code: 
  
  
  
  """simple docstring"""
  
  
  
  
  from collections import OrderedDict
  from typing import Any, List, Mapping, Optional
  
  from ... import PretrainedConfig, PreTrainedTokenizer
  from ...onnx import OnnxConfig, PatchingSpec
  from ...utils import TensorType, is_torch_available, logging
  
  
  _lowerCAmelCase       :      Any							=							logging.get_logger(__name__)
  
  _lowerCAmelCase       :      Tuple							=							{
      "microsoft/layoutlm-base-uncased": (
          "https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json"
      ),
      "microsoft/layoutlm-large-uncased": (
          "https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json"
      ),
  }
  
  
  
  
  
  
  class 			UpperCAmelCase_						(       _UpperCamelCase		):
        __SCREAMING_SNAKE_CASE		:     List[str]				  =				'layoutlm'
  
  
  
        def __init__(  self  :			List[str]  ,			A  :			int=3_0_5_2_2  ,			A  :			Any=7_6_8  ,			A  :			List[str]=1_2  ,			A  :			List[str]=1_2  ,			A  :			List[Any]=3_0_7_2  ,			A  :			int="gelu"  ,			A  :			str=0.1  ,			A  :			Any=0.1  ,			A  :			Union[str, Any]=5_1_2  ,			A  :			Optional[int]=2  ,			A  :			Any=0.02  ,			A  :			Optional[Any]=1e-12  ,			A  :			Tuple=0  ,			A  :			Dict="absolute"  ,			A  :			Optional[int]=True  ,			A  :			List[str]=1_0_2_4  ,			**A  :			Union[str, Any]  ,			):
             super().__init__(pad_token_id=A  ,			**A		)
             _UpperCAmelCase    :			Any              =					vocab_size
             _UpperCAmelCase    :			Optional[int]              =					hidden_size
             _UpperCAmelCase    :			int              =					num_hidden_layers
             _UpperCAmelCase    :			Tuple              =					num_attention_heads
             _UpperCAmelCase    :			Optional[Any]              =					hidden_act
             _UpperCAmelCase    :			Dict              =					intermediate_size
             _UpperCAmelCase    :			Union[str, Any]              =					hidden_dropout_prob
             _UpperCAmelCase    :			Dict              =					attention_probs_dropout_prob
             _UpperCAmelCase    :			Union[str, Any]              =					max_position_embeddings
             _UpperCAmelCase    :			List[Any]              =					type_vocab_size
             _UpperCAmelCase    :			List[Any]              =					initializer_range
             _UpperCAmelCase    :			List[str]              =					layer_norm_eps
             _UpperCAmelCase    :			Dict              =					position_embedding_type
             _UpperCAmelCase    :			List[Any]              =					use_cache
             _UpperCAmelCase    :			Optional[Any]              =					max_ad_position_embeddings
  
  
  
  
  
  
  
  class 			UpperCAmelCase_						(       _UpperCamelCase		):
  
  
  
        def __init__(  self  :			List[Any]  ,			A  :			PretrainedConfig  ,			A  :			str = "default"  ,			A  :			List[PatchingSpec] = None  ,			):
             super().__init__(A  ,			task=A  ,			patching_specs=A		)
             _UpperCAmelCase    :			Any              =					config.max_ad_position_embeddings - 1
  
  
  
        @property
        def 		snake_case_      (  self  :			int		):
             return OrderedDict(
                 [
                     ("input_ids", {0: "batch", 1: "sequence"}),
                     ("bbox", {0: "batch", 1: "sequence"}),
                     ("attention_mask", {0: "batch", 1: "sequence"}),
                     ("token_type_ids", {0: "batch", 1: "sequence"}),
                 ]		)
  
  
  
        def 		snake_case_      (  self  :			Dict  ,			A  :			PreTrainedTokenizer  ,			A  :			int = -1  ,			A  :			int = -1  ,			A  :			bool = False  ,			A  :			Optional[TensorType] = None  ,			):
             _UpperCAmelCase    :			int              =					super().generate_dummy_inputs(
                 A  ,			batch_size=A  ,			seq_length=A  ,			is_pair=A  ,			framework=A		)
  
             # Generate a dummy bbox
             _UpperCAmelCase    :			Any              =					[4_8, 8_4, 7_3, 1_2_8]
  
             if not framework == TensorType.PYTORCH:
                  raise NotImplementedError("Exporting LayoutLM to ONNX is currently only supported for PyTorch."		)
  
             if not is_torch_available():
                  raise ValueError("Cannot generate dummy inputs without PyTorch installed."		)
             import torch
  
             _UpperCAmelCase       , _UpperCAmelCase    :			Optional[int]              =					input_dict["input_ids"].shape
             _UpperCAmelCase    :			Tuple              =					torch.tensor([*[box] * seq_length]		).tile(A  ,			1  ,			1		)
             return input_dict
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  from arguments import TokenizerTrainingArguments
  from datasets import load_dataset
  from tqdm import tqdm
  
  from transformers import AutoTokenizer, HfArgumentParser
  from transformers.models.gpta.tokenization_gpta import bytes_to_unicode
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		str=10       )						->     List[Any]:
  
  
  
  
  
       '''simple docstring'''
  
       for _ in tqdm(range(0		,						args.n_examples		,						SCREAMING_SNAKE_CASE__       )       ):
            yield [next(SCREAMING_SNAKE_CASE__       )[args.text_column] for _ in range(SCREAMING_SNAKE_CASE__       )]
  
  
  # Configuration
  _lowerCAmelCase       :      Optional[Any]							=							HfArgumentParser(TokenizerTrainingArguments)
  _lowerCAmelCase       :      Dict							=							parser.parse_args()
  
  # Base tokenizer
  _lowerCAmelCase       :      int							=							AutoTokenizer.from_pretrained(args.base_tokenizer)
  _lowerCAmelCase       :      List[str]							=							list(bytes_to_unicode().values())
  
  # Load dataset
  _lowerCAmelCase       :      Dict							=							load_dataset(args.dataset_name, split="train", streaming=True)
  _lowerCAmelCase       :      List[Any]							=							iter(dataset)
  
  
  # Training and saving
  _lowerCAmelCase       :      List[str]							=							tokenizer.train_new_from_iterator(
      batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
  )
  new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
  