


"""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   lowercase__							(				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 = 512			,      SCREAMING_SNAKE_CASE = None			,      SCREAMING_SNAKE_CASE = True			,      SCREAMING_SNAKE_CASE = True			,      SCREAMING_SNAKE_CASE = True			,      **SCREAMING_SNAKE_CASE			,      )     ->     List[str]:
      super().__init__()
      if use_fast_bert_tokenizer:
         _lowerCamelCase  :				Tuple      =		FastBertTokenizer(
             SCREAMING_SNAKE_CASE			,      token_out_type=tf.intaa			,      lower_case_nfd_strip_accents=SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)
      else:
         _lowerCamelCase  :				Tuple      =		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[str]      =		BertTokenizerLayer(
             SCREAMING_SNAKE_CASE			,      token_out_type=tf.intaa			,      lower_case=SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)

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





   @classmethod
   def 		UpperCamelCase_     (       cls			,      SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)     ->     List[str]:  # noqa: F821
      _lowerCamelCase  :				List[Any]      =		kwargs.pop("""do_lower_case"""			,      SCREAMING_SNAKE_CASE)
      _lowerCamelCase  :				Dict      =		tokenizer.do_lower_case if do_lower_case is None else do_lower_case
      _lowerCamelCase  :				List[Any]      =		kwargs.pop("""cls_token_id"""			,      SCREAMING_SNAKE_CASE)
      _lowerCamelCase  :				Dict      =		tokenizer.cls_token_id if cls_token_id is None else cls_token_id
      _lowerCamelCase  :				Optional[int]      =		kwargs.pop("""sep_token_id"""			,      SCREAMING_SNAKE_CASE)
      _lowerCamelCase  :				Tuple      =		tokenizer.sep_token_id if sep_token_id is None else sep_token_id
      _lowerCamelCase  :				int      =		kwargs.pop("""pad_token_id"""			,      SCREAMING_SNAKE_CASE)
      _lowerCamelCase  :				Dict      =		tokenizer.pad_token_id if pad_token_id is None else pad_token_id

      _lowerCamelCase  :				Optional[Any]      =		tokenizer.get_vocab()
      _lowerCamelCase  :				Any      =		sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]			,      key=lambda SCREAMING_SNAKE_CASE: x[1])
      _lowerCamelCase  :				Dict      =		[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 		UpperCamelCase_     (       cls			,      SCREAMING_SNAKE_CASE			,      *SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)     ->     Dict:
      try:
         _lowerCamelCase  :				Dict      =		BertTokenizer.from_pretrained(SCREAMING_SNAKE_CASE			,      *SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)
      except:  # noqa: E722
         from .tokenization_bert_fast import BertTokenizerFast

         _lowerCamelCase  :				Union[str, Any]      =		BertTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE			,      *SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)
      return cls.from_tokenizer(SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     Any:
      if self.do_lower_case:
         _lowerCamelCase  :				List[Any]      =		case_fold_utfa(SCREAMING_SNAKE_CASE)
      _lowerCamelCase  :				Any      =		self.tf_tokenizer.tokenize(SCREAMING_SNAKE_CASE)
      return tokens.merge_dims(1			,      -1)





   def 		UpperCamelCase_     (       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			,      )     ->     Optional[int]:
      if padding is None:
         _lowerCamelCase  :				Union[str, 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:
         _lowerCamelCase  :				Optional[int]      =		self.max_length
      if truncation is None:
         _lowerCamelCase  :				Any      =		self.truncation
      if pad_to_multiple_of is None:
         _lowerCamelCase  :				str      =		self.pad_to_multiple_of
      if return_token_type_ids is None:
         _lowerCamelCase  :				Union[str, Any]      =		self.return_token_type_ids
      if return_attention_mask is None:
         _lowerCamelCase  :				Any      =		self.return_attention_mask
      if not isinstance(SCREAMING_SNAKE_CASE			,      tf.Tensor):
         _lowerCamelCase  :				Dict      =		tf.convert_to_tensor(SCREAMING_SNAKE_CASE)
      if text_pair is not None and not isinstance(SCREAMING_SNAKE_CASE			,      tf.Tensor):
         _lowerCamelCase  :				List[str]      =		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  :				str      =		text[:, 0], text[:, 1]
      _lowerCamelCase  :				Union[str, Any]      =		self.unpaired_tokenize(SCREAMING_SNAKE_CASE)
      if text_pair is None:  # Unpaired text
         if truncation:
            _lowerCamelCase  :				List[str]      =		text[:, : max_length - 2]  # Allow room for special tokens
         _lowerCamelCase  :				int      =		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  :				Dict      =		self.paired_trimmer.trim([text, text_pair])
         _lowerCamelCase  :				Union[str, 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  :				Optional[int]      =		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  :				Dict      =		max_length

      _lowerCamelCase  :				Optional[int]      =		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  :				str      =		attention_mask
      if return_token_type_ids:
         _lowerCamelCase  :				Union[str, Any]      =		pad_model_inputs(
             SCREAMING_SNAKE_CASE			,      max_seq_length=SCREAMING_SNAKE_CASE			,      pad_value=self.pad_token_id)
         _lowerCamelCase  :				List[str]      =		token_type_ids
      return output





   def 		UpperCamelCase_     (       self)     ->     Union[str, Any]:
      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 itertools import permutations

def 							_snake_case			(      __snake_case       :			int = 5      ):




   """simple docstring"""







   if gon_side < 3 or gon_side > 5:
      raise ValueError("""gon_side must be in the range [3, 5]"""      )

   # Since it's 16, we know 10 is on the outer ring
   # Put the big numbers at the end so that they are never the first number
   _lowerCamelCase  :				Dict      =		list(range(gon_side + 1	,   0	,   -1      )      )
   _lowerCamelCase  :				Optional[int]      =		list(range(gon_side + 2	,   gon_side * 2 + 1      )      )

   for perm in permutations(small_numbers + big_numbers      ):
      _lowerCamelCase  :				List[str]      =		generate_gon_ring(__snake_case	,   list(__snake_case      )      )
      if is_magic_gon(__snake_case      ):
         return int("""""".join(str(__snake_case      ) for n in numbers      )      )

   _lowerCamelCase  :				Union[str, Any]      =		F'Magic {gon_side}-gon ring is impossible'
   raise ValueError(__snake_case      )

def 							_snake_case			(      __snake_case       :			int	,   __snake_case       :			list[int]      ):




   """simple docstring"""







   _lowerCamelCase  :				Union[str, Any]      =		[0] * (gon_side * 3)
   _lowerCamelCase  :				Dict      =		perm[0:3]
   perm.append(perm[1]      )

   _lowerCamelCase  :				Tuple      =		1 if gon_side < 5 else 2

   for i in range(1	,   len(__snake_case      ) // 3 + magic_number      ):
      _lowerCamelCase  :				Dict      =		perm[2 * i + 1]
      _lowerCamelCase  :				str      =		result[3 * i - 1]
      _lowerCamelCase  :				Dict      =		perm[2 * i + 2]

   return result

def 							_snake_case			(      __snake_case       :			list[int]      ):




   """simple docstring"""







   if len(__snake_case      ) % 3 != 0:
      raise ValueError("""a gon ring should have a length that is a multiple of 3"""      )

   if min(numbers[::3]      ) != numbers[0]:
      return False

   _lowerCamelCase  :				str      =		sum(numbers[:3]      )

   return all(sum(numbers[i : i + 3]      ) == total for i in range(3	,   len(__snake_case      )	,   3      )      )


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

