

# 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

a_      =     get_logger()

a_      =     None






class _UpperCamelCase				(				TensorFormatter[Mapping, 'jax.Array', Mapping] ):
			'''simple docstring'''







			def __init__(      self				:			Optional[Any]   ,   a				:			str=None   ,   a				:			List[Any]=None   ,   **a				:			Any						)				->							Optional[Any]:

				"""simple docstring"""

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

				if isinstance(a   ,   a						):
					raise ValueError(
					    F"Expected {device} to be a `str` not {type(a						)}, 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`."						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							device if isinstance(a   ,   a						) 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:
					SCREAMING_SNAKE_CASE :   str					  =							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]						)}."						)
					SCREAMING_SNAKE_CASE :   Any					  =							str(jax.devices()[0]						)
				SCREAMING_SNAKE_CASE :   Any					  =							jnp_array_kwargs
			@staticmethod
			def 				__UpperCamelCase			(      )				->							Dict[str, "jaxlib.xla_extension.Device"]:

				"""simple docstring"""

				import jax

				return {str(a						): device for device in jax.devices()}
			def 				__UpperCamelCase			(      self				:			Dict   ,   a				:			Tuple						)				->							str:

				"""simple docstring"""

				import jax
				import jax.numpy as jnp

				if isinstance(a   ,   a						) and column:
					if all(
					    isinstance(a   ,   jax.Array						) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column						):
						return jnp.stack(a   ,   axis=0						)
				return column
			def 				__UpperCamelCase			(      self				:			Dict   ,   a				:			str						)				->							str:

				"""simple docstring"""

				import jax
				import jax.numpy as jnp

				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.tolist()

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							{}

				if isinstance(a   ,   (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:
						SCREAMING_SNAKE_CASE :   Dict					  =							{"dtype": jnp.intaa}
					else:
						SCREAMING_SNAKE_CASE :   str					  =							{"dtype": jnp.intaa}
				elif isinstance(a   ,   (np.number, np.ndarray)						) and np.issubdtype(value.dtype   ,   np.floating						):
					SCREAMING_SNAKE_CASE :   int					  =							{"dtype": jnp.floataa}
				elif config.PIL_AVAILABLE and "PIL" in sys.modules:
					import PIL.Image

					if isinstance(a   ,   PIL.Image.Image						):
						SCREAMING_SNAKE_CASE :   Dict					  =							np.asarray(a						)

        # 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:
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							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(a   ,   **{**default_dtype, **self.jnp_array_kwargs}						)
			def 				__UpperCamelCase			(      self				:			Any   ,   a				:			List[str]						)				->							Dict:

				"""simple docstring"""

				import jax

				# 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   ,   jax.Array						):
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							data_struct.__array__()
				# support for nested types like struct of list of struct
				if isinstance(a   ,   np.ndarray						):
					if data_struct.dtype == object:  # jax arrays 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 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			dict						)				->							Dict:

				"""simple docstring"""

				return map_nested(self._recursive_tensorize   ,   a   ,   map_list=a						)
			def 				__UpperCamelCase			(      self				:			Dict   ,   a				:			pa.Table						)				->							Mapping:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.numpy_arrow_extractor().extract_row(a						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							self.python_features_decoder.decode_row(a						)
				return self.recursive_tensorize(a						)
			def 				__UpperCamelCase			(      self				:			Optional[int]   ,   a				:			pa.Table						)				->							"jax.Array":

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							self.numpy_arrow_extractor().extract_column(a						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.python_features_decoder.decode_column(a   ,   pa_table.column_names[0]						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							self.recursive_tensorize(a						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							self._consolidate(a						)
				return column






			def 				__UpperCamelCase			(      self				:			List[Any]   ,   a				:			pa.Table						)				->							Mapping:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							self.numpy_arrow_extractor().extract_batch(a						)
				SCREAMING_SNAKE_CASE :   str					  =							self.python_features_decoder.decode_batch(a						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							self.recursive_tensorize(a						)
				for column_name in batch:
					SCREAMING_SNAKE_CASE :   List[Any]					  =							self._consolidate(batch[column_name]						)
				return batch





def      lowerCamelCase_						(			)	->			List[str]:





				"""simple docstring"""


				__lowerCamelCase									=       0
				for i in range(1					,	1001 ):
								total += i**i
				return str(UpperCamelCase__ )[-10:]


if __name__ == "__main__":
					print(solution())


