


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 							a       (      tf.keras.layers.Layer ):





     def __init__(     self       :					Optional[int]     ,						SCREAMING_SNAKE_CASE_       :					List     ,						SCREAMING_SNAKE_CASE_       :					bool     ,						SCREAMING_SNAKE_CASE_       :					int = None     ,						SCREAMING_SNAKE_CASE_       :					int = None     ,						SCREAMING_SNAKE_CASE_       :					int = None     ,						SCREAMING_SNAKE_CASE_       :					str = "longest"     ,						SCREAMING_SNAKE_CASE_       :					bool = True     ,						SCREAMING_SNAKE_CASE_       :					int = 512     ,						SCREAMING_SNAKE_CASE_       :					int = None     ,						SCREAMING_SNAKE_CASE_       :					bool = True     ,						SCREAMING_SNAKE_CASE_       :					bool = True     ,						SCREAMING_SNAKE_CASE_       :					bool = True     ,						**SCREAMING_SNAKE_CASE_       :					Any     ,						):
        super().__init__()
        if use_fast_bert_tokenizer:
           __lowerCamelCase:					List[Any]	 =						FastBertTokenizer(
               _snake_case     ,						token_out_type=tf.intaa     ,						lower_case_nfd_strip_accents=_snake_case     ,						**_snake_case				)
        else:
           __lowerCamelCase:					List[Any]	 =						tf.lookup.StaticVocabularyTable(
               tf.lookup.KeyValueTensorInitializer(
                   keys=_snake_case     ,						key_dtype=tf.string     ,						values=tf.range(tf.size(_snake_case     ,						out_type=tf.intaa				)     ,						dtype=tf.intaa				)     ,						value_dtype=tf.intaa     ,						)     ,						num_oov_buckets=1     ,						)
           __lowerCamelCase:					Any	 =						BertTokenizerLayer(
               _snake_case     ,						token_out_type=tf.intaa     ,						lower_case=_snake_case     ,						**_snake_case				)

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





     @classmethod
     def 			SCREAMING_SNAKE_CASE__							(     cls       :					Optional[Any]     ,						SCREAMING_SNAKE_CASE_       :					"PreTrainedTokenizerBase"     ,						**SCREAMING_SNAKE_CASE_       :					str				):  # noqa: F821
        __lowerCamelCase:					Tuple	 =						kwargs.pop("""do_lower_case"""     ,						_snake_case				)
        __lowerCamelCase:					Any	 =						tokenizer.do_lower_case if do_lower_case is None else do_lower_case
        __lowerCamelCase:					int	 =						kwargs.pop("""cls_token_id"""     ,						_snake_case				)
        __lowerCamelCase:					List[str]	 =						tokenizer.cls_token_id if cls_token_id is None else cls_token_id
        __lowerCamelCase:					List[Any]	 =						kwargs.pop("""sep_token_id"""     ,						_snake_case				)
        __lowerCamelCase:					str	 =						tokenizer.sep_token_id if sep_token_id is None else sep_token_id
        __lowerCamelCase:					Dict	 =						kwargs.pop("""pad_token_id"""     ,						_snake_case				)
        __lowerCamelCase:					Union[str, Any]	 =						tokenizer.pad_token_id if pad_token_id is None else pad_token_id

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





     @classmethod
     def 			SCREAMING_SNAKE_CASE__							(     cls       :					List[str]     ,						SCREAMING_SNAKE_CASE_       :					Union[str, os.PathLike]     ,						*SCREAMING_SNAKE_CASE_       :					Union[str, Any]     ,						**SCREAMING_SNAKE_CASE_       :					Union[str, Any]				):


        try:
           __lowerCamelCase:					List[str]	 =						BertTokenizer.from_pretrained(_snake_case     ,						*_snake_case     ,						**_snake_case				)
        except:  # noqa: E722
           from .tokenization_bert_fast import BertTokenizerFast

           __lowerCamelCase:					List[Any]	 =						BertTokenizerFast.from_pretrained(_snake_case     ,						*_snake_case     ,						**_snake_case				)
        return cls.from_tokenizer(_snake_case     ,						**_snake_case				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					int     ,						SCREAMING_SNAKE_CASE_       :					str				):


        if self.do_lower_case:
           __lowerCamelCase:					int	 =						case_fold_utfa(_snake_case				)
        __lowerCamelCase:					Union[str, Any]	 =						self.tf_tokenizer.tokenize(_snake_case				)
        return tokens.merge_dims(1     ,						-1				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					int     ,						SCREAMING_SNAKE_CASE_       :					str     ,						SCREAMING_SNAKE_CASE_       :					str=None     ,						SCREAMING_SNAKE_CASE_       :					Union[str, Any]=None     ,						SCREAMING_SNAKE_CASE_       :					str=None     ,						SCREAMING_SNAKE_CASE_       :					int=None     ,						SCREAMING_SNAKE_CASE_       :					Optional[int]=None     ,						SCREAMING_SNAKE_CASE_       :					Optional[Any]=None     ,						SCREAMING_SNAKE_CASE_       :					List[str]=None     ,						):


        if padding is None:
           __lowerCamelCase:					Tuple	 =						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:					Union[str, Any]	 =						self.max_length
        if truncation is None:
           __lowerCamelCase:					Tuple	 =						self.truncation
        if pad_to_multiple_of is None:
           __lowerCamelCase:					int	 =						self.pad_to_multiple_of
        if return_token_type_ids is None:
           __lowerCamelCase:					List[Any]	 =						self.return_token_type_ids
        if return_attention_mask is None:
           __lowerCamelCase:					str	 =						self.return_attention_mask
        if not isinstance(_snake_case     ,						tf.Tensor				):
           __lowerCamelCase:					List[Any]	 =						tf.convert_to_tensor(_snake_case				)
        if text_pair is not None and not isinstance(_snake_case     ,						tf.Tensor				):
           __lowerCamelCase:					int	 =						tf.convert_to_tensor(_snake_case				)
        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 ,  __lowerCamelCase:					Tuple	 =						text[:, 0], text[:, 1]
        __lowerCamelCase:					Optional[int]	 =						self.unpaired_tokenize(_snake_case				)
        if text_pair is None:  # Unpaired text
           if truncation:
              __lowerCamelCase:					Tuple	 =						text[:, : max_length - 2]  # Allow room for special tokens
           __lowerCamelCase ,  __lowerCamelCase:					Optional[int]	 =						combine_segments(
               (text,)     ,						start_of_sequence_id=self.cls_token_id     ,						end_of_segment_id=self.sep_token_id				)
        else:  # Paired text
           __lowerCamelCase:					List[str]	 =						self.unpaired_tokenize(_snake_case				)
           if truncation:
              __lowerCamelCase ,  __lowerCamelCase:					Optional[int]	 =						self.paired_trimmer.trim([text, text_pair]				)
           __lowerCamelCase ,  __lowerCamelCase:					int	 =						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:					Tuple	 =						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:					Any	 =						pad_to_multiple_of * (-tf.math.floordiv(-pad_length     ,						_snake_case				))
        else:
           __lowerCamelCase:					Optional[int]	 =						max_length

        __lowerCamelCase ,  __lowerCamelCase:					List[str]	 =						pad_model_inputs(_snake_case     ,						max_seq_length=_snake_case     ,						pad_value=self.pad_token_id				)
        __lowerCamelCase:					Optional[int]	 =						{"""input_ids""": input_ids}
        if return_attention_mask:
           __lowerCamelCase:					Optional[int]	 =						attention_mask
        if return_token_type_ids:
           __lowerCamelCase ,  __lowerCamelCase:					str	 =						pad_model_inputs(
               _snake_case     ,						max_seq_length=_snake_case     ,						pad_value=self.pad_token_id				)
           __lowerCamelCase:					Union[str, Any]	 =						token_type_ids
        return output







     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[Any]				):
        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,
        }








from .stack import Stack


def   A		(__A  :       str			)  -> bool:







     """simple docstring"""


     UpperCAmelCase_		=				Stack()
     UpperCAmelCase_		=				{'''(''': ''')''', '''[''': ''']''', '''{''': '''}'''}
     for bracket in parentheses:
          if bracket in bracket_pairs:
               stack.push(__A			)
          elif bracket in (")", "]", "}"):
               if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                    return False
     return stack.is_empty()


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

    testmod()

    snake_case_					:					Dict       						=			["((()))", "((())", "(()))"]
    print("Balanced parentheses demonstration:\n")
    for example in examples:
        snake_case_					:					Optional[int]       						=			"" if balanced_parentheses(example) else "not "
        print(f"{example} is {not_str}balanced")






