

"""simple docstring"""




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			:  Union[str, Any]      ,			__UpperCAmelCase			:  List      ,			__UpperCAmelCase			:  bool      ,			__UpperCAmelCase			:  int = None      ,			__UpperCAmelCase			:  int = None      ,			__UpperCAmelCase			:  int = None      ,			__UpperCAmelCase			:  str = "longest"      ,			__UpperCAmelCase			:  bool = True      ,			__UpperCAmelCase			:  int = 5_1_2      ,			__UpperCAmelCase			:  int = None      ,			__UpperCAmelCase			:  bool = True      ,			__UpperCAmelCase			:  bool = True      ,			__UpperCAmelCase			:  bool = True      ,			**__UpperCAmelCase			:  Tuple      ,			)				->      Optional[Any]:
						super().__init__()
						if use_fast_bert_tokenizer:
									SCREAMING_SNAKE_CASE__									= FastBertTokenizer(
									    __lowercase      ,			token_out_type=tf.intaa      ,			lower_case_nfd_strip_accents=__lowercase      ,			**__lowercase    )
						else:
									SCREAMING_SNAKE_CASE__									= 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      ,			)
									SCREAMING_SNAKE_CASE__									= BertTokenizerLayer(
									    __lowercase      ,			token_out_type=tf.intaa      ,			lower_case=__lowercase      ,			**__lowercase    )

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

			@classmethod
			def    SCREAMING_SNAKE_CASE			(   cls			:  Any      ,			__UpperCAmelCase			:  "PreTrainedTokenizerBase"      ,			**__UpperCAmelCase			:  Any    )				->      Tuple:  # noqa: F821
						SCREAMING_SNAKE_CASE__									= kwargs.pop("""do_lower_case"""      ,			__lowercase    )
						SCREAMING_SNAKE_CASE__									= tokenizer.do_lower_case if do_lower_case is None else do_lower_case
						SCREAMING_SNAKE_CASE__									= kwargs.pop("""cls_token_id"""      ,			__lowercase    )
						SCREAMING_SNAKE_CASE__									= tokenizer.cls_token_id if cls_token_id is None else cls_token_id
						SCREAMING_SNAKE_CASE__									= kwargs.pop("""sep_token_id"""      ,			__lowercase    )
						SCREAMING_SNAKE_CASE__									= tokenizer.sep_token_id if sep_token_id is None else sep_token_id
						SCREAMING_SNAKE_CASE__									= kwargs.pop("""pad_token_id"""      ,			__lowercase    )
						SCREAMING_SNAKE_CASE__									= tokenizer.pad_token_id if pad_token_id is None else pad_token_id

						SCREAMING_SNAKE_CASE__									= tokenizer.get_vocab()
						SCREAMING_SNAKE_CASE__									= sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]      ,			key=lambda __UpperCAmelCase    : x[1]    )
						SCREAMING_SNAKE_CASE__									= [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    SCREAMING_SNAKE_CASE			(   cls			:  str      ,			__UpperCAmelCase			:  Union[str, os.PathLike]      ,			*__UpperCAmelCase			:  Optional[int]      ,			**__UpperCAmelCase			:  int    )				->      Optional[Any]:






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

									SCREAMING_SNAKE_CASE__									= BertTokenizerFast.from_pretrained(__lowercase      ,			*__lowercase      ,			**__lowercase    )
						return cls.from_tokenizer(__lowercase      ,			**__lowercase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  int      ,			__UpperCAmelCase			:  Tuple    )				->      int:






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

			def    SCREAMING_SNAKE_CASE			(   self			:  Dict      ,			__UpperCAmelCase			:  Optional[Any]      ,			__UpperCAmelCase			:  Optional[int]=None      ,			__UpperCAmelCase			:  Any=None      ,			__UpperCAmelCase			:  str=None      ,			__UpperCAmelCase			:  Optional[int]=None      ,			__UpperCAmelCase			:  List[str]=None      ,			__UpperCAmelCase			:  str=None      ,			__UpperCAmelCase			:  Tuple=None      ,			)				->      Union[str, Any]:






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

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





			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]    )				->      List[str]:
						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,
						}





"""simple docstring"""




import math

def 		A    (       snake_case__      ):




			'''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(snake_case__      ) + 1      )						,			6      ):
						if number % i == 0 or number % (i + 2) == 0:
									return False
			return True

def 		A    (       snake_case__ = 10_00						,			snake_case__ = 10_00      ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= [0, 0, 0]  # length, a, b
			for a in range((a_limit * -1) + 1						,			snake_case__      ):
						for b in range(2						,			snake_case__      ):
									if is_prime(snake_case__      ):
												SCREAMING_SNAKE_CASE__									= 0
												SCREAMING_SNAKE_CASE__									= 0
												while is_prime((n**2) + (a * n) + b      ):
															count += 1
															n += 1
												if count > longest[0]:
															SCREAMING_SNAKE_CASE__									= [count, a, b]
			SCREAMING_SNAKE_CASE__									= longest[1] * longest[2]
			return ans


if __name__ == "__main__":
						print(solution(1_000, 1_000))


