
"""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 					A__			(      tf.keras.layers.Layer):

		def __init__( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = "longest"			,						_SCREAMING_SNAKE_CASE = True			,						_SCREAMING_SNAKE_CASE = 5_12			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = True			,						_SCREAMING_SNAKE_CASE = True			,						_SCREAMING_SNAKE_CASE = True			,						**_SCREAMING_SNAKE_CASE			,						):
			super().__init__()
			if use_fast_bert_tokenizer:
				__lowerCAmelCase     :		str		     = FastBertTokenizer(
				    _SCREAMING_SNAKE_CASE			,						token_out_type=tf.intaa			,						lower_case_nfd_strip_accents=_SCREAMING_SNAKE_CASE			,						**_SCREAMING_SNAKE_CASE					)
			else:
				__lowerCAmelCase     :		Optional[int]		     = tf.lookup.StaticVocabularyTable(
				    tf.lookup.KeyValueTensorInitializer(
				        keys=_SCREAMING_SNAKE_CASE			,						key_dtype=tf.string			,						values=tf.range(tf.size(_SCREAMING_SNAKE_CASE			,						out_type=tf.intaa					)			,						dtype=tf.intaa					)			,						value_dtype=tf.intaa			,						)			,						num_oov_buckets=1			,						)
				__lowerCAmelCase     :		List[Any]		     = BertTokenizerLayer(
				    _SCREAMING_SNAKE_CASE			,						token_out_type=tf.intaa			,						lower_case=_SCREAMING_SNAKE_CASE			,						**_SCREAMING_SNAKE_CASE					)

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

		@classmethod
		def 				__lowerCamelCase				( cls			,						_SCREAMING_SNAKE_CASE			,						**_SCREAMING_SNAKE_CASE					):  # noqa: F821
			__lowerCAmelCase     :		Optional[Any]		     = kwargs.pop('do_lower_case'			,						_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		Optional[int]		     = tokenizer.do_lower_case if do_lower_case is None else do_lower_case
			__lowerCAmelCase     :		Tuple		     = kwargs.pop('cls_token_id'			,						_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		str		     = tokenizer.cls_token_id if cls_token_id is None else cls_token_id
			__lowerCAmelCase     :		List[str]		     = kwargs.pop('sep_token_id'			,						_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		str		     = tokenizer.sep_token_id if sep_token_id is None else sep_token_id
			__lowerCAmelCase     :		Tuple		     = kwargs.pop('pad_token_id'			,						_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		Optional[Any]		     = tokenizer.pad_token_id if pad_token_id is None else pad_token_id

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

		@classmethod
		def 				__lowerCamelCase				( cls			,						_SCREAMING_SNAKE_CASE			,						*_SCREAMING_SNAKE_CASE			,						**_SCREAMING_SNAKE_CASE					):
			try:
				__lowerCAmelCase     :		Any		     = BertTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE			,						*_SCREAMING_SNAKE_CASE			,						**_SCREAMING_SNAKE_CASE					)
			except:  # noqa: E722
				from .tokenization_bert_fast import BertTokenizerFast

				__lowerCAmelCase     :		str		     = BertTokenizerFast.from_pretrained(_SCREAMING_SNAKE_CASE			,						*_SCREAMING_SNAKE_CASE			,						**_SCREAMING_SNAKE_CASE					)
			return cls.from_tokenizer(_SCREAMING_SNAKE_CASE			,						**_SCREAMING_SNAKE_CASE					)

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE					):
			if self.do_lower_case:
				__lowerCAmelCase     :		int		     = case_fold_utfa(_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		Optional[int]		     = self.tf_tokenizer.tokenize(_SCREAMING_SNAKE_CASE					)
			return tokens.merge_dims(1			,						-1					)

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

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







		def 				__lowerCamelCase				( self					):
			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"""







def        __lowerCAmelCase							(_UpperCamelCase = 10**6						):

	if n <= 0:
		raise ValueError('Please enter an integer greater than 0'						)

	__lowerCAmelCase     :		Optional[Any]		     = list(range(n + 1						)						)
	for number in range(2			,    n + 1						):
		if phi[number] == number:
			phi[number] -= 1
			for multiple in range(number * 2			,    n + 1			,    _UpperCamelCase						):
				__lowerCAmelCase     :		Tuple		     = (phi[multiple] // number) * (number - 1)

	__lowerCAmelCase     :		Any		     = 1
	for number in range(1			,    n + 1						):
		if (answer / phi[answer]) < (number / phi[number]):
			__lowerCAmelCase     :		List[Any]		     = number

	return answer


if __name__ == "__main__":
							print(solution())