






"""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

lowercase_				     =     get_logger()

lowercase_				     =     None


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


   '''simple docstring'''

   def __init__(	self :     Tuple,       _lowerCamelCase :     Optional[int]=None,       _lowerCamelCase :     Optional[int]=None,       **_lowerCamelCase :     Optional[int]  ):

     '''simple docstring'''
     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 				_SCREAMING_SNAKE_CASE				(	):

     '''simple docstring'''
     import jax

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


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str],       _lowerCamelCase :     Optional[int]  ):

     '''simple docstring'''
     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 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any],       _lowerCamelCase :     Union[str, Any]  ):

     '''simple docstring'''
     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 				_SCREAMING_SNAKE_CASE				(	self :     List[Any],       _lowerCamelCase :     str  ):

     '''simple docstring'''
     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 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any],       _lowerCamelCase :     dict  ):

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


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any],       _lowerCamelCase :     pa.Table  ):

     '''simple docstring'''
     __A									= self.numpy_arrow_extractor().extract_row(_lowerCamelCase  )
     __A									= self.python_features_decoder.decode_row(_lowerCamelCase  )
     return self.recursive_tensorize(_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int],       _lowerCamelCase :     pa.Table  ):

     '''simple docstring'''
     __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 				_SCREAMING_SNAKE_CASE				(	self :     int,       _lowerCamelCase :     pa.Table  ):

     '''simple docstring'''
     __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












"""simple docstring"""


from __future__ import annotations

from typing import Any



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if not postfix_notation:
    return 0

  __A									= {'''+''', '''-''', '''*''', '''/'''}
  __A									= []

  for token in postfix_notation:
    if token in operations:
      __A			,    __A									= stack.pop(), stack.pop()
      if token == "+":
        stack.append(a + b )
      elif token == "-":
        stack.append(a - b )
      elif token == "*":
        stack.append(a * b )
      else:
        if a * b < 0 and a % b != 0:
          stack.append(a // b + 1 )
        else:
          stack.append(a // b )
    else:
      stack.append(int(__UpperCamelCase ) )

  return stack.pop()


if __name__ == "__main__":
       import doctest

       doctest.testmod()




