


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, is_vision_available, logging


if is_vision_available():
   import PIL


lowerCamelCase__							:						str							     =   logging.get_logger(__name__)
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							List[Any]	   =       ['pixel_values']

     def __init__(					self:Dict     ,		_a:bool = True     ,		_a:Dict[str, int] = None     ,		_a:float = None     ,		_a:PILImageResampling = PILImageResampling.BILINEAR     ,		_a:bool = True     ,		_a:Union[int, float] = 1 / 2_55     ,		_a:bool = True     ,		_a:Optional[Union[float, List[float]]] = None     ,		_a:Optional[Union[float, List[float]]] = None     ,		**_a:Union[str, Any]     ,		):
       super().__init__(**_a   )
       snake_case__						= size if size is not None else {'''shortest_edge''': 3_84}
       snake_case__						= get_size_dict(_a     ,		default_to_square=_a   )

       snake_case__						= do_resize
       snake_case__						= size
       # Default value set here for backwards compatibility where the value in config is None
       snake_case__						= crop_pct if crop_pct is not None else 2_24 / 2_56
       snake_case__						= resample
       snake_case__						= do_rescale
       snake_case__						= rescale_factor
       snake_case__						= do_normalize
       snake_case__						= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       snake_case__						= image_std if image_std is not None else IMAGENET_STANDARD_STD

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:np.ndarray     ,		_a:Dict[str, int]     ,		_a:float     ,		_a:PILImageResampling = PILImageResampling.BICUBIC     ,		_a:Optional[Union[str, ChannelDimension]] = None     ,		**_a:Optional[int]     ,		):
       snake_case__						= get_size_dict(_a     ,		default_to_square=_a   )
       if "shortest_edge" not in size:
         raise ValueError(F"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""   )
       snake_case__						= size['''shortest_edge''']

       if shortest_edge < 3_84:
         # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
         snake_case__						= int(shortest_edge / crop_pct   )
         snake_case__						= get_resize_output_image_size(_a     ,		size=_a     ,		default_to_square=_a   )
         snake_case__						= resize(image=_a     ,		size=_a     ,		resample=_a     ,		data_format=_a     ,		**_a   )
         # then crop to (shortest_edge, shortest_edge)
         return center_crop(image=_a     ,		size=(shortest_edge, shortest_edge)     ,		data_format=_a     ,		**_a   )
       else:
         # warping (no cropping) when evaluated at 384 or larger
         return resize(
             _a     ,		size=(shortest_edge, shortest_edge)     ,		resample=_a     ,		data_format=_a     ,		**_a   )

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

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







     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:ImageInput     ,		_a:bool = None     ,		_a:Dict[str, int] = None     ,		_a:float = None     ,		_a:PILImageResampling = None     ,		_a:bool = None     ,		_a:float = None     ,		_a:bool = None     ,		_a:Optional[Union[float, List[float]]] = None     ,		_a:Optional[Union[float, List[float]]] = None     ,		_a:Optional[Union[str, TensorType]] = None     ,		_a:ChannelDimension = ChannelDimension.FIRST     ,		**_a:Any     ,		):
       snake_case__						= do_resize if do_resize is not None else self.do_resize
       snake_case__						= crop_pct if crop_pct is not None else self.crop_pct
       snake_case__						= resample if resample is not None else self.resample
       snake_case__						= do_rescale if do_rescale is not None else self.do_rescale
       snake_case__						= rescale_factor if rescale_factor is not None else self.rescale_factor
       snake_case__						= do_normalize if do_normalize is not None else self.do_normalize
       snake_case__						= image_mean if image_mean is not None else self.image_mean
       snake_case__						= image_std if image_std is not None else self.image_std

       snake_case__						= size if size is not None else self.size
       snake_case__						= get_size_dict(_a     ,		default_to_square=_a   )

       snake_case__						= make_list_of_images(_a   )

       if not valid_images(_a   ):
         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_resize and size["shortest_edge"] < 3_84 and crop_pct is None:
         raise ValueError('''crop_pct must be specified if size < 384.'''   )

       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.
       snake_case__						= [to_numpy_array(_a   ) for image in images]

       if do_resize:
         snake_case__						= [self.resize(image=_a     ,		size=_a     ,		crop_pct=_a     ,		resample=_a   ) for image in images]

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

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

       snake_case__						= [to_channel_dimension_format(_a     ,		_a   ) for image in images]

       snake_case__						= {'''pixel_values''': images}
       return BatchFeature(data=_a     ,		tensor_type=_a   )





def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase = 100				)    ->				int:
  snake_case__						= set()

  snake_case__						= 0

  snake_case__						= n + 1  # maximum limit

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


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

