"""simple docstring"""


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

UpperCamelCase						:				Tuple             =      get_logger()

UpperCamelCase						:				Optional[dict]             =      None



class 			lowerCamelCase__						(   TensorFormatter[Mapping, """jax.Array""", Mapping]			):







						def __init__(							self   :    Tuple			,			_lowercase   :    Optional[Any]=None			,			_lowercase   :    Optional[Any]=None			,			**_lowercase   :    Optional[int]		):
								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`.'		)
								A					       = 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:
										A					       = 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]		)}.'		)
										A					       = str(jax.devices()[0]		)
								A					       = jnp_array_kwargs







						@staticmethod
						def __a						(							):
								import jax

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







						def __a						(							self   :    Union[str, Any]			,			_lowercase   :    str		):
								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 __a						(							self   :    Dict			,			_lowercase   :    Dict		):
								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()

								A					       = {}

								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:
												A					       = {"dtype": jnp.intaa}
										else:
												A					       = {"dtype": jnp.intaa}
								elif isinstance(__A			,			(np.number, np.ndarray)		) and np.issubdtype(value.dtype			,			np.floating		):
										A					       = {"dtype": jnp.floataa}
								elif config.PIL_AVAILABLE and "PIL" in sys.modules:
										import PIL.Image

										if isinstance(__A			,			PIL.Image.Image		):
												A					       = 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:
										A					       = 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 __a						(							self   :    Tuple			,			_lowercase   :    Optional[int]		):
								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		):
										A					       = 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 __a						(							self   :    Tuple			,			_lowercase   :    dict		):
								return map_nested(self._recursive_tensorize			,			__A			,			map_list=__A		)







						def __a						(							self   :    Union[str, Any]			,			_lowercase   :    pa.Table		):
								A					       = self.numpy_arrow_extractor().extract_row(__A		)
								A					       = self.python_features_decoder.decode_row(__A		)
								return self.recursive_tensorize(__A		)







						def __a						(							self   :    Any			,			_lowercase   :    pa.Table		):
								A					       = self.numpy_arrow_extractor().extract_column(__A		)
								A					       = self.python_features_decoder.decode_column(__A			,			pa_table.column_names[0]		)
								A					       = self.recursive_tensorize(__A		)
								A					       = self._consolidate(__A		)
								return column







						def __a						(							self   :    Optional[int]			,			_lowercase   :    pa.Table		):
								A					       = self.numpy_arrow_extractor().extract_batch(__A		)
								A					       = self.python_features_decoder.decode_batch(__A		)
								A					       = self.recursive_tensorize(__A		)
								for column_name in batch:
										A					       = self._consolidate(batch[column_name]		)
								return batch









"""simple docstring"""


from __future__ import annotations







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		if not nums:
				return 0
		A					       = nums[0]
		A					       = 0
		for num in nums[1:]:
				A       , A					       = (
				    max_excluding + num,
				    max(UpperCamelCase__ ,			UpperCamelCase__	),
				)
		return max(UpperCamelCase__ ,			UpperCamelCase__	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()





