




"""simple docstring"""







import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available





def 							lowercase					(*snake_case__   : List[Any] )     ->  str:


				'''simple docstring'''






				if not isinstance(__lowercase      ,       __lowercase ):
								lowerCAmelCase  =				list(__lowercase )
				for i in range(len(__lowercase ) ):
								lowerCAmelCase  =				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 							lowercase					(snake_case__   : Exception )     ->  Any:


				'''simple docstring'''






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





def 							lowercase					(snake_case__   : callable = None      ,       snake_case__   : int = 128 )     ->  int:


				'''simple docstring'''






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

				lowerCAmelCase  =				starting_batch_size

				def decorator(*snake_case__   : List[str]      ,       **snake_case__   : Union[str, 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()
								lowerCAmelCase  =				list(inspect.signature(__lowercase ).parameters.keys() )
								# Guard against user error
								if len(__lowercase ) < (len(__lowercase ) + 1):
												lowerCAmelCase  =				', '.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(__lowercase      ,       *__lowercase      ,       **__lowercase )
												except Exception as e:
																if should_reduce_batch_size(__lowercase ):
																				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"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a      =							{
    'configuration_time_series_transformer': [
        'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TimeSeriesTransformerConfig',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a      =							[
		    'TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TimeSeriesTransformerForPrediction',
		    'TimeSeriesTransformerModel',
		    'TimeSeriesTransformerPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    TimeSeriesTransformerConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_time_series_transformer import (
				    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TimeSeriesTransformerForPrediction,
				    TimeSeriesTransformerModel,
				    TimeSeriesTransformerPreTrainedModel,
				)

else:
		import sys

		a      =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





