

"""simple docstring"""





def   _A  (  UpperCamelCase_					:	int = 100)     ->						int:
   '''simple docstring'''


   __lowercase								=						set()

   __lowercase								=						0

   __lowercase								=						n + 1  # maximum limit

   for a in range(2,      UpperCamelCase_):
      for b in range(2,      UpperCamelCase_):
         __lowercase								=						a**b  # calculates the current power
         collect_powers.add(UpperCamelCase_)  # adds the result to the set
   return len(UpperCamelCase_)


if __name__ == "__main__":
   print('Number of terms ', solution(int(str(input()).strip())))







"""simple docstring"""





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,
    get_resize_output_image_size,
    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, logging


_a							=							logging.get_logger(__name__)




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 __UpperCAmelCase     : Optional[Any]       		=     ["pixel_values"]


 def __init__(      self							:      int, UpperCAmelCase__							:      bool = True, UpperCAmelCase__							:      Optional[Dict[str, int]] = None, UpperCAmelCase__							:      PILImageResampling = PILImageResampling.BILINEAR, UpperCAmelCase__							:      bool = True, UpperCAmelCase__							:      Dict[str, int] = None, UpperCAmelCase__							:      bool = True, UpperCAmelCase__							:      Union[int, float] = 1 / 2_5_5, UpperCAmelCase__							:      bool = True, UpperCAmelCase__							:      Optional[Union[float, List[float]]] = None, UpperCAmelCase__							:      Optional[Union[float, List[float]]] = None, **UpperCAmelCase__							:      str, ):
    super().__init__(**UpperCAmelCase__     )
    __lowercase								=						size if size is not None else {"shortest_edge": 2_5_6}
    __lowercase								=						get_size_dict(UpperCAmelCase__, default_to_square=UpperCAmelCase__     )
    __lowercase								=						crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
    __lowercase								=						get_size_dict(UpperCAmelCase__     )
    __lowercase								=						do_resize
    __lowercase								=						size
    __lowercase								=						resample
    __lowercase								=						do_center_crop
    __lowercase								=						crop_size
    __lowercase								=						do_rescale
    __lowercase								=						rescale_factor
    __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


 def  _lowercase			(      self							:      int, UpperCAmelCase__							:      np.ndarray, UpperCAmelCase__							:      Dict[str, int], UpperCAmelCase__							:      PILImageResampling = PILImageResampling.BICUBIC, UpperCAmelCase__							:      Optional[Union[str, ChannelDimension]] = None, **UpperCAmelCase__							:      Dict, ):
    __lowercase								=						get_size_dict(UpperCAmelCase__, default_to_square=UpperCAmelCase__     )
    if "shortest_edge" not in size:
       raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""     )
    __lowercase								=						get_resize_output_image_size(UpperCAmelCase__, size=size["shortest_edge"], default_to_square=UpperCAmelCase__     )
    return resize(UpperCAmelCase__, size=UpperCAmelCase__, resample=UpperCAmelCase__, data_format=UpperCAmelCase__, **UpperCAmelCase__     )


 def  _lowercase			(      self							:      Dict, UpperCAmelCase__							:      np.ndarray, UpperCAmelCase__							:      Dict[str, int], UpperCAmelCase__							:      Optional[Union[str, ChannelDimension]] = None, **UpperCAmelCase__							:      Dict, ):
    __lowercase								=						get_size_dict(UpperCAmelCase__     )
    return center_crop(UpperCAmelCase__, size=(size["height"], size["width"]), data_format=UpperCAmelCase__, **UpperCAmelCase__     )


 def  _lowercase			(      self							:      Optional[int], UpperCAmelCase__							:      np.ndarray, UpperCAmelCase__							:      float, UpperCAmelCase__							:      Optional[Union[str, ChannelDimension]] = None, **UpperCAmelCase__							:      Union[str, Any]     ):
    return rescale(UpperCAmelCase__, scale=UpperCAmelCase__, data_format=UpperCAmelCase__, **UpperCAmelCase__     )


 def  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      np.ndarray, UpperCAmelCase__							:      Union[float, List[float]], UpperCAmelCase__							:      Union[float, List[float]], UpperCAmelCase__							:      Optional[Union[str, ChannelDimension]] = None, **UpperCAmelCase__							:      int, ):
    return normalize(UpperCAmelCase__, mean=UpperCAmelCase__, std=UpperCAmelCase__, data_format=UpperCAmelCase__, **UpperCAmelCase__     )



 def  _lowercase			(      self							:      Any, UpperCAmelCase__							:      ImageInput, UpperCAmelCase__							:      Optional[bool] = None, UpperCAmelCase__							:      Dict[str, int] = None, UpperCAmelCase__							:      PILImageResampling = None, UpperCAmelCase__							:      bool = None, UpperCAmelCase__							:      Dict[str, int] = None, UpperCAmelCase__							:      Optional[bool] = None, UpperCAmelCase__							:      Optional[float] = None, UpperCAmelCase__							:      Optional[bool] = None, UpperCAmelCase__							:      Optional[Union[float, List[float]]] = None, UpperCAmelCase__							:      Optional[Union[float, List[float]]] = None, UpperCAmelCase__							:      Optional[Union[str, TensorType]] = None, UpperCAmelCase__							:      Union[str, ChannelDimension] = ChannelDimension.FIRST, **UpperCAmelCase__							:      Optional[int], ):
    __lowercase								=						do_resize if do_resize is not None else self.do_resize
    __lowercase								=						size if size is not None else self.size
    __lowercase								=						get_size_dict(UpperCAmelCase__, default_to_square=UpperCAmelCase__     )
    __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								=						crop_size if crop_size is not None else self.crop_size
    __lowercase								=						get_size_dict(UpperCAmelCase__     )
    __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								=						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								=						make_list_of_images(UpperCAmelCase__     )

    if not valid_images(UpperCAmelCase__     ):
       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:
       raise ValueError("Size 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(UpperCAmelCase__     ) for image in images]

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

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

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

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

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

    __lowercase								=						{"pixel_values": images}
    return BatchFeature(data=UpperCAmelCase__, tensor_type=UpperCAmelCase__     )




