
import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer




class 	lowercase_      (			tf.keras.layers.Layer					):


  '''simple docstring'''







  def __init__(			self      :			Optional[Any]      ,							UpperCamelCase__      :			List      ,							UpperCamelCase__      :			bool      ,							UpperCamelCase__      :			int = None      ,							UpperCamelCase__      :			int = None      ,							UpperCamelCase__      :			int = None      ,							UpperCamelCase__      :			str = "longest"      ,							UpperCamelCase__      :			bool = True      ,							UpperCamelCase__      :			int = 512      ,							UpperCamelCase__      :			int = None      ,							UpperCamelCase__      :			bool = True      ,							UpperCamelCase__      :			bool = True      ,							UpperCamelCase__      :			bool = True      ,							**UpperCamelCase__      :			Optional[Any]      ,							)      ->     Union[str, Any]:







    """simple docstring"""






    super().__init__()
    if use_fast_bert_tokenizer:
      __magic_name__    					=	FastBertTokenizer(
          UpperCamelCase__      ,							token_out_type=tf.intaa      ,							lower_case_nfd_strip_accents=UpperCamelCase__      ,							**UpperCamelCase__   )
    else:
      __magic_name__    					=	tf.lookup.StaticVocabularyTable(
          tf.lookup.KeyValueTensorInitializer(
              keys=UpperCamelCase__      ,							key_dtype=tf.string      ,							values=tf.range(tf.size(UpperCamelCase__      ,							out_type=tf.intaa   )      ,							dtype=tf.intaa   )      ,							value_dtype=tf.intaa      ,							)      ,							num_oov_buckets=1      ,							)
      __magic_name__    					=	BertTokenizerLayer(
          UpperCamelCase__      ,							token_out_type=tf.intaa      ,							lower_case=UpperCamelCase__      ,							**UpperCamelCase__   )

    __magic_name__    					=	vocab_list
    __magic_name__    					=	do_lower_case
    __magic_name__    					=	cls_token_id or vocab_list.index("""[CLS]"""   )
    __magic_name__    					=	sep_token_id or vocab_list.index("""[SEP]"""   )
    __magic_name__    					=	pad_token_id or vocab_list.index("""[PAD]"""   )
    __magic_name__    					=	ShrinkLongestTrimmer(max_length - 3      ,							axis=1   )  # Allow room for special tokens
    __magic_name__    					=	max_length
    __magic_name__    					=	padding
    __magic_name__    					=	truncation
    __magic_name__    					=	pad_to_multiple_of
    __magic_name__    					=	return_token_type_ids
    __magic_name__    					=	return_attention_mask
  @classmethod
  def 							_lowercase							(			cls      :			Optional[int]      ,							UpperCamelCase__      :			"PreTrainedTokenizerBase"      ,							**UpperCamelCase__      :			Optional[int]   )      ->     Dict:  # noqa: F821







    """simple docstring"""






    __magic_name__    					=	kwargs.pop("""do_lower_case"""      ,							UpperCamelCase__   )
    __magic_name__    					=	tokenizer.do_lower_case if do_lower_case is None else do_lower_case
    __magic_name__    					=	kwargs.pop("""cls_token_id"""      ,							UpperCamelCase__   )
    __magic_name__    					=	tokenizer.cls_token_id if cls_token_id is None else cls_token_id
    __magic_name__    					=	kwargs.pop("""sep_token_id"""      ,							UpperCamelCase__   )
    __magic_name__    					=	tokenizer.sep_token_id if sep_token_id is None else sep_token_id
    __magic_name__    					=	kwargs.pop("""pad_token_id"""      ,							UpperCamelCase__   )
    __magic_name__    					=	tokenizer.pad_token_id if pad_token_id is None else pad_token_id

    __magic_name__    					=	tokenizer.get_vocab()
    __magic_name__    					=	sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]      ,							key=lambda UpperCamelCase__   : x[1]   )
    __magic_name__    					=	[entry[0] for entry in vocab]
    return cls(
        vocab_list=UpperCamelCase__      ,							do_lower_case=UpperCamelCase__      ,							cls_token_id=UpperCamelCase__      ,							sep_token_id=UpperCamelCase__      ,							pad_token_id=UpperCamelCase__      ,							**UpperCamelCase__      ,							)
  @classmethod
  def 							_lowercase							(			cls      :			List[str]      ,							UpperCamelCase__      :			Union[str, os.PathLike]      ,							*UpperCamelCase__      :			Optional[int]      ,							**UpperCamelCase__      :			List[str]   )      ->     List[str]:







    """simple docstring"""






    try:
      __magic_name__    					=	BertTokenizer.from_pretrained(UpperCamelCase__      ,							*UpperCamelCase__      ,							**UpperCamelCase__   )
    except:  # noqa: E722
      from .tokenization_bert_fast import BertTokenizerFast

      __magic_name__    					=	BertTokenizerFast.from_pretrained(UpperCamelCase__      ,							*UpperCamelCase__      ,							**UpperCamelCase__   )
    return cls.from_tokenizer(UpperCamelCase__      ,							**UpperCamelCase__   )
  def 							_lowercase							(			self      :			Union[str, Any]      ,							UpperCamelCase__      :			int   )      ->     int:







    """simple docstring"""






    if self.do_lower_case:
      __magic_name__    					=	case_fold_utfa(UpperCamelCase__   )
    __magic_name__    					=	self.tf_tokenizer.tokenize(UpperCamelCase__   )
    return tokens.merge_dims(1      ,							-1   )
  def 							_lowercase							(			self      :			Union[str, Any]      ,							UpperCamelCase__      :			List[Any]      ,							UpperCamelCase__      :			Dict=None      ,							UpperCamelCase__      :			Optional[int]=None      ,							UpperCamelCase__      :			Union[str, Any]=None      ,							UpperCamelCase__      :			List[Any]=None      ,							UpperCamelCase__      :			int=None      ,							UpperCamelCase__      :			Any=None      ,							UpperCamelCase__      :			Optional[Any]=None      ,							)      ->     Optional[int]:







    """simple docstring"""






    if padding is None:
      __magic_name__    					=	self.padding
    if padding not in ("longest", "max_length"):
      raise ValueError("""Padding must be either 'longest' or 'max_length'!"""   )
    if max_length is not None and text_pair is not None:
      # Because we have to instantiate a Trimmer to do it properly
      raise ValueError("""max_length cannot be overridden at call time when truncating paired texts!"""   )
    if max_length is None:
      __magic_name__    					=	self.max_length
    if truncation is None:
      __magic_name__    					=	self.truncation
    if pad_to_multiple_of is None:
      __magic_name__    					=	self.pad_to_multiple_of
    if return_token_type_ids is None:
      __magic_name__    					=	self.return_token_type_ids
    if return_attention_mask is None:
      __magic_name__    					=	self.return_attention_mask
    if not isinstance(UpperCamelCase__      ,							tf.Tensor   ):
      __magic_name__    					=	tf.convert_to_tensor(UpperCamelCase__   )
    if text_pair is not None and not isinstance(UpperCamelCase__      ,							tf.Tensor   ):
      __magic_name__    					=	tf.convert_to_tensor(UpperCamelCase__   )
    if text_pair is not None:
      if text.shape.rank > 1:
        raise ValueError("""text argument should not be multidimensional when a text pair is supplied!"""   )
      if text_pair.shape.rank > 1:
        raise ValueError("""text_pair should not be multidimensional!"""   )
    if text.shape.rank == 2:
      __magic_name__					,   __magic_name__    					=	text[:, 0], text[:, 1]
    __magic_name__    					=	self.unpaired_tokenize(UpperCamelCase__   )
    if text_pair is None:  # Unpaired text
      if truncation:
        __magic_name__    					=	text[:, : max_length - 2]  # Allow room for special tokens
      __magic_name__					,   __magic_name__    					=	combine_segments(
          (text,)      ,							start_of_sequence_id=self.cls_token_id      ,							end_of_segment_id=self.sep_token_id   )
    else:  # Paired text
      __magic_name__    					=	self.unpaired_tokenize(UpperCamelCase__   )
      if truncation:
        __magic_name__					,   __magic_name__    					=	self.paired_trimmer.trim([text, text_pair]   )
      __magic_name__					,   __magic_name__    					=	combine_segments(
          (text, text_pair)      ,							start_of_sequence_id=self.cls_token_id      ,							end_of_segment_id=self.sep_token_id   )
    if padding == "longest":
      __magic_name__    					=	input_ids.bounding_shape(axis=1   )
      if pad_to_multiple_of is not None:
        # No ceiling division in tensorflow, so we negate floordiv instead
        __magic_name__    					=	pad_to_multiple_of * (-tf.math.floordiv(-pad_length      ,							UpperCamelCase__   ))
    else:
      __magic_name__    					=	max_length

    __magic_name__					,   __magic_name__    					=	pad_model_inputs(UpperCamelCase__      ,							max_seq_length=UpperCamelCase__      ,							pad_value=self.pad_token_id   )
    __magic_name__    					=	{"""input_ids""": input_ids}
    if return_attention_mask:
      __magic_name__    					=	attention_mask
    if return_token_type_ids:
      __magic_name__					,   __magic_name__    					=	pad_model_inputs(
          UpperCamelCase__      ,							max_seq_length=UpperCamelCase__      ,							pad_value=self.pad_token_id   )
      __magic_name__    					=	token_type_ids
    return output





  def 							_lowercase							(			self      :			str   )      ->     List[Any]:







    """simple docstring"""






    return {
        "vocab_list": self.vocab_list,
        "do_lower_case": self.do_lower_case,
        "cls_token_id": self.cls_token_id,
        "sep_token_id": self.sep_token_id,
        "pad_token_id": self.pad_token_id,
    }



def a__   (	A_ ):






  '''simple docstring'''





  __magic_name__    					=	str(A_ ).strip()
  if not bin_string:
    raise ValueError("""Empty string was passed to the function""" )
  __magic_name__    					=	bin_string[0] == """-"""
  if is_negative:
    __magic_name__    					=	bin_string[1:]
  if not all(char in """01""" for char in bin_string ):
    raise ValueError("""Non-binary value was passed to the function""" )
  __magic_name__    					=	0
  for char in bin_string:
    __magic_name__    					=	2 * decimal_number + int(A_ )
  return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
    from doctest import testmod

    testmod()

