"""simple docstring"""






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		(_SCREAMING_SNAKE_CASE     ,     unittest.TestCase   ):






					'''simple docstring'''


					a 						=		DebertaTokenizer
					a 						=		True
					a 						=		DebertaTokenizerFast



					def       lowerCAmelCase_       (    self      :					Tuple		)				->							int:
									super().setUp()

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

									SCREAMING_SNAKE_CASE__			=					os.path.join(self.tmpdirname ,			VOCAB_FILES_NAMES["vocab_file"]		)
									SCREAMING_SNAKE_CASE__			=					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(_snake_case		) + "\n"		)
									with open(self.merges_file ,			"w" ,			encoding="utf-8"		) as fp:
													fp.write("\n".join(_snake_case		)		)



					def       lowerCAmelCase_       (    self      :					Optional[int] ,			**_snake_case      :					str		)				->							List[str]:
									kwargs.update(self.special_tokens_map		)
									return self.tokenizer_class.from_pretrained(self.tmpdirname ,			**_snake_case		)



					def       lowerCAmelCase_       (    self      :					Optional[int] ,			_snake_case      :					List[Any]		)				->							Union[str, Any]:
									SCREAMING_SNAKE_CASE__			=					"lower newer"
									SCREAMING_SNAKE_CASE__			=					"lower newer"
									return input_text, output_text



					def       lowerCAmelCase_       (    self      :					Optional[int]		)				->							int:
									SCREAMING_SNAKE_CASE__			=					self.get_tokenizer()
									SCREAMING_SNAKE_CASE__			=					"lower newer"
									SCREAMING_SNAKE_CASE__			=					["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
									SCREAMING_SNAKE_CASE__			=					tokenizer.tokenize(_snake_case		)
									self.assertListEqual(_snake_case ,			_snake_case		)

									SCREAMING_SNAKE_CASE__			=					tokens + [tokenizer.unk_token]
									SCREAMING_SNAKE_CASE__			=					[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case		) ,			_snake_case		)



					def       lowerCAmelCase_       (    self      :					Union[str, Any]		)				->							str:
									SCREAMING_SNAKE_CASE__			=					self.get_tokenizer()
									SCREAMING_SNAKE_CASE__			=					tokenizer("Hello" ,			"World"		)
									SCREAMING_SNAKE_CASE__			=					[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
									self.assertListEqual(tokd["token_type_ids"] ,			_snake_case		)



					@slow
					def       lowerCAmelCase_       (    self      :					List[Any]		)				->							Optional[int]:
									SCREAMING_SNAKE_CASE__			=					self.tokenizer_class.from_pretrained("microsoft/deberta-base"		)

									SCREAMING_SNAKE_CASE__			=					tokenizer.encode("sequence builders" ,			add_special_tokens=_snake_case		)
									SCREAMING_SNAKE_CASE__			=					tokenizer.encode("multi-sequence build" ,			add_special_tokens=_snake_case		)

									SCREAMING_SNAKE_CASE__			=					tokenizer.encode(
									    "sequence builders" ,			add_special_tokens=_snake_case ,			add_prefix_space=_snake_case		)
									SCREAMING_SNAKE_CASE__			=					tokenizer.encode(
									    "sequence builders" ,			"multi-sequence build" ,			add_special_tokens=_snake_case ,			add_prefix_space=_snake_case		)

									SCREAMING_SNAKE_CASE__			=					tokenizer.build_inputs_with_special_tokens(_snake_case		)
									SCREAMING_SNAKE_CASE__			=					tokenizer.build_inputs_with_special_tokens(_snake_case ,			_snake_case		)

									assert encoded_sentence == encoded_text_from_decode
									assert encoded_pair == encoded_pair_from_decode



					@slow
					def       lowerCAmelCase_       (    self      :					str		)				->							Union[str, Any]:
									SCREAMING_SNAKE_CASE__			=					[self.tokenizer_class]
									if self.test_rust_tokenizer:
													tokenizer_classes.append(self.rust_tokenizer_class		)

									for tokenizer_class in tokenizer_classes:
													SCREAMING_SNAKE_CASE__			=					tokenizer_class.from_pretrained("microsoft/deberta-base"		)

													SCREAMING_SNAKE_CASE__			=					[
													    "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.",
													]

													SCREAMING_SNAKE_CASE__			=					tokenizer(_snake_case ,			padding=_snake_case		)
													SCREAMING_SNAKE_CASE__			=					[tokenizer.decode(_snake_case ,			skip_special_tokens=_snake_case		) for seq in encoding["input_ids"]]

													# fmt: off
													SCREAMING_SNAKE_CASE__			=					{
													    "input_ids": [
													        [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 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, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 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, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 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

													SCREAMING_SNAKE_CASE__			=					[
													    "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 ,			_snake_case		)

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












def       _SCREAMING_SNAKE_CASE (							__snake_case = 1_0_0_0 )				->						int:
       _UpperCAmelCase	,			_UpperCAmelCase							=    1, 1
       _UpperCAmelCase							=    []
       for i in range(1   ,      n + 1 ):
              _UpperCAmelCase							=    prev_numerator + 2 * prev_denominator
              _UpperCAmelCase							=    prev_numerator + prev_denominator
              if len(str(__snake_case ) ) > len(str(__snake_case ) ):
                     result.append(__snake_case )
              _UpperCAmelCase							=    numerator
              _UpperCAmelCase							=    denominator

       return len(__snake_case )


if __name__ == "__main__":
  print(F"{solution() = }")