




'''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      __UpperCAmelCase							(    TensorFormatter[Mapping, np.ndarray, Mapping]							):



     '''simple docstring'''





     def __init__(  self    :	int			,					_lowercase    :	Optional[Any]=None			,					**_lowercase    :	int)						->       List[Any]:
        super().__init__(features=_lowercase)
        A_       		=   np_array_kwargs



     def 						__snake_case	(  self    :	int			,					_lowercase    :	Optional[Any])						->       List[str]:
        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 						__snake_case	(  self    :	Dict			,					_lowercase    :	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 						__snake_case	(  self    :	str			,					_lowercase    :	Tuple)						->       Optional[int]:
        # 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 						__snake_case	(  self    :	Optional[int]			,					_lowercase    :	dict)						->       Tuple:
        return map_nested(self._recursive_tensorize			,					_lowercase			,					map_list=_lowercase)



     def 						__snake_case	(  self    :	str			,					_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 						__snake_case	(  self    :	str			,					_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 						__snake_case	(  self    :	List[str]			,					_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










'''simple docstring'''
def  lowerCamelCase(       SCREAMING_SNAKE_CASE_    )		->		list:
			A_       		=   [True for i in range(n + 1    )]
			A_       		=   [i - 1 for i in range(n + 1    )]
			A_       		=   []
			for i in range(2					,n + 1    ):
						if is_prime[i]:
									primes.append(SCREAMING_SNAKE_CASE_    )
						for j in range(0					,len(SCREAMING_SNAKE_CASE_    )    ):
									if i * primes[j] >= n:
												break
									A_       		=   False

									if i % primes[j] == 0:
												A_       		=   totients[i] * primes[j]
												break

									A_       		=   totients[i] * (primes[j] - 1)

			return totients


if __name__ == "__main__":
							import doctest

							doctest.testmod()




