





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


SCREAMING_SNAKE_CASE_      :			Any		  =      logging.get_logger(__name__)

class 		snake_case_      (       lowerCamelCase__	):







	'''simple docstring'''
	__UpperCamelCase        =		['pixel_values']




	def __init__(	self       :    List[str]				,			__lowerCamelCase       :    List[Any] = True				,			__lowerCamelCase       :    Tuple = None				,			__lowerCamelCase       :    Dict = PIL.Image.NEAREST				,			__lowerCamelCase       :    List[str] = False				,			__lowerCamelCase       :    Optional[Any] = None				,			__lowerCamelCase       :    Union[str, Any] = 1 / 255				,			__lowerCamelCase       :    Union[str, Any] = False				,			__lowerCamelCase       :    Optional[Any] = True				,			__lowerCamelCase       :    List[str] = True				,			__lowerCamelCase       :    List[Any] = None				,			__lowerCamelCase       :    int = None				,			__lowerCamelCase       :    Union[str, Any] = True				,			**__lowerCamelCase       :    Union[str, Any]				,			)					->   None:

			'''simple docstring'''




			super().__init__(**lowercase_      )
			__lowercase					=    size if size is not None else {'height': 346, 'width': 346}
			__lowercase					=    get_size_dict(lowercase_      )
			__lowercase					=    crop_size if crop_size is not None else {'height': 289, 'width': 289}
			__lowercase					=    get_size_dict(lowercase_				,			param_name='crop_size'      )

			__lowercase					=    do_resize
			__lowercase					=    size
			__lowercase					=    resample
			__lowercase					=    do_center_crop
			__lowercase					=    crop_size
			__lowercase					=    do_rescale
			__lowercase					=    rescale_factor
			__lowercase					=    rescale_offset
			__lowercase					=    do_normalize
			__lowercase					=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
			__lowercase					=    image_std if image_std is not None else IMAGENET_STANDARD_STD
			__lowercase					=    include_top




	def       UpperCAmelCase (	self       :    Optional[Any]				,			__lowerCamelCase       :    Tuple				,			__lowerCamelCase       :    Optional[int]				,			__lowerCamelCase       :    Optional[int] = PIL.Image.NEAREST				,			__lowerCamelCase       :    Dict = None				,			**__lowerCamelCase       :    Tuple				,			)					->   np.ndarray:

			'''simple docstring'''




			__lowercase					=    get_size_dict(lowercase_      )
			if "height" not in size or "width" not in size:
					raise ValueError(F"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"      )
			return resize(
			    lowercase_				,			size=(size['height'], size['width'])				,			resample=lowercase_				,			data_format=lowercase_				,			**lowercase_      )




	def       UpperCAmelCase (	self       :    Optional[int]				,			__lowerCamelCase       :    Optional[int]				,			__lowerCamelCase       :    Union[str, Any]				,			__lowerCamelCase       :    Optional[Any] = None				,			**__lowerCamelCase       :    Tuple				,			)					->   np.ndarray:

			'''simple docstring'''




			__lowercase					=    get_size_dict(lowercase_      )
			if "height" not in size or "width" not in size:
					raise ValueError(F"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"      )
			return center_crop(lowercase_				,			size=(size['height'], size['width'])				,			data_format=lowercase_				,			**lowercase_      )




	def       UpperCAmelCase (	self       :    Union[str, Any]				,			__lowerCamelCase       :    Dict				,			__lowerCamelCase       :    Any				,			__lowerCamelCase       :    Any = True				,			__lowerCamelCase       :    Optional[Any] = None				,			**__lowerCamelCase       :    List[Any]				,			)					->   Union[str, Any]:

			'''simple docstring'''




			if offset:
					__lowercase					=    (image - 127.5) * scale
					if data_format is not None:
							__lowercase					=    to_channel_dimension_format(lowercase_				,			lowercase_      )
					__lowercase					=    rescaled_image.astype(np.floataa      )
			else:
					__lowercase					=    rescale(lowercase_				,			scale=lowercase_				,			data_format=lowercase_				,			**lowercase_      )
			return rescaled_image




	def       UpperCAmelCase (	self       :    Optional[int]				,			__lowerCamelCase       :    str				,			__lowerCamelCase       :    Optional[Any]				,			__lowerCamelCase       :    Union[str, Any]				,			__lowerCamelCase       :    Tuple = None				,			**__lowerCamelCase       :    Any				,			)					->   np.ndarray:

			'''simple docstring'''




			return normalize(lowercase_				,			mean=lowercase_				,			std=lowercase_				,			data_format=lowercase_				,			**lowercase_      )






	def       UpperCAmelCase (	self       :    int				,			__lowerCamelCase       :    str				,			__lowerCamelCase       :    Union[str, Any] = None				,			__lowerCamelCase       :    Optional[Any] = None				,			__lowerCamelCase       :    Optional[int]=None				,			__lowerCamelCase       :    str = None				,			__lowerCamelCase       :    int = None				,			__lowerCamelCase       :    Optional[int] = None				,			__lowerCamelCase       :    List[str] = None				,			__lowerCamelCase       :    Optional[Any] = None				,			__lowerCamelCase       :    Dict = None				,			__lowerCamelCase       :    Optional[Any] = None				,			__lowerCamelCase       :    List[Any] = None				,			__lowerCamelCase       :    List[str] = None				,			__lowerCamelCase       :    Tuple = None				,			__lowerCamelCase       :    Dict = ChannelDimension.FIRST				,			**__lowerCamelCase       :    Tuple				,			)					->   PIL.Image.Image:

			'''simple docstring'''




			__lowercase					=    do_resize if do_resize is not None else self.do_resize
			__lowercase					=    resample if resample is not None else self.resample
			__lowercase					=    do_center_crop if do_center_crop is not None else self.do_center_crop
			__lowercase					=    do_rescale if do_rescale is not None else self.do_rescale
			__lowercase					=    rescale_factor if rescale_factor is not None else self.rescale_factor
			__lowercase					=    rescale_offset if rescale_offset is not None else self.rescale_offset
			__lowercase					=    do_normalize if do_normalize is not None else self.do_normalize
			__lowercase					=    image_mean if image_mean is not None else self.image_mean
			__lowercase					=    image_std if image_std is not None else self.image_std
			__lowercase					=    include_top if include_top is not None else self.include_top

			__lowercase					=    size if size is not None else self.size
			__lowercase					=    get_size_dict(lowercase_      )
			__lowercase					=    crop_size if crop_size is not None else self.crop_size
			__lowercase					=    get_size_dict(lowercase_				,			param_name='crop_size'      )

			__lowercase					=    make_list_of_images(lowercase_      )

			if not valid_images(lowercase_      ):
					raise ValueError(
					    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
					    'torch.Tensor, tf.Tensor or jax.ndarray.'      )

			if do_resize and size is None or resample is None:
					raise ValueError('Size and resample must be specified if do_resize is True.'      )

			if do_center_crop and crop_size is None:
					raise ValueError('Crop size must be specified if do_center_crop is True.'      )

			if do_rescale and rescale_factor is None:
					raise ValueError('Rescale factor must be specified if do_rescale is True.'      )

			if do_normalize and (image_mean is None or image_std is None):
					raise ValueError('Image mean and std must be specified if do_normalize is True.'      )

			# All transformations expect numpy arrays.
			__lowercase					=    [to_numpy_array(lowercase_      ) for image in images]

			if do_resize:
					__lowercase					=    [self.resize(image=lowercase_				,			size=lowercase_				,			resample=lowercase_      ) for image in images]

			if do_center_crop:
					__lowercase					=    [self.center_crop(image=lowercase_				,			size=lowercase_      ) for image in images]

			if do_rescale:
					__lowercase					=    [self.rescale(image=lowercase_				,			scale=lowercase_				,			offset=lowercase_      ) for image in images]

			if do_normalize:
					__lowercase					=    [self.normalize(image=lowercase_				,			mean=lowercase_				,			std=lowercase_      ) for image in images]

			if include_top:
					__lowercase					=    [self.normalize(image=lowercase_				,			mean=[0, 0, 0]				,			std=lowercase_      ) for image in images]

			__lowercase					=    [to_channel_dimension_format(lowercase_				,			lowercase_      ) for image in images]

			__lowercase					=    {'pixel_values': images}
			return BatchFeature(data=lowercase_				,			tensor_type=lowercase_      )











from math import floor, sqrt



def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						int:
		__lowercase					=    0.0
		__lowercase					=    1.0
		__lowercase					=    int(sqrt(snake_case       )       )
		__lowercase					=    root
		__lowercase					=    0
		while integer_part != 2 * root:
				__lowercase					=    denominator * integer_part - numerator
				__lowercase					=    (n - numerator**2) / denominator
				__lowercase					=    int((root + numerator) / denominator       )
				period += 1
		return period



def    SCREAMING_SNAKE_CASE							(			snake_case = 10_000       )		->						int:
		__lowercase					=    0
		for i in range(2	,      n + 1       ):
				__lowercase					=    sqrt(snake_case       )
				if sr - floor(snake_case       ) != 0 and continuous_fraction_period(snake_case       ) % 2 == 1:
						count_odd_periods += 1
		return count_odd_periods


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




