



'''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()
A__    :   str           =					logging.get_logger(__name__)





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

      __snake_case :   List[str]													=    'huggingface/label-files'
      if "ade20k-full" in model_name:
            # this should be ok
            __snake_case :   Tuple													=    8_47
            __snake_case :   List[str]													=    'maskformer-ade20k-full-id2label.json'
      elif "ade" in model_name:
            # this should be ok
            __snake_case :   str													=    1_50
            __snake_case :   Optional[int]													=    'ade20k-id2label.json'
      elif "coco-stuff" in model_name:
            # this should be ok
            __snake_case :   List[Any]													=    1_71
            __snake_case :   List[Any]													=    'maskformer-coco-stuff-id2label.json'
      elif "coco" in model_name:
            # TODO
            __snake_case :   List[str]													=    1_33
            __snake_case :   str													=    'coco-panoptic-id2label.json'
      elif "cityscapes" in model_name:
            # this should be ok
            __snake_case :   Union[str, Any]													=    19
            __snake_case :   Optional[Any]													=    'cityscapes-id2label.json'
      elif "vistas" in model_name:
            # this should be ok
            __snake_case :   Optional[int]													=    65
            __snake_case :   int													=    'mapillary-vistas-id2label.json'

      __snake_case :   List[str]													=    json.load(open(hf_hub_download(_UpperCAmelCase ,_UpperCAmelCase ,repo_type='dataset'				) ,'r'				)				)
      __snake_case :   str													=    {int(_UpperCAmelCase				): v for k, v in idalabel.items()}

      return config





def   a_   (		_UpperCAmelCase    :      int				)    ->  Union[str, Any]:
      __snake_case :   List[Any]													=    []
      # 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_   (		_UpperCAmelCase    :      Any ,_UpperCAmelCase    :      List[Any] ,_UpperCAmelCase    :      Optional[Any]				)    ->  int:
      __snake_case :   Union[str, Any]													=    dct.pop(_UpperCAmelCase				)
      __snake_case :   Tuple													=    val





def   a_   (		_UpperCAmelCase    :      Optional[int] ,_UpperCAmelCase    :      Optional[int]				)    ->  Union[str, Any]:
      __snake_case :   Optional[Any]													=    [int(backbone_config.embed_dim * 2**i				) for i in range(len(backbone_config.depths				)				)]
      for i in range(len(backbone_config.depths				)				):
            __snake_case :   List[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)
                  __snake_case :   Union[str, Any]													=    state_dict.pop(f'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''				)
                  __snake_case :   Optional[int]													=    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
                  __snake_case :   Optional[int]													=    in_proj_weight[:dim, :]
                  __snake_case :   Tuple													=    in_proj_bias[: dim]
                  __snake_case :   Dict													=    in_proj_weight[
                      dim : dim * 2, :
                  ]
                  __snake_case :   Union[str, Any]													=    in_proj_bias[
                      dim : dim * 2
                  ]
                  __snake_case :   str													=    in_proj_weight[
                      -dim :, :
                  ]
                  __snake_case :   Optional[int]													=    in_proj_bias[-dim :]
                  # fmt: on





def   a_   (		_UpperCAmelCase    :      Union[str, Any] ,_UpperCAmelCase    :      int				)    ->  Union[str, Any]:
      # fmt: off
      __snake_case :   Optional[Any]													=    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)
            __snake_case :   str													=    state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''				)
            __snake_case :   List[str]													=    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
            __snake_case :   Union[str, Any]													=    in_proj_weight[: hidden_size, :]
            __snake_case :   Any													=    in_proj_bias[:config.hidden_size]
            __snake_case :   Dict													=    in_proj_weight[hidden_size : hidden_size * 2, :]
            __snake_case :   List[str]													=    in_proj_bias[hidden_size : hidden_size * 2]
            __snake_case :   Optional[Any]													=    in_proj_weight[-hidden_size :, :]
            __snake_case :   Tuple													=    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)
            __snake_case :   List[str]													=    state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''				)
            __snake_case :   int													=    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
            __snake_case :   Optional[int]													=    in_proj_weight[: hidden_size, :]
            __snake_case :   List[Any]													=    in_proj_bias[:config.hidden_size]
            __snake_case :   str													=    in_proj_weight[hidden_size : hidden_size * 2, :]
            __snake_case :   Dict													=    in_proj_bias[hidden_size : hidden_size * 2]
            __snake_case :   Union[str, Any]													=    in_proj_weight[-hidden_size :, :]
            __snake_case :   Tuple													=    in_proj_bias[-hidden_size :]
      # fmt: on





def   a_   (		)    ->  torch.Tensor:
      __snake_case :   Optional[int]													=    'http://images.cocodataset.org/val2017/000000039769.jpg'
      __snake_case :   Dict													=    Image.open(requests.get(_UpperCAmelCase ,stream=_UpperCAmelCase				).raw				)
      return im





@torch.no_grad()
def   a_   (		_UpperCAmelCase    :      str ,_UpperCAmelCase    :      str ,_UpperCAmelCase    :      str ,_UpperCAmelCase    :      bool = False				)    ->  List[Any]:
      __snake_case :   Dict													=    get_maskformer_config(_UpperCAmelCase				)

      # load original state_dict
      with open(_UpperCAmelCase ,'rb'				) as f:
            __snake_case :   Any													=    pickle.load(_UpperCAmelCase				)
      __snake_case :   List[Any]													=    data['model']

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

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

      # update to torch tensors
      for key, value in state_dict.items():
            __snake_case :   int													=    torch.from_numpy(_UpperCAmelCase				)

      # load 🤗 model
      __snake_case :   List[Any]													=    MaskFormerForInstanceSegmentation(_UpperCAmelCase				)
      model.eval()

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

      __snake_case							,     __snake_case :   Dict													=    model.load_state_dict(_UpperCAmelCase ,strict=_UpperCAmelCase				)
      assert missing_keys == [
          "model.pixel_level_module.encoder.model.layernorm.weight",
          "model.pixel_level_module.encoder.model.layernorm.bias",
      ]
      assert len(_UpperCAmelCase				) == 0, f'''Unexpected keys: {unexpected_keys}'''

      # verify results
      __snake_case :   Union[str, Any]													=    prepare_img()
      if "vistas" in model_name:
            __snake_case :   int													=    65
      elif "cityscapes" in model_name:
            __snake_case :   List[str]													=    6_55_35
      else:
            __snake_case :   Any													=    2_55
      __snake_case :   Tuple													=    True if 'ade' in model_name else False
      __snake_case :   Any													=    MaskFormerImageProcessor(ignore_index=_UpperCAmelCase ,reduce_labels=_UpperCAmelCase				)

      __snake_case :   Union[str, Any]													=    image_processor(_UpperCAmelCase ,return_tensors='pt'				)

      __snake_case :   Optional[Any]													=    model(**_UpperCAmelCase				)

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

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

      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__    :   int           =					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__    :   Dict           =					parser.parse_args()
    convert_maskformer_checkpoint(
        args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )








'''simple docstring'''





import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= ['''image_processor''', '''tokenizer''']
 A__				= '''AutoImageProcessor'''
 A__				= '''AutoTokenizer'''
 def __init__(   self						:							List[str]      ,     __a						:							Optional[int]=None      ,     __a						:							Any=None      ,     **__a						:							Optional[int]  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   str													=    None
       if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'      ,     __a      ,     )
             __snake_case :   Tuple													=    kwargs.pop('feature_extractor'  )

       __snake_case :   Union[str, Any]													=    image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
             raise ValueError('You need to specify an `image_processor`.'  )
       if tokenizer is None:
             raise ValueError('You need to specify a `tokenizer`.'  )

       super().__init__(__a      ,     __a  )
       __snake_case :   Any													=    self.image_processor
       __snake_case :   int													=    False
 def __call__(   self						:							str      ,     *__a						:							List[str]      ,     **__a						:							List[str]  )      ->   Dict:







       '''simple docstring'''
       # For backward compatibility
       if self._in_target_context_manager:
             return self.current_processor(*__a      ,     **__a  )

       __snake_case :   List[Any]													=    kwargs.pop('images'      ,     __a  )
       __snake_case :   Any													=    kwargs.pop('text'      ,     __a  )
       if len(__a  ) > 0:
             __snake_case :   List[Any]													=    args[0]
             __snake_case :   Dict													=    args[1:]

       if images is None and text is None:
             raise ValueError('You need to specify either an `images` or `text` input to process.'  )

       if images is not None:
             __snake_case :   int													=    self.image_processor(__a      ,     *__a      ,     **__a  )
       if text is not None:
             __snake_case :   Tuple													=    self.tokenizer(__a      ,     **__a  )

       if text is None:
             return inputs
       elif images is None:
             return encodings
       else:
             __snake_case :   List[str]													=    encodings['input_ids']
             return inputs
 def A_   (   self						:							str      ,     *__a						:							Union[str, Any]      ,     **__a						:							Optional[Any]  )      ->   List[str]:







       '''simple docstring'''
       return self.tokenizer.batch_decode(*__a      ,     **__a  )
 def A_   (   self						:							Any      ,     *__a						:							List[Any]      ,     **__a						:							Dict  )      ->   List[Any]:







       '''simple docstring'''
       return self.tokenizer.decode(*__a      ,     **__a  )
 @contextmanager
 def A_   (   self						:							str  )      ->   List[Any]:







       '''simple docstring'''
       warnings.warn(
           '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
           'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
           'your images inputs, or in a separate call.'  )
       __snake_case :   Union[str, Any]													=    True
       __snake_case :   List[Any]													=    self.tokenizer
       yield
       __snake_case :   str													=    self.image_processor
       __snake_case :   Tuple													=    False
 def A_   (   self						:							List[str]      ,     __a						:							Tuple      ,     __a						:							Union[str, Any]=False      ,     __a						:							str=None  )      ->   List[str]:







       '''simple docstring'''
       if added_vocab is None:
             __snake_case :   List[Any]													=    self.tokenizer.get_added_vocab()

       __snake_case :   Tuple													=    {}

       while tokens:
             __snake_case :   Optional[Any]													=    re.search(r'<s_(.*?)>'      ,     __a      ,     re.IGNORECASE  )
             if start_token is None:
                   break
             __snake_case :   Any													=    start_token.group(1  )
             __snake_case :   Dict													=    re.search(rf'''</s_{key}>'''      ,     __a      ,     re.IGNORECASE  )
             __snake_case :   List[str]													=    start_token.group()
             if end_token is None:
                   __snake_case :   List[Any]													=    tokens.replace(__a      ,     ''  )
             else:
                   __snake_case :   int													=    end_token.group()
                   __snake_case :   Optional[int]													=    re.escape(__a  )
                   __snake_case :   List[str]													=    re.escape(__a  )
                   __snake_case :   Optional[int]													=    re.search(f'''{start_token_escaped}(.*?){end_token_escaped}'''      ,     __a      ,     re.IGNORECASE  )
                   if content is not None:
                         __snake_case :   Tuple													=    content.group(1  ).strip()
                         if r"<s_" in content and r"</s_" in content:  # non-leaf node
                               __snake_case :   str													=    self.tokenajson(__a      ,     is_inner_value=__a      ,     added_vocab=__a  )
                               if value:
                                     if len(__a  ) == 1:
                                           __snake_case :   int													=    value[0]
                                     __snake_case :   Union[str, Any]													=    value
                         else:  # leaf nodes
                               __snake_case :   Any													=    []
                               for leaf in content.split(r'<sep/>'  ):
                                     __snake_case :   str													=    leaf.strip()
                                     if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                                           __snake_case :   Any													=    leaf[1:-2]  # for categorical special tokens
                                     output[key].append(__a  )
                               if len(output[key]  ) == 1:
                                     __snake_case :   int													=    output[key][0]

                   __snake_case :   Union[str, Any]													=    tokens[tokens.find(__a  ) + len(__a  ) :].strip()
                   if tokens[:6] == r"<sep/>":  # non-leaf nodes
                         return [output] + self.tokenajson(tokens[6:]      ,     is_inner_value=__a      ,     added_vocab=__a  )

       if len(__a  ):
             return [output] if is_inner_value else output
       else:
             return [] if is_inner_value else {"text_sequence": tokens}
 @property
 def A_   (   self						:							Optional[int]  )      ->   Any:







       '''simple docstring'''
       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'      ,     __a      ,     )
       return self.image_processor_class





 @property
 def A_   (   self						:							Union[str, Any]  )      ->   Optional[int]:







       '''simple docstring'''
       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'      ,     __a      ,     )
       return self.image_processor



