"""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]    ):



      def __init__(						self   ,      lowercase=None   ,      lowercase=None   ,      **lowercase)     ->      str:





        '''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`.')
        a__:					Union[str, Any]      							=     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:
          a__:					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])}.')
          a__:					int      							=     str(jax.devices()[0])
        a__:					List[Any]      							=     jnp_array_kwargs



      @staticmethod
      def   lowerCamelCase_       (						)     ->      Dict[str, "jaxlib.xla_extension.Device"]:





        '''simple docstring'''





        import jax

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



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      str:





        '''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   ,      lowercase)     ->      Dict:





        '''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()

        a__:					Union[str, Any]      							=     {}

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

          if isinstance(__UpperCAmelCase   ,      PIL.Image.Image):
            a__:					Tuple      							=     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:
          a__:					List[Any]      							=     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   ,      lowercase)     ->      Any:





        '''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):
          a__:					int      							=     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   ,      lowercase)     ->      Tuple:





        '''simple docstring'''





        return map_nested(self._recursive_tensorize   ,      __UpperCAmelCase   ,      map_list=__UpperCAmelCase)



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      Mapping:





        '''simple docstring'''





        a__:					List[str]      							=     self.numpy_arrow_extractor().extract_row(__UpperCAmelCase)
        a__:					List[Any]      							=     self.python_features_decoder.decode_row(__UpperCAmelCase)
        return self.recursive_tensorize(__UpperCAmelCase)



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      "jax.Array":





        '''simple docstring'''





        a__:					int      							=     self.numpy_arrow_extractor().extract_column(__UpperCAmelCase)
        a__:					int      							=     self.python_features_decoder.decode_column(__UpperCAmelCase   ,      pa_table.column_names[0])
        a__:					List[str]      							=     self.recursive_tensorize(__UpperCAmelCase)
        a__:					List[Any]      							=     self._consolidate(__UpperCAmelCase)
        return column



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      Mapping:





        '''simple docstring'''





        a__:					Any      							=     self.numpy_arrow_extractor().extract_batch(__UpperCAmelCase)
        a__:					List[str]      							=     self.python_features_decoder.decode_batch(__UpperCAmelCase)
        a__:					List[Any]      							=     self.recursive_tensorize(__UpperCAmelCase)
        for column_name in batch:
          a__:					Tuple      							=     self._consolidate(batch[column_name])
        return batch



"""simple docstring"""
def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Tuple:
  _enforce_args(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
  if n == 0:
    return 0
  a__:					List[Any]      							=     float('-inf'       )
  for i in range(1		,					n + 1       ):
    a__:					Optional[Any]      							=     max(
        _SCREAMING_SNAKE_CASE		,					prices[i - 1] + naive_cut_rod_recursive(n - i		,					_SCREAMING_SNAKE_CASE       )       )

  return max_revue







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->str:
  _enforce_args(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
  a__:					str      							=     [float('-inf'       ) for _ in range(n + 1       )]
  return _top_down_cut_rod_recursive(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Optional[Any]:
  if max_rev[n] >= 0:
    return max_rev[n]
  elif n == 0:
    return 0
  else:
    a__:					Dict      							=     float('-inf'       )
    for i in range(1		,					n + 1       ):
      a__:					Optional[Any]      							=     max(
          _SCREAMING_SNAKE_CASE		,					prices[i - 1] + _top_down_cut_rod_recursive(n - i		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )		,					)

    a__:					Optional[int]      							=     max_revenue

  return max_rev[n]







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Dict:
  _enforce_args(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )

  # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
  # length 0.
  a__:					str      							=     [float('-inf'       ) for _ in range(n + 1       )]
  a__:					Tuple      							=     0

  for i in range(1		,					n + 1       ):
    a__:					List[str]      							=     max_rev[i]
    for j in range(1		,					i + 1       ):
      a__:					Tuple      							=     max(_SCREAMING_SNAKE_CASE		,					prices[j - 1] + max_rev[i - j]       )

    a__:					Union[str, Any]      							=     max_revenue_i

  return max_rev[n]







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Any:
  if n < 0:
    a__:					Optional[int]      							=     F'n must be greater than or equal to 0. Got n = {n}'
    raise ValueError(_SCREAMING_SNAKE_CASE       )

  if n > len(_SCREAMING_SNAKE_CASE       ):
    a__:					List[str]      							=     (
        'Each integral piece of rod must have a corresponding price. '
        F'Got n = {n} but length of prices = {len(_SCREAMING_SNAKE_CASE       )}'
    )
    raise ValueError(_SCREAMING_SNAKE_CASE       )







def 						__a       (     )     ->str:
  a__:					int      							=     [6, 10, 12, 15, 20, 23]
  a__:					Union[str, Any]      							=     len(_SCREAMING_SNAKE_CASE       )

  # the best revenue comes from cutting the rod into 6 pieces, each
  # of length 1 resulting in a revenue of 6 * 6 = 36.
  a__:					Any      							=     36

  a__:					Optional[int]      							=     top_down_cut_rod(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
  a__:					List[Any]      							=     bottom_up_cut_rod(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
  a__:					int      							=     naive_cut_rod_recursive(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )

  assert expected_max_revenue == max_rev_top_down
  assert max_rev_top_down == max_rev_bottom_up
  assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
 main()


