import torch
from torch import autograd






class 		a__						( autograd.Function ):

					@staticmethod
					def     __UpperCamelCase       (   _A				:	Any,_A				:	Union[str, Any],_A				:	int,_A				:	Optional[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   inputs.numel()
							SCREAMING_SNAKE_CASE_  :		int    				=   int(0.005 * nb_elems   ) + 1
							if sigmoid:
									SCREAMING_SNAKE_CASE_  :		List[Any]    				=   (torch.sigmoid(__SCREAMING_SNAKE_CASE   ) > threshold).type(inputs.type()   )
							else:
									SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   (inputs > threshold).type(inputs.type()   )
							if mask.sum() < nb_min:
									# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
									SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   inputs.flatten().kthvalue(max(nb_elems - nb_min,1   )   ).values
									SCREAMING_SNAKE_CASE_  :		List[str]    				=   (inputs > k_threshold).type(inputs.type()   )
							return mask






					@staticmethod
					def     __UpperCamelCase       (   _A				:	Union[str, Any],_A				:	Optional[Any]   ):







							"""simple docstring"""






							return gradOutput, None, None







class 		a__						( autograd.Function ):

					@staticmethod
					def     __UpperCamelCase       (   _A				:	Any,_A				:	str,_A				:	Dict   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   inputs.clone()
							SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   inputs.flatten().sort(descending=__SCREAMING_SNAKE_CASE   )
							SCREAMING_SNAKE_CASE_  :		int    				=   int(threshold * inputs.numel()   )

							# flat_out and mask access the same memory.
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   mask.flatten()
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   0
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   1
							return mask






					@staticmethod
					def     __UpperCamelCase       (   _A				:	Optional[Any],_A				:	str   ):







							"""simple docstring"""






							return gradOutput, None







class 		a__						( UpperCamelCase_ ):

					@staticmethod
					def     __UpperCamelCase       (   _A				:	List[str],_A				:	Optional[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   inputs.clone()
							SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   inputs.abs().flatten().sort(descending=__SCREAMING_SNAKE_CASE   )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   int(threshold * inputs.numel()   )

							# flat_out and mask access the same memory.
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   mask.flatten()
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   0
							SCREAMING_SNAKE_CASE_  :		str    				=   1
							return mask

from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    '''cross_attention''',
    '''0.20.0''',
    '''Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.''',
    standard_warn=False,
)


__lowerCamelCase :     str          =  AttentionProcessor






class 		a__						( A__ ):

					def __init__(   self				:	Tuple,*_A				:	Union[str, Any],**_A				:	Any   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"   )   )} instead.'
							deprecate("cross_attention","0.20.0",_A,standard_warn=_A   )
							super().__init__(*_A,**_A   )







class 		a__						( A__ ):

					def __init__(   self				:	str,*_A				:	Optional[Any],**_A				:	List[str]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"   )   )} instead.'
							deprecate("cross_attention","0.20.0",_A,standard_warn=_A   )
							super().__init__(*_A,**_A   )







class 		a__						( A__ ):

					def __init__(   self				:	str,*_A				:	Dict,**_A				:	Optional[int]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"   )   )} instead.'
							deprecate("cross_attention","0.20.0",_A,standard_warn=_A   )
							super().__init__(*_A,**_A   )







class 		a__						( A__ ):

					def __init__(   self				:	List[Any],*_A				:	List[str],**_A				:	str   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple    				=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"   )   )} instead.'
							deprecate("cross_attention","0.20.0",_A,standard_warn=_A   )
							super().__init__(*_A,**_A   )







class 		a__						( A__ ):

					def __init__(   self				:	Optional[int],*_A				:	Any,**_A				:	int   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"   )   )} instead.'
							deprecate("cross_attention","0.20.0",_A,standard_warn=_A   )
							super().__init__(*_A,**_A   )







class 		a__						( A__ ):

					def __init__(   self				:	Union[str, Any],*_A				:	Union[str, Any],**_A				:	Any   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"   )   )} instead.'
							deprecate("cross_attention","0.20.0",_A,standard_warn=_A   )
							super().__init__(*_A,**_A   )







class 		a__						( A__ ):

					def __init__(   self				:	Dict,*_A				:	Dict,**_A				:	Optional[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"   )   )} instead.'
							deprecate("cross_attention","0.20.0",_A,standard_warn=_A   )
							super().__init__(*_A,**_A   )







class 		a__						( A__ ):

					def __init__(   self				:	int,*_A				:	Dict,**_A				:	Union[str, Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str    				=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"   )   )} instead.'
							deprecate("cross_attention","0.20.0",_A,standard_warn=_A   )
							super().__init__(*_A,**_A   )
