






'''simple docstring'''







def     _UpperCamelCase				(  snake_case__ = 1_000				):
      __A		 =   []
      __A		 =   []
      __A		 =   3
      __A		 =   5
      __A		 =   1
      while True:
            __A		 =   z * temp
            if result < n:
                  zmulti.append(snake_case__				)
                  temp += 1
            else:
                  __A		 =   1
                  break
      while True:
            __A		 =   x * temp
            if result < n:
                  xmulti.append(snake_case__				)
                  temp += 1
            else:
                  break
      __A		 =   list(set(xmulti + zmulti				)				)
      return sum(snake_case__				)


if __name__ == "__main__":
       print(F"""{solution() = }""")










'''simple docstring'''







import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter






class        a__     (      TensorFormatter[Mapping, np.ndarray, Mapping]    ):
     '''simple docstring'''







     def __init__(self     :						Tuple					,					lowercase__     :						Dict=None					,					**lowercase__     :						List[Any]       )	->							str:
           super().__init__(features=lowercase__       )
           __A		 =   np_array_kwargs






     def      _UpperCamelCase (self     :						int					,					lowercase__     :						Tuple       )	->							List[Any]:
           if isinstance(lowercase__					,					lowercase__       ):
                 if column and all(
                     isinstance(lowercase__					,					np.ndarray       ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column       ):
                       return np.stack(lowercase__       )
                 else:
                       # don't use np.array(column, dtype=object)
                       # since it fails in certain cases
                       # see https://stackoverflow.com/q/51005699
                       __A		 =   np.empty(len(lowercase__       )					,					dtype=lowercase__       )
                       __A		 =   column
                       return out
           return column






     def      _UpperCamelCase (self     :						Optional[Any]					,					lowercase__     :						Optional[int]       )	->							Tuple:
           if isinstance(lowercase__					,					(str, bytes, type(lowercase__       ))       ):
                 return value
           elif isinstance(lowercase__					,					(np.character, np.ndarray)       ) and np.issubdtype(value.dtype					,					np.character       ):
                 return value
           elif isinstance(lowercase__					,					np.number       ):
                 return value

           __A		 =   {}

           if isinstance(lowercase__					,					np.ndarray       ) and np.issubdtype(value.dtype					,					np.integer       ):
                 __A		 =   {"dtype": np.intaa}
           elif isinstance(lowercase__					,					np.ndarray       ) and np.issubdtype(value.dtype					,					np.floating       ):
                 __A		 =   {"dtype": np.floataa}
           elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                 import PIL.Image

                 if isinstance(lowercase__					,					PIL.Image.Image       ):
                       return np.asarray(lowercase__					,					**self.np_array_kwargs       )

           return np.asarray(lowercase__					,					**{**default_dtype, **self.np_array_kwargs}       )






     def      _UpperCamelCase (self     :						List[Any]					,					lowercase__     :						str       )	->							Optional[Any]:
           # support for torch, tf, jax etc.
           if config.TORCH_AVAILABLE and "torch" in sys.modules:
                 import torch

                 if isinstance(lowercase__					,					torch.Tensor       ):
                       return self._tensorize(data_struct.detach().cpu().numpy()[()]       )
           if hasattr(lowercase__					,					"__array__"       ) and not isinstance(lowercase__					,					(np.ndarray, np.character, np.number)       ):
                 __A		 =   data_struct.__array__()
           # support for nested types like struct of list of struct
           if isinstance(lowercase__					,					np.ndarray       ):
                 if data_struct.dtype == object:
                       return self._consolidate([self.recursive_tensorize(lowercase__       ) for substruct in data_struct]       )
           if isinstance(lowercase__					,					(list, tuple)       ):
                 return self._consolidate([self.recursive_tensorize(lowercase__       ) for substruct in data_struct]       )
           return self._tensorize(lowercase__       )






     def      _UpperCamelCase (self     :						Optional[Any]					,					lowercase__     :						dict       )	->							Dict:
           return map_nested(self._recursive_tensorize					,					lowercase__					,					map_list=lowercase__       )






     def      _UpperCamelCase (self     :						Optional[int]					,					lowercase__     :						pa.Table       )	->							Mapping:
           __A		 =   self.numpy_arrow_extractor().extract_row(lowercase__       )
           __A		 =   self.python_features_decoder.decode_row(lowercase__       )
           return self.recursive_tensorize(lowercase__       )






     def      _UpperCamelCase (self     :						List[Any]					,					lowercase__     :						pa.Table       )	->							np.ndarray:
           __A		 =   self.numpy_arrow_extractor().extract_column(lowercase__       )
           __A		 =   self.python_features_decoder.decode_column(lowercase__					,					pa_table.column_names[0]       )
           __A		 =   self.recursive_tensorize(lowercase__       )
           __A		 =   self._consolidate(lowercase__       )
           return column






     def      _UpperCamelCase (self     :						Optional[Any]					,					lowercase__     :						pa.Table       )	->							Mapping:
           __A		 =   self.numpy_arrow_extractor().extract_batch(lowercase__       )
           __A		 =   self.python_features_decoder.decode_batch(lowercase__       )
           __A		 =   self.recursive_tensorize(lowercase__       )
           for column_name in batch:
                 __A		 =   self._consolidate(batch[column_name]       )
           return batch


