
"""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 ,			__UpperCamelCase=None ,			**__UpperCamelCase  )->					List[Any]:
										super().__init__(features=_A  )
										UpperCAmelCase__							: Any 							=						torch_tensor_kwargs
										import torch  # noqa import torch at initialization



					def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					int:
										import torch

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



					def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					int:
										import torch

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

										UpperCAmelCase__							: Any 							=						{}

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

															if isinstance(_A ,			PIL.Image.Image  ):
																				UpperCAmelCase__							: Optional[int] 							=						np.asarray(_A  )
										return torch.tensor(_A ,			**{**default_dtype, **self.torch_tensor_kwargs}  )



					def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Tuple:
										import torch

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



					def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Union[str, Any]:
										return map_nested(self._recursive_tensorize ,			_A ,			map_list=_A  )



					def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[Any]:
										UpperCAmelCase__							: List[Any] 							=						self.numpy_arrow_extractor().extract_row(_A  )
										UpperCAmelCase__							: List[Any] 							=						self.python_features_decoder.decode_row(_A  )
										return self.recursive_tensorize(_A  )



					def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[Any]:
										UpperCAmelCase__							: List[str] 							=						self.numpy_arrow_extractor().extract_column(_A  )
										UpperCAmelCase__							: Dict 							=						self.python_features_decoder.decode_column(_A ,			pa_table.column_names[0]  )
										UpperCAmelCase__							: Any 							=						self.recursive_tensorize(_A  )
										UpperCAmelCase__							: List[Any] 							=						self._consolidate(_A  )
										return column



					def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Optional[Any]:
										UpperCAmelCase__							: Optional[int] 							=						self.numpy_arrow_extractor().extract_batch(_A  )
										UpperCAmelCase__							: Optional[Any] 							=						self.python_features_decoder.decode_batch(_A  )
										UpperCAmelCase__							: Dict 							=						self.recursive_tensorize(_A  )
										for column_name in batch:
															UpperCAmelCase__							: Dict 							=						self._consolidate(batch[column_name]  )
										return batch





"""simple docstring"""




from math import isqrt



def 							a__ (				lowerCAmelCase	:       int		):






     '''simple docstring'''
     UpperCAmelCase__							: Tuple 							=						[True] * max_number
     for i in range(2       ,    isqrt(max_number - 1		) + 1		):
          if is_prime[i]:
               for j in range(i**2       ,    lowerCAmelCase       ,    lowerCAmelCase		):
                    UpperCAmelCase__							: List[Any] 							=						False

     return [i for i in range(2       ,    lowerCAmelCase		) if is_prime[i]]



def 							a__ (				lowerCAmelCase	:       int = 10**8		):






     '''simple docstring'''
     UpperCAmelCase__							: Dict 							=						calculate_prime_numbers(max_number // 2		)

     UpperCAmelCase__							: Optional[int] 							=						0
     UpperCAmelCase__							: Optional[int] 							=						0
     UpperCAmelCase__							: Tuple 							=						len(lowerCAmelCase		) - 1
     while left <= right:
          while prime_numbers[left] * prime_numbers[right] >= max_number:
               right -= 1
          semiprimes_count += right - left + 1
          left += 1

     return semiprimes_count


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



