

import argparse
import json
import os

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_									=    logging.get_logger(__name__)
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      int )				-> str:





    '''simple docstring'''






    A__        =  ConvNextVaConfig()

    if "atto" in checkpoint_url:
        A__        =  [2, 2, 6, 2]
        A__        =  [40, 80, 160, 320]
    if "femto" in checkpoint_url:
        A__        =  [2, 2, 6, 2]
        A__        =  [48, 96, 192, 384]
    if "pico" in checkpoint_url:
        A__        =  [2, 2, 6, 2]
        A__        =  [64, 128, 256, 512]
    if "nano" in checkpoint_url:
        A__        =  [2, 2, 8, 2]
        A__        =  [80, 160, 320, 640]
    if "tiny" in checkpoint_url:
        A__        =  [3, 3, 9, 3]
        A__        =  [96, 192, 384, 768]
    if "base" in checkpoint_url:
        A__        =  [3, 3, 27, 3]
        A__        =  [128, 256, 512, 1024]
    if "large" in checkpoint_url:
        A__        =  [3, 3, 27, 3]
        A__        =  [192, 384, 768, 1536]
    if "huge" in checkpoint_url:
        A__        =  [3, 3, 27, 3]
        A__        =  [352, 704, 1408, 2816]

    A__        =  1000
    A__        =  'imagenet-1k-id2label.json'
    A__        =  (1, 1000)

    A__        =  'huggingface/label-files'
    A__        =  num_labels
    A__        =  json.load(open(hf_hub_download(_UpperCAmelCase       ,     _UpperCAmelCase       ,     repo_type='dataset' )       ,     'r' ) )
    A__        =  {int(_UpperCAmelCase ): v for k, v in idalabel.items()}

    A__        =  idalabel
    A__        =  {v: k for k, v in idalabel.items()}
    A__        =  hidden_sizes
    A__        =  depths

    return config, expected_shape
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      List[str] )				-> Union[str, Any]:





    '''simple docstring'''






    if "downsample_layers.0.0" in name:
        A__        =  name.replace('downsample_layers.0.0'       ,     'embeddings.patch_embeddings' )
    if "downsample_layers.0.1" in name:
        A__        =  name.replace('downsample_layers.0.1'       ,     'embeddings.norm' )  # we rename to layernorm later on
    if "downsample_layers.1.0" in name:
        A__        =  name.replace('downsample_layers.1.0'       ,     'stages.1.downsampling_layer.0' )
    if "downsample_layers.1.1" in name:
        A__        =  name.replace('downsample_layers.1.1'       ,     'stages.1.downsampling_layer.1' )
    if "downsample_layers.2.0" in name:
        A__        =  name.replace('downsample_layers.2.0'       ,     'stages.2.downsampling_layer.0' )
    if "downsample_layers.2.1" in name:
        A__        =  name.replace('downsample_layers.2.1'       ,     'stages.2.downsampling_layer.1' )
    if "downsample_layers.3.0" in name:
        A__        =  name.replace('downsample_layers.3.0'       ,     'stages.3.downsampling_layer.0' )
    if "downsample_layers.3.1" in name:
        A__        =  name.replace('downsample_layers.3.1'       ,     'stages.3.downsampling_layer.1' )
    if "stages" in name and "downsampling_layer" not in name:
        # stages.0.0. for instance should be renamed to stages.0.layers.0.
        A__        =  name[: len('stages.0' )] + '.layers' + name[len('stages.0' ) :]
    if "gamma" in name:
        A__        =  name.replace('gamma'       ,     'weight' )
    if "beta" in name:
        A__        =  name.replace('beta'       ,     'bias' )
    if "stages" in name:
        A__        =  name.replace('stages'       ,     'encoder.stages' )
    if "norm" in name:
        A__        =  name.replace('norm'       ,     'layernorm' )
    if "head" in name:
        A__        =  name.replace('head'       ,     'classifier' )

    return name
def 		_snake_case(       )				-> Union[str, Any]:





    '''simple docstring'''






    A__        =  'http://images.cocodataset.org/val2017/000000039769.jpg'
    A__        =  Image.open(requests.get(_UpperCAmelCase       ,     stream=_UpperCAmelCase ).raw )
    return im
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      List[Any] )				-> List[str]:





    '''simple docstring'''






    if "224" in checkpoint_url:
        A__        =  224
        A__        =  224 / 256
    elif "384" in checkpoint_url:
        A__        =  384
        A__        =  None
    else:
        A__        =  512
        A__        =  None

    return ConvNextImageProcessor(
        size=_UpperCAmelCase       ,     crop_pct=_UpperCAmelCase       ,     image_mean=[0.485, 0.456, 0.406]       ,     image_std=[0.229, 0.224, 0.225]       ,     resample=PILImageResampling.BICUBIC       ,     )
@torch.no_grad()
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Optional[int]       ,     SCREAMING_SNAKE_CASE__     :      str       ,     SCREAMING_SNAKE_CASE__     :      Dict       ,     SCREAMING_SNAKE_CASE__     :      Tuple )				-> List[str]:





    '''simple docstring'''






    print('Downloading original model from checkpoint...' )
    # define ConvNeXTV2 configuration based on URL
    A__ ,		A__        =  get_convnextva_config(_UpperCAmelCase )
    # load original state_dict from URL
    A__        =  torch.hub.load_state_dict_from_url(_UpperCAmelCase )['model']

    print('Converting model parameters...' )
    # rename keys
    for key in state_dict.copy().keys():
        A__        =  state_dict.pop(_UpperCAmelCase )
        A__        =  val
    # add prefix to all keys expect classifier head
    for key in state_dict.copy().keys():
        A__        =  state_dict.pop(_UpperCAmelCase )
        if not key.startswith('classifier' ):
            A__        =  'convnextv2.' + key
        A__        =  val

    # load HuggingFace model
    A__        =  ConvNextVaForImageClassification(_UpperCAmelCase )
    model.load_state_dict(_UpperCAmelCase )
    model.eval()

    # Check outputs on an image, prepared by ConvNextImageProcessor
    A__        =  convert_preprocessor(_UpperCAmelCase )
    A__        =  preprocessor(images=prepare_img()       ,     return_tensors='pt' )
    A__        =  model(**_UpperCAmelCase ).logits

    # note: the logits below were obtained without center cropping
    if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
        A__        =  torch.tensor([-0.3930, 0.1747, -0.5246, 0.4177, 0.4295] )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
        A__        =  torch.tensor([-0.1727, -0.5341, -0.7818, -0.4745, -0.6566] )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
        A__        =  torch.tensor([-0.0333, 0.1563, -0.9137, 0.1054, 0.0381] )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
        A__        =  torch.tensor([-0.1744, -0.1555, -0.0713, 0.0950, -0.1431] )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
        A__        =  torch.tensor([0.9996, 0.1966, -0.4386, -0.3472, 0.6661] )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
        A__        =  torch.tensor([-0.2553, -0.6708, -0.1359, 0.2518, -0.2488] )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
        A__        =  torch.tensor([-0.0673, -0.5627, -0.3753, -0.2722, 0.0178] )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
        A__        =  torch.tensor([-0.6377, -0.7458, -0.2150, 0.1184, -0.0597] )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
        A__        =  torch.tensor([1.0799, 0.2322, -0.8860, 1.0219, 0.6231] )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
        A__        =  torch.tensor([0.3766, 0.4917, -1.1426, 0.9942, 0.6024] )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
        A__        =  torch.tensor([0.4220, -0.6919, -0.4317, -0.2881, -0.6609] )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
        A__        =  torch.tensor([0.1082, -0.8286, -0.5095, 0.4681, -0.8085] )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
        A__        =  torch.tensor([-0.2419, -0.6221, 0.2176, -0.0980, -0.7527] )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
        A__        =  torch.tensor([0.0391, -0.4371, 0.3786, 0.1251, -0.2784] )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
        A__        =  torch.tensor([-0.0504, 0.5636, -0.1729, -0.6507, -0.3949] )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
        A__        =  torch.tensor([0.3560, 0.9486, 0.3149, -0.2667, -0.5138] )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
        A__        =  torch.tensor([-0.2469, -0.4550, -0.5853, -0.0810, 0.0309] )
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
        A__        =  torch.tensor([-0.3090, 0.0802, -0.0682, -0.1979, -0.2826] )
    else:
        raise ValueError(f'Unknown URL: {checkpoint_url}' )

    assert torch.allclose(logits[0, :5]       ,     _UpperCAmelCase       ,     atol=1E-3 )
    assert logits.shape == expected_shape
    print('Model outputs match the original results!' )

    if save_model:
        print('Saving model to local...' )
        # Create folder to save model
        if not os.path.isdir(_UpperCAmelCase ):
            os.mkdir(_UpperCAmelCase )

        model.save_pretrained(_UpperCAmelCase )
        preprocessor.save_pretrained(_UpperCAmelCase )

    A__        =  'convnextv2'
    if "atto" in checkpoint_url:
        model_name += "-atto"
    if "femto" in checkpoint_url:
        model_name += "-femto"
    if "pico" in checkpoint_url:
        model_name += "-pico"
    if "nano" in checkpoint_url:
        model_name += "-nano"
    elif "tiny" in checkpoint_url:
        model_name += "-tiny"
    elif "base" in checkpoint_url:
        model_name += "-base"
    elif "large" in checkpoint_url:
        model_name += "-large"
    elif "huge" in checkpoint_url:
        model_name += "-huge"
    if "22k" in checkpoint_url and "1k" not in checkpoint_url:
        model_name += "-22k"
    elif "22k" in checkpoint_url and "1k" in checkpoint_url:
        model_name += "-22k-1k"
    elif "1k" in checkpoint_url:
        model_name += "-1k"
    if "224" in checkpoint_url:
        model_name += "-224"
    elif "384" in checkpoint_url:
        model_name += "-384"
    elif "512" in checkpoint_url:
        model_name += "-512"

    if push_to_hub:
        print(f'Pushing {model_name} to the hub...' )
        model.push_to_hub(_UpperCAmelCase )
        preprocessor.push_to_hub(_UpperCAmelCase )


if __name__ == "__main__":
     lowercase_									=    argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--checkpoint_url",
         default="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt",
         type=str,
         help="URL of the original ConvNeXTV2 checkpoint you\'d like to convert.",
     )
     parser.add_argument(
         "--pytorch_dump_folder_path",
         default="model",
         type=str,
         help="Path to the output PyTorch model directory.",
     )
     parser.add_argument("--save_model", action="store_true", help="Save model to local")
     parser.add_argument("--push_to_hub", action="store_true", help="Push model and image preprocessor to the hub")

     lowercase_									=    parser.parse_args()
     convert_convnextva_checkpoint(
         args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
     )






from math import floor, sqrt





def 	__snake_case      (     _UpperCAmelCase							):
    __a					=							0.0
    __a					=							1.0
    __a					=							int(sqrt(_UpperCAmelCase							)							)
    __a					=							root
    __a					=							0
    while integer_part != 2 * root:
        __a					=							denominator * integer_part - numerator
        __a					=							(n - numerator**2) / denominator
        __a					=							int((root + numerator) / denominator							)
        period += 1
    return period





def 	__snake_case      (     _UpperCAmelCase = 10000							):
    __a					=							0
    for i in range(2  ,					n + 1							):
        __a					=							sqrt(_UpperCAmelCase							)
        if sr - floor(_UpperCAmelCase							) != 0 and continuous_fraction_period(_UpperCAmelCase							) % 2 == 1:
            count_odd_periods += 1
    return count_odd_periods


if __name__ == "__main__":
 print(f'{solution(int(input().strip()))}')





