
'''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  _UpperCAmelCase ( TensorFormatter[Mapping, "torch.Tensor", Mapping]    ):

     """simple docstring"""

     def __init__(      self							,		lowerCAmelCase_=None							,		**lowerCAmelCase_ ):

       '''simple docstring'''

       super().__init__(features=lowerCAmelCase_ )
       a_      :	Dict      =							torch_tensor_kwargs
       import torch  # noqa import torch at initialization


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       import torch

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


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       import torch

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

       a_      :	List[str]      =							{}

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

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


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       import torch

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


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

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


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	str      =							self.numpy_arrow_extractor().extract_row(lowerCAmelCase_ )
       a_      :	Any      =							self.python_features_decoder.decode_row(lowerCAmelCase_ )
       return self.recursive_tensorize(lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	List[Any]      =							self.numpy_arrow_extractor().extract_column(lowerCAmelCase_ )
       a_      :	Optional[Any]      =							self.python_features_decoder.decode_column(lowerCAmelCase_							,		pa_table.column_names[0] )
       a_      :	str      =							self.recursive_tensorize(lowerCAmelCase_ )
       a_      :	Optional[Any]      =							self._consolidate(lowerCAmelCase_ )
       return column






     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Optional[int]      =							self.numpy_arrow_extractor().extract_batch(lowerCAmelCase_ )
       a_      :	Dict      =							self.python_features_decoder.decode_batch(lowerCAmelCase_ )
       a_      :	Optional[Any]      =							self.recursive_tensorize(lowerCAmelCase_ )
       for column_name in batch:
         a_      :	str      =							self._consolidate(batch[column_name] )
       return batch




'''simple docstring'''

from __future__ import annotations






def 	_snake_case						(      A_       :    int   ):
  """simple docstring"""







  a_      :	Optional[Any]      =							2
  a_      :	int      =							[]
  while i * i <= n:
    if n % i:
      i += 1
    else:
      n //= i
      factors.append(A_   )
  if n > 1:
    factors.append(A_   )
  return factors


if __name__ == "__main__":
    import doctest

    doctest.testmod()


