'''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   ):
      '''simple docstring'''

      def __init__(	self						:    Union[str, Any]						, A						:    List						, A						:    bool						, A						:    int = None						, A						:    int = None						, A						:    int = None						, A						:    str = "longest"						, A						:    bool = True						, A						:    int = 512						, A						:    int = None						, A						:    bool = True						, A						:    bool = True						, A						:    bool = True						, **A						:    int						, ):
             super().__init__()
             if use_fast_bert_tokenizer:
                    _UpperCAmelCase					:  int											=   FastBertTokenizer(
                        A						, token_out_type=tf.intaa						, lower_case_nfd_strip_accents=A						, **A	)
             else:
                    _UpperCAmelCase					:  Optional[int]											=   tf.lookup.StaticVocabularyTable(
                        tf.lookup.KeyValueTensorInitializer(
                            keys=A						, key_dtype=tf.string						, values=tf.range(tf.size(A						, out_type=tf.intaa	)						, dtype=tf.intaa	)						, value_dtype=tf.intaa						, )						, num_oov_buckets=1						, )
                    _UpperCAmelCase					:  Optional[Any]											=   BertTokenizerLayer(
                        A						, token_out_type=tf.intaa						, lower_case=A						, **A	)

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




      @classmethod
      def 					_A     (	cls						:    int						, A						:    "PreTrainedTokenizerBase"						, **A						:    Tuple	):  # noqa: F821
             _UpperCAmelCase					:  Union[str, Any]											=   kwargs.pop("do_lower_case"						, A	)
             _UpperCAmelCase					:  Optional[int]											=   tokenizer.do_lower_case if do_lower_case is None else do_lower_case
             _UpperCAmelCase					:  Any											=   kwargs.pop("cls_token_id"						, A	)
             _UpperCAmelCase					:  int											=   tokenizer.cls_token_id if cls_token_id is None else cls_token_id
             _UpperCAmelCase					:  Optional[int]											=   kwargs.pop("sep_token_id"						, A	)
             _UpperCAmelCase					:  Union[str, Any]											=   tokenizer.sep_token_id if sep_token_id is None else sep_token_id
             _UpperCAmelCase					:  Dict											=   kwargs.pop("pad_token_id"						, A	)
             _UpperCAmelCase					:  List[Any]											=   tokenizer.pad_token_id if pad_token_id is None else pad_token_id

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




      @classmethod
      def 					_A     (	cls						:    Any						, A						:    Union[str, os.PathLike]						, *A						:    int						, **A						:    str	):
             try:
                    _UpperCAmelCase					:  Optional[Any]											=   BertTokenizer.from_pretrained(A						, *A						, **A	)
             except:  # noqa: E722
                    from .tokenization_bert_fast import BertTokenizerFast

                    _UpperCAmelCase					:  Optional[int]											=   BertTokenizerFast.from_pretrained(A						, *A						, **A	)
             return cls.from_tokenizer(A						, **A	)




      def 					_A     (	self						:    List[Any]						, A						:    Union[str, Any]	):
             if self.do_lower_case:
                    _UpperCAmelCase					:  List[Any]											=   case_fold_utfa(A	)
             _UpperCAmelCase					:  List[Any]											=   self.tf_tokenizer.tokenize(A	)
             return tokens.merge_dims(1						, -1	)




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

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





      def 					_A     (	self						:    Tuple	):
             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

from collections.abc import Generator

def      UpperCamelCase_							( _UpperCAmelCase  :				int   )   ->   Generator[int, None, None]:


       """simple docstring"""







       if not isinstance(_UpperCAmelCase      ,     _UpperCAmelCase   ) or n < 1:
              raise Exception("Sequence only defined for positive integers"   )

       yield n
       while n != 1:
              if n % 2 == 0:
                     n //= 2
              else:
                     _UpperCAmelCase					:  Optional[Any]											=   3 * n + 1
              yield n

def      UpperCamelCase_							( )   ->   Any:


       """simple docstring"""







       _UpperCAmelCase					:  str											=   43
       _UpperCAmelCase					:  Optional[int]											=   tuple(collatz_sequence(_UpperCAmelCase   )   )
       print(_UpperCAmelCase   )
       print(F"""Collatz sequence from {n} took {len(_UpperCAmelCase   )} steps."""   )


if __name__ == "__main__":
  main()

