






"""simple docstring"""


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


lowercase_								=	logging.get_logger(__name__)

lowercase_								=	{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase_								=	{
    'vocab_file': {
        'microsoft/layoutlm-base-uncased': (
            'https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt'
        ),
        'microsoft/layoutlm-large-uncased': (
            'https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'microsoft/layoutlm-base-uncased': (
            'https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json'
        ),
        'microsoft/layoutlm-large-uncased': (
            'https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json'
        ),
    },
}

lowercase_								=	{
    'microsoft/layoutlm-base-uncased': 512,
    'microsoft/layoutlm-large-uncased': 512,
}

lowercase_								=	{
    'microsoft/layoutlm-base-uncased': {'do_lower_case': True},
    'microsoft/layoutlm-large-uncased': {'do_lower_case': True},
}


class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      Any							=					VOCAB_FILES_NAMES
   A_		:      Tuple							=					PRETRAINED_VOCAB_FILES_MAP
   A_		:      Optional[int]							=					PRETRAINED_INIT_CONFIGURATION
   A_		:      List[Any]							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A_		:      Optional[int]							=					LayoutLMTokenizer


   def __init__(      self					:	List[Any],			_lowerCamelCase					:	Tuple=None,			_lowerCamelCase					:	Optional[Any]=None,			_lowerCamelCase					:	Optional[int]=True,			_lowerCamelCase					:	Optional[Any]="[UNK]",			_lowerCamelCase					:	Any="[SEP]",			_lowerCamelCase					:	Optional[int]="[PAD]",			_lowerCamelCase					:	Any="[CLS]",			_lowerCamelCase					:	Any="[MASK]",			_lowerCamelCase					:	int=True,			_lowerCamelCase					:	str=None,			**_lowerCamelCase					:	Tuple,			):

     '''simple docstring'''
     super().__init__(
         _lowerCamelCase,			tokenizer_file=_lowerCamelCase,			do_lower_case=_lowerCamelCase,			unk_token=_lowerCamelCase,			sep_token=_lowerCamelCase,			pad_token=_lowerCamelCase,			cls_token=_lowerCamelCase,			mask_token=_lowerCamelCase,			tokenize_chinese_chars=_lowerCamelCase,			strip_accents=_lowerCamelCase,			**_lowerCamelCase,			)

     __A		     =					json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
     if (
         normalizer_state.get('''lowercase''',			_lowerCamelCase    ) != do_lower_case
         or normalizer_state.get('''strip_accents''',			_lowerCamelCase    ) != strip_accents
         or normalizer_state.get('''handle_chinese_chars''',			_lowerCamelCase    ) != tokenize_chinese_chars
     ):
       __A		     =					getattr(_lowerCamelCase,			normalizer_state.pop('''type'''    )    )
       __A		     =					do_lower_case
       __A		     =					strip_accents
       __A		     =					tokenize_chinese_chars
       __A		     =					normalizer_class(**_lowerCamelCase    )

     __A		     =					do_lower_case


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple,			_lowerCamelCase					:	List[Any],			_lowerCamelCase					:	Dict=None    ):

     '''simple docstring'''
     __A		     =					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

     if token_ids_a:
       output += token_ids_a + [self.sep_token_id]

     return output


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any],			_lowerCamelCase					:	List[int],			_lowerCamelCase					:	Optional[List[int]] = None    ):

     '''simple docstring'''
     __A		     =					[self.sep_token_id]
     __A		     =					[self.cls_token_id]
     if token_ids_a is None:
       return len(cls + token_ids_a + sep    ) * [0]
     return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any],			_lowerCamelCase					:	str,			_lowerCamelCase					:	Optional[str] = None    ):

     '''simple docstring'''
     __A		     =					self._tokenizer.model.save(_lowerCamelCase,			name=_lowerCamelCase    )
     return tuple(_lowerCamelCase    )








'''simple docstring'''






import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


a_       :     List[str] 							=     logging.get_logger(__name__)

a_       :     Union[str, 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 			a  (  _SCREAMING_SNAKE_CASE							):
 _lowerCAmelCase			       =						"""oneformer"""
 _lowerCAmelCase			       =						{"""hidden_size""": """hidden_dim"""}
 def __init__(			self   ,					__magic_name__ = None   ,					__magic_name__ = 2_55   ,					__magic_name__ = 1_50   ,					__magic_name__ = 0.1   ,					__magic_name__ = 2.0   ,					__magic_name__ = 5.0   ,					__magic_name__ = 5.0   ,					__magic_name__ = 0.5   ,					__magic_name__ = 0.0_7   ,					__magic_name__ = 1_25_44   ,					__magic_name__ = 3.0   ,					__magic_name__ = 0.7_5   ,					__magic_name__ = 0.0_2   ,					__magic_name__ = 1.0   ,					__magic_name__ = 1e-05   ,					__magic_name__ = False   ,					__magic_name__ = True   ,					__magic_name__ = True   ,					__magic_name__ = [4, 8, 16, 32]   ,					__magic_name__ = 77   ,					__magic_name__ = 2_56   ,					__magic_name__ = 77   ,					__magic_name__ = 6   ,					__magic_name__ = 4_94_08   ,					__magic_name__ = 2   ,					__magic_name__ = 16   ,					__magic_name__ = 2_56   ,					__magic_name__ = 2_56   ,					__magic_name__ = 2_56   ,					__magic_name__ = 10_24   ,					__magic_name__ = "GN"   ,					__magic_name__ = 6   ,					__magic_name__ = 10   ,					__magic_name__ = True   ,					__magic_name__ = 8   ,					__magic_name__ = 0.1   ,					__magic_name__ = 20_48   ,					__magic_name__ = False   ,					__magic_name__ = False   ,					__magic_name__ = 2   ,					__magic_name__ = 4   ,					**__magic_name__   ,					)       ->				Dict:
      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=__magic_name__   ,					out_features=['stage1', 'stage2', 'stage3', 'stage4']   ,					)
      elif isinstance(__magic_name__   ,					__magic_name__    ):
           _a         =				backbone_config.get('model_type'    )
           _a         =				CONFIG_MAPPING[backbone_model_type]
           _a         =				config_class.from_dict(__magic_name__    )

      _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__(**__magic_name__    )




 def 				__UpperCAmelCase		(			self    )       ->				Dict[str, any]:
      _a         =				copy.deepcopy(self.__dict__    )
      _a         =				self.backbone_config.to_dict()
      _a         =				self.__class__.model_type
      return output

