



"""simple docstring"""





# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

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


if TYPE_CHECKING:
  import torch
class 				lowercase__       (   TensorFormatter[Mapping, '''torch.Tensor''', Mapping]				):







       '''simple docstring'''


       def __init__(							self      :  List[Any]		,    _UpperCAmelCase      :  Optional[Any]=None		,    **_UpperCAmelCase      :  Any   )			->						int:


             '''simple docstring'''




             super().__init__(features=_UpperCAmelCase   )
             UpperCAmelCase_      			=							torch_tensor_kwargs
             import torch  # noqa import torch at initialization



       def 					lowercase__    (							self      :  List[Any]		,    _UpperCAmelCase      :  Tuple   )			->						str:


             '''simple docstring'''




             import torch

             if isinstance(_UpperCAmelCase		,    _UpperCAmelCase   ) and column:
                   if all(
                       isinstance(_UpperCAmelCase		,    torch.Tensor   ) and x.shape == column[0].shape and x.dtype == column[0].dtype
                       for x in column   ):
                         return torch.stack(_UpperCAmelCase   )
             return column



       def 					lowercase__    (							self      :  List[str]		,    _UpperCAmelCase      :  List[str]   )			->						List[str]:


             '''simple docstring'''




             import torch

             if isinstance(_UpperCAmelCase		,    (str, bytes, type(_UpperCAmelCase   ))   ):
                   return value
             elif isinstance(_UpperCAmelCase		,    (np.character, np.ndarray)   ) and np.issubdtype(value.dtype		,    np.character   ):
                   return value.tolist()

             UpperCAmelCase_      			=							{}

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

                   if isinstance(_UpperCAmelCase		,    PIL.Image.Image   ):
                         UpperCAmelCase_      			=							np.asarray(_UpperCAmelCase   )
             return torch.tensor(_UpperCAmelCase		,    **{**default_dtype, **self.torch_tensor_kwargs}   )



       def 					lowercase__    (							self      :  Tuple		,    _UpperCAmelCase      :  List[Any]   )			->						List[str]:


             '''simple docstring'''




             import torch

             # support for torch, tf, jax etc.
             if hasattr(_UpperCAmelCase		,    "__array__"   ) and not isinstance(_UpperCAmelCase		,    torch.Tensor   ):
                   UpperCAmelCase_      			=							data_struct.__array__()
             # support for nested types like struct of list of struct
             if isinstance(_UpperCAmelCase		,    np.ndarray   ):
                   if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
                         return self._consolidate([self.recursive_tensorize(_UpperCAmelCase   ) for substruct in data_struct]   )
             elif isinstance(_UpperCAmelCase		,    (list, tuple)   ):
                   return self._consolidate([self.recursive_tensorize(_UpperCAmelCase   ) for substruct in data_struct]   )
             return self._tensorize(_UpperCAmelCase   )



       def 					lowercase__    (							self      :  Optional[Any]		,    _UpperCAmelCase      :  dict   )			->						Union[str, Any]:


             '''simple docstring'''




             return map_nested(self._recursive_tensorize		,    _UpperCAmelCase		,    map_list=_UpperCAmelCase   )



       def 					lowercase__    (							self      :  Optional[int]		,    _UpperCAmelCase      :  pa.Table   )			->						Mapping:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.numpy_arrow_extractor().extract_row(_UpperCAmelCase   )
             UpperCAmelCase_      			=							self.python_features_decoder.decode_row(_UpperCAmelCase   )
             return self.recursive_tensorize(_UpperCAmelCase   )



       def 					lowercase__    (							self      :  Any		,    _UpperCAmelCase      :  pa.Table   )			->						"torch.Tensor":


             '''simple docstring'''




             UpperCAmelCase_      			=							self.numpy_arrow_extractor().extract_column(_UpperCAmelCase   )
             UpperCAmelCase_      			=							self.python_features_decoder.decode_column(_UpperCAmelCase		,    pa_table.column_names[0]   )
             UpperCAmelCase_      			=							self.recursive_tensorize(_UpperCAmelCase   )
             UpperCAmelCase_      			=							self._consolidate(_UpperCAmelCase   )
             return column







       def 					lowercase__    (							self      :  Optional[int]		,    _UpperCAmelCase      :  pa.Table   )			->						Mapping:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.numpy_arrow_extractor().extract_batch(_UpperCAmelCase   )
             UpperCAmelCase_      			=							self.python_features_decoder.decode_batch(_UpperCAmelCase   )
             UpperCAmelCase_      			=							self.recursive_tensorize(_UpperCAmelCase   )
             for column_name in batch:
                   UpperCAmelCase_      			=							self._consolidate(batch[column_name]   )
             return batch









"""simple docstring"""





def        a__     (				lowerCAmelCase__ = 2000000				):
      UpperCAmelCase_      			=							[0 for i in range(n + 1				)]
      UpperCAmelCase_      			=							1
      UpperCAmelCase_      			=							1

      for i in range(2     ,      int(n**0.5				) + 1				):
            if primality_list[i] == 0:
                  for j in range(i * i     ,      n + 1     ,      lowerCAmelCase__				):
                        UpperCAmelCase_      			=							1
      UpperCAmelCase_      			=							0
      for i in range(lowerCAmelCase__				):
            if primality_list[i] == 0:
                  sum_of_primes += i
      return sum_of_primes


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




