


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 					__lowerCamelCase	(tf.keras.layers.Layer		):







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






            '''simple docstring'''




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

            __UpperCamelCase				=	vocab_list
            __UpperCamelCase				=	do_lower_case
            __UpperCamelCase				=	cls_token_id or vocab_list.index('[CLS]'			)
            __UpperCamelCase				=	sep_token_id or vocab_list.index('[SEP]'			)
            __UpperCamelCase				=	pad_token_id or vocab_list.index('[PAD]'			)
            __UpperCamelCase				=	ShrinkLongestTrimmer(max_length - 3,axis=1			)  # Allow room for special tokens
            __UpperCamelCase				=	max_length
            __UpperCamelCase				=	padding
            __UpperCamelCase				=	truncation
            __UpperCamelCase				=	pad_to_multiple_of
            __UpperCamelCase				=	return_token_type_ids
            __UpperCamelCase				=	return_attention_mask







       @classmethod
       def 						snake_case_					(	cls:				str,A_:				"PreTrainedTokenizerBase",**A_:				Any			):  # noqa: F821






            '''simple docstring'''




            __UpperCamelCase				=	kwargs.pop('do_lower_case',A_			)
            __UpperCamelCase				=	tokenizer.do_lower_case if do_lower_case is None else do_lower_case
            __UpperCamelCase				=	kwargs.pop('cls_token_id',A_			)
            __UpperCamelCase				=	tokenizer.cls_token_id if cls_token_id is None else cls_token_id
            __UpperCamelCase				=	kwargs.pop('sep_token_id',A_			)
            __UpperCamelCase				=	tokenizer.sep_token_id if sep_token_id is None else sep_token_id
            __UpperCamelCase				=	kwargs.pop('pad_token_id',A_			)
            __UpperCamelCase				=	tokenizer.pad_token_id if pad_token_id is None else pad_token_id

            __UpperCamelCase				=	tokenizer.get_vocab()
            __UpperCamelCase				=	sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()],key=lambda A_			: x[1]			)
            __UpperCamelCase				=	[entry[0] for entry in vocab]
            return cls(
                vocab_list=A_,do_lower_case=A_,cls_token_id=A_,sep_token_id=A_,pad_token_id=A_,**A_,)







       @classmethod
       def 						snake_case_					(	cls:				Any,A_:				Union[str, os.PathLike],*A_:				Optional[Any],**A_:				Dict			):






            '''simple docstring'''




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

                 __UpperCamelCase				=	BertTokenizerFast.from_pretrained(A_,*A_,**A_			)
            return cls.from_tokenizer(A_,**A_			)







       def 						snake_case_					(	self:				Optional[int],A_:				Tuple			):






            '''simple docstring'''




            if self.do_lower_case:
                 __UpperCamelCase				=	case_fold_utfa(A_			)
            __UpperCamelCase				=	self.tf_tokenizer.tokenize(A_			)
            return tokens.merge_dims(1,-1			)







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






            '''simple docstring'''




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

            __UpperCamelCase,  __UpperCamelCase				=	pad_model_inputs(A_,max_seq_length=A_,pad_value=self.pad_token_id			)
            __UpperCamelCase				=	{'input_ids': input_ids}
            if return_attention_mask:
                 __UpperCamelCase				=	attention_mask
            if return_token_type_ids:
                 __UpperCamelCase,  __UpperCamelCase				=	pad_model_inputs(
                     A_,max_seq_length=A_,pad_value=self.pad_token_id			)
                 __UpperCamelCase				=	token_type_ids
            return output







       def 						snake_case_					(	self:				Optional[int]			):






            '''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,
            }






import math






def 						_A						(						_lowercase				)			->				bool:


     """simple docstring"""


     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5			,       int(math.sqrt(_lowercase				) + 1				)			,       6				):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True






def 						_A						(						_lowercase = 2_00_00_00				)			->				int:


     """simple docstring"""


     return sum(num for num in range(3			,       _lowercase			,       2				) if is_prime(_lowercase				)				) + 2 if n > 2 else 0


if __name__ == "__main__":
    print(f"""{solution() = }""")


