import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          OpenAIGPTConfig,
          OpenAIGPTDoubleHeadsModel,
          OpenAIGPTForSequenceClassification,
          OpenAIGPTLMHeadModel,
          OpenAIGPTModel,
      )


class   lowerCamelCase					:
  """simple docstring"""







  def __init__(							self :       str  ,     __magic_name__ :       Tuple  ,     __magic_name__ :       List[Any]=13  ,     __magic_name__ :       Any=7  ,     __magic_name__ :       int=True  ,     __magic_name__ :       int=True  ,     __magic_name__ :       Any=True  ,     __magic_name__ :       Dict=99  ,     __magic_name__ :       List[Any]=32  ,     __magic_name__ :       Any=5  ,     __magic_name__ :       Tuple=4  ,     __magic_name__ :       Optional[Any]=37  ,     __magic_name__ :       Union[str, Any]="gelu"  ,     __magic_name__ :       Optional[int]=0.1  ,     __magic_name__ :       Optional[int]=0.1  ,     __magic_name__ :       int=512  ,     __magic_name__ :       Tuple=16  ,     __magic_name__ :       Optional[int]=2  ,     __magic_name__ :       List[str]=0.02  ,     __magic_name__ :       List[str]=3  ,     __magic_name__ :       Union[str, Any]=4  ,     __magic_name__ :       List[str]=None  ,     )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  parent
        SCREAMING_SNAKE_CASE_          =  batch_size
        SCREAMING_SNAKE_CASE_          =  seq_length
        SCREAMING_SNAKE_CASE_          =  is_training
        SCREAMING_SNAKE_CASE_          =  use_token_type_ids
        SCREAMING_SNAKE_CASE_          =  use_labels
        SCREAMING_SNAKE_CASE_          =  vocab_size
        SCREAMING_SNAKE_CASE_          =  hidden_size
        SCREAMING_SNAKE_CASE_          =  num_hidden_layers
        SCREAMING_SNAKE_CASE_          =  num_attention_heads
        SCREAMING_SNAKE_CASE_          =  intermediate_size
        SCREAMING_SNAKE_CASE_          =  hidden_act
        SCREAMING_SNAKE_CASE_          =  hidden_dropout_prob
        SCREAMING_SNAKE_CASE_          =  attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE_          =  max_position_embeddings
        SCREAMING_SNAKE_CASE_          =  type_vocab_size
        SCREAMING_SNAKE_CASE_          =  type_sequence_label_size
        SCREAMING_SNAKE_CASE_          =  initializer_range
        SCREAMING_SNAKE_CASE_          =  num_labels
        SCREAMING_SNAKE_CASE_          =  num_choices
        SCREAMING_SNAKE_CASE_          =  scope
        SCREAMING_SNAKE_CASE_          =  self.vocab_size - 1







  def    __A     (							self :       int   )  ->	int:
        SCREAMING_SNAKE_CASE_          =  ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size   )

        SCREAMING_SNAKE_CASE_          =  None
        if self.use_token_type_ids:
              SCREAMING_SNAKE_CASE_          =  ids_tensor([self.batch_size, self.seq_length]  ,     self.type_vocab_size   )

        SCREAMING_SNAKE_CASE_          =  None
        SCREAMING_SNAKE_CASE_          =  None
        SCREAMING_SNAKE_CASE_          =  None
        if self.use_labels:
              SCREAMING_SNAKE_CASE_          =  ids_tensor([self.batch_size]  ,     self.type_sequence_label_size   )
              SCREAMING_SNAKE_CASE_          =  ids_tensor([self.batch_size, self.seq_length]  ,     self.num_labels   )
              SCREAMING_SNAKE_CASE_          =  ids_tensor([self.batch_size]  ,     self.num_choices   )

        SCREAMING_SNAKE_CASE_          =  OpenAIGPTConfig(
            vocab_size=self.vocab_size  ,     n_embd=self.hidden_size  ,     n_layer=self.num_hidden_layers  ,     n_head=self.num_attention_heads  ,     n_positions=self.max_position_embeddings  ,     pad_token_id=self.pad_token_id  ,     )

        SCREAMING_SNAKE_CASE_          =  ids_tensor([self.num_hidden_layers, self.num_attention_heads]  ,     2   )

        return (
            config,
            input_ids,
            head_mask,
            token_type_ids,
            sequence_labels,
            token_labels,
            choice_labels,
        )







  def    __A     (							self :       int  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       str  ,     __magic_name__ :       Optional[int]  ,     *__magic_name__ :       Optional[Any]   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  OpenAIGPTModel(config=__magic_name__   )
        model.to(__magic_name__   )
        model.eval()

        SCREAMING_SNAKE_CASE_          =  model(__magic_name__  ,     token_type_ids=__magic_name__  ,     head_mask=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  model(__magic_name__  ,     token_type_ids=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )

        self.parent.assertEqual(result.last_hidden_state.shape  ,     (self.batch_size, self.seq_length, self.hidden_size)   )







  def    __A     (							self :       Dict  ,     __magic_name__ :       Any  ,     __magic_name__ :       int  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       Any  ,     *__magic_name__ :       Union[str, Any]   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  OpenAIGPTLMHeadModel(__magic_name__   )
        model.to(__magic_name__   )
        model.eval()

        SCREAMING_SNAKE_CASE_          =  model(__magic_name__  ,     token_type_ids=__magic_name__  ,     labels=__magic_name__   )
        self.parent.assertEqual(result.loss.shape  ,     ()   )
        self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.seq_length, self.vocab_size)   )







  def    __A     (							self :       int  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       Optional[int]  ,     *__magic_name__ :       List[Any]   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  OpenAIGPTDoubleHeadsModel(__magic_name__   )
        model.to(__magic_name__   )
        model.eval()

        SCREAMING_SNAKE_CASE_          =  model(__magic_name__  ,     token_type_ids=__magic_name__  ,     labels=__magic_name__   )
        self.parent.assertEqual(result.loss.shape  ,     ()   )
        self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.seq_length, self.vocab_size)   )







  def    __A     (							self :       Any  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Any  ,     __magic_name__ :       Any  ,     *__magic_name__ :       Tuple   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  self.num_labels
        SCREAMING_SNAKE_CASE_          =  OpenAIGPTForSequenceClassification(__magic_name__   )
        model.to(__magic_name__   )
        model.eval()

        SCREAMING_SNAKE_CASE_          =  ids_tensor([self.batch_size]  ,     self.type_sequence_label_size   )
        SCREAMING_SNAKE_CASE_          =  model(__magic_name__  ,     token_type_ids=__magic_name__  ,     labels=__magic_name__   )
        self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.num_labels)   )







  def    __A     (							self :       List[Any]   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  self.prepare_config_and_inputs()
        (
            (
            SCREAMING_SNAKE_CASE_
        )     , (
            SCREAMING_SNAKE_CASE_
        )     , (
            SCREAMING_SNAKE_CASE_
        )     , (
            SCREAMING_SNAKE_CASE_
        )     , (
            SCREAMING_SNAKE_CASE_
        )     , (
            SCREAMING_SNAKE_CASE_
        )     , (
            SCREAMING_SNAKE_CASE_
        )     , 
        )          =  config_and_inputs
        SCREAMING_SNAKE_CASE_          =  {
            "input_ids": input_ids,
            "token_type_ids": token_type_ids,
            "head_mask": head_mask,
        }

        return config, inputs_dict



@require_torch
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      unittest.TestCase					):
  """simple docstring"""







  lowerCamelCase__     =						(
      (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
      if is_torch_available()
      else ()
  )
  lowerCamelCase__     =						(
      (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
  )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
  lowerCamelCase__     =						(
      {
          '''feature-extraction''': OpenAIGPTModel,
          '''text-classification''': OpenAIGPTForSequenceClassification,
          '''text-generation''': OpenAIGPTLMHeadModel,
          '''zero-shot''': OpenAIGPTForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )







  def    __A     (							self :       Dict  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       Any  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       Dict   )  ->	Dict:
        if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
              # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
              # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
              # tiny config could not be created.
              return True

        return False







  def    __A     (							self :       List[Any]  ,     __magic_name__ :       Any  ,     __magic_name__ :       int  ,     __magic_name__ :       str=False   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  super()._prepare_for_class(__magic_name__  ,     __magic_name__  ,     return_labels=__magic_name__   )

        if return_labels:
              if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
                    SCREAMING_SNAKE_CASE_          =  torch.zeros(
                        (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)  ,     dtype=torch.long  ,     device=__magic_name__  ,     )
                    SCREAMING_SNAKE_CASE_          =  inputs_dict["labels"]
                    SCREAMING_SNAKE_CASE_          =  inputs_dict["labels"]
                    SCREAMING_SNAKE_CASE_          =  torch.zeros(
                        (self.model_tester.batch_size, self.model_tester.num_choices)  ,     dtype=torch.long  ,     device=__magic_name__  ,     )
                    SCREAMING_SNAKE_CASE_          =  torch.zeros(
                        self.model_tester.batch_size  ,     dtype=torch.long  ,     device=__magic_name__   )
        return inputs_dict







  def    __A     (							self :       Any   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  OpenAIGPTModelTester(self   )
        SCREAMING_SNAKE_CASE_          =  ConfigTester(self  ,     config_class=__magic_name__  ,     n_embd=37   )







  def    __A     (							self :       List[Any]   )  ->	str:
        self.config_tester.run_common_tests()







  def    __A     (							self :       Union[str, Any]   )  ->	int:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_openai_gpt_model(*__magic_name__   )







  def    __A     (							self :       List[str]   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_lm_head_model(*__magic_name__   )







  def    __A     (							self :       Union[str, Any]   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_double_lm_head_model(*__magic_name__   )







  def    __A     (							self :       Dict   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*__magic_name__   )







  @slow
  def    __A     (							self :       List[Any]   )  ->	List[str]:
        for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE_          =  OpenAIGPTModel.from_pretrained(__magic_name__   )
              self.assertIsNotNone(__magic_name__   )



@require_torch
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  @slow
  def    __A     (							self :       Any   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  OpenAIGPTLMHeadModel.from_pretrained("openai-gpt"   )
        model.to(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([[481, 4_735, 544]]  ,     dtype=torch.long  ,     device=__magic_name__   )  # the president is
        SCREAMING_SNAKE_CASE_          =  [
            481,
            4_735,
            544,
            246,
            963,
            870,
            762,
            239,
            244,
            40_477,
            244,
            249,
            719,
            881,
            487,
            544,
            240,
            244,
            603,
            481,
        ]  # the president is a very good man. " \n " i\'m sure he is, " said the

        SCREAMING_SNAKE_CASE_          =  model.generate(__magic_name__  ,     do_sample=__magic_name__   )
        self.assertListEqual(output_ids[0].tolist()  ,     __magic_name__   )



from __future__ import annotations


def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  str(__UpperCamelCase				)
      return n == n[::-1]


def      a__ (		__UpperCamelCase = 1_0_0_0_0_0_0				):
      SCREAMING_SNAKE_CASE_          =  0

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


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


