class_docstr: Generic processors for the sequence samples.
class_docstr_tok: ["Generic","processors","for","the","sequence","samples","."]
docstr: Args:
      vocab_dict_type: initial vocab dict type, one of `text` `labeling`.
      **kwargs:
docstr_tok: ["Args",":","vocab_dict_type",":","initial","vocab","dict","type",",","one","of","`","text","`","`","labeling","`",".","*","*","kwargs",":"]
code: def __init__(
      self,
      build_in_vocab: str = "text",
      min_count: int = 3,
      build_vocab_from_labels: bool = False,
      **kwargs: Any,
  ) -> None:
      """
  
      Args:
          vocab_dict_type: initial vocab dict type, one of `text` `labeling`.
          **kwargs:
      """
      super(SequenceProcessor, self).__init__(**kwargs)
  
      self.build_in_vocab = build_in_vocab
      self.min_count = min_count
      self.build_vocab_from_labels = build_vocab_from_labels
  
      if build_in_vocab == "text":
          self._initial_vocab_dic = {
              self.token_pad: 0,
              self.token_unk: 1,
              self.token_bos: 2,
              self.token_eos: 3,
          }
      elif build_in_vocab == "labeling":
          self._initial_vocab_dic = {self.token_pad: 0}
      else:
          self._initial_vocab_dic = {}
  
      self._showed_seq_len_warning = False
  
file_path: Kashgari/kashgari/processors/sequence_processor.py
imports: import collections
  import operator
  from typing import Dict, List, Any, Optional, Union
  import numpy
  import tqdm
  from tensorflow.keras.preprocessing.sequence import pad_sequences
  from kashgari.generators import CorpusGenerator
  from kashgari.logger import logger
  from kashgari.processors.abc_processor import ABCProcessor
  from kashgari.types import TextSamplesVar
label_desc: Use image node_id 2 to create a new SequenceProcessor object from inherited base classes: ABCProcessor with example: obj = SequenceProcessor(build_in_vocab, min_count, build_vocab_from_labels)