


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				:     Dict   , __lowerCamelCase				:     Union[str, Any]   , __lowerCamelCase				:     Optional[Any]   , __lowerCamelCase				:     Any = None   , __lowerCamelCase				:     int = None   , __lowerCamelCase				:     List[Any] = None   , __lowerCamelCase				:     str = "longest"   , __lowerCamelCase				:     Any = True   , __lowerCamelCase				:     Tuple = 512   , __lowerCamelCase				:     List[Any] = None   , __lowerCamelCase				:     Optional[Any] = True   , __lowerCamelCase				:     Dict = True   , __lowerCamelCase				:     Optional[Any] = True   , **__lowerCamelCase				:     Tuple   , ):


             '''simple docstring'''



             super().__init__()
             if use_fast_bert_tokenizer:
                   lowerCamelCase__						:							List[str]       =    FastBertTokenizer(
                       lowercase_   , token_out_type=tf.intaa   , lower_case_nfd_strip_accents=lowercase_   , **lowercase_			)
             else:
                   lowerCamelCase__						:							Optional[int]       =    tf.lookup.StaticVocabularyTable(
                       tf.lookup.KeyValueTensorInitializer(
                           keys=lowercase_   , key_dtype=tf.string   , values=tf.range(tf.size(lowercase_   , out_type=tf.intaa			)   , dtype=tf.intaa			)   , value_dtype=tf.intaa   , )   , num_oov_buckets=1   , )
                   lowerCamelCase__						:							List[str]       =    BertTokenizerLayer(
                       lowercase_   , token_out_type=tf.intaa   , lower_case=lowercase_   , **lowercase_			)

             lowerCamelCase__						:							List[Any]       =    vocab_list
             lowerCamelCase__						:							Any       =    do_lower_case
             lowerCamelCase__						:							Dict       =    cls_token_id or vocab_list.index("[CLS]"			)
             lowerCamelCase__						:							Any       =    sep_token_id or vocab_list.index("[SEP]"			)
             lowerCamelCase__						:							List[str]       =    pad_token_id or vocab_list.index("[PAD]"			)
             lowerCamelCase__						:							str       =    ShrinkLongestTrimmer(max_length - 3   , axis=1			)  # Allow room for special tokens
             lowerCamelCase__						:							List[str]       =    max_length
             lowerCamelCase__						:							Any       =    padding
             lowerCamelCase__						:							List[Any]       =    truncation
             lowerCamelCase__						:							List[Any]       =    pad_to_multiple_of
             lowerCamelCase__						:							Any       =    return_token_type_ids
             lowerCamelCase__						:							Dict       =    return_attention_mask





       @classmethod
       def 		lowerCAmelCase	(  cls				:     List[str]   , __lowerCamelCase				:     str   , **__lowerCamelCase				:     Dict			):  # noqa: F821


             '''simple docstring'''



             lowerCamelCase__						:							Tuple       =    kwargs.pop("do_lower_case"   , lowercase_			)
             lowerCamelCase__						:							Optional[Any]       =    tokenizer.do_lower_case if do_lower_case is None else do_lower_case
             lowerCamelCase__						:							Any       =    kwargs.pop("cls_token_id"   , lowercase_			)
             lowerCamelCase__						:							List[str]       =    tokenizer.cls_token_id if cls_token_id is None else cls_token_id
             lowerCamelCase__						:							Dict       =    kwargs.pop("sep_token_id"   , lowercase_			)
             lowerCamelCase__						:							Any       =    tokenizer.sep_token_id if sep_token_id is None else sep_token_id
             lowerCamelCase__						:							Optional[Any]       =    kwargs.pop("pad_token_id"   , lowercase_			)
             lowerCamelCase__						:							Any       =    tokenizer.pad_token_id if pad_token_id is None else pad_token_id

             lowerCamelCase__						:							Tuple       =    tokenizer.get_vocab()
             lowerCamelCase__						:							List[Any]       =    sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]   , key=lambda __lowerCamelCase			: x[1]			)
             lowerCamelCase__						:							Tuple       =    [entry[0] for entry in vocab]
             return cls(
                 vocab_list=lowercase_   , do_lower_case=lowercase_   , cls_token_id=lowercase_   , sep_token_id=lowercase_   , pad_token_id=lowercase_   , **lowercase_   , )





       @classmethod
       def 		lowerCAmelCase	(  cls				:     Any   , __lowerCamelCase				:     int   , *__lowerCamelCase				:     Any   , **__lowerCamelCase				:     Tuple			):


             '''simple docstring'''



             try:
                   lowerCamelCase__						:							List[Any]       =    BertTokenizer.from_pretrained(lowercase_   , *lowercase_   , **lowercase_			)
             except:  # noqa: E722
                   from .tokenization_bert_fast import BertTokenizerFast

                   lowerCamelCase__						:							Optional[Any]       =    BertTokenizerFast.from_pretrained(lowercase_   , *lowercase_   , **lowercase_			)
             return cls.from_tokenizer(lowercase_   , **lowercase_			)





       def 		lowerCAmelCase	(  self				:     Any   , __lowerCamelCase				:     List[Any]			):


             '''simple docstring'''



             if self.do_lower_case:
                   lowerCamelCase__						:							List[Any]       =    case_fold_utfa(lowercase_			)
             lowerCamelCase__						:							Any       =    self.tf_tokenizer.tokenize(lowercase_			)
             return tokens.merge_dims(1   , -1			)





       def 		lowerCAmelCase	(  self				:     Any   , __lowerCamelCase				:     Optional[int]   , __lowerCamelCase				:     Any=None   , __lowerCamelCase				:     Optional[Any]=None   , __lowerCamelCase				:     int=None   , __lowerCamelCase				:     Tuple=None   , __lowerCamelCase				:     str=None   , __lowerCamelCase				:     Union[str, Any]=None   , __lowerCamelCase				:     Optional[int]=None   , ):


             '''simple docstring'''



             if padding is None:
                   lowerCamelCase__						:							List[Any]       =    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:
                   lowerCamelCase__						:							Optional[Any]       =    self.max_length
             if truncation is None:
                   lowerCamelCase__						:							Dict       =    self.truncation
             if pad_to_multiple_of is None:
                   lowerCamelCase__						:							int       =    self.pad_to_multiple_of
             if return_token_type_ids is None:
                   lowerCamelCase__						:							Dict       =    self.return_token_type_ids
             if return_attention_mask is None:
                   lowerCamelCase__						:							int       =    self.return_attention_mask
             if not isinstance(lowercase_   , tf.Tensor			):
                   lowerCamelCase__						:							Any       =    tf.convert_to_tensor(lowercase_			)
             if text_pair is not None and not isinstance(lowercase_   , tf.Tensor			):
                   lowerCamelCase__						:							Optional[int]       =    tf.convert_to_tensor(lowercase_			)
             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:
                   lowerCamelCase__						:							List[str]       =    text[:, 0], text[:, 1]
             lowerCamelCase__						:							Optional[int]       =    self.unpaired_tokenize(lowercase_			)
             if text_pair is None:  # Unpaired text
                   if truncation:
                         lowerCamelCase__						:							str       =    text[:, : max_length - 2]  # Allow room for special tokens
                   lowerCamelCase__						:							List[Any]       =    combine_segments(
                       (text,)   , start_of_sequence_id=self.cls_token_id   , end_of_segment_id=self.sep_token_id			)
             else:  # Paired text
                   lowerCamelCase__						:							str       =    self.unpaired_tokenize(lowercase_			)
                   if truncation:
                         lowerCamelCase__						:							List[Any]       =    self.paired_trimmer.trim([text, text_pair]			)
                   lowerCamelCase__						:							Optional[Any]       =    combine_segments(
                       (text, text_pair)   , start_of_sequence_id=self.cls_token_id   , end_of_segment_id=self.sep_token_id			)
             if padding == "longest":
                   lowerCamelCase__						:							str       =    input_ids.bounding_shape(axis=1			)
                   if pad_to_multiple_of is not None:
                         # No ceiling division in tensorflow, so we negate floordiv instead
                         lowerCamelCase__						:							List[str]       =    pad_to_multiple_of * (-tf.math.floordiv(-pad_length   , lowercase_			))
             else:
                   lowerCamelCase__						:							int       =    max_length

             lowerCamelCase__						:							Dict       =    pad_model_inputs(lowercase_   , max_seq_length=lowercase_   , pad_value=self.pad_token_id			)
             lowerCamelCase__						:							Tuple       =    {"""input_ids""": input_ids}
             if return_attention_mask:
                   lowerCamelCase__						:							List[str]       =    attention_mask
             if return_token_type_ids:
                   lowerCamelCase__						:							List[Any]       =    pad_model_inputs(
                       lowercase_   , max_seq_length=lowercase_   , pad_value=self.pad_token_id			)
                   lowerCamelCase__						:							Optional[Any]       =    token_type_ids
             return output





       def 		lowerCAmelCase	(  self				:     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  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




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


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

		testmod()

