


'''simple docstring'''
def       a				(      __a       )  ->     Union[str, Any]:



						'''simple docstring'''






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


if __name__ == "__main__":
		import doctest

		doctest.testmod()



'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
		import jax
		import jaxlib

__snake_case											=					get_logger()

__snake_case											=					None
class        lowercase     (							TensorFormatter[Mapping, 'jax.Array', Mapping] ):

		"""simple docstring"""

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

							'''simple docstring'''


							super().__init__(features=UpperCamelCase_							)
							import jax
							from jaxlib.xla_client import Device

							if isinstance(UpperCamelCase_   ,       UpperCamelCase_							):
												raise ValueError(
												    F'''Expected {device} to be a `str` not {type(UpperCamelCase_							)}, as `jaxlib.xla_extension.Device` '''
												    '''is not serializable neither with `pickle` nor with `dill`. Instead you can surround '''
												    '''the device with `str()` to get its string identifier that will be internally mapped '''
												    '''to the actual `jaxlib.xla_extension.Device`.'''							)
							UpperCamelCase__       :Tuple							 =			device if isinstance(UpperCamelCase_   ,       UpperCamelCase_							) else str(jax.devices()[0]							)
							# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
							# with `pickle` nor with `dill`, so we need to use a global variable instead
							global DEVICE_MAPPING
							if DEVICE_MAPPING is None:
												UpperCamelCase__       :Optional[Any]							 =			self._map_devices_to_str()
							if self.device not in list(DEVICE_MAPPING.keys()							):
												logger.warning(
												    F'''Device with string identifier {self.device} not listed among the available '''
												    F'''devices: {list(DEVICE_MAPPING.keys()							)}, so falling back to the default '''
												    F'''device: {str(jax.devices()[0]							)}.'''							)
												UpperCamelCase__       :Optional[int]							 =			str(jax.devices()[0]							)
							UpperCamelCase__       :Tuple							 =			jnp_array_kwargs




		@staticmethod
		def    lowerCAmelCase__    (		):

							'''simple docstring'''


							import jax

							return {str(UpperCamelCase_							): device for device in jax.devices()}




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							import jax
							import jax.numpy as jnp

							if isinstance(UpperCamelCase_   ,       UpperCamelCase_							) and column:
												if all(
												    isinstance(UpperCamelCase_   ,       jax.Array							) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column							):
																	return jnp.stack(UpperCamelCase_   ,       axis=0							)
							return column




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							import jax
							import jax.numpy as jnp

							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()

							UpperCamelCase__       :Optional[int]							 =			{}

							if isinstance(UpperCamelCase_   ,       (np.number, np.ndarray)							) and np.issubdtype(value.dtype   ,       np.integer							):
												# the default int precision depends on the jax config
												# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
												if jax.config.jax_enable_xaa:
																	UpperCamelCase__       :List[str]							 =			{'''dtype''': jnp.intaa}
												else:
																	UpperCamelCase__       :Union[str, Any]							 =			{'''dtype''': jnp.intaa}
							elif isinstance(UpperCamelCase_   ,       (np.number, np.ndarray)							) and np.issubdtype(value.dtype   ,       np.floating							):
												UpperCamelCase__       :Optional[Any]							 =			{'''dtype''': jnp.floataa}
							elif config.PIL_AVAILABLE and "PIL" in sys.modules:
												import PIL.Image

												if isinstance(UpperCamelCase_   ,       PIL.Image.Image							):
																	UpperCamelCase__       :str							 =			np.asarray(UpperCamelCase_							)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
							global DEVICE_MAPPING
							if DEVICE_MAPPING is None:
												UpperCamelCase__       :Dict							 =			self._map_devices_to_str()

							with jax.default_device(DEVICE_MAPPING[self.device]							):
												# calling jnp.array on a np.ndarray does copy the data
												# see https://github.com/google/jax/issues/4486
												return jnp.array(UpperCamelCase_   ,       **{**default_dtype, **self.jnp_array_kwargs}							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							import jax

							# support for torch, tf, jax etc.
							if config.TORCH_AVAILABLE and "torch" in sys.modules:
												import torch

												if isinstance(UpperCamelCase_   ,       torch.Tensor							):
																	return self._tensorize(data_struct.detach().cpu().numpy()[()]							)
							if hasattr(UpperCamelCase_   ,       '''__array__'''							) and not isinstance(UpperCamelCase_   ,       jax.Array							):
												UpperCamelCase__       :int							 =			data_struct.__array__()
							# support for nested types like struct of list of struct
							if isinstance(UpperCamelCase_   ,       np.ndarray							):
												if data_struct.dtype == object:  # jax arrays 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    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			self.numpy_arrow_extractor().extract_row(UpperCamelCase_							)
							UpperCamelCase__       :List[Any]							 =			self.python_features_decoder.decode_row(UpperCamelCase_							)
							return self.recursive_tensorize(UpperCamelCase_							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			self.numpy_arrow_extractor().extract_column(UpperCamelCase_							)
							UpperCamelCase__       :Tuple							 =			self.python_features_decoder.decode_column(UpperCamelCase_   ,       pa_table.column_names[0]							)
							UpperCamelCase__       :Dict							 =			self.recursive_tensorize(UpperCamelCase_							)
							UpperCamelCase__       :str							 =			self._consolidate(UpperCamelCase_							)
							return column




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :int							 =			self.numpy_arrow_extractor().extract_batch(UpperCamelCase_							)
							UpperCamelCase__       :Union[str, Any]							 =			self.python_features_decoder.decode_batch(UpperCamelCase_							)
							UpperCamelCase__       :List[str]							 =			self.recursive_tensorize(UpperCamelCase_							)
							for column_name in batch:
												UpperCamelCase__       :Optional[int]							 =			self._consolidate(batch[column_name]							)
							return batch