




from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_ = False						)   ->       list[float]:



       """simple docstring"""

       if radian_mode:
              return [magnitude * cos(lowercase_						), magnitude * sin(lowercase_						)]
       return [magnitude * cos(radians(lowercase_						)						), magnitude * sin(radians(lowercase_						)						)]







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_ = 10**-1						)   ->       bool:



       """simple docstring"""

       A__       =    cross(lowercase_ ,				lowercase_						)
       A__       =    sum(lowercase_						)
       return abs(lowercase_						) < eps


if __name__ == "__main__":
  # Test to check if it works
  _lowerCamelCase	:		Optional[Any]							       =   array(
      [
          polar_force(718.4, 180 - 30),
          polar_force(879.54, 45),
          polar_force(100, -90),
      ]
  )

  _lowerCamelCase	:		NDArray[floataa]							       =   array([[0, 0], [0, 0], [0, 0]])

  assert in_static_equilibrium(forces, location)

  # Problem 1 in image_data/2D_problems.jpg
  _lowerCamelCase	:		Union[str, Any]							       =   array(
      [
          polar_force(30 * 9.81, 15),
          polar_force(215, 180 - 45),
          polar_force(264, 90 - 30),
      ]
  )

  _lowerCamelCase	:		Dict							       =   array([[0, 0], [0, 0], [0, 0]])

  assert in_static_equilibrium(forces, location)

  # Problem in image_data/2D_problems_1.jpg
  _lowerCamelCase	:		Dict							       =   array([[0, -2000], [0, -1200], [0, 15600], [0, -12400]])

  _lowerCamelCase	:		Optional[Any]							       =   array([[0, 0], [6, 0], [10, 0], [12, 0]])

  assert in_static_equilibrium(forces, location)

  import doctest

  doctest.testmod()





import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available


def   __SCREAMING_SNAKE_CASE					(*SCREAMING_SNAKE_CASE__       ):
						if not isinstance(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
												snake_case_   =	list(SCREAMING_SNAKE_CASE__       )
						for i in range(len(SCREAMING_SNAKE_CASE__       )       ):
												snake_case_   =	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   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	[
						    '''CUDA out of memory.''',  # CUDA OOM
						    '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
						    '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
						]
						if isinstance(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ) and len(exception.args       ) == 1:
												return any(err in exception.args[0] for err in _statements       )
						return False


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__ = None  ,		SCREAMING_SNAKE_CASE__ = 128       ):
						if function is None:
												return functools.partial(SCREAMING_SNAKE_CASE__  ,		starting_batch_size=SCREAMING_SNAKE_CASE__       )

						snake_case_   =	starting_batch_size

						def decorator(*SCREAMING_SNAKE_CASE__  ,		**SCREAMING_SNAKE_CASE__       ):
												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()
												snake_case_   =	list(inspect.signature(SCREAMING_SNAKE_CASE__       ).parameters.keys()       )
												# Guard against user error
												if len(SCREAMING_SNAKE_CASE__       ) < (len(SCREAMING_SNAKE_CASE__       ) + 1):
																		snake_case_   =	''', '''.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(SCREAMING_SNAKE_CASE__  ,		*SCREAMING_SNAKE_CASE__  ,		**SCREAMING_SNAKE_CASE__       )
																		except Exception as e:
																								if should_reduce_batch_size(SCREAMING_SNAKE_CASE__       ):
																														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