import os
import re
import shutil
import sys
import tempfile
import unittest

import black


lowerCAmelCase_ : Optional[Any]						  =      os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
lowerCAmelCase_ : int						  =      """    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
"""





class 							lowercase						(						unittest.TestCase ):


   def __UpperCAmelCase       (   self				:	List[str])      ->					Union[str, Any]:
         lowercase_     =      tempfile.mkdtemp()
         os.makedirs(os.path.join(self.transformer_dir   ,		"models/bert/"))
         lowercase_     =      self.transformer_dir
         shutil.copy(
             os.path.join(_lowerCAmelCase   ,		"src/transformers/models/bert/modeling_bert.py")   ,		os.path.join(self.transformer_dir   ,		"models/bert/modeling_bert.py")   ,		)


   def __UpperCAmelCase       (   self				:	Any)      ->					List[str]:
         lowercase_     =      "src/transformers"
         shutil.rmtree(self.transformer_dir)


   def __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	Tuple   ,		__lowerCAmelCase				:	Optional[Any]=None)      ->					str:
         lowercase_     =      comment + F'\nclass {class_name}(nn.Module):\n' + class_code
         if overwrite_result is not None:
               lowercase_     =      comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
         lowercase_     =      black.Mode(target_versions={black.TargetVersion.PYaa}   ,		line_length=119)
         lowercase_     =      black.format_str(_lowerCAmelCase   ,		mode=_lowerCAmelCase)
         lowercase_     =      os.path.join(self.transformer_dir   ,		"new_code.py")
         with open(_lowerCAmelCase   ,		"w"   ,		newline="\n") as f:
               f.write(_lowerCAmelCase)
         if overwrite_result is None:
               self.assertTrue(len(check_copies.is_copy_consistent(_lowerCAmelCase)) == 0)
         else:
               check_copies.is_copy_consistent(f.name   ,		overwrite=_lowerCAmelCase)
               with open(_lowerCAmelCase   ,		"r") as f:
                     self.assertTrue(f.read()   ,		_lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					str:
         lowercase_     =      check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead")
         self.assertEqual(_lowerCAmelCase   ,		_lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	List[str])      ->					List[str]:
         self.check_copy_consistency(
             "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"   ,		"BertLMPredictionHead"   ,		REFERENCE_CODE + "\n"   ,		)

         # With no empty line at the end
         self.check_copy_consistency(
             "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"   ,		"BertLMPredictionHead"   ,		_lowerCAmelCase   ,		)

         # Copy consistency with rename
         self.check_copy_consistency(
             "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"   ,		"TestModelLMPredictionHead"   ,		re.sub("Bert"   ,		"TestModel"   ,		_lowerCAmelCase)   ,		)

         # Copy consistency with a really long name
         lowercase_     =      "TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
         self.check_copy_consistency(
             F'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'   ,		F'{long_class_name}LMPredictionHead'   ,		re.sub("Bert"   ,		_lowerCAmelCase   ,		_lowerCAmelCase)   ,		)

         # Copy consistency with overwrite
         self.check_copy_consistency(
             "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"   ,		"TestModelLMPredictionHead"   ,		_lowerCAmelCase   ,		overwrite_result=re.sub("Bert"   ,		"TestModel"   ,		_lowerCAmelCase)   ,		)


   def __UpperCAmelCase       (   self				:	List[Any])      ->					List[str]:
         lowercase_     =      check_copies.LOCALIZED_READMES["README_zh-hans.md"]

         lowercase_     =      (
             "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
             " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
             " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
             " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
             " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
             " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
             " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
             " method has been applied to compress GPT2 into"
             " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
             " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
             " Multilingual BERT into"
             " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
             " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
             " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
             " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
             " Luong, Quoc V. Le, Christopher D. Manning."
         )
         lowercase_     =      (
             "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
             " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
             " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
             " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
         )
         lowercase_     =      (
             "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
             " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
             " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
             " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
             " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
             " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
             " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
             " method has been applied to compress GPT2 into"
             " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
             " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
             " Multilingual BERT into"
             " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
             " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
             " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
             " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
             " Christopher D. Manning 发布。\n"
         )

         lowercase_ ,							lowercase_     =      check_copies.convert_to_localized_md(
             _lowerCAmelCase   ,		_lowerCAmelCase   ,		localized_readme["format_model_list"])

         self.assertFalse(_lowerCAmelCase)
         self.assertEqual(_lowerCAmelCase   ,		_lowerCAmelCase)

         lowercase_ ,							lowercase_     =      check_copies.convert_to_localized_md(
             _lowerCAmelCase   ,		_lowerCAmelCase   ,		localized_readme["format_model_list"])

         # Check whether the number of models is equal to README.md after conversion.
         self.assertTrue(_lowerCAmelCase)

         lowercase_     =      (
             "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
             " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
             " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
             " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
         )
         lowercase_     =      (
             "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
             " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
             " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
             " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
         )
         lowercase_     =      (
             "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
             " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
             " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
             " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
         )

         lowercase_ ,							lowercase_     =      check_copies.convert_to_localized_md(
             _lowerCAmelCase   ,		_lowerCAmelCase   ,		localized_readme["format_model_list"])

         # Check if the model link is synchronized.
         self.assertEqual(_lowerCAmelCase   ,		_lowerCAmelCase)







'''simple docstring'''

from __future__ import annotations


def __a (	__lowerCamelCase			:						int | str				)	->							bool:
      '''simple docstring'''


      lowercase_     =      str(__lowerCamelCase				)
      return n == n[::-1]


def __a (	__lowerCamelCase			:						int = 1_000_000				)	->							Optional[int]:
      '''simple docstring'''


      lowercase_     =      0

      for i in range(1 ,		__lowerCamelCase				):
            if is_palindrome(__lowerCamelCase				) and is_palindrome(bin(__lowerCamelCase				).split("b"				)[1]				):
                  total += i
      return total


if __name__ == "__main__":
       print(solution(int(str(input().strip()))))






