






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







   """simple docstring"""







   def __init__( self   ,   _A=None   ,   **_A			)    ->   List[str]:
    super().__init__(features=_A			)
    __a    :	Optional[int]									=						np_array_kwargs



   def    __magic_name__    ( self   ,   _A			)    ->   Optional[Any]:
    if isinstance(_A   ,   _A			):
     if column and all(
         isinstance(_A   ,   np.ndarray			) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column			):
      return np.stack(_A			)
     else:
      # don't use np.array(column, dtype=object)
      # since it fails in certain cases
      # see https://stackoverflow.com/q/51005699
      __a    :	Optional[int]									=						np.empty(len(_A			)   ,   dtype=_A			)
      __a    :	str									=						column
      return out
    return column



   def    __magic_name__    ( self   ,   _A			)    ->   Any:
    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
    elif isinstance(_A   ,   np.number			):
     return value

    __a    :	Dict									=						{}

    if isinstance(_A   ,   np.ndarray			) and np.issubdtype(value.dtype   ,   np.integer			):
     __a    :	List[Any]									=						{'dtype': np.intaa}
    elif isinstance(_A   ,   np.ndarray			) and np.issubdtype(value.dtype   ,   np.floating			):
     __a    :	Any									=						{'dtype': np.floataa}
    elif config.PIL_AVAILABLE and "PIL" in sys.modules:
     import PIL.Image

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

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



   def    __magic_name__    ( self   ,   _A			)    ->   Tuple:
    # support for torch, tf, jax etc.
    if config.TORCH_AVAILABLE and "torch" in sys.modules:
     import torch

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



   def    __magic_name__    ( self   ,   _A			)    ->   Optional[Any]:
    return map_nested(self._recursive_tensorize   ,   _A   ,   map_list=_A			)



   def    __magic_name__    ( self   ,   _A			)    ->   Mapping:
    __a    :	List[str]									=						self.numpy_arrow_extractor().extract_row(_A			)
    __a    :	str									=						self.python_features_decoder.decode_row(_A			)
    return self.recursive_tensorize(_A			)



   def    __magic_name__    ( self   ,   _A			)    ->   np.ndarray:
    __a    :	Any									=						self.numpy_arrow_extractor().extract_column(_A			)
    __a    :	Optional[Any]									=						self.python_features_decoder.decode_column(_A   ,   pa_table.column_names[0]			)
    __a    :	List[str]									=						self.recursive_tensorize(_A			)
    __a    :	Optional[Any]									=						self._consolidate(_A			)
    return column



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












'''simple docstring'''


def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__ = 30		):
 __a    :	Tuple									=						exponent + 2
 __a    :	str									=						(1 + 5**0.5) / 2
 __a    :	Any									=						(phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5

 return int(SCREAMING_SNAKE_CASE__		)


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




