


"""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()
__lowercase    =				logging.get_logger(__name__)


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







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

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

  a						:		List[Any]											=      json.load(open(hf_hub_download(A_  ,       A_  ,       repo_type="dataset"	)  ,       "r"	)	)
  a						:		Optional[int]											=      {int(A_	): v for k, v in idalabel.items()}

  return config


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







  a						:		List[Any]											=      []
  # 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 					lowercase      (    A_  ,       A_  ,       A_	)->				Union[str, Any]:
  '''simple docstring'''







  a						:		Optional[int]											=      dct.pop(A_	)
  a						:		List[str]											=      val


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







  a						:		str											=      [int(backbone_config.embed_dim * 2**i	) for i in range(len(backbone_config.depths	)	)]
  for i in range(len(backbone_config.depths	)	):
    a						:		Optional[int]											=      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						:		Optional[Any]											=      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						:		Tuple											=      in_proj_bias[: dim]
      a						:		Optional[int]											=      in_proj_weight[
          dim : dim * 2, :
      ]
      a						:		Optional[Any]											=      in_proj_bias[
          dim : dim * 2
      ]
      a						:		List[Any]											=      in_proj_weight[
          -dim :, :
      ]
      a						:		List[str]											=      in_proj_bias[-dim :]
      # fmt: on


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







  a						:		Union[str, Any]											=      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						:		List[Any]											=      state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''	)
    a						:		Optional[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						:		str											=      in_proj_weight[: hidden_size, :]
    a						:		List[Any]											=      in_proj_bias[:config.hidden_size]
    a						:		Optional[int]											=      in_proj_weight[hidden_size : hidden_size * 2, :]
    a						:		Tuple											=      in_proj_bias[hidden_size : hidden_size * 2]
    a						:		Any											=      in_proj_weight[-hidden_size :, :]
    a						:		str											=      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						:		Tuple											=      state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''	)
    a						:		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						:		str											=      in_proj_weight[: hidden_size, :]
    a						:		Optional[Any]											=      in_proj_bias[:config.hidden_size]
    a						:		Union[str, Any]											=      in_proj_weight[hidden_size : hidden_size * 2, :]
    a						:		Optional[Any]											=      in_proj_bias[hidden_size : hidden_size * 2]
    a						:		Any											=      in_proj_weight[-hidden_size :, :]
    a						:		Optional[int]											=      in_proj_bias[-hidden_size :]
  # fmt: on


def 					lowercase      (    )->				Dict:
  '''simple docstring'''







  a						:		Optional[Any]											=      "http://images.cocodataset.org/val2017/000000039769.jpg"
  a						:		List[str]											=      Image.open(requests.get(A_  ,       stream=A_	).raw	)
  return im


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







  a						:		Dict											=      get_maskformer_config(A_	)

  # load original state_dict
  with open(A_  ,       "rb"	) as f:
    a						:		Optional[Any]											=      pickle.load(A_	)
  a						:		Union[str, Any]											=      data["model"]

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

  # rename keys
  a						:		Any											=      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						:		Optional[Any]											=      torch.from_numpy(A_	)

  # load 🤗 model
  a						:		Optional[int]											=      MaskFormerForInstanceSegmentation(A_	)
  model.eval()

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

  a							,		a						:		int											=      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						:		Optional[int]											=      prepare_img()
  if "vistas" in model_name:
    a						:		Dict											=      65
  elif "cityscapes" in model_name:
    a						:		Optional[Any]											=      65_535
  else:
    a						:		List[str]											=      255
  a						:		Dict											=      True if "ade" in model_name else False
  a						:		Any											=      MaskFormerImageProcessor(ignore_index=A_  ,       reduce_labels=A_	)

  a						:		Dict											=      image_processor(A_  ,       return_tensors="pt"	)

  a						:		List[str]											=      model(**A_	)

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

  if model_name == "maskformer-swin-tiny-ade":
    a						:		str											=      torch.tensor(
        [[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]]	)
  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__":
 __lowercase    =				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."""
 )

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








"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


__lowercase    =				logging.get_logger(__name__)






class 						_A  (  _a  ):







    """simple docstring"""




    def __init__( self    :   List[Any]    ,			*__UpperCAmelCase    :   int    ,			**__UpperCAmelCase    :   List[Any]):
      warnings.warn(
          "The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
          " use DPTImageProcessor instead."    ,			__UpperCAmelCase    ,			)
      super().__init__(*__UpperCAmelCase    ,			**__UpperCAmelCase)






