

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 	a (     A__      :  str	)       -> Optional[Any]:





      """simple docstring"""



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

      _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(A__ ,  A__ ,  repo_type='dataset'	) ,  'r'	)	)
      _lowercase      			={int(A__	): v for k, v in idalabel.items()}

      return config
def 	a (     A__      :  Dict	)       -> List[str]:





      """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 	a (     A__      :  int ,  A__      :  Any ,  A__      :  Optional[int]	)       -> str:





      """simple docstring"""



      _lowercase      			=dct.pop(A__	)
      _lowercase      			=val
def 	a (     A__      :  Optional[int] ,  A__      :  int	)       -> Any:





      """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 	a (     A__      :  str ,  A__      :  int	)       -> List[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 	a (     )       -> torch.Tensor:





      """simple docstring"""



      _lowercase      			='http://images.cocodataset.org/val2017/000000039769.jpg'
      _lowercase      			=Image.open(requests.get(A__ ,  stream=A__	).raw	)
      return im
@torch.no_grad()
def 	a (     A__      :  str ,  A__      :  str ,  A__      :  str ,  A__      :  bool = False	)       -> Union[str, Any]:





      """simple docstring"""



      _lowercase      			=get_maskformer_config(A__	)

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

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

      # rename keys
      _lowercase      			=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():
            _lowercase      			=torch.from_numpy(A__	)

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

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

      _lowercase			,			_lowercase      			=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
      _lowercase      			=prepare_img()
      if "vistas" in model_name:
            _lowercase      			=65
      elif "cityscapes" in model_name:
            _lowercase      			=65535
      else:
            _lowercase      			=255
      _lowercase      			=True if 'ade' in model_name else False
      _lowercase      			=MaskFormerImageProcessor(ignore_index=A__ ,  reduce_labels=A__	)

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

      _lowercase      			=model(**A__	)

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

      if model_name == "maskformer-swin-tiny-ade":
            _lowercase      			=torch.tensor(
                [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]	)
      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
  )





import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowercase_   							=  False
lowercase_   							=  True
lowercase_   							=  False


if __name__ == "__main__":
  lowercase_   							=  argparse.ArgumentParser()

  parser.add_argument(
      '--repo_path',
      default=None,
      type=str,
      required=True,
      help='The config json file corresponding to the architecture.',
  )

  parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

  lowercase_   							=  parser.parse_args()

  lowercase_   							=  {
      'image_size': 'sample_size',
      'num_res_blocks': 'layers_per_block',
      'block_channels': 'block_out_channels',
      'down_blocks': 'down_block_types',
      'up_blocks': 'up_block_types',
      'downscale_freq_shift': 'freq_shift',
      'resnet_num_groups': 'norm_num_groups',
      'resnet_act_fn': 'act_fn',
      'resnet_eps': 'norm_eps',
      'num_head_channels': 'attention_head_dim',
  }

  lowercase_   							=  {
      'time_steps': 'time_proj',
      'mid': 'mid_block',
      'downsample_blocks': 'down_blocks',
      'upsample_blocks': 'up_blocks',
  }

  lowercase_   							=  '' if has_file(args.repo_path, 'config.json') else 'unet'

  with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
    lowercase_   							=  reader.read()
    lowercase_   							=  json.loads(text)

  if do_only_config:
    for key in config_parameters_to_change.keys():
      config.pop(key, None)

  if has_file(args.repo_path, 'config.json'):
    lowercase_   							=  UNetaDModel(**config)
  else:
    lowercase_   							=  UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
    lowercase_   							=  class_name(**config)

  if do_only_config:
    model.save_config(os.path.join(args.repo_path, subfolder))

  lowercase_   							=  dict(model.config)

  if do_only_renaming:
    for key, value in config_parameters_to_change.items():
      if key in config:
        lowercase_   							=  config[key]
        del config[key]

    lowercase_   							=  [k.replace('UNetRes', '') for k in config['down_block_types']]
    lowercase_   							=  [k.replace('UNetRes', '') for k in config['up_block_types']]

  if do_only_weights:
    lowercase_   							=  torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))

    lowercase_   							=  {}
    for param_key, param_value in state_dict.items():
      if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
        continue
      lowercase_   							=  False
      for key, new_key in key_parameters_to_change.items():
        if not has_changed and param_key.split('.')[0] == key:
          lowercase_   							=  param_value
          lowercase_   							=  True
      if not has_changed:
        lowercase_   							=  param_value

    model.load_state_dict(new_state_dict)
    model.save_pretrained(os.path.join(args.repo_path, subfolder))


