





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        lowercase_			(       TensorFormatter[Mapping, np.ndarray, Mapping]							):

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






        '''simple docstring'''

        super().__init__(features=UpperCamelCase__       )
        snake_case__      							=			np_array_kwargs

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        if isinstance(UpperCamelCase__				,   UpperCamelCase__       ):
         if column and all(
             isinstance(UpperCamelCase__				,   np.ndarray       ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column       ):
          return np.stack(UpperCamelCase__       )
         else:
          # don't use np.array(column, dtype=object)
          # since it fails in certain cases
          # see https://stackoverflow.com/q/51005699
          snake_case__      							=			np.empty(len(UpperCamelCase__       )				,   dtype=UpperCamelCase__       )
          snake_case__      							=			column
          return out
        return column

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        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
        elif isinstance(UpperCamelCase__				,   np.number       ):
         return value

        snake_case__      							=			{}

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

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

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

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        if config.TORCH_AVAILABLE and "torch" in sys.modules:
         import torch

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

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			self.numpy_arrow_extractor().extract_row(UpperCamelCase__       )
        snake_case__      							=			self.python_features_decoder.decode_row(UpperCamelCase__       )
        return self.recursive_tensorize(UpperCamelCase__       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

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








"""simple docstring"""


import math





def 							a__     (					_UpperCAmelCase		)      ->					list:

 '''simple docstring'''






 if n <= 0:
  raise ValueError("Only positive integers have prime factors"		)
 snake_case__      							=			[]
 while n % 2 == 0:
  pf.append(2		)
  snake_case__      							=			int(n / 2		)
 for i in range(3		,       int(math.sqrt(_UpperCAmelCase		)		) + 1		,       2		):
  while n % i == 0:
   pf.append(_UpperCAmelCase		)
   snake_case__      							=			int(n / i		)
 if n > 2:
  pf.append(_UpperCAmelCase		)
 return pf





def 							a__     (					_UpperCAmelCase		)      ->					int:

 '''simple docstring'''






 if n <= 0:
  raise ValueError("Only positive numbers are accepted"		)
 snake_case__      							=			1
 snake_case__      							=			1
 while n % 2 == 0:
  temp += 1
  snake_case__      							=			int(n / 2		)
 div *= temp
 for i in range(3		,       int(math.sqrt(_UpperCAmelCase		)		) + 1		,       2		):
  snake_case__      							=			1
  while n % i == 0:
   temp += 1
   snake_case__      							=			int(n / i		)
  div *= temp
 if n > 1:
  div *= 2
 return div





def 							a__     (					_UpperCAmelCase		)      ->					int:

 '''simple docstring'''






 if n <= 0:
  raise ValueError("Only positive numbers are accepted"		)
 snake_case__      							=			1
 snake_case__      							=			1
 while n % 2 == 0:
  temp += 1
  snake_case__      							=			int(n / 2		)
 if temp > 1:
  s *= (2**temp - 1) / (2 - 1)
 for i in range(3		,       int(math.sqrt(_UpperCAmelCase		)		) + 1		,       2		):
  snake_case__      							=			1
  while n % i == 0:
   temp += 1
   snake_case__      							=			int(n / i		)
  if temp > 1:
   s *= (i**temp - 1) / (i - 1)
 return int(_UpperCAmelCase		)





def 							a__     (					_UpperCAmelCase		)      ->					int:

 '''simple docstring'''






 snake_case__      							=			n
 for x in set(prime_factors(_UpperCAmelCase		)		):
  s *= (x - 1) / x
 return int(_UpperCAmelCase		)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

