





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()
__A     =    logging.get_logger(__name__)





def 					lowerCAmelCase_			(	__a		)					->		Optional[Any]:







  """simple docstring"""





  lowerCamelCase__:				Dict        =SwinConfig.from_pretrained(
      "microsoft/swin-tiny-patch4-window7-224"     ,   out_features=["stage1", "stage2", "stage3", "stage4"]		)
  lowerCamelCase__:				Any        =MaskFormerConfig(backbone_config=__a		)

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

  lowerCamelCase__:				Tuple        =json.load(open(hf_hub_download(__a     ,   __a     ,   repo_type="dataset"		)     ,   "r"		)		)
  lowerCamelCase__:				Tuple        ={int(__a		): v for k, v in idalabel.items()}

  return config





def 					lowerCAmelCase_			(	__a		)					->		Optional[Any]:







  """simple docstring"""





  lowerCamelCase__:				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 					lowerCAmelCase_			(	__a     ,   __a     ,   __a		)					->		int:







  """simple docstring"""





  lowerCamelCase__:				Tuple        =dct.pop(__a		)
  lowerCamelCase__:				Optional[int]        =val





def 					lowerCAmelCase_			(	__a     ,   __a		)					->		List[str]:







  """simple docstring"""





  lowerCamelCase__:				List[str]        =[int(backbone_config.embed_dim * 2**i		) for i in range(len(backbone_config.depths		)		)]
  for i in range(len(backbone_config.depths		)		):
    lowerCamelCase__:				Optional[Any]        =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)
      lowerCamelCase__:				Tuple        =state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""		)
      lowerCamelCase__:				Union[str, 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
      lowerCamelCase__:				int        =in_proj_weight[:dim, :]
      lowerCamelCase__:				Any        =in_proj_bias[: dim]
      lowerCamelCase__:				str        =in_proj_weight[
          dim : dim * 2, :
      ]
      lowerCamelCase__:				str        =in_proj_bias[
          dim : dim * 2
      ]
      lowerCamelCase__:				Union[str, Any]        =in_proj_weight[
          -dim :, :
      ]
      lowerCamelCase__:				Optional[int]        =in_proj_bias[-dim :]
      # fmt: on





def 					lowerCAmelCase_			(	__a     ,   __a		)					->		Optional[Any]:







  """simple docstring"""





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





def 					lowerCAmelCase_			(	)					->		torch.Tensor:







  """simple docstring"""





  lowerCamelCase__:				str        ="http://images.cocodataset.org/val2017/000000039769.jpg"
  lowerCamelCase__:				Any        =Image.open(requests.get(__a     ,   stream=__a		).raw		)
  return im





@torch.no_grad()
def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a     ,   __a = False		)					->		List[Any]:







  """simple docstring"""





  lowerCamelCase__:				Any        =get_maskformer_config(__a		)

  # load original state_dict
  with open(__a     ,   "rb"		) as f:
    lowerCamelCase__:				List[Any]        =pickle.load(__a		)
  lowerCamelCase__:				Any        =data["model"]

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

  # rename keys
  lowerCamelCase__:				Optional[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():
    lowerCamelCase__:				List[str]        =torch.from_numpy(__a		)

  # load 🤗 model
  lowerCamelCase__:				Union[str, Any]        =MaskFormerForInstanceSegmentation(__a		)
  model.eval()

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

  lowerCamelCase__  , lowerCamelCase__:				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
  lowerCamelCase__:				str        =prepare_img()
  if "vistas" in model_name:
    lowerCamelCase__:				List[Any]        =65
  elif "cityscapes" in model_name:
    lowerCamelCase__:				Optional[int]        =65535
  else:
    lowerCamelCase__:				int        =255
  lowerCamelCase__:				Tuple        =True if "ade" in model_name else False
  lowerCamelCase__:				Union[str, Any]        =MaskFormerImageProcessor(ignore_index=__a     ,   reduce_labels=__a		)

  lowerCamelCase__:				Union[str, Any]        =image_processor(__a     ,   return_tensors="pt"		)

  lowerCamelCase__:				Dict        =model(**__a		)

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

  if model_name == "maskformer-swin-tiny-ade":
    lowerCamelCase__:				Optional[int]        =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__":
     __A     =    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."
     )

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








def 					lowerCAmelCase_			(	__a     ,   __a		)					->		Tuple:







  """simple docstring"""





  assert x is not None
  assert y is not None

  lowerCamelCase__:				Any        =len(__a		)
  lowerCamelCase__:				int        =len(__a		)

  # declaring the array for storing the dp values
  lowerCamelCase__:				List[Any]        =[[0] * (n + 1) for _ in range(m + 1		)]  # noqa: E741

  for i in range(1     ,   m + 1		):
    for j in range(1     ,   n + 1		):
      lowerCamelCase__:				str        =1 if x[i - 1] == y[j - 1] else 0

      lowerCamelCase__:				str        =max(l[i - 1][j]     ,   l[i][j - 1]     ,   l[i - 1][j - 1] + match		)

  lowerCamelCase__:				Any        =""
  lowerCamelCase__  , lowerCamelCase__:				str        =m, n
  while i > 0 and j > 0:
    lowerCamelCase__:				Union[str, Any]        =1 if x[i - 1] == y[j - 1] else 0

    if l[i][j] == l[i - 1][j - 1] + match:
      if match == 1:
        lowerCamelCase__:				Any        =x[i - 1] + seq
      i -= 1
      j -= 1
    elif l[i][j] == l[i - 1][j]:
      i -= 1
    else:
      j -= 1

  return l[m][n], seq


if __name__ == "__main__":
     __A     =    "AGGTAB"
     __A     =    "GXTXAYB"
     __A     =    4
     __A     =    "GTAB"

     __A ,     __A     =    longest_common_subsequence(a, b)
     print("len =", ln, ", sub-sequence =", subseq)
     import doctest

     doctest.testmod()

