose(outputs.class_queries_logits[0, :3, :3] ,					a_ ,					atol=1E-4					)
  print('Looks ok!'					)

  if pytorch_dump_folder_path is not None:
    print(f"""Saving model and image processor to {pytorch_dump_folder_path}"""					)
    Path(a_					).mkdir(exist_ok=a_					)
    model.save_pretrained(a_					)
    image_processor.save_pretrained(a_					)

  if push_to_hub:
    print('Pushing model and image processor to the hub...'					)
    model.push_to_hub(f"""nielsr/{model_name}"""					)
    image_processor.push_to_hub(f"""nielsr/{model_name}"""					)


if __name__ == "__main__":
   lowerCamelCase_     :						int						=					argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--model_name',
       default='maskformer-swin-tiny-ade',
       type=str,
       help=('Name of the MaskFormer model you\'d like to convert',),
   )
   parser.add_argument(
       '--checkpoint_path',
       default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
       type=str,
       help='Path to the original state dict (.pth file).',
   )
   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.'
   )

   lowerCamelCase_     :						Optional[Any]						=					parser.parse_args()
   convert_maskformer_checkpoint(
       args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )

import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
					SCREAMING_SNAKE_CASE							:Any							 =				{
					    'linear': PIL.Image.Resampling.BILINEAR,
					    'bilinear': PIL.Image.Resampling.BILINEAR,
					    'bicubic': PIL.Image.Resampling.BICUBIC,
					    'lanczos': PIL.Image.Resampling.LANCZOS,
					    'nearest': PIL.Image.Resampling.NEAREST,
					}
else:
					SCREAMING_SNAKE_CASE							:int							 =				{
					    'linear': PIL.Image.LINEAR,
					    'bilinear': PIL.Image.BILINEAR,
					    'bicubic': PIL.Image.BICUBIC,
					    'lanczos': PIL.Image.LANCZOS,
					    'nearest': PIL.Image.NEAREST,
					}


def 						UpperCAmelCase			(	a_						)		->	Optional[Any]:







				"""simple docstring"""



				__A   			= (images / 2 + 0.5).clamp(0		,   1						)
				__A   			= images.cpu().permute(0		,   2		,   3		,   1						).float().numpy()
				__A   			= numpy_to_pil(a_						)
				return images


def 						UpperCAmelCase			(	a_						)		->	int:







				"""simple docstring"""



				if images.ndim == 3:
								__A   			= images[None, ...]
				__A   			= (images * 2_5_5).round().astype("uint8"						)
				if images.shape[-1] == 1:
								# special case for grayscale (single channel) images
								__A   			= [Image.fromarray(image.squeeze()		,   mode="L"						) for image in images]
				else:
								__A   			= [Image.fromarray(a_						) for image in images]

				return pil_images
