


"""simple docstring"""






import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available
def      lowerCamelCase       (     *_UpperCamelCase		:      Optional[Any]							)		->       Optional[Any]:

							'''simple docstring'''



							if not isinstance(_UpperCamelCase       ,			_UpperCamelCase							):
														__UpperCAmelCase   :						Optional[int]      							=					list(_UpperCamelCase							)
							for i in range(len(_UpperCamelCase							)							):
														__UpperCAmelCase   :						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		:      Exception							)		->       bool:

							'''simple docstring'''



							__UpperCAmelCase   :						List[str]      							=					[
							    """CUDA out of memory.""",  # CUDA OOM
							    """cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""",  # CUDNN SNAFU
							    """DefaultCPUAllocator: can't allocate memory""",  # CPU OOM
							]
							if isinstance(_UpperCamelCase       ,			_UpperCamelCase							) and len(exception.args							) == 1:
														return any(err in exception.args[0] for err in _statements							)
							return False
def      lowerCamelCase       (     _UpperCamelCase		:      callable = None       ,			_UpperCamelCase		:      int = 1_2_8							)		->       int:

							'''simple docstring'''



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

							__UpperCAmelCase   :						List[str]      							=					starting_batch_size

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




"""simple docstring"""






import numpy as np

UpperCAmelCase    :     Optional[Any]   						=		[
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]





class      lowerCamelCase__   :







					"""simple docstring"""


					def __init__(       self    : Union[str, Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					np.array(UpperCamelCase  )






					def lowerCamelCase__							(       self    : int ,       UpperCamelCase    : str  ):
												'''simple docstring'''






												__UpperCAmelCase							,__UpperCAmelCase   :						Optional[Any]      							=					np.where(letter == self.SQUARE  )
												__UpperCAmelCase   :						Optional[Any]      							=					np.concatenate([indexa + 1, indexa + 1]  )
												return indexes






					def lowerCamelCase__							(       self    : Any ,       UpperCamelCase    : int ,       UpperCamelCase    : int  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					self.SQUARE[indexa - 1, indexa - 1]
												return letter






					def lowerCamelCase__							(       self    : Optional[int] ,       UpperCamelCase    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					message.lower()
												__UpperCAmelCase   :						List[Any]      							=					message.replace(""" """ ,       """"""  )
												__UpperCAmelCase   :						List[Any]      							=					message.replace("""j""" ,       """i"""  )

												__UpperCAmelCase   :						Optional[int]      							=					np.empty((2, len(UpperCamelCase  ))  )
												for letter_index in range(len(UpperCamelCase  )  ):
																			__UpperCAmelCase   :						List[Any]      							=					self.letter_to_numbers(message[letter_index]  )

																			__UpperCAmelCase   :						str      							=					numbers[0]
																			__UpperCAmelCase   :						int      							=					numbers[1]

												__UpperCAmelCase   :						Union[str, Any]      							=					first_step.reshape(2 * len(UpperCamelCase  )  )
												__UpperCAmelCase   :						Optional[Any]      							=					""""""
												for numbers_index in range(len(UpperCamelCase  )  ):
																			__UpperCAmelCase   :						Any      							=					int(second_step[numbers_index * 2]  )
																			__UpperCAmelCase   :						Any      							=					int(second_step[(numbers_index * 2) + 1]  )
																			__UpperCAmelCase   :						str      							=					self.numbers_to_letter(UpperCamelCase ,       UpperCamelCase  )
																			__UpperCAmelCase   :						Optional[Any]      							=					encoded_message + letter

												return encoded_message






					def lowerCamelCase__							(       self    : Optional[int] ,       UpperCamelCase    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Any      							=					message.lower()
												message.replace(""" """ ,       """"""  )
												__UpperCAmelCase   :						int      							=					np.empty(2 * len(UpperCamelCase  )  )
												for letter_index in range(len(UpperCamelCase  )  ):
																			__UpperCAmelCase   :						Any      							=					self.letter_to_numbers(message[letter_index]  )
																			__UpperCAmelCase   :						Any      							=					numbers[0]
																			__UpperCAmelCase   :						Dict      							=					numbers[1]

												__UpperCAmelCase   :						str      							=					first_step.reshape((2, len(UpperCamelCase  ))  )
												__UpperCAmelCase   :						Union[str, Any]      							=					""""""
												for numbers_index in range(len(UpperCamelCase  )  ):
																			__UpperCAmelCase   :						Optional[int]      							=					int(second_step[0, numbers_index]  )
																			__UpperCAmelCase   :						Tuple      							=					int(second_step[1, numbers_index]  )
																			__UpperCAmelCase   :						Tuple      							=					self.numbers_to_letter(UpperCamelCase ,       UpperCamelCase  )
																			__UpperCAmelCase   :						Any      							=					decoded_message + letter

												return decoded_message
