





# 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

__lowerCamelCase	:					Optional[Any]			  =					get_logger()

__lowerCamelCase	:					Optional[dict]			  =					None



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





      def __init__(			self	,							A_=None	,							A_=None	,							**A_       ):
       '''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     : int	     =       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     : Union[str, 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]       )}."""       )
        UpperCamelCase     : Any	     =       str(jax.devices()[0]       )
       UpperCamelCase     : Optional[Any]	     =       jnp_array_kwargs





      @staticmethod
      def 	__UpperCamelCase(			):
       '''simple docstring'''




       import jax

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





      def 	__UpperCamelCase(			self	,							A_       ):
       '''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 	__UpperCamelCase(			self	,							A_       ):
       '''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     : Any	     =       {}

       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     : Any	     =       {"dtype": jnp.intaa}
        else:
         UpperCamelCase     : Any	     =       {"dtype": jnp.intaa}
       elif isinstance(A_	,							(np.number, np.ndarray)       ) and np.issubdtype(value.dtype	,							np.floating       ):
        UpperCamelCase     : Tuple	     =       {"dtype": jnp.floataa}
       elif config.PIL_AVAILABLE and "PIL" in sys.modules:
        import PIL.Image

        if isinstance(A_	,							PIL.Image.Image       ):
         UpperCamelCase     : str	     =       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     : Optional[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(A_	,							**{**default_dtype, **self.jnp_array_kwargs}       )





      def 	__UpperCamelCase(			self	,							A_       ):
       '''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     : Optional[int]	     =       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 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[Any]	     =       self.numpy_arrow_extractor().extract_row(A_       )
       UpperCamelCase     : Tuple	     =       self.python_features_decoder.decode_row(A_       )
       return self.recursive_tensorize(A_       )





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[Any]	     =       self.numpy_arrow_extractor().extract_batch(A_       )
       UpperCamelCase     : Optional[Any]	     =       self.python_features_decoder.decode_batch(A_       )
       UpperCamelCase     : Dict	     =       self.recursive_tensorize(A_       )
       for column_name in batch:
        UpperCamelCase     : Optional[Any]	     =       self._consolidate(batch[column_name]       )
       return batch







from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       )						->			list[float]:
 UpperCamelCase     ,   UpperCamelCase     : List[Any]	     =       coefficient_matrix.shape
 UpperCamelCase     ,   UpperCamelCase     : Optional[int]	     =       constant_matrix.shape

 if rowsa != colsa:
  UpperCamelCase     : List[Any]	     =       F"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
  raise ValueError(_lowerCAmelCase					)

 if colsa != 1:
  UpperCamelCase     : Any	     =       F"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
  raise ValueError(_lowerCAmelCase					)

 if rowsa != rowsa:
  UpperCamelCase     : Tuple	     =       (
      "Coefficient and constant matrices dimensions must be nxn and nx1 but "
      F"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
  )
  raise ValueError(_lowerCAmelCase					)

 if len(_lowerCAmelCase					) != rowsa:
  UpperCamelCase     : Any	     =       (
      "Number of initial values must be equal to number of rows in coefficient "
      F"""matrix but received {len(_lowerCAmelCase					)} and {rowsa}"""
  )
  raise ValueError(_lowerCAmelCase					)

 if iterations <= 0:
  raise ValueError("Iterations must be at least 1"					)

 UpperCamelCase     : NDArray[floataa]	     =       np.concatenate(
     (coefficient_matrix, constant_matrix)							,       axis=1					)

 UpperCamelCase     ,   UpperCamelCase     : str	     =       table.shape

 strictly_diagonally_dominant(_lowerCAmelCase					)

 # Iterates the whole matrix for given number of times
 for _ in range(_lowerCAmelCase					):
  UpperCamelCase     : Optional[Any]	     =       []
  for row in range(_lowerCAmelCase					):
   UpperCamelCase     : Optional[int]	     =       0
   for col in range(_lowerCAmelCase					):
    if col == row:
     UpperCamelCase     : Union[str, Any]	     =       table[row][col]
    elif col == cols - 1:
     UpperCamelCase     : List[Any]	     =       table[row][col]
    else:
     temp += (-1) * table[row][col] * init_val[col]
   UpperCamelCase     : Dict	     =       (temp + val) / denom
   new_val.append(_lowerCAmelCase					)
  UpperCamelCase     : List[str]	     =       new_val

 return [float(_lowerCAmelCase					) for i in new_val]



def 		A_    (					_lowerCAmelCase					)						->			bool:
 UpperCamelCase     ,   UpperCamelCase     : Dict	     =       table.shape

 UpperCamelCase     : List[Any]	     =       True

 for i in range(0							,       _lowerCAmelCase					):
  UpperCamelCase     : Union[str, Any]	     =       0
  for j in range(0							,       cols - 1					):
   if i == j:
    continue
   else:
    total += table[i][j]

  if table[i][i] <= total:
   raise ValueError("Coefficient matrix is not strictly diagonally dominant"					)

 return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
    import doctest

    doctest.testmod()
