





import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available






def 	lowerCamelCase_		(							*_UpperCamelCase					)						->		int:






				"""simple docstring"""


				if not isinstance(snake_case__					,							snake_case__					):
								snake_case_		:       Any           =    list(snake_case__					)
				for i in range(len(snake_case__					)					):
								snake_case_		:       Optional[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 	lowerCamelCase_		(							_UpperCamelCase					)						->		List[Any]:






				"""simple docstring"""


				snake_case_		:       Any           =    [
				    """CUDA out of memory.""",  # CUDA OOM
				    """cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""",  # CUDNN SNAFU
				    """DefaultCPUAllocator: can't allocate memory""",  # CPU OOM
				]
				if isinstance(snake_case__					,							snake_case__					) and len(exception.args					) == 1:
								return any(err in exception.args[0] for err in _statements					)
				return False






def 	lowerCamelCase_		(							_UpperCamelCase = None					,							_UpperCamelCase = 128					)						->		Optional[Any]:






				"""simple docstring"""


				if function is None:
								return functools.partial(snake_case__					,							starting_batch_size=snake_case__					)

				snake_case_		:       Dict           =    starting_batch_size

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








def 	UpperCamelCase	(							snake_case__  : list	):




     '''simple docstring'''






     if not grid or not grid[0]:
          raise TypeError("""The grid does not contain the appropriate information"""	)

     for cell_n in range(1						,len(grid[0]	)	):
          grid[0][cell_n] += grid[0][cell_n - 1]
     __snake_case      :str 							=    grid[0]

     for row_n in range(1						,len(snake_case__	)	):
          __snake_case      :Optional[int] 							=    grid[row_n]
          __snake_case      :Optional[Any] 							=    fill_row(snake_case__						,snake_case__	)
          __snake_case      :Dict 							=    grid[row_n]

     return grid[-1][-1]






def 	UpperCamelCase	(							snake_case__  : list						,snake_case__  : list	):




     '''simple docstring'''






     current_row[0] += row_above[0]
     for cell_n in range(1						,len(snake_case__	)	):
          current_row[cell_n] += min(current_row[cell_n - 1]						,row_above[cell_n]	)

     return current_row


if __name__ == "__main__":
     import doctest

     doctest.testmod()


