'''simple docstring'''






import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class lowercase_       (		_UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   AltDiffusionPipeline
   __lowerCAmelCase         =   TEXT_TO_IMAGE_PARAMS
   __lowerCAmelCase         =   TEXT_TO_IMAGE_BATCH_PARAMS
   __lowerCAmelCase         =   TEXT_TO_IMAGE_IMAGE_PARAMS
   __lowerCAmelCase         =   TEXT_TO_IMAGE_IMAGE_PARAMS
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			List[str]:
        torch.manual_seed(0     )
        _A    		=  UNetaDConditionModel(
            block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=4, out_channels=4, down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D'), up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D'), cross_attention_dim=32, )
        _A    		=  DDIMScheduler(
            beta_start=0.00_085, beta_end=0.012, beta_schedule='scaled_linear', clip_sample=UpperCamelCase__, set_alpha_to_one=UpperCamelCase__, )
        torch.manual_seed(0     )
        _A    		=  AutoencoderKL(
            block_out_channels=[32, 64], in_channels=3, out_channels=3, down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'], up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'], latent_channels=4, )

        # TODO: address the non-deterministic text encoder (fails for save-load tests)
        # torch.manual_seed(0)
        # text_encoder_config = RobertaSeriesConfig(
        #     hidden_size=32,
        #     project_dim=32,
        #     intermediate_size=37,
        #     layer_norm_eps=1e-05,
        #     num_attention_heads=4,
        #     num_hidden_layers=5,
        #     vocab_size=5002,
        # )
        # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

        torch.manual_seed(0     )
        _A    		=  CLIPTextConfig(
            bos_token_id=0, eos_token_id=2, hidden_size=32, projection_dim=32, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=50_02, )
        _A    		=  CLIPTextModel(UpperCamelCase__     )

        _A    		=  XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'     )
        _A    		=  77

        _A    		=  {
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'safety_checker': None,
            'feature_extractor': None,
        }
        return components
   def 	__UpperCAmelCase   (      self      :			Union[str, Any], UpperCamelCase__      :			str, UpperCamelCase__      :			str=0     )  ->			Tuple:
        if str(UpperCamelCase__     ).startswith('mps'     ):
             _A    		=  torch.manual_seed(UpperCamelCase__     )
        else:
             _A    		=  torch.Generator(device=UpperCamelCase__     ).manual_seed(UpperCamelCase__     )
        _A    		=  {
            'prompt': 'A painting of a squirrel eating a burger',
            'generator': generator,
            'num_inference_steps': 2,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
        }
        return inputs
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Dict:
        super().test_attention_slicing_forward_pass(expected_max_diff=3e-3     )
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Any:
        super().test_inference_batch_single_identical(expected_max_diff=3e-3     )
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Union[str, Any]:
        _A    		=  'cpu'  # ensure determinism for the device-dependent torch.Generator

        _A    		=  self.get_dummy_components()
        torch.manual_seed(0     )
        _A    		=  RobertaSeriesConfig(
            hidden_size=32, project_dim=32, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, vocab_size=50_02, )
        # TODO: remove after fixing the non-deterministic text encoder
        _A    		=  RobertaSeriesModelWithTransformation(UpperCamelCase__     )
        _A    		=  text_encoder

        _A    		=  AltDiffusionPipeline(**UpperCamelCase__     )
        _A    		=  alt_pipe.to(UpperCamelCase__     )
        alt_pipe.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  self.get_dummy_inputs(UpperCamelCase__     )
        _A    		=  'A photo of an astronaut'
        _A    		=  alt_pipe(**UpperCamelCase__     )
        _A    		=  output.images
        _A    		=  image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        _A    		=  np.array(
            [0.5_748_162, 0.60_447_145, 0.48_821_217, 0.50_100_636, 0.5_431_185, 0.45_763_683, 0.49_657_696, 0.48_132_733, 0.47_573_093]     )

        assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2

   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Optional[Any]:
        _A    		=  'cpu'  # ensure determinism for the device-dependent torch.Generator

        _A    		=  self.get_dummy_components()
        _A    		=  PNDMScheduler(skip_prk_steps=UpperCamelCase__     )
        torch.manual_seed(0     )
        _A    		=  RobertaSeriesConfig(
            hidden_size=32, project_dim=32, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, vocab_size=50_02, )
        # TODO: remove after fixing the non-deterministic text encoder
        _A    		=  RobertaSeriesModelWithTransformation(UpperCamelCase__     )
        _A    		=  text_encoder
        _A    		=  AltDiffusionPipeline(**UpperCamelCase__     )
        _A    		=  alt_pipe.to(UpperCamelCase__     )
        alt_pipe.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  self.get_dummy_inputs(UpperCamelCase__     )
        _A    		=  alt_pipe(**UpperCamelCase__     )
        _A    		=  output.images
        _A    		=  image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        _A    		=  np.array(
            [0.51_605_093, 0.5_707_241, 0.47_365_507, 0.50_578_886, 0.5_633_877, 0.4_642_503, 0.5_182_081, 0.48_763_484, 0.49_084_237]     )

        assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2







@slow
@require_torch_gpu
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			str     )  ->			Optional[Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			str:
        # make sure here that pndm scheduler skips prk
        _A    		=  AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion', safety_checker=UpperCamelCase__     )
        _A    		=  alt_pipe.to(UpperCamelCase__     )
        alt_pipe.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  'A painting of a squirrel eating a burger'
        _A    		=  torch.manual_seed(0     )
        _A    		=  alt_pipe([prompt], generator=UpperCamelCase__, guidance_scale=6.0, num_inference_steps=20, output_type='np'     )

        _A    		=  output.images

        _A    		=  image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        _A    		=  np.array([0.1_010, 0.0_800, 0.0_794, 0.0_885, 0.0_843, 0.0_762, 0.0_769, 0.0_729, 0.0_586]     )

        assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2

   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Dict:
        _A    		=  DDIMScheduler.from_pretrained('BAAI/AltDiffusion', subfolder='scheduler'     )

        _A    		=  AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion', scheduler=UpperCamelCase__, safety_checker=UpperCamelCase__     )
        _A    		=  alt_pipe.to(UpperCamelCase__     )
        alt_pipe.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  'A painting of a squirrel eating a burger'
        _A    		=  torch.manual_seed(0     )

        _A    		=  alt_pipe([prompt], generator=UpperCamelCase__, num_inference_steps=2, output_type='numpy'     )
        _A    		=  output.images

        _A    		=  image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        _A    		=  np.array([0.4_019, 0.4_052, 0.3_810, 0.4_119, 0.3_916, 0.3_982, 0.4_651, 0.4_195, 0.5_323]     )

        assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2




'''simple docstring'''






from __future__ import annotations





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int | str				):
     _A    		=  str(__snake_case				)
     return n == n[::-1]





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int = 1_0_0_0_0_0_0				):
     _A    		=  0

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


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



