


'''simple docstring'''







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 tensorflow as tf
class 		UpperCamelCase__    (       TensorFormatter[Mapping, "tf.Tensor", Mapping]		):



     """simple docstring"""






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

            '''simple docstring'''

            super().__init__(features=__a      )
            _lowerCAmelCase			:							Any      	=				tf_tensor_kwargs
            import tensorflow as tf  # noqa: import tf at initialization







     def a		(       self					,		snake_case__      ):

            '''simple docstring'''

            import tensorflow as tf

            if isinstance(__a					,		__a      ) and column:
                   if all(
                       isinstance(__a					,		tf.Tensor      ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column      ):
                          return tf.stack(__a      )
                   elif all(
                       isinstance(__a					,		(tf.Tensor, tf.RaggedTensor)      ) and x.ndim == 1 and x.dtype == column[0].dtype
                       for x in column      ):
                          # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
                          return tf.ragged.stack(__a      )

            return column







     def a		(       self					,		snake_case__      ):

            '''simple docstring'''

            import tensorflow as tf

            if value is None:
                   return value

            _lowerCAmelCase			:							str      	=				{}

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

                   if isinstance(__a					,		PIL.Image.Image      ):
                          _lowerCAmelCase			:							Tuple      	=				np.asarray(__a      )

            return tf.convert_to_tensor(__a					,		**{**default_dtype, **self.tf_tensor_kwargs}      )







     def a		(       self					,		snake_case__      ):

            '''simple docstring'''

            import tensorflow as tf

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







     def a		(       self					,		snake_case__      ):

            '''simple docstring'''

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







     def a		(       self					,		snake_case__      ):

            '''simple docstring'''

            _lowerCAmelCase			:							Optional[int]      	=				self.numpy_arrow_extractor().extract_row(__a      )
            _lowerCAmelCase			:							Optional[int]      	=				self.python_features_decoder.decode_row(__a      )
            return self.recursive_tensorize(__a      )







     def a		(       self					,		snake_case__      ):

            '''simple docstring'''

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







     def a		(       self					,		snake_case__      ):

            '''simple docstring'''

            _lowerCAmelCase			:							List[str]      	=				self.numpy_arrow_extractor().extract_batch(__a      )
            _lowerCAmelCase			:							Any      	=				self.python_features_decoder.decode_batch(__a      )
            _lowerCAmelCase			:							int      	=				self.recursive_tensorize(__a      )
            for column_name in batch:
                   _lowerCAmelCase			:							Dict      	=				self._consolidate(batch[column_name]      )
            return batch








"""simple docstring"""


def     lowercase__			(    lowercase_	)    ->						str:




      """simple docstring"""

      return " ".join(sorted(set(sentence.split()	)	)	)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
