



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  :     Optional[Any]					,				_a  :     int					,				_a  :     Tuple					,				_a  :     List[str] = None					,				_a  :     str = None					,				_a  :     str = None					,				_a  :     Union[str, Any] = "longest"					,				_a  :     Any = True					,				_a  :     int = 5_1_2					,				_a  :     int = None					,				_a  :     Optional[int] = True					,				_a  :     Optional[Any] = True					,				_a  :     List[str] = True					,				**_a  :     List[str]					,				):
													super().__init__()
													if use_fast_bert_tokenizer:
																				a__:				Optional[Any] 							=FastBertTokenizer(
																				    lowerCAmelCase__					,				token_out_type=tf.intaa					,				lower_case_nfd_strip_accents=lowerCAmelCase__					,				**lowerCAmelCase__	)
													else:
																				a__:				int 							=tf.lookup.StaticVocabularyTable(
																				    tf.lookup.KeyValueTensorInitializer(
																				        keys=lowerCAmelCase__					,				key_dtype=tf.string					,				values=tf.range(tf.size(lowerCAmelCase__					,				out_type=tf.intaa	)					,				dtype=tf.intaa	)					,				value_dtype=tf.intaa					,				)					,				num_oov_buckets=1					,				)
																				a__:				List[str] 							=BertTokenizerLayer(
																				    lowerCAmelCase__					,				token_out_type=tf.intaa					,				lower_case=lowerCAmelCase__					,				**lowerCAmelCase__	)

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


						@classmethod
						def      _lowerCamelCase	(       cls  :     List[str]					,				_a  :     Union[str, Any]					,				**_a  :     List[str]	):  # noqa: F821
													a__:				Any 							=kwargs.pop("do_lower_case"					,				lowerCAmelCase__	)
													a__:				Dict 							=tokenizer.do_lower_case if do_lower_case is None else do_lower_case
													a__:				Optional[Any] 							=kwargs.pop("cls_token_id"					,				lowerCAmelCase__	)
													a__:				int 							=tokenizer.cls_token_id if cls_token_id is None else cls_token_id
													a__:				Any 							=kwargs.pop("sep_token_id"					,				lowerCAmelCase__	)
													a__:				Any 							=tokenizer.sep_token_id if sep_token_id is None else sep_token_id
													a__:				int 							=kwargs.pop("pad_token_id"					,				lowerCAmelCase__	)
													a__:				Optional[Any] 							=tokenizer.pad_token_id if pad_token_id is None else pad_token_id

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


						@classmethod
						def      _lowerCamelCase	(       cls  :     str					,				_a  :     Union[str, Any]					,				*_a  :     Dict					,				**_a  :     Optional[int]	):




													try:
																				a__:				int 							=BertTokenizer.from_pretrained(lowerCAmelCase__					,				*lowerCAmelCase__					,				**lowerCAmelCase__	)
													except:  # noqa: E722
																				from .tokenization_bert_fast import BertTokenizerFast

																				a__:				Dict 							=BertTokenizerFast.from_pretrained(lowerCAmelCase__					,				*lowerCAmelCase__					,				**lowerCAmelCase__	)
													return cls.from_tokenizer(lowerCAmelCase__					,				**lowerCAmelCase__	)


						def      _lowerCamelCase	(       self  :     int					,				_a  :     List[str]	):




													if self.do_lower_case:
																				a__:				str 							=case_fold_utfa(lowerCAmelCase__	)
													a__:				Optional[int] 							=self.tf_tokenizer.tokenize(lowerCAmelCase__	)
													return tokens.merge_dims(1					,				-1	)


						def      _lowerCamelCase	(       self  :     str					,				_a  :     Union[str, Any]					,				_a  :     Union[str, Any]=None					,				_a  :     Tuple=None					,				_a  :     int=None					,				_a  :     List[str]=None					,				_a  :     int=None					,				_a  :     Union[str, Any]=None					,				_a  :     Union[str, Any]=None					,				):




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

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







						def      _lowerCamelCase	(       self  :     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,
													}









def 		a__       (							A__ = 2_0							):

			try:
						SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= int(A__							)
			except (TypeError, ValueError):
						raise TypeError('Parameter n must be int or castable to int.'							)
			if n <= 0:
						raise ValueError('Parameter n must be greater than or equal to one.'							)
			SCREAMING_SNAKE_CASE_						:			int       						= 0
			while 1:
						i += n * (n - 1)
						SCREAMING_SNAKE_CASE_						:			List[str]       						= 0
						for j in range(2,	A__							):
									if i % j != 0:
												SCREAMING_SNAKE_CASE_						:			str       						= 1
												break
						if nfound == 0:
									if i == 0:
												SCREAMING_SNAKE_CASE_						:			Tuple       						= 1
									return i
			return None


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





