





import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available


def  SCREAMING_SNAKE_CASE   ( *_UpperCAmelCase				)    ->  Optional[Any]:
    if not isinstance(_UpperCAmelCase		,			_UpperCAmelCase				):
        lowerCamelCase__					:   Tuple      				=	list(_UpperCAmelCase				)
    for i in range(len(_UpperCAmelCase				)				):
        lowerCamelCase__					:   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  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  bool:
    lowerCamelCase__					:   Any      				=	[
        '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  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase = None		,			_UpperCAmelCase = 128				)    ->  List[str]:
    if function is None:
        return functools.partial(_UpperCAmelCase		,			starting_batch_size=_UpperCAmelCase				)

    lowerCamelCase__					:   Tuple      				=	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()
        lowerCamelCase__					:   Optional[Any]      				=	list(inspect.signature(_UpperCAmelCase				).parameters.keys()				)
        # Guard against user error
        if len(_UpperCAmelCase				) < (len(_UpperCAmelCase				) + 1):
            lowerCamelCase__					:   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












from ..utils import DummyObject, requires_backends

class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   Tuple    ,  *UpperCAmelCase						:   str    ,  **UpperCAmelCase						:   Optional[int]			)    ->			List[str]:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   Union[str, Any]    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   List[str]			)    ->			int:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   Any    ,  *UpperCAmelCase						:   List[Any]    ,  **UpperCAmelCase						:   Tuple			)    ->			Union[str, Any]:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)


class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   List[Any]    ,  *UpperCAmelCase						:   Union[str, Any]    ,  **UpperCAmelCase						:   Dict			)    ->			Any:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   str    ,  *UpperCAmelCase						:   Union[str, Any]    ,  **UpperCAmelCase						:   Union[str, Any]			)    ->			Tuple:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   List[Any]    ,  *UpperCAmelCase						:   str    ,  **UpperCAmelCase						:   Dict			)    ->			str:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)


class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   Dict    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   Dict			)    ->			Optional[int]:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   Optional[Any]    ,  *UpperCAmelCase						:   Union[str, Any]    ,  **UpperCAmelCase						:   Any			)    ->			Any:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   List[str]    ,  *UpperCAmelCase						:   Any    ,  **UpperCAmelCase						:   Dict			)    ->			Tuple:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)


class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   Union[str, Any]    ,  *UpperCAmelCase						:   Optional[int]    ,  **UpperCAmelCase						:   Optional[int]			)    ->			Any:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   List[Any]    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   int			)    ->			Dict:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   Dict    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   Optional[int]			)    ->			Optional[int]:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)


class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   List[str]    ,  *UpperCAmelCase						:   List[Any]    ,  **UpperCAmelCase						:   List[str]			)    ->			Optional[int]:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   List[Any]    ,  *UpperCAmelCase						:   Optional[Any]    ,  **UpperCAmelCase						:   Optional[Any]			)    ->			str:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   List[Any]    ,  *UpperCAmelCase						:   Optional[int]    ,  **UpperCAmelCase						:   Any			)    ->			Any:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)


class      lowerCAmelCase							(      metaclass=__UpperCamelCase    ):
 UpperCAmelCase__									=	["""torch""", """transformers""", """onnx"""]
 def __init__(   self						:   List[str]    ,  *UpperCAmelCase						:   Tuple    ,  **UpperCAmelCase						:   Optional[int]			)    ->			Optional[Any]:
     requires_backends(self    ,  ['torch', 'transformers', 'onnx']			)
 @classmethod
 def   A_			(   cls						:   Union[str, Any]    ,  *UpperCAmelCase						:   List[Any]    ,  **UpperCAmelCase						:   Optional[Any]			)    ->			Tuple:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)







 @classmethod
 def   A_			(   cls						:   Tuple    ,  *UpperCAmelCase						:   List[str]    ,  **UpperCAmelCase						:   List[str]			)    ->			Tuple:
     requires_backends(cls    ,  ['torch', 'transformers', 'onnx']			)





