



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]					):





   def __init__(						self							,  _lowerCAmelCase=None							,  **_lowerCAmelCase      ):
         super().__init__(features=_lowerCAmelCase      )
         _lowerCAmelCase			      =	np_array_kwargs





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





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase      ):
         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
         elif isinstance(_lowerCAmelCase							,  np.number      ):
               return value

         _lowerCAmelCase			      =	{}

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

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

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





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase      ):
         # support for torch, tf, jax etc.
         if config.TORCH_AVAILABLE and "torch" in sys.modules:
               import torch

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





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase      ):
         return map_nested(self._recursive_tensorize							,  _lowerCAmelCase							,  map_list=_lowerCAmelCase      )





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase      ):
         _lowerCAmelCase			      =	self.numpy_arrow_extractor().extract_row(_lowerCAmelCase      )
         _lowerCAmelCase			      =	self.python_features_decoder.decode_row(_lowerCAmelCase      )
         return self.recursive_tensorize(_lowerCAmelCase      )





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase      ):
         _lowerCAmelCase			      =	self.numpy_arrow_extractor().extract_column(_lowerCAmelCase      )
         _lowerCAmelCase			      =	self.python_features_decoder.decode_column(_lowerCAmelCase							,  pa_table.column_names[0]      )
         _lowerCAmelCase			      =	self.recursive_tensorize(_lowerCAmelCase      )
         _lowerCAmelCase			      =	self._consolidate(_lowerCAmelCase      )
         return column





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase      ):
         _lowerCAmelCase			      =	self.numpy_arrow_extractor().extract_batch(_lowerCAmelCase      )
         _lowerCAmelCase			      =	self.python_features_decoder.decode_batch(_lowerCAmelCase      )
         _lowerCAmelCase			      =	self.recursive_tensorize(_lowerCAmelCase      )
         for column_name in batch:
               _lowerCAmelCase			      =	self._consolidate(batch[column_name]      )
         return batch




def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						str					)->str:
      return " ".join(sorted(set(sentence.split()					)					)					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()