

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

  def __init__(  self		:			Optional[Any]					,A		:			List[Any]=None					,A		:			Tuple=None					,**A		:			Dict    ):






   '''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`."""    )
   UpperCAmelCase__					:  Union[str, Any]					   =					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:
    UpperCAmelCase__					:  Optional[int]					   =					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__					:  Tuple					   =					str(jax.devices()[0]    )
   UpperCAmelCase__					:  str					   =					jnp_array_kwargs

  @staticmethod
  def    __lowercase       (  ):






   '''simple docstring'''
   import jax

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

  def    __lowercase       (  self		:			Optional[int]					,A		:			Optional[int]    ):






   '''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    __lowercase       (  self		:			Optional[Any]					,A		:			Union[str, Any]    ):






   '''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()

   UpperCAmelCase__					:  str					   =					{}

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

    if isinstance(A					,PIL.Image.Image    ):
     UpperCAmelCase__					:  Tuple					   =					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:
    UpperCAmelCase__					:  str					   =					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    __lowercase       (  self		:			str					,A		:			List[str]    ):






   '''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    ):
    UpperCAmelCase__					:  str					   =					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    __lowercase       (  self		:			Union[str, Any]					,A		:			dict    ):






   '''simple docstring'''
   return map_nested(self._recursive_tensorize					,A					,map_list=A    )

  def    __lowercase       (  self		:			Optional[Any]					,A		:			pa.Table    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					self.numpy_arrow_extractor().extract_row(A    )
   UpperCAmelCase__					:  Optional[int]					   =					self.python_features_decoder.decode_row(A    )
   return self.recursive_tensorize(A    )

  def    __lowercase       (  self		:			str					,A		:			pa.Table    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[int]					   =					self.numpy_arrow_extractor().extract_column(A    )
   UpperCAmelCase__					:  Tuple					   =					self.python_features_decoder.decode_column(A					,pa_table.column_names[0]    )
   UpperCAmelCase__					:  int					   =					self.recursive_tensorize(A    )
   UpperCAmelCase__					:  Any					   =					self._consolidate(A    )
   return column




  def    __lowercase       (  self		:			Optional[Any]					,A		:			pa.Table    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					self.numpy_arrow_extractor().extract_batch(A    )
   UpperCAmelCase__					:  List[str]					   =					self.python_features_decoder.decode_batch(A    )
   UpperCAmelCase__					:  Union[str, Any]					   =					self.recursive_tensorize(A    )
   for column_name in batch:
    UpperCAmelCase__					:  List[Any]					   =					self._consolidate(batch[column_name]    )
   return batch








"""simple docstring"""




import random
from typing import Any
def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 for _ in range(len(__UpperCamelCase      )      ):
  UpperCAmelCase__					:  Dict					   =					random.randint(0							,    len(__UpperCamelCase      ) - 1      )
  UpperCAmelCase__					:  Tuple					   =					random.randint(0							,    len(__UpperCamelCase      ) - 1      )
  UpperCAmelCase__   ,      UpperCAmelCase__					:  int					   =					data[b], data[a]
 return data


if __name__ == "__main__":
      __UpperCAmelCase =				[0, 1, 2, 3, 4, 5, 6, 7]
      __UpperCAmelCase =				['python', 'says', 'hello', '!']
      print('Fisher-Yates Shuffle:')
      print('List', integers, strings)
      print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))





