code: 
  
  
  
  """simple docstring"""
  
  
  
  
  import warnings
  from typing import List, Optional, Union
  
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
  from ...utils import TensorType
  
  
  
  
  
  
  class 			UpperCAmelCase_						(       _UpperCamelCase		):
        __SCREAMING_SNAKE_CASE		:     List[str]				  =				['image_processor', 'tokenizer']
        __SCREAMING_SNAKE_CASE		:     Union[str, Any]				  =				'LayoutLMv2ImageProcessor'
        __SCREAMING_SNAKE_CASE		:     List[Any]				  =				('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')
  
  
  
        def __init__(  self  :			Dict  ,			A  :			str=None  ,			A  :			str=None  ,			**A  :			str		):
             _UpperCAmelCase    :			Dict              =					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  ,			)
                  _UpperCAmelCase    :			int              =					kwargs.pop("feature_extractor"		)
  
             _UpperCAmelCase    :			List[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		)
  
  
  
        def __call__(  self  :			str  ,			A  :			Any  ,			A  :			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,			A  :			Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None  ,			A  :			Union[List[List[int]], List[List[List[int]]]] = None  ,			A  :			Optional[Union[List[int], List[List[int]]]] = None  ,			A  :			bool = True  ,			A  :			Union[bool, str, PaddingStrategy] = False  ,			A  :			Union[bool, str, TruncationStrategy] = False  ,			A  :			Optional[int] = None  ,			A  :			int = 0  ,			A  :			Optional[int] = None  ,			A  :			Optional[bool] = None  ,			A  :			Optional[bool] = None  ,			A  :			bool = False  ,			A  :			bool = False  ,			A  :			bool = False  ,			A  :			bool = False  ,			A  :			bool = True  ,			A  :			Optional[Union[str, TensorType]] = None  ,			**A  :			Any  ,			):
             # verify input
             if self.image_processor.apply_ocr and (boxes is not None):
                  raise ValueError(
                      "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."		)
  
             if self.image_processor.apply_ocr and (word_labels is not None):
                  raise ValueError(
                      "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."		)
  
             if return_overflowing_tokens is True and return_offsets_mapping is False:
                  raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."		)
  
             # first, apply the image processor
             _UpperCAmelCase    :			Dict              =					self.image_processor(images=A  ,			return_tensors=A		)
  
             # second, apply the tokenizer
             if text is not None and self.image_processor.apply_ocr and text_pair is None:
                  if isinstance(A  ,			A		):
                       _UpperCAmelCase    :			Union[str, Any]              =					[text]  # add batch dimension (as the image processor always adds a batch dimension)
                  _UpperCAmelCase    :			List[str]              =					features["words"]
  
             _UpperCAmelCase    :			Union[str, Any]              =					self.tokenizer(
                 text=text if text is not None else features["words"]  ,			text_pair=text_pair if text_pair is not None else None  ,			boxes=boxes if boxes is not None else features["boxes"]  ,			word_labels=A  ,			add_special_tokens=A  ,			padding=A  ,			truncation=A  ,			max_length=A  ,			stride=A  ,			pad_to_multiple_of=A  ,			return_token_type_ids=A  ,			return_attention_mask=A  ,			return_overflowing_tokens=A  ,			return_special_tokens_mask=A  ,			return_offsets_mapping=A  ,			return_length=A  ,			verbose=A  ,			return_tensors=A  ,			**A  ,			)
  
             # add pixel values
             _UpperCAmelCase    :			Tuple              =					features.pop("pixel_values"		)
             if return_overflowing_tokens is True:
                  _UpperCAmelCase    :			Optional[Any]              =					self.get_overflowing_images(A  ,			encoded_inputs["overflow_to_sample_mapping"]		)
             _UpperCAmelCase    :			Dict              =					images
  
             return encoded_inputs
  
  
  
        def 		snake_case_      (  self  :			List[str]  ,			A  :			Union[str, Any]  ,			A  :			List[str]		):
             # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
             _UpperCAmelCase    :			int              =					[]
             for sample_idx in overflow_to_sample_mapping:
                  images_with_overflow.append(images[sample_idx]		)
  
             if len(A		) != len(A		):
                  raise ValueError(
                      "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
                      f' {len(A		)} and {len(A		)}'		)
  
             return images_with_overflow
  
  
  
        def 		snake_case_      (  self  :			Union[str, Any]  ,			*A  :			Tuple  ,			**A  :			Dict		):
             return self.tokenizer.batch_decode(*A  ,			**A		)
  
  
  
        def 		snake_case_      (  self  :			int  ,			*A  :			Union[str, Any]  ,			**A  :			List[str]		):
             return self.tokenizer.decode(*A  ,			**A		)
  
  
  
        @property
        def 		snake_case_      (  self  :			str		):
             return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
  
  
  
        @property
        def 		snake_case_      (  self  :			Union[str, Any]		):
             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 		snake_case_      (  self  :			int		):
             warnings.warn(
                 "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."  ,			A  ,			)
             return self.image_processor
  
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)
  