



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



 """simple docstring"""

 def __init__(    self					:					Optional[int]			,						__lowerCAmelCase					:					int=None			,						__lowerCAmelCase					:					Dict=None			,						**__lowerCAmelCase					:					int      )   ->	Optional[int]:
    super().__init__(features=__lowerCAmelCase      )
    import jax
    from jaxlib.xla_client import Device

    if isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ):
       raise ValueError(
           f'''Expected {device} to be a `str` not {type(__lowerCAmelCase      )}, 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(__lowerCAmelCase			,						__lowerCAmelCase      ) 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     snake_case_      (    )   ->	Dict[str, "jaxlib.xla_extension.Device"]:
    import jax

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

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					Union[str, Any]      )   ->	Optional[int]:
    import jax
    import jax.numpy as jnp

    if isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ) and column:
       if all(
           isinstance(__lowerCAmelCase			,						jax.Array      ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column      ):
          return jnp.stack(__lowerCAmelCase			,						axis=0      )
    return column

 def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					Union[str, Any]      )   ->	Optional[int]:
    import jax
    import jax.numpy as jnp

    if isinstance(__lowerCAmelCase			,						(str, bytes, type(__lowerCAmelCase      ))      ):
       return value
    elif isinstance(__lowerCAmelCase			,						(np.character, np.ndarray)      ) and np.issubdtype(value.dtype			,						np.character      ):
       return value.tolist()

    _A      =       {}

    if isinstance(__lowerCAmelCase			,						(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(__lowerCAmelCase			,						(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(__lowerCAmelCase			,						PIL.Image.Image      ):
          _A      =       np.asarray(__lowerCAmelCase      )

        # 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(__lowerCAmelCase			,						**{**default_dtype, **self.jnp_array_kwargs}      )

 def     snake_case_      (    self					:					Union[str, Any]			,						__lowerCAmelCase					:					str      )   ->	Optional[int]:
    import jax

    # support for torch, tf, jax etc.
    if config.TORCH_AVAILABLE and "torch" in sys.modules:
       import torch

       if isinstance(__lowerCAmelCase			,						torch.Tensor      ):
          return self._tensorize(data_struct.detach().cpu().numpy()[()]      )
    if hasattr(__lowerCAmelCase			,						'''__array__'''      ) and not isinstance(__lowerCAmelCase			,						jax.Array      ):
       _A      =       data_struct.__array__()
    # support for nested types like struct of list of struct
    if isinstance(__lowerCAmelCase			,						np.ndarray      ):
       if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
          return self._consolidate([self.recursive_tensorize(__lowerCAmelCase      ) for substruct in data_struct]      )
    elif isinstance(__lowerCAmelCase			,						(list, tuple)      ):
       return self._consolidate([self.recursive_tensorize(__lowerCAmelCase      ) for substruct in data_struct]      )
    return self._tensorize(__lowerCAmelCase      )

 def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					dict      )   ->	Optional[Any]:
    return map_nested(self._recursive_tensorize			,						__lowerCAmelCase			,						map_list=__lowerCAmelCase      )

 def     snake_case_      (    self					:					Tuple			,						__lowerCAmelCase					:					pa.Table      )   ->	Mapping:
    _A      =       self.numpy_arrow_extractor().extract_row(__lowerCAmelCase      )
    _A      =       self.python_features_decoder.decode_row(__lowerCAmelCase      )
    return self.recursive_tensorize(__lowerCAmelCase      )

 def     snake_case_      (    self					:					Union[str, Any]			,						__lowerCAmelCase					:					pa.Table      )   ->	"jax.Array":
    _A      =       self.numpy_arrow_extractor().extract_column(__lowerCAmelCase      )
    _A      =       self.python_features_decoder.decode_column(__lowerCAmelCase			,						pa_table.column_names[0]      )
    _A      =       self.recursive_tensorize(__lowerCAmelCase      )
    _A      =       self._consolidate(__lowerCAmelCase      )
    return column

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					pa.Table      )   ->	Mapping:
    _A      =       self.numpy_arrow_extractor().extract_batch(__lowerCAmelCase      )
    _A      =       self.python_features_decoder.decode_batch(__lowerCAmelCase      )
    _A      =       self.recursive_tensorize(__lowerCAmelCase      )
    for column_name in batch:
       _A      =       self._consolidate(batch[column_name]      )
    return batch








def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:list				)  ->     bool:
   if not isinstance(_snake_case	,   _snake_case				):
      raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''				)
   if len(_snake_case				) == 0:
      raise ValueError('''Input list must be a non empty list'''				)
   if len(_snake_case				) == 1:
      return True
   _A      =       series[1] - series[0]
   for index in range(len(_snake_case				) - 1				):
      if series[index + 1] - series[index] != common_diff:
         return False
   return True

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:list				)  ->     float:
   if not isinstance(_snake_case	,   _snake_case				):
      raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''				)
   if len(_snake_case				) == 0:
      raise ValueError('''Input list must be a non empty list'''				)
   _A      =       0
   for val in series:
      answer += val
   return answer / len(_snake_case				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



