import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available



def 		A_    (					*_lowerCAmelCase					)						->			List[Any]:
 if not isinstance(_lowerCAmelCase							,       _lowerCAmelCase					):
  UpperCamelCase     : Any	     =       list(_lowerCAmelCase					)
 for i in range(len(_lowerCAmelCase					)					):
  UpperCamelCase     : int	     =       None
 gc.collect()
 if is_xpu_available():
  torch.xpu.empty_cache()
 elif is_npu_available():
  torch.npu.empty_cache()
 else:
  torch.cuda.empty_cache()
 return objects



def 		A_    (					_lowerCAmelCase					)						->			Union[str, Any]:
 UpperCamelCase     : Dict	     =       [
     '''CUDA out of memory.''',  # CUDA OOM
     '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
     '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
 ]
 if isinstance(_lowerCAmelCase							,       _lowerCAmelCase					) and len(exception.args					) == 1:
  return any(err in exception.args[0] for err in _statements					)
 return False



def 		A_    (					_lowerCAmelCase = None							,       _lowerCAmelCase = 128					)						->			List[Any]:
 if function is None:
  return functools.partial(_lowerCAmelCase							,       starting_batch_size=_lowerCAmelCase					)

 UpperCamelCase     : Dict	     =       starting_batch_size

 def decorator(*_lowerCAmelCase							,       **_lowerCAmelCase					):
  nonlocal batch_size
  gc.collect()
  if is_xpu_available():
   torch.xpu.empty_cache()
  elif is_npu_available():
   torch.npu.empty_cache()
  else:
   torch.cuda.empty_cache()
  UpperCamelCase     : Any	     =       list(inspect.signature(_lowerCAmelCase					).parameters.keys()					)
  # Guard against user error
  if len(_lowerCAmelCase					) < (len(_lowerCAmelCase					) + 1):
   UpperCamelCase     : Optional[int]	     =       ''', '''.join([F"""{arg}={value}""" for arg, value in zip(params[1:]							,       args[1:]					)]					)
   raise TypeError(
       F"""Batch size was passed into `{function.__name__}` as the first argument when called."""
       F"""Remove this as the decorator already does so: `{function.__name__}({arg_str})`"""					)
  while True:
   if batch_size == 0:
    raise RuntimeError("No executable batch size found, reached zero."					)
   try:
    return function(_lowerCAmelCase							,       *_lowerCAmelCase							,       **_lowerCAmelCase					)
   except Exception as e:
    if should_reduce_batch_size(_lowerCAmelCase					):
     gc.collect()
     if is_xpu_available():
      torch.xpu.empty_cache()
     elif is_npu_available():
      torch.npu.empty_cache()
     else:
      torch.cuda.empty_cache()
     batch_size //= 2
    else:
     raise

 return decorator







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()
