






"""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				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase = None				,						_lowerCamelCase = None				,						_lowerCamelCase = None				,						_lowerCamelCase = "longest"				,						_lowerCamelCase = True				,						_lowerCamelCase = 5_1_2				,						_lowerCamelCase = None				,						_lowerCamelCase = True				,						_lowerCamelCase = True				,						_lowerCamelCase = True				,						**_lowerCamelCase				,						):
      super().__init__()
      if use_fast_bert_tokenizer:
        lowercase				       =			FastBertTokenizer(
            _lowerCamelCase				,						token_out_type=tf.intaa				,						lower_case_nfd_strip_accents=_lowerCamelCase				,						**_lowerCamelCase			)
      else:
        lowercase				       =			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				,						)
        lowercase				       =			BertTokenizerLayer(
            _lowerCamelCase				,						token_out_type=tf.intaa				,						lower_case=_lowerCamelCase				,						**_lowerCamelCase			)

      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				,						_lowerCamelCase				,						**_lowerCamelCase			):  # noqa: F821
      lowercase				       =			kwargs.pop('do_lower_case'				,						_lowerCamelCase			)
      lowercase				       =			tokenizer.do_lower_case if do_lower_case is None else do_lower_case
      lowercase				       =			kwargs.pop('cls_token_id'				,						_lowerCamelCase			)
      lowercase				       =			tokenizer.cls_token_id if cls_token_id is None else cls_token_id
      lowercase				       =			kwargs.pop('sep_token_id'				,						_lowerCamelCase			)
      lowercase				       =			tokenizer.sep_token_id if sep_token_id is None else sep_token_id
      lowercase				       =			kwargs.pop('pad_token_id'				,						_lowerCamelCase			)
      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 _lowerCamelCase			: x[1]			)
      lowercase				       =			[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      UpperCamelCase_ (     cls				,						_lowerCamelCase				,						*_lowerCamelCase				,						**_lowerCamelCase			):
      try:
        lowercase				       =			BertTokenizer.from_pretrained(_lowerCamelCase				,						*_lowerCamelCase				,						**_lowerCamelCase			)
      except:  # noqa: E722
        from .tokenization_bert_fast import BertTokenizerFast

        lowercase				       =			BertTokenizerFast.from_pretrained(_lowerCamelCase				,						*_lowerCamelCase				,						**_lowerCamelCase			)
      return cls.from_tokenizer(_lowerCamelCase				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      if self.do_lower_case:
        lowercase				       =			case_fold_utfa(_lowerCamelCase			)
      lowercase				       =			self.tf_tokenizer.tokenize(_lowerCamelCase			)
      return tokens.merge_dims(1				,						-1			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase=None				,						_lowerCamelCase=None				,						_lowerCamelCase=None				,						_lowerCamelCase=None				,						_lowerCamelCase=None				,						_lowerCamelCase=None				,						_lowerCamelCase=None				,						):
      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(_lowerCamelCase				,						tf.Tensor			):
        lowercase				       =			tf.convert_to_tensor(_lowerCamelCase			)
      if text_pair is not None and not isinstance(_lowerCamelCase				,						tf.Tensor			):
        lowercase				       =			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:
        lowercase       ,	lowercase				       =			text[:, 0], text[:, 1]
      lowercase				       =			self.unpaired_tokenize(_lowerCamelCase			)
      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(_lowerCamelCase			)
        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				,						_lowerCamelCase			))
      else:
        lowercase				       =			max_length

      lowercase       ,	lowercase				       =			pad_model_inputs(_lowerCamelCase				,						max_seq_length=_lowerCamelCase				,						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(
            _lowerCamelCase				,						max_seq_length=_lowerCamelCase				,						pad_value=self.pad_token_id			)
        lowercase				       =			token_type_ids
      return output







    def      UpperCamelCase_ (     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"""




import math






def 							lowerCAmelCase			(   UpperCamelCase_:				int       )							->				list:







  '''simple docstring'''







  if n <= 0:
    raise ValueError("Only positive integers have prime factors"       )
  _a					      =						[]
  while n % 2 == 0:
    pf.append(2       )
    _a					      =						int(n / 2       )
  for i in range(3  ,			int(math.sqrt(UpperCamelCase_       )       ) + 1  ,			2       ):
    while n % i == 0:
      pf.append(UpperCamelCase_       )
      _a					      =						int(n / i       )
  if n > 2:
    pf.append(UpperCamelCase_       )
  return pf






def 							lowerCAmelCase			(   UpperCamelCase_:				int       )							->				int:







  '''simple docstring'''







  if n <= 0:
    raise ValueError("Only positive numbers are accepted"       )
  _a					      =						1
  _a					      =						1
  while n % 2 == 0:
    temp += 1
    _a					      =						int(n / 2       )
  div *= temp
  for i in range(3  ,			int(math.sqrt(UpperCamelCase_       )       ) + 1  ,			2       ):
    _a					      =						1
    while n % i == 0:
      temp += 1
      _a					      =						int(n / i       )
    div *= temp
  if n > 1:
    div *= 2
  return div






def 							lowerCAmelCase			(   UpperCamelCase_:				int       )							->				int:







  '''simple docstring'''







  if n <= 0:
    raise ValueError("Only positive numbers are accepted"       )
  _a					      =						1
  _a					      =						1
  while n % 2 == 0:
    temp += 1
    _a					      =						int(n / 2       )
  if temp > 1:
    s *= (2**temp - 1) / (2 - 1)
  for i in range(3  ,			int(math.sqrt(UpperCamelCase_       )       ) + 1  ,			2       ):
    _a					      =						1
    while n % i == 0:
      temp += 1
      _a					      =						int(n / i       )
    if temp > 1:
      s *= (i**temp - 1) / (i - 1)
  return int(UpperCamelCase_       )






def 							lowerCAmelCase			(   UpperCamelCase_:				int       )							->				int:







  '''simple docstring'''







  _a					      =						n
  for x in set(prime_factors(UpperCamelCase_       )       ):
    s *= (x - 1) / x
  return int(UpperCamelCase_       )


if __name__ == "__main__":
     import doctest

     doctest.testmod()


