

'''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	:						Dict    ,			lowercase__	:						List    ,			lowercase__	:						bool    ,			lowercase__	:						int = None    ,			lowercase__	:						int = None    ,			lowercase__	:						int = None    ,			lowercase__	:						str = "longest"    ,			lowercase__	:						bool = True    ,			lowercase__	:						int = 5_12    ,			lowercase__	:						int = None    ,			lowercase__	:						bool = True    ,			lowercase__	:						bool = True    ,			lowercase__	:						bool = True    ,			**lowercase__	:						int    ,			)						->int:

							"""simple docstring"""



							super().__init__()
							if use_fast_bert_tokenizer:
								_lowercase							=     FastBertTokenizer(
								    lowercase__    ,			token_out_type=tf.intaa    ,			lower_case_nfd_strip_accents=lowercase__    ,			**lowercase__)
							else:
								_lowercase							=     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    ,			)
								_lowercase							=     BertTokenizerLayer(
								    lowercase__    ,			token_out_type=tf.intaa    ,			lower_case=lowercase__    ,			**lowercase__)

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

						@classmethod
						def 	_UpperCAmelCase							(		cls	:						Optional[Any]    ,			lowercase__	:						"PreTrainedTokenizerBase"    ,			**lowercase__	:						Union[str, Any])						->Optional[int]:  # noqa: F821

							"""simple docstring"""



							_lowercase							=     kwargs.pop("""do_lower_case"""    ,			lowercase__)
							_lowercase							=     tokenizer.do_lower_case if do_lower_case is None else do_lower_case
							_lowercase							=     kwargs.pop("""cls_token_id"""    ,			lowercase__)
							_lowercase							=     tokenizer.cls_token_id if cls_token_id is None else cls_token_id
							_lowercase							=     kwargs.pop("""sep_token_id"""    ,			lowercase__)
							_lowercase							=     tokenizer.sep_token_id if sep_token_id is None else sep_token_id
							_lowercase							=     kwargs.pop("""pad_token_id"""    ,			lowercase__)
							_lowercase							=     tokenizer.pad_token_id if pad_token_id is None else pad_token_id

							_lowercase							=     tokenizer.get_vocab()
							_lowercase							=     sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]    ,			key=lambda lowercase__: x[1])
							_lowercase							=     [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 	_UpperCAmelCase							(		cls	:						Union[str, Any]    ,			lowercase__	:						Union[str, os.PathLike]    ,			*lowercase__	:						Tuple    ,			**lowercase__	:						int)						->str:

							"""simple docstring"""



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

								_lowercase							=     BertTokenizerFast.from_pretrained(lowercase__    ,			*lowercase__    ,			**lowercase__)
							return cls.from_tokenizer(lowercase__    ,			**lowercase__)

						def 	_UpperCAmelCase							(		self	:						Dict    ,			lowercase__	:						str)						->Any:

							"""simple docstring"""



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

						def 	_UpperCAmelCase							(		self	:						List[str]    ,			lowercase__	:						str    ,			lowercase__	:						List[Any]=None    ,			lowercase__	:						Tuple=None    ,			lowercase__	:						int=None    ,			lowercase__	:						Dict=None    ,			lowercase__	:						Optional[Any]=None    ,			lowercase__	:						Optional[Any]=None    ,			lowercase__	:						List[str]=None    ,			)						->Optional[int]:

							"""simple docstring"""



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

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


						def 	_UpperCAmelCase							(		self	:						Optional[Any])						->Tuple:

							"""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,
							}







'''simple docstring'''







from __future__ import annotations

import math
import random






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_							,       snake_case_	):
	_lowercase							=     d * e - 1
	_lowercase							=     0
	_lowercase							=     0
	while p == 0:
		_lowercase							=     random.randint(2							,       n - 1	)
		_lowercase							=     k
		while True:
			if t % 2 == 0:
				_lowercase							=     t // 2
				_lowercase							=     (g**t) % n
				_lowercase							=     math.gcd(x - 1							,       snake_case_	)
				if x > 1 and y > 1:
					_lowercase							=     y
					_lowercase							=     n // y
					break  # find the correct factors
			else:
				break  # t is not divisible by 2, break and choose another g
	return sorted([p, q]	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




