






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()
_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)





def        _snake_case   (_snake_case				:       str)					->	Optional[Any]:
   _lowercase              =SwinConfig.from_pretrained(
       'microsoft/swin-tiny-patch4-window7-224'      ,    out_features=['stage1', 'stage2', 'stage3', 'stage4'])
   _lowercase              =MaskFormerConfig(backbone_config=_snake_case)

   _lowercase              ='huggingface/label-files'
   if "ade20k-full" in model_name:
      # this should be ok
      _lowercase              =847
      _lowercase              ='maskformer-ade20k-full-id2label.json'
   elif "ade" in model_name:
      # this should be ok
      _lowercase              =150
      _lowercase              ='ade20k-id2label.json'
   elif "coco-stuff" in model_name:
      # this should be ok
      _lowercase              =171
      _lowercase              ='maskformer-coco-stuff-id2label.json'
   elif "coco" in model_name:
      # TODO
      _lowercase              =133
      _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(_snake_case      ,    _snake_case      ,    repo_type='dataset')      ,    'r'))
   _lowercase              ={int(_snake_case): v for k, v in idalabel.items()}

   return config





def        _snake_case   (_snake_case				:       Optional[int])					->	Optional[int]:
   _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        _snake_case   (_snake_case				:       Any      ,    _snake_case				:       int      ,    _snake_case				:       List[str])					->	List[Any]:
   _lowercase              =dct.pop(_snake_case)
   _lowercase              =val





def        _snake_case   (_snake_case				:       str      ,    _snake_case				:       Tuple)					->	List[Any]:
   _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        _snake_case   (_snake_case				:       Tuple      ,    _snake_case				:       int)					->	Union[str, Any]:
   # fmt: off
   _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        _snake_case   ()					->	torch.Tensor:
   _lowercase              ='http://images.cocodataset.org/val2017/000000039769.jpg'
   _lowercase              =Image.open(requests.get(_snake_case      ,    stream=_snake_case).raw)
   return im





@torch.no_grad()
def        _snake_case   (_snake_case				:       str      ,    _snake_case				:       str      ,    _snake_case				:       str      ,    _snake_case				:       bool = False)					->	List[Any]:
   _lowercase              =get_maskformer_config(_snake_case)

   # load original state_dict
   with open(_snake_case      ,    'rb') as f:
      _lowercase              =pickle.load(_snake_case)
   _lowercase              =data['model']

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

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

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

   # load 🤗 model
   _lowercase              =MaskFormerForInstanceSegmentation(_snake_case)
   model.eval()

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

   _lowercase       ,       _lowercase              =model.load_state_dict(_snake_case      ,    strict=_snake_case)
   assert missing_keys == [
       "model.pixel_level_module.encoder.model.layernorm.weight",
       "model.pixel_level_module.encoder.model.layernorm.bias",
   ]
   assert len(_snake_case) == 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_5535
   else:
      _lowercase              =255
   _lowercase              =True if 'ade' in model_name else False
   _lowercase              =MaskFormerImageProcessor(ignore_index=_snake_case      ,    reduce_labels=_snake_case)

   _lowercase              =image_processor(_snake_case      ,    return_tensors='pt')

   _lowercase              =model(**_snake_case)

   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]      ,    _snake_case      ,    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(_snake_case).mkdir(exist_ok=_snake_case)
      model.save_pretrained(_snake_case)
      image_processor.save_pretrained(_snake_case)

   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__":
    _SCREAMING_SNAKE_CASE   =  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."
    )

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










import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =  {
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		List[str]					='''mvp'''
       __lowerCAmelCase    :		List[Any]					=['''past_key_values''']
       __lowerCAmelCase    :		Optional[Any]					={'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





       def __init__(				self	:Any,      snake_case	:Any=5_0267,      snake_case	:List[str]=1024,      snake_case	:List[str]=12,      snake_case	:List[Any]=4096,      snake_case	:List[Any]=16,      snake_case	:Optional[int]=12,      snake_case	:Optional[int]=4096,      snake_case	:List[str]=16,      snake_case	:Optional[Any]=0.0,      snake_case	:Tuple=0.0,      snake_case	:str="gelu",      snake_case	:int=1024,      snake_case	:Any=0.1,      snake_case	:Dict=0.0,      snake_case	:Optional[Any]=0.0,      snake_case	:int=0.0_2,      snake_case	:Union[str, Any]=0.0,      snake_case	:Tuple=False,      snake_case	:Optional[int]=True,      snake_case	:Any=1,      snake_case	:Union[str, Any]=0,      snake_case	:Optional[Any]=2,      snake_case	:Union[str, Any]=True,      snake_case	:Tuple=2,      snake_case	:Any=2,      snake_case	:List[Any]=False,      snake_case	:Optional[int]=100,      snake_case	:Union[str, Any]=800,      **snake_case	:Tuple,      ):



          """simple docstring"""

          _lowercase              =vocab_size
          _lowercase              =max_position_embeddings
          _lowercase              =d_model
          _lowercase              =encoder_ffn_dim
          _lowercase              =encoder_layers
          _lowercase              =encoder_attention_heads
          _lowercase              =decoder_ffn_dim
          _lowercase              =decoder_layers
          _lowercase              =decoder_attention_heads
          _lowercase              =dropout
          _lowercase              =attention_dropout
          _lowercase              =activation_dropout
          _lowercase              =activation_function
          _lowercase              =init_std
          _lowercase              =encoder_layerdrop
          _lowercase              =decoder_layerdrop
          _lowercase              =classifier_dropout
          _lowercase              =use_cache
          _lowercase              =encoder_layers
          _lowercase              =scale_embedding  # scale factor will be sqrt(d_model) if True
          _lowercase              =use_prompt
          _lowercase              =prompt_length
          _lowercase              =prompt_mid_dim

          super().__init__(
              pad_token_id=snake_case,      bos_token_id=snake_case,      eos_token_id=snake_case,      is_encoder_decoder=snake_case,      decoder_start_token_id=snake_case,      forced_eos_token_id=snake_case,      **snake_case,      )

          if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated',      snake_case):
             _lowercase              =self.bos_token_id
             warnings.warn(
                 f'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
                 'The config can simply be saved and uploaded again to be fixed.')


