




import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class       __lowerCAmelCase				(							lowerCAmelCase					,       unittest.TestCase):
       _a           =  DebertaTokenizer
       _a           =  True
       _a           =  DebertaTokenizerFast






       def     SCREAMING_SNAKE_CASE			(      self:    int ):
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         lowercase			:Dict							=  [
             "l",
             "o",
             "w",
             "e",
             "r",
             "s",
             "t",
             "i",
             "d",
             "n",
             "\u0120",
             "\u0120l",
             "\u0120n",
             "\u0120lo",
             "\u0120low",
             "er",
             "\u0120lowest",
             "\u0120newer",
             "\u0120wider",
             "[UNK]",
         ]
         lowercase			:List[Any]							=  dict(zip(_lowerCAmelCase				,    range(len(_lowerCAmelCase ) ) ) )
         lowercase			:str							=  ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
         lowercase			:Dict							=  {"unk_token": "[UNK]"}

         lowercase			:int							=  os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES["vocab_file"] )
         lowercase			:List[Any]							=  os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES["merges_file"] )
         with open(self.vocab_file				,    "w"				,    encoding="utf-8" ) as fp:
           fp.write(json.dumps(_lowerCAmelCase ) + "\n" )
         with open(self.merges_file				,    "w"				,    encoding="utf-8" ) as fp:
           fp.write("\n".join(_lowerCAmelCase ) )






       def     SCREAMING_SNAKE_CASE			(      self:    int				,    **_lowerCAmelCase:    Optional[Any] ):
         kwargs.update(self.special_tokens_map )
         return self.tokenizer_class.from_pretrained(self.tmpdirname				,    **_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    str				,    _lowerCAmelCase:    Optional[Any] ):
         lowercase			:Any							=  "lower newer"
         lowercase			:Dict							=  "lower newer"
         return input_text, output_text






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[Any] ):
         lowercase			:List[Any]							=  self.get_tokenizer()
         lowercase			:str							=  "lower newer"
         lowercase			:List[Any]							=  ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
         lowercase			:List[str]							=  tokenizer.tokenize(_lowerCAmelCase )
         self.assertListEqual(_lowerCAmelCase				,    _lowerCAmelCase )

         lowercase			:Dict							=  tokens + [tokenizer.unk_token]
         lowercase			:str							=  [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase )				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    List[str] ):
         lowercase			:Any							=  self.get_tokenizer()
         lowercase			:Optional[int]							=  tokenizer("Hello"				,    "World" )
         lowercase			:Optional[Any]							=  [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
         self.assertListEqual(tokd["token_type_ids"]				,    _lowerCAmelCase )






       @slow
       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         lowercase			:int							=  self.tokenizer_class.from_pretrained("microsoft/deberta-base" )

         lowercase			:Any							=  tokenizer.encode("sequence builders"				,    add_special_tokens=_lowerCAmelCase )
         lowercase			:List[Any]							=  tokenizer.encode("multi-sequence build"				,    add_special_tokens=_lowerCAmelCase )

         lowercase			:str							=  tokenizer.encode(
             "sequence builders"				,    add_special_tokens=_lowerCAmelCase				,    add_prefix_space=_lowerCAmelCase )
         lowercase			:List[str]							=  tokenizer.encode(
             "sequence builders"				,    "multi-sequence build"				,    add_special_tokens=_lowerCAmelCase				,    add_prefix_space=_lowerCAmelCase )

         lowercase			:Optional[Any]							=  tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase )
         lowercase			:Union[str, Any]							=  tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase				,    _lowerCAmelCase )

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode






       @slow
       def     SCREAMING_SNAKE_CASE			(      self:    Optional[Any] ):
         lowercase			:List[str]							=  [self.tokenizer_class]
         if self.test_rust_tokenizer:
           tokenizer_classes.append(self.rust_tokenizer_class )

         for tokenizer_class in tokenizer_classes:
           lowercase			:Union[str, Any]							=  tokenizer_class.from_pretrained("microsoft/deberta-base" )

           lowercase			:Optional[int]							=  [
               "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
               "ALBERT incorporates two parameter reduction techniques",
               "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
               " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
               " vocabulary embedding.",
           ]

           lowercase			:Tuple							=  tokenizer(_lowerCAmelCase				,    padding=_lowerCAmelCase )
           lowercase			:int							=  [tokenizer.decode(_lowerCAmelCase				,    skip_special_tokens=_lowerCAmelCase ) for seq in encoding["input_ids"]]

           # fmt: off
           lowercase			:Any							=  {
               "input_ids": [
                   [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2]
               ],
               "token_type_ids": [
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
               ],
               "attention_mask": [
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
               ]
           }
           # fmt: on

           lowercase			:Optional[int]							=  [
               "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
               "ALBERT incorporates two parameter reduction techniques",
               "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
               " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
               " vocabulary embedding.",
           ]

           self.assertDictEqual(encoding.data				,    _lowerCAmelCase )

           for expected, decoded in zip(_lowerCAmelCase				,    _lowerCAmelCase ):
             self.assertEqual(_lowerCAmelCase				,    _lowerCAmelCase )









import os
from math import logaa



def 					UpperCAmelCase__     (   lowerCamelCase = "base_exp.txt"      ):
  lowercase			:float							=  0
  lowercase			:str							=  0
  for i, line in enumerate(open(os.path.join(os.path.dirname(lowerCamelCase      ),	lowerCamelCase      )      )      ):
    lowercase		,			lowercase			:str							=  list(map(lowerCamelCase,	line.split(","      )      )      )
    if x * logaa(lowerCamelCase      ) > largest:
      lowercase			:Optional[Any]							=  x * logaa(lowerCamelCase      )
      lowercase			:Any							=  i + 1
  return result


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



