


# 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     :    Dict 	=				get_logger()

__UpperCamelCase     :    Optional[dict] 	=				None



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

   '''simple docstring'''







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






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

   @staticmethod
   def 	_snake_case     (	):






         '''simple docstring'''



         import jax

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

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






         '''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 	_snake_case     (	self	:			int							,	_lowerCamelCase	:			Optional[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()

         __lowerCamelCase  :   Optional[Any]							    =    {}

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

               if isinstance(_lowerCamelCase							,	PIL.Image.Image				):
                     __lowerCamelCase  :   Dict							    =    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:
               __lowerCamelCase  :   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(_lowerCamelCase							,	**{**default_dtype, **self.jnp_array_kwargs}				)

   def 	_snake_case     (	self	:			Any							,	_lowerCamelCase	:			List[Any]				):






         '''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				):
               __lowerCamelCase  :   List[Any]							    =    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	:			str							,	_lowerCamelCase	:			List[str]				):






         '''simple docstring'''



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

   def 	_snake_case     (	self	:			Optional[int]							,	_lowerCamelCase	:			Union[str, Any]				):






         '''simple docstring'''



         __lowerCamelCase  :   List[str]							    =    self.numpy_arrow_extractor().extract_row(_lowerCamelCase				)
         __lowerCamelCase  :   Tuple							    =    self.python_features_decoder.decode_row(_lowerCamelCase				)
         return self.recursive_tensorize(_lowerCamelCase				)

   def 	_snake_case     (	self	:			str							,	_lowerCamelCase	:			Dict				):






         '''simple docstring'''



         __lowerCamelCase  :   Optional[Any]							    =    self.numpy_arrow_extractor().extract_column(_lowerCamelCase				)
         __lowerCamelCase  :   Tuple							    =    self.python_features_decoder.decode_column(_lowerCamelCase							,	pa_table.column_names[0]				)
         __lowerCamelCase  :   Optional[Any]							    =    self.recursive_tensorize(_lowerCamelCase				)
         __lowerCamelCase  :   Optional[Any]							    =    self._consolidate(_lowerCamelCase				)
         return column


   def 	_snake_case     (	self	:			List[Any]							,	_lowerCamelCase	:			Tuple				):






         '''simple docstring'''



         __lowerCamelCase  :   str							    =    self.numpy_arrow_extractor().extract_batch(_lowerCamelCase				)
         __lowerCamelCase  :   Tuple							    =    self.python_features_decoder.decode_batch(_lowerCamelCase				)
         __lowerCamelCase  :   str							    =    self.recursive_tensorize(_lowerCamelCase				)
         for column_name in batch:
               __lowerCamelCase  :   Union[str, Any]							    =    self._consolidate(batch[column_name]				)
         return batch








from __future__ import annotations

from typing import Any
class    __UpperCamelCase			:

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = 0      ) ->			None:
  a__							,			a__												= row, column
  a__												= [[default_value for c in range(SCREAMING_SNAKE_CASE      )] for r in range(SCREAMING_SNAKE_CASE      )]

 def __str__(	self      ) ->			str:
  a__												= f"Matrix consist of {self.row} rows and {self.column} columns\n"

  # Make string identifier
  a__												= 0
  for row_vector in self.array:
   for obj in row_vector:
    a__												= max(SCREAMING_SNAKE_CASE		,				len(str(SCREAMING_SNAKE_CASE      )      )      )
  a__												= f"%{max_element_length}s"

  # Make string and return
  def single_line(SCREAMING_SNAKE_CASE      ) -> str:
   nonlocal string_format_identifier
   a__												= '''['''
   line += ", ".join(string_format_identifier % (obj,) for obj in row_vector      )
   line += "]"
   return line

  s += "\n".join(single_line(SCREAMING_SNAKE_CASE      ) for row_vector in self.array      )
  return s

 def __repr__(	self      ) ->			str:
  return str(self      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			bool:
  if not (isinstance(SCREAMING_SNAKE_CASE		,				(list, tuple)      ) and len(SCREAMING_SNAKE_CASE      ) == 2):
   return False
  elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
   return False
  else:
   return True

 def __getitem__(	self		,				SCREAMING_SNAKE_CASE      ) ->			Any:
  assert self.validate_indicies(SCREAMING_SNAKE_CASE      )
  return self.array[loc[0]][loc[1]]

 def __setitem__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			None:
  assert self.validate_indicies(SCREAMING_SNAKE_CASE      )
  a__												= value

 def __add__(	self		,				SCREAMING_SNAKE_CASE      ) ->			Matrix:
  assert isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
  assert self.row == another.row and self.column == another.column

  # Add
  a__												= Matrix(self.row		,				self.column      )
  for r in range(self.row      ):
   for c in range(self.column      ):
    a__												= self[r, c] + another[r, c]
  return result

 def __neg__(	self      ) ->			Matrix:
  a__												= Matrix(self.row		,				self.column      )
  for r in range(self.row      ):
   for c in range(self.column      ):
    a__												= -self[r, c]
  return result

 def __sub__(	self		,				SCREAMING_SNAKE_CASE      ) ->			Matrix:
  return self + (-another)

 def __mul__(	self		,				SCREAMING_SNAKE_CASE      ) ->			Matrix:

  if isinstance(SCREAMING_SNAKE_CASE		,				(int, float)      ):  # Scalar multiplication
   a__												= Matrix(self.row		,				self.column      )
   for r in range(self.row      ):
    for c in range(self.column      ):
     a__												= self[r, c] * another
   return result
  elif isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ):  # Matrix multiplication
   assert self.column == another.row
   a__												= Matrix(self.row		,				another.column      )
   for r in range(self.row      ):
    for c in range(another.column      ):
     for i in range(self.column      ):
      result[r, c] += self[r, i] * another[i, c]
   return result
  else:
   a__												= f"Unsupported type given for another ({type(SCREAMING_SNAKE_CASE      )})"
   raise TypeError(SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self      ) ->			Matrix:
  a__												= Matrix(self.column		,				self.row      )
  for r in range(self.row      ):
   for c in range(self.column      ):
    a__												= self[r, c]
  return result



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			Any:
  assert isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) and isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
  assert self.row == self.column == u.row == v.row  # u, v should be column vector
  assert u.column == v.column == 1  # u, v should be column vector

  # Calculate
  a__												= v.transpose()
  a__												= (v_t * self * u)[0, 0] + 1
  if numerator_factor == 0:
   return None  # It's not invertable
  return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":






     def     __a					(		):
      # a^(-1)
      a__												= Matrix(3							,  3							,  0       )
      for i in range(3       ):
       a__												= 1
      print(f"a^(-1) is {ainv}"       )
      # u, v
      a__												= Matrix(3							,  1							,  0       )
      a__							,			a__							,			a__												= 1, 2, -3
      a__												= Matrix(3							,  1							,  0       )
      a__							,			a__							,			a__												= 4, -2, 5
      print(f"u is {u}"       )
      print(f"v is {v}"       )
      print(f"uv^T is {u * v.transpose()}"       )
      # Sherman Morrison
      print(f"(a + uv^T)^(-1) is {ainv.sherman_morrison(__UpperCAmelCase							,  __UpperCAmelCase       )}"       )






     def     __a					(		):
      import doctest

      doctest.testmod()

     testa()





