






"""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		:  Optional[Any]						=		logging.get_logger(__name__)
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  str    ):





  '''simple docstring'''





  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							    =       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(__snake_case		,					__snake_case		,					repo_type='dataset'    )		,					'r'    )    )
  lowercase							    =       {int(__snake_case    ): v for k, v in idalabel.items()}

  return config
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Union[str, Any]    ):





  '''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 (    __snake_case			:  str		,					__snake_case			:  str		,					__snake_case			:  List[str]    ):





  '''simple docstring'''





  lowercase							    =       dct.pop(__snake_case    )
  lowercase							    =       val
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[Any]		,					__snake_case			:  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 (    __snake_case			:  List[Any]		,					__snake_case			:  str    ):





  '''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 (    ):





  '''simple docstring'''





  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 						_SCREAMING_SNAKE_CASE (    __snake_case			:  str		,					__snake_case			:  str		,					__snake_case			:  str		,					__snake_case			:  bool = False    ):





  '''simple docstring'''





  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_55_35
  else:
    lowercase							    =       2_55
  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(
        [[3.6353, -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]		,					__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__":
    _UpperCamelCase		:  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		:  List[Any]						=		parser.parse_args()
    convert_maskformer_checkpoint(
        args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )










"""simple docstring"""



import argparse
import json

from tqdm import tqdm
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       argparse.ArgumentParser()

  # Required parameters
  parser.add_argument(
      '--src_path'		,					type=__snake_case		,					default='biencoder-nq-dev.json'		,					help='Path to raw DPR training data'		,					)
  parser.add_argument(
      '--evaluation_set'		,					type=__snake_case		,					help='where to store parsed evaluation_set file'		,					)
  parser.add_argument(
      '--gold_data_path'		,					type=__snake_case		,					help='where to store parsed gold_data_path file'		,					)
  lowercase							    =       parser.parse_args()

  with open(args.src_path		,					'r'    ) as src_file, open(args.evaluation_set		,					'w'    ) as eval_file, open(
      args.gold_data_path		,					'w'    ) as gold_file:
    lowercase							    =       json.load(__snake_case    )
    for dpr_record in tqdm(__snake_case    ):
      lowercase							    =       dpr_record['question']
      lowercase							    =       [context['title'] for context in dpr_record['positive_ctxs']]
      eval_file.write(question + '\n'    )
      gold_file.write('\t'.join(__snake_case    ) + '\n'    )


if __name__ == "__main__":
    main()


