code: 
  
  
  
  
  
  import copy
  from typing import Dict, Optional
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  from ..auto import CONFIG_MAPPING
  
  
  __lowerCAmelCase							:			Tuple				=logging.get_logger(__name__)
  
  __lowerCAmelCase							:			Any				={
      "shi-labs/oneformer_ade20k_swin_tiny": (
          "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
      ),
      # See all OneFormer models at https://huggingface.co/models?filter=oneformer
  }
  
  
  
  
  
  
  class      UpperCAmelCase			( a__       ):
   __lowercase     	=       "oneformer"
   __lowercase     	=       {"hidden_size": "hidden_dim"}
   def __init__(       self   :Union[str, Any]		, lowercase_   :Optional[Any] = None		, lowercase_   :Optional[Any] = 2_55		, lowercase_   :Tuple = 1_50		, lowercase_   :Optional[Any] = 0.1		, lowercase_   :Any = 2.0		, lowercase_   :Any = 5.0		, lowercase_   :Any = 5.0		, lowercase_   :Any = 0.5		, lowercase_   :Optional[Any] = 0.0_7		, lowercase_   :Optional[int] = 1_25_44		, lowercase_   :Union[str, Any] = 3.0		, lowercase_   :Optional[int] = 0.7_5		, lowercase_   :int = 0.0_2		, lowercase_   :Tuple = 1.0		, lowercase_   :str = 1E-05		, lowercase_   :Tuple = False		, lowercase_   :Union[str, Any] = True		, lowercase_   :List[str] = True		, lowercase_   :int = [4, 8, 16, 32]		, lowercase_   :List[str] = 77		, lowercase_   :Any = 2_56		, lowercase_   :int = 77		, lowercase_   :Any = 6		, lowercase_   :Dict = 4_94_08		, lowercase_   :Dict = 2		, lowercase_   :int = 16		, lowercase_   :Any = 2_56		, lowercase_   :str = 2_56		, lowercase_   :Union[str, Any] = 2_56		, lowercase_   :Dict = 10_24		, lowercase_   :List[str] = "GN"		, lowercase_   :Dict = 6		, lowercase_   :List[Any] = 10		, lowercase_   :Optional[Any] = True		, lowercase_   :int = 8		, lowercase_   :List[Any] = 0.1		, lowercase_   :str = 20_48		, lowercase_   :int = False		, lowercase_   :List[Any] = False		, lowercase_   :Tuple = 2		, lowercase_   :List[Any] = 4		, **lowercase_   :List[str]		, )->							Optional[int]:
       if backbone_config is None:
           logger.info("`backbone_config` is unset. Initializing the config with the default `Swin` backbone."							)
           A__       =    CONFIG_MAPPING['swin'](
               image_size=2_24		, in_channels=3		, patch_size=4		, embed_dim=96		, depths=[2, 2, 6, 2]		, num_heads=[3, 6, 12, 24]		, window_size=7		, drop_path_rate=0.3		, use_absolute_embeddings=SCREAMING_SNAKE_CASE_		, out_features=["stage1", "stage2", "stage3", "stage4"]		, )
       elif isinstance(SCREAMING_SNAKE_CASE_		, SCREAMING_SNAKE_CASE_							):
           A__       =    backbone_config.get("model_type"							)
           A__       =    CONFIG_MAPPING[backbone_model_type]
           A__       =    config_class.from_dict(SCREAMING_SNAKE_CASE_							)
  
       A__       =    backbone_config
  
       A__       =    ignore_value
       A__       =    num_queries
       A__       =    no_object_weight
       A__       =    class_weight
       A__       =    mask_weight
       A__       =    dice_weight
       A__       =    contrastive_weight
       A__       =    contrastive_temperature
       A__       =    train_num_points
       A__       =    oversample_ratio
       A__       =    importance_sample_ratio
       A__       =    init_std
       A__       =    init_xavier_std
       A__       =    layer_norm_eps
       A__       =    is_training
       A__       =    use_auxiliary_loss
       A__       =    output_auxiliary_logits
       A__       =    strides
       A__       =    task_seq_len
       A__       =    text_encoder_width
       A__       =    text_encoder_context_length
       A__       =    text_encoder_num_layers
       A__       =    text_encoder_vocab_size
       A__       =    text_encoder_proj_layers
       A__       =    text_encoder_n_ctx
       A__       =    conv_dim
       A__       =    mask_dim
       A__       =    hidden_dim
       A__       =    encoder_feedforward_dim
       A__       =    norm
       A__       =    encoder_layers
       A__       =    decoder_layers
       A__       =    use_task_norm
       A__       =    num_attention_heads
       A__       =    dropout
       A__       =    dim_feedforward
       A__       =    pre_norm
       A__       =    enforce_input_proj
       A__       =    query_dec_layers
       A__       =    common_stride
       A__       =    decoder_layers
  
       super().__init__(**SCREAMING_SNAKE_CASE_							)
  
  
  
   def   UpperCAmelCase_							(       self   :Dict							)->							Dict[str, any]:
       A__       =    copy.deepcopy(self.__dict__							)
       A__       =    self.backbone_config.to_dict()
       A__       =    self.__class__.model_type
       return output
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import torch
  
  from transformers import AutoTokenizer
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   def __init__(       self   :Any		, lowercase_   :Dict							)->							Any:
       A__       =    AutoTokenizer.from_pretrained(lowercase_							)
   def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :int							)->							Optional[int]:
  
       if isinstance(lowercase_		, lowercase_							) and all(isinstance(_x		, lowercase_							) for _x in x							):
           A__       =    None
           for l in x:
               A__       =    self.tokenizer(
                   lowercase_		, padding="max_length"		, max_length=3_84		, truncation=lowercase_		, return_tensors="pt"		, )
               A__       =    torch.tensor([len(lowercase_							)]							)
               if d is not None:
                   for k in d.keys():
                       A__       =    torch.cat((d[k], t[k])		, 0							)
               else:
                   A__       =    t
  
           A__       =    torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"							)							)
           A__       =    torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"							)							)
  
       elif isinstance(lowercase_		, lowercase_							) and all(isinstance(_x		, lowercase_							) for _x in x							):
           A__       =    self.tokenizer(
               lowercase_		, padding="max_length"		, max_length=3_84		, truncation=lowercase_		, return_tensors="pt"		, )
  
       else:
           raise Exception(
               "Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"
               " strings` for supports are supported."							)
  
       return d
  
  
  
   def   UpperCAmelCase_							(       self   :Union[str, Any]		, lowercase_   :List[str]		, lowercase_   :str		, lowercase_   :str		, lowercase_   :List[Any]		, lowercase_   :Dict=0.7_0							)->							int:
       A__       =    []
       for idx in range(len(W_query["input_ids"]							)							):
           A__       =    A__       =    range(p_start.shape[1]							)
  
           A__       =    []
           for start_id in start_indexes:
               for end_id in end_indexes:
                   if start_id < end_id:
                       output.append(
                           (
                               start_id,
                               end_id,
                               p_start[idx][start_id].item(),
                               p_end[idx][end_id].item(),
                           )							)
  
           output.sort(key=lambda lowercase_							: (tup[2] * tup[3])		, reverse=lowercase_							)
           A__       =    []
           for k in range(len(lowercase_							)							):
               if output[k][2] * output[k][3] >= thresh:
                   A__,			A__       =    output[k][0], output[k][1]
                   A__       =    self.tokenizer.decode(W_query["input_ids"][idx][c_start_pos:c_end_pos]							)
                   temp.append((decoded, output[k][2] * output[k][3])							)
  
           final_outputs.append(lowercase_							)
  
       return final_outputs
  
  
  
  