



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,
)


__snake_case		:int 						=	AttentionProcessor

class        _A			(      snake_case_       ):



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


           '''simple docstring'''

           __a					=							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'''	,						lowercase_	,						standard_warn=lowercase_)
           super().__init__(*lowercase_	,						**lowercase_)


class        _A			(      snake_case_       ):



       def __init__(						self				:	Any	,						*__SCREAMING_SNAKE_CASE				:	Tuple	,						**__SCREAMING_SNAKE_CASE				:	Any):


           '''simple docstring'''

           __a					=							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'''	,						lowercase_	,						standard_warn=lowercase_)
           super().__init__(*lowercase_	,						**lowercase_)


class        _A			(      snake_case_       ):



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


           '''simple docstring'''

           __a					=							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'''	,						lowercase_	,						standard_warn=lowercase_)
           super().__init__(*lowercase_	,						**lowercase_)


class        _A			(      snake_case_       ):



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


           '''simple docstring'''

           __a					=							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'''	,						lowercase_	,						standard_warn=lowercase_)
           super().__init__(*lowercase_	,						**lowercase_)


class        _A			(      snake_case_       ):



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


           '''simple docstring'''

           __a					=							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'''	,						lowercase_	,						standard_warn=lowercase_)
           super().__init__(*lowercase_	,						**lowercase_)


class        _A			(      snake_case_       ):



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


           '''simple docstring'''

           __a					=							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'''	,						lowercase_	,						standard_warn=lowercase_)
           super().__init__(*lowercase_	,						**lowercase_)


class        _A			(      snake_case_       ):



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


           '''simple docstring'''

           __a					=							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'''	,						lowercase_	,						standard_warn=lowercase_)
           super().__init__(*lowercase_	,						**lowercase_)


class        _A			(      snake_case_       ):



       def __init__(						self				:	Dict	,						*__SCREAMING_SNAKE_CASE				:	str	,						**__SCREAMING_SNAKE_CASE				:	List[str]):


           '''simple docstring'''

           __a					=							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'''	,						lowercase_	,						standard_warn=lowercase_)
           super().__init__(*lowercase_	,						**lowercase_)













"""simple docstring"""


import torch
from torch import autograd






class __a ( autograd.Function					):



  @staticmethod
  def     A_					(lowercase_	:		Optional[Any]					,      lowercase_	:		torch.tensor					,      lowercase_	:		float					,      lowercase_	:		bool )		->				List[str]:
   __A						:							str      =						inputs.numel()
   __A						:							str      =						int(0.005 * nb_elems ) + 1
   if sigmoid:
    __A						:							Dict      =						(torch.sigmoid(lowercase_ ) > threshold).type(inputs.type() )
   else:
    __A						:							Tuple      =						(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
    __A						:							Any      =						inputs.flatten().kthvalue(max(nb_elems - nb_min					,      1 ) ).values
    __A						:							Union[str, Any]      =						(inputs > k_threshold).type(inputs.type() )
   return mask



  @staticmethod
  def     A_					(lowercase_	:		str					,      lowercase_	:		Any )		->				int:
   return gradOutput, None, None






class __a ( autograd.Function					):



  @staticmethod
  def     A_					(lowercase_	:		List[Any]					,      lowercase_	:		torch.tensor					,      lowercase_	:		float )		->				Dict:
   __A						:							List[Any]      =						inputs.clone()
   __A     ,       __A						:							Optional[int]      =						inputs.flatten().sort(descending=lowercase_ )
   __A						:							Tuple      =						int(threshold * inputs.numel() )

   # flat_out and mask access the same memory.
   __A						:							Optional[Any]      =						mask.flatten()
   __A						:							Dict      =						0
   __A						:							Any      =						1
   return mask



  @staticmethod
  def     A_					(lowercase_	:		Tuple					,      lowercase_	:		Optional[Any] )		->				str:
   return gradOutput, None






class __a ( snake_case_					):



  @staticmethod
  def     A_					(lowercase_	:		torch.tensor					,      lowercase_	:		float )		->				List[str]:
   __A						:							List[str]      =						inputs.clone()
   __A     ,       __A						:							List[Any]      =						inputs.abs().flatten().sort(descending=lowercase_ )
   __A						:							Dict      =						int(threshold * inputs.numel() )

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





