



import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available

def   lowerCAmelCase__						(							*_SCREAMING_SNAKE_CASE       :			Optional[Any]    ):
    """simple docstring"""







    if not isinstance(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    ):
        __a       =		list(_SCREAMING_SNAKE_CASE    )
    for i in range(len(_SCREAMING_SNAKE_CASE    )    ):
        __a       =		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__						(							_SCREAMING_SNAKE_CASE       :			Exception    ):
    """simple docstring"""







    __a       =		[
        """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   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			callable = None				,     _SCREAMING_SNAKE_CASE       :			int = 128    ):
    """simple docstring"""







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

    __a       =		starting_batch_size

    def decorator(*_SCREAMING_SNAKE_CASE       :			Any				,     **_SCREAMING_SNAKE_CASE       :			Optional[int]    ):
        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()
        __a       =		list(inspect.signature(_SCREAMING_SNAKE_CASE    ).parameters.keys()    )
        # Guard against user error
        if len(_SCREAMING_SNAKE_CASE    ) < (len(_SCREAMING_SNAKE_CASE    ) + 1):
            __a       =		""", """.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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


lowerCamelCase__			= {"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= ["""SpeechEncoderDecoderModel"""]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= ["""FlaxSpeechEncoderDecoderModel"""]

if TYPE_CHECKING:
						from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
						import sys

						lowerCamelCase__			= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
