code: 
  """simple docstring"""
  
  
  
  
  
  
  
  import random
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNetaDModel, VQModel
  from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch
  
  
  enable_full_determinism()
  
  
  
  
  
  
  class     __a			(					unittest.TestCase			):
  
  
  
  
  
  
  
         @property
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					1
            _UpperCAmelCase   	=					3
            _UpperCAmelCase   	=					(32, 32)
  
            _UpperCAmelCase   	=					floats_tensor((batch_size, num_channels) + sizes							,rng=random.Random(0		)		).to(lowerCamelCase_		)
            return image
  
  
  
  
  
  
  
         @property
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            torch.manual_seed(0		)
            _UpperCAmelCase   	=					UNetaDModel(
                block_out_channels=(32, 64)							,layers_per_block=2							,sample_size=32							,in_channels=6							,out_channels=3							,down_block_types=("DownBlock2D", "AttnDownBlock2D")							,up_block_types=("AttnUpBlock2D", "UpBlock2D")							,)
            return model
  
  
  
  
  
  
  
         @property
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            torch.manual_seed(0		)
            _UpperCAmelCase   	=					VQModel(
                block_out_channels=[32, 64]							,in_channels=3							,out_channels=3							,down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]							,up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]							,latent_channels=3							,)
            return model
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					"cpu"
            _UpperCAmelCase   	=					self.dummy_uncond_unet
            _UpperCAmelCase   	=					DDIMScheduler()
            _UpperCAmelCase   	=					self.dummy_vq_model
  
            _UpperCAmelCase   	=					LDMSuperResolutionPipeline(unet=lowerCamelCase_							,vqvae=lowerCamelCase_							,scheduler=lowerCamelCase_		)
            ldm.to(lowerCamelCase_		)
            ldm.set_progress_bar_config(disable=lowerCamelCase_		)
  
            _UpperCAmelCase   	=					self.dummy_image.to(lowerCamelCase_		)
  
            _UpperCAmelCase   	=					torch.Generator(device=lowerCamelCase_		).manual_seed(0		)
            _UpperCAmelCase   	=					ldm(image=lowerCamelCase_							,generator=lowerCamelCase_							,num_inference_steps=2							,output_type="numpy"		).images
  
            _UpperCAmelCase   	=					image[0, -3:, -3:, -1]
  
            assert image.shape == (1, 64, 64, 3)
            _UpperCAmelCase   	=					np.array([0.8_6_7_8, 0.8_2_4_5, 0.6_3_8_1, 0.6_8_3_0, 0.4_3_8_5, 0.5_5_9_9, 0.4_6_4_1, 0.6_2_0_1, 0.5_1_5_0]		)
  
            assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
  
  
  
  
  
  
  
         @unittest.skipIf(torch_device != "cuda"							,"This test requires a GPU"		)
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.dummy_uncond_unet
            _UpperCAmelCase   	=					DDIMScheduler()
            _UpperCAmelCase   	=					self.dummy_vq_model
  
            # put models in fp16
            _UpperCAmelCase   	=					unet.half()
            _UpperCAmelCase   	=					vqvae.half()
  
            _UpperCAmelCase   	=					LDMSuperResolutionPipeline(unet=lowerCamelCase_							,vqvae=lowerCamelCase_							,scheduler=lowerCamelCase_		)
            ldm.to(lowerCamelCase_		)
            ldm.set_progress_bar_config(disable=lowerCamelCase_		)
  
            _UpperCAmelCase   	=					self.dummy_image.to(lowerCamelCase_		)
  
            _UpperCAmelCase   	=					ldm(lowerCamelCase_							,num_inference_steps=2							,output_type="numpy"		).images
  
            assert image.shape == (1, 64, 64, 3)
  
  
  
  
  
  
  @slow
  @require_torch
  class     __a			(					unittest.TestCase			):
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					load_image(
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                "/vq_diffusion/teddy_bear_pool.png"		)
            _UpperCAmelCase   	=					init_image.resize((64, 64)							,resample=PIL_INTERPOLATION["lanczos"]		)
  
            _UpperCAmelCase   	=					LDMSuperResolutionPipeline.from_pretrained("duongna/ldm-super-resolution"							,device_map="auto"		)
            ldm.set_progress_bar_config(disable=lowerCamelCase_		)
  
            _UpperCAmelCase   	=					torch.manual_seed(0		)
            _UpperCAmelCase   	=					ldm(image=lowerCamelCase_							,generator=lowerCamelCase_							,num_inference_steps=20							,output_type="numpy"		).images
  
            _UpperCAmelCase   	=					image[0, -3:, -3:, -1]
  
            assert image.shape == (1, 256, 256, 3)
            _UpperCAmelCase   	=					np.array([0.7_6_4_4, 0.7_6_7_9, 0.7_6_4_2, 0.7_6_3_3, 0.7_6_6_6, 0.7_5_6_0, 0.7_4_2_5, 0.7_2_5_7, 0.6_9_0_7]		)
  
            assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  
  
  from math import floor, sqrt
  def 			__UpperCAmelCase		(		a_			:		int				)     ->       int:
     _UpperCAmelCase   	=					0.0
     _UpperCAmelCase   	=					1.0
     _UpperCAmelCase   	=					int(sqrt(a_				)				)
     _UpperCAmelCase   	=					root
     _UpperCAmelCase   	=					0
     while integer_part != 2 * root:
        _UpperCAmelCase   	=					denominator * integer_part - numerator
        _UpperCAmelCase   	=					(n - numerator**2) / denominator
        _UpperCAmelCase   	=					int((root + numerator) / denominator				)
        period += 1
     return period
  def 			__UpperCAmelCase		(		a_			:		int = 1_00_00				)     ->       int:
     _UpperCAmelCase   	=					0
     for i in range(2    ,  n + 1				):
        _UpperCAmelCase   	=					sqrt(a_				)
        if sr - floor(a_				) != 0 and continuous_fraction_period(a_				) % 2 == 1:
           count_odd_periods += 1
     return count_odd_periods
  
  
  if __name__ == "__main__":
    print(f'''{solution(int(input().strip()))}''')
  
  