






"""simple docstring"""


from collections import namedtuple

_UpperCamelCase :    List[Any]      =			namedtuple("""from_to""", """from_ to""")

_UpperCamelCase :    Union[str, Any]      =			{
    """atm""": from_to(1, 1),
    """pascal""": from_to(0.0_0_0_0_0_9_8, 10_13_25),
    """bar""": from_to(0.9_8_6_9_2_3, 1.0_1_3_2_5),
    """kilopascal""": from_to(0.0_0_9_8_6_9_2_3, 1_0_1.3_2_5),
    """megapascal""": from_to(9.8_6_9_2_3, 0.1_0_1_3_2_5),
    """psi""": from_to(0.0_6_8_0_4_6, 1_4.6_9_5_9),
    """inHg""": from_to(0.0_3_3_4_2_1_1, 2_9.9_2_1_3),
    """torr""": from_to(0.0_0_1_3_1_5_7_9, 7_60),
}




def 					a			(  UpperCamelCase_				:					float  ,						UpperCamelCase_				:					str  ,						UpperCamelCase_				:					str		)	->		float:
 if from_type not in PRESSURE_CONVERSION:
  raise ValueError(
      F"Invalid 'from_type' value: {from_type!r}  Supported values are:\n"
      + ', '.join(UpperCamelCase_		)		)
 if to_type not in PRESSURE_CONVERSION:
  raise ValueError(
      F"Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"
      + ', '.join(UpperCamelCase_		)		)
 return (
     value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
 )


if __name__ == "__main__":
   import doctest

   doctest.testmod()













"""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





