


'''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 torch


class  lowerCAmelCase_						(       TensorFormatter[Mapping, """torch.Tensor""", Mapping]      ):





		"""simple docstring"""



		def __init__(					self     :							Dict				,							SCREAMING_SNAKE_CASE__     :							Tuple=None				,							**SCREAMING_SNAKE_CASE__     :							List[Any]						):





							'''simple docstring'''

							super().__init__(features=UpperCamelCase_						)
							__a       =   torch_tensor_kwargs
							import torch  # noqa import torch at initialization

		def __a						(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							int						):





							'''simple docstring'''

							import torch

							if isinstance(UpperCamelCase_				,							UpperCamelCase_						) and column:
												if all(
												    isinstance(UpperCamelCase_				,							torch.Tensor						) and x.shape == column[0].shape and x.dtype == column[0].dtype
												    for x in column						):
																	return torch.stack(UpperCamelCase_						)
							return column

		def __a						(					self     :							int				,							SCREAMING_SNAKE_CASE__     :							Dict						):





							'''simple docstring'''

							import torch

							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.tolist()

							__a       =   {}

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

												if isinstance(UpperCamelCase_				,							PIL.Image.Image						):
																	__a       =   np.asarray(UpperCamelCase_						)
							return torch.tensor(UpperCamelCase_				,							**{**default_dtype, **self.torch_tensor_kwargs}						)

		def __a						(					self     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							str						):





							'''simple docstring'''

							import torch

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

		def __a						(					self     :							str				,							SCREAMING_SNAKE_CASE__     :							str						):





							'''simple docstring'''

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

		def __a						(					self     :							Dict				,							SCREAMING_SNAKE_CASE__     :							Dict						):





							'''simple docstring'''

							__a       =   self.numpy_arrow_extractor().extract_row(UpperCamelCase_						)
							__a       =   self.python_features_decoder.decode_row(UpperCamelCase_						)
							return self.recursive_tensorize(UpperCamelCase_						)

		def __a						(					self     :							Any				,							SCREAMING_SNAKE_CASE__     :							List[str]						):





							'''simple docstring'''

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


		def __a						(					self     :							Any				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]						):





							'''simple docstring'''

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










'''simple docstring'''







def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					str:
     """simple docstring"""




     return " ".join(input_str.split()[::-1]		)


if __name__ == "__main__":
 import doctest

 doctest.testmod()






