



"""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()
UpperCAmelCase						:		Dict			= logging.get_logger(__name__)


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						Dict:







  '''simple docstring'''


  lowercase_								= SwinConfig.from_pretrained(
      """microsoft/swin-tiny-patch4-window7-224""" ,  out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]							)
  lowercase_								= MaskFormerConfig(backbone_config=__lowerCAmelCase							)

  lowercase_								= """huggingface/label-files"""
  if "ade20k-full" in model_name:
    # this should be ok
    lowercase_								= 8_47
    lowercase_								= """maskformer-ade20k-full-id2label.json"""
  elif "ade" in model_name:
    # this should be ok
    lowercase_								= 1_50
    lowercase_								= """ade20k-id2label.json"""
  elif "coco-stuff" in model_name:
    # this should be ok
    lowercase_								= 1_71
    lowercase_								= """maskformer-coco-stuff-id2label.json"""
  elif "coco" in model_name:
    # TODO
    lowercase_								= 1_33
    lowercase_								= """coco-panoptic-id2label.json"""
  elif "cityscapes" in model_name:
    # this should be ok
    lowercase_								= 19
    lowercase_								= """cityscapes-id2label.json"""
  elif "vistas" in model_name:
    # this should be ok
    lowercase_								= 65
    lowercase_								= """mapillary-vistas-id2label.json"""

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

  return config


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						Dict:







  '''simple docstring'''


  lowercase_								= []
  # 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 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						str:







  '''simple docstring'''


  lowercase_								= dct.pop(__lowerCAmelCase							)
  lowercase_								= val


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						Tuple:







  '''simple docstring'''


  lowercase_								= [int(backbone_config.embed_dim * 2**i							) for i in range(len(backbone_config.depths							)							)]
  for i in range(len(backbone_config.depths							)							):
    lowercase_								= 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)
      lowercase_								= state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''							)
      lowercase_								= 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
      lowercase_								= in_proj_weight[:dim, :]
      lowercase_								= in_proj_bias[: dim]
      lowercase_								= in_proj_weight[
          dim : dim * 2, :
      ]
      lowercase_								= in_proj_bias[
          dim : dim * 2
      ]
      lowercase_								= in_proj_weight[
          -dim :, :
      ]
      lowercase_								= in_proj_bias[-dim :]
      # fmt: on


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						Any:







  '''simple docstring'''


  lowercase_								= 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)
    lowercase_								= state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''							)
    lowercase_								= 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
    lowercase_								= in_proj_weight[: hidden_size, :]
    lowercase_								= in_proj_bias[:config.hidden_size]
    lowercase_								= in_proj_weight[hidden_size : hidden_size * 2, :]
    lowercase_								= in_proj_bias[hidden_size : hidden_size * 2]
    lowercase_								= in_proj_weight[-hidden_size :, :]
    lowercase_								= 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)
    lowercase_								= state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''							)
    lowercase_								= 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
    lowercase_								= in_proj_weight[: hidden_size, :]
    lowercase_								= in_proj_bias[:config.hidden_size]
    lowercase_								= in_proj_weight[hidden_size : hidden_size * 2, :]
    lowercase_								= in_proj_bias[hidden_size : hidden_size * 2]
    lowercase_								= in_proj_weight[-hidden_size :, :]
    lowercase_								= in_proj_bias[-hidden_size :]
  # fmt: on


def 			_SCREAMING_SNAKE_CASE						() ->						torch.Tensor:







  '''simple docstring'''


  lowercase_								= """http://images.cocodataset.org/val2017/000000039769.jpg"""
  lowercase_								= Image.open(requests.get(__lowerCAmelCase ,  stream=__lowerCAmelCase							).raw							)
  return im


@torch.no_grad()
def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase = False							) ->						Any:







  '''simple docstring'''


  lowercase_								= get_maskformer_config(__lowerCAmelCase							)

  # load original state_dict
  with open(__lowerCAmelCase ,  """rb"""							) as f:
    lowercase_								= pickle.load(__lowerCAmelCase							)
  lowercase_								= data["""model"""]

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

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

  # update to torch tensors
  for key, value in state_dict.items():
    lowercase_								= torch.from_numpy(__lowerCAmelCase							)

  # load 🤗 model
  lowercase_								= MaskFormerForInstanceSegmentation(__lowerCAmelCase							)
  model.eval()

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

  lowercase_					,    lowercase_								= model.load_state_dict(__lowerCAmelCase ,  strict=__lowerCAmelCase							)
  assert missing_keys == [
      "model.pixel_level_module.encoder.model.layernorm.weight",
      "model.pixel_level_module.encoder.model.layernorm.bias",
  ]
  assert len(__lowerCAmelCase							) == 0, F'''Unexpected keys: {unexpected_keys}'''

  # verify results
  lowercase_								= prepare_img()
  if "vistas" in model_name:
    lowercase_								= 65
  elif "cityscapes" in model_name:
    lowercase_								= 6_55_35
  else:
    lowercase_								= 2_55
  lowercase_								= True if """ade""" in model_name else False
  lowercase_								= MaskFormerImageProcessor(ignore_index=__lowerCAmelCase ,  reduce_labels=__lowerCAmelCase							)

  lowercase_								= image_processor(__lowerCAmelCase ,  return_tensors="""pt"""							)

  lowercase_								= model(**__lowerCAmelCase							)

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

  if model_name == "maskformer-swin-tiny-ade":
    lowercase_								= 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] ,  __lowerCAmelCase ,  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(__lowerCAmelCase							).mkdir(exist_ok=__lowerCAmelCase							)
    model.save_pretrained(__lowerCAmelCase							)
    image_processor.save_pretrained(__lowerCAmelCase							)

  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__":
       UpperCAmelCase						:		Union[str, Any]			= 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."
       )

       UpperCAmelCase						:		Tuple			= parser.parse_args()
       convert_maskformer_checkpoint(
           args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
       )






"""simple docstring"""
def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase=2_81_23							) ->						Optional[Any]:







  '''simple docstring'''


  lowercase_								= [1] * (limit + 1)

  for i in range(2 ,  int(limit**0.5							) + 1							):
    sum_divs[i * i] += i
    for k in range(i + 1 ,  limit // i + 1							):
      sum_divs[k * i] += k + i

  lowercase_								= set()
  lowercase_								= 0

  for n in range(1 ,  limit + 1							):
    if sum_divs[n] > n:
      abundants.add(__lowerCAmelCase							)

    if not any((n - a in abundants) for a in abundants							):
      res += n

  return res


if __name__ == "__main__":
       print(solution())

