




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

__SCREAMING_SNAKE_CASE 						=get_logger()

__SCREAMING_SNAKE_CASE 						=None






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







				def __init__(							self:   str		,					_lowerCamelCase:   List[str]=None		,					_lowerCamelCase:   Dict=None		,					**_lowerCamelCase:   Optional[Any]		):
											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_   	=    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_   	=    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_   	=    str(jax.devices()[0]		)
											SCREAMING_SNAKE_CASE_   	=    jnp_array_kwargs


				@staticmethod
				def     _A							(							):
											import jax

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


				def     _A							(							self:   int		,					_lowerCamelCase:   List[Any]		):
											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:   List[str]		,					_lowerCamelCase:   Optional[Any]		):
											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_   	=    {}

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

																		if isinstance(_A		,					PIL.Image.Image		):
																									SCREAMING_SNAKE_CASE_   	=    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_   	=    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:   Union[str, Any]		,					_lowerCamelCase:   str		):
											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_   	=    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:   Union[str, Any]		,					_lowerCamelCase:   List[str]		):
											return map_nested(self._recursive_tensorize		,					_A		,					map_list=_A		)


				def     _A							(							self:   Tuple		,					_lowerCamelCase:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    self.numpy_arrow_extractor().extract_row(_A		)
											SCREAMING_SNAKE_CASE_   	=    self.python_features_decoder.decode_row(_A		)
											return self.recursive_tensorize(_A		)


				def     _A							(							self:   Optional[int]		,					_lowerCamelCase:   Dict		):
											SCREAMING_SNAKE_CASE_   	=    self.numpy_arrow_extractor().extract_column(_A		)
											SCREAMING_SNAKE_CASE_   	=    self.python_features_decoder.decode_column(_A		,					pa_table.column_names[0]		)
											SCREAMING_SNAKE_CASE_   	=    self.recursive_tensorize(_A		)
											SCREAMING_SNAKE_CASE_   	=    self._consolidate(_A		)
											return column





				def     _A							(							self:   Tuple		,					_lowerCamelCase:   Any		):
											SCREAMING_SNAKE_CASE_   	=    self.numpy_arrow_extractor().extract_batch(_A		)
											SCREAMING_SNAKE_CASE_   	=    self.python_features_decoder.decode_batch(_A		)
											SCREAMING_SNAKE_CASE_   	=    self.recursive_tensorize(_A		)
											for column_name in batch:
																		SCREAMING_SNAKE_CASE_   	=    self._consolidate(batch[column_name]		)
											return batch






def 			a   (_lowerCAmelCase     ):
							if number > 0:
														raise ValueError('''input must be a negative integer'''     )
							SCREAMING_SNAKE_CASE_   	=    len(bin(_lowerCAmelCase     )[3:]     )
							SCREAMING_SNAKE_CASE_   	=    bin(abs(_lowerCAmelCase     ) - (1 << binary_number_length)     )[3:]
							SCREAMING_SNAKE_CASE_   	=    (
							    (
							        '''1'''
							        + '''0''' * (binary_number_length - len(_lowerCAmelCase     ))
							        + twos_complement_number
							    )
							    if number < 0
							    else '''0'''
							)
							return "0b" + twos_complement_number


if __name__ == "__main__":
			import doctest

			doctest.testmod()


