

'''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

UpperCamelCase_							=					get_logger()

UpperCamelCase_							=					None





class 		__SCREAMING_SNAKE_CASE       ( TensorFormatter[Mapping, 'jax.Array', Mapping]			):







				def __init__(					self    :		Union[str, Any]		,				UpperCAmelCase__    :		List[str]=None		,				UpperCAmelCase__    :		Tuple=None		,				**UpperCAmelCase__    :		Dict      ):
										'''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`.'''      )
										lowercase   :       Dict								=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:
																lowercase   :       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]      )}.'''      )
																lowercase   :       List[Any]								=str(jax.devices()[0]      )
										lowercase   :       Optional[Any]								=jnp_array_kwargs







				@staticmethod
				def    lowerCamelCase_  (					):
										'''simple docstring'''
										import jax

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







				def    lowerCamelCase_  (					self    :		Any		,				UpperCAmelCase__    :		List[Any]      ):
										'''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    :		int		,				UpperCAmelCase__    :		Union[str, Any]      ):
										'''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()

										lowercase   :       Dict								={}

										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:
																						lowercase   :       Tuple								={'''dtype''': jnp.intaa}
																else:
																						lowercase   :       Optional[int]								={'''dtype''': jnp.intaa}
										elif isinstance(UpperCAmelCase__		,				(np.number, np.ndarray)      ) and np.issubdtype(value.dtype		,				np.floating      ):
																lowercase   :       Union[str, Any]								={'''dtype''': jnp.floataa}
										elif config.PIL_AVAILABLE and "PIL" in sys.modules:
																import PIL.Image

																if isinstance(UpperCAmelCase__		,				PIL.Image.Image      ):
																						lowercase   :       int								=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:
																lowercase   :       int								=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    :		Union[str, Any]		,				UpperCAmelCase__    :		Optional[int]      ):
										'''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      ):
																lowercase   :       Any								=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    :		int		,				UpperCAmelCase__    :		dict      ):
										'''simple docstring'''
										return map_nested(self._recursive_tensorize		,				UpperCAmelCase__		,				map_list=UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Optional[int]		,				UpperCAmelCase__    :		pa.Table      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=self.numpy_arrow_extractor().extract_row(UpperCAmelCase__      )
										lowercase   :       str								=self.python_features_decoder.decode_row(UpperCAmelCase__      )
										return self.recursive_tensorize(UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Union[str, Any]		,				UpperCAmelCase__    :		pa.Table      ):
										'''simple docstring'''
										lowercase   :       str								=self.numpy_arrow_extractor().extract_column(UpperCAmelCase__      )
										lowercase   :       Dict								=self.python_features_decoder.decode_column(UpperCAmelCase__		,				pa_table.column_names[0]      )
										lowercase   :       str								=self.recursive_tensorize(UpperCAmelCase__      )
										lowercase   :       Tuple								=self._consolidate(UpperCAmelCase__      )
										return column







				def    lowerCamelCase_  (					self    :		str		,				UpperCAmelCase__    :		pa.Table      ):
										'''simple docstring'''
										lowercase   :       int								=self.numpy_arrow_extractor().extract_batch(UpperCAmelCase__      )
										lowercase   :       Optional[int]								=self.python_features_decoder.decode_batch(UpperCAmelCase__      )
										lowercase   :       List[str]								=self.recursive_tensorize(UpperCAmelCase__      )
										for column_name in batch:
																lowercase   :       Optional[int]								=self._consolidate(batch[column_name]      )
										return batch








'''simple docstring'''





def 							_lowerCAmelCase (      __magic_name__	:    int      ,   __magic_name__	:    Optional[Any]	)							->     str:
						lowercase   :       Optional[Any]								=[0 for i in range(r + 1	)]
						# nc0 = 1
						lowercase   :       Optional[Any]								=1
						for i in range(1      ,   n + 1	):
												# to compute current row from previous row.
												lowercase   :       str								=min(__magic_name__      ,   __magic_name__	)
												while j > 0:
																		c[j] += c[j - 1]
																		j -= 1
						return c[r]







print(binomial_coefficient(n=10, r=5))





