code: 
  
  
  import torch
  from torch import autograd
  
  
  
  
  
  class        lowercase_				( autograd.Function):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							@staticmethod
  							def 						lowercase__     (   _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		inputs.numel()
  													a_					=		int(0.0_0_5 * nb_elems		) + 1
  													if sigmoid:
  																			a_					=		(torch.sigmoid(_UpperCAmelCase		) > threshold).type(inputs.type()		)
  													else:
  																			a_					=		(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_					=		inputs.flatten().kthvalue(max(nb_elems - nb_min			,    1		)		).values
  																			a_					=		(inputs > k_threshold).type(inputs.type()		)
  													return mask
  
  
  
  
  
  
  							@staticmethod
  							def 						lowercase__     (   _UpperCAmelCase			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return gradOutput, None, None
  
  
  
  
  
  class        lowercase_				( autograd.Function):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							@staticmethod
  							def 						lowercase__     (   _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		inputs.clone()
  													a_			,   a_					=		inputs.flatten().sort(descending=_UpperCAmelCase		)
  													a_					=		int(threshold * inputs.numel()		)
  
  													# flat_out and mask access the same memory.
  													a_					=		mask.flatten()
  													a_					=		0
  													a_					=		1
  													return mask
  
  
  
  
  
  
  							@staticmethod
  							def 						lowercase__     (   _UpperCAmelCase			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return gradOutput, None
  
  
  
  
  
  class        lowercase_				( UpperCamelCase__):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							@staticmethod
  							def 						lowercase__     (   _UpperCAmelCase			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		inputs.clone()
  													a_			,   a_					=		inputs.abs().flatten().sort(descending=_UpperCAmelCase		)
  													a_					=		int(threshold * inputs.numel()		)
  
  													# flat_out and mask access the same memory.
  													a_					=		mask.flatten()
  													a_					=		0
  													a_					=		1
  													return mask
style_context: 
  
  
  import abc
  import copy
  import dataclasses
  from dataclasses import dataclass
  from typing import ClassVar, Dict, Type, TypeVar
  
  from ..features import Features
  
  
  A_    :					Tuple             =TypeVar("""T""", bound="""TaskTemplate""")
  
  
  
  
  
  @dataclass(frozen=UpperCamelCase__)
  class        lowercase_				( abc.ABC):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		42
  							snake_case_        =		42
  							snake_case_        =		42
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return copy.deepcopy(self		)
  
  
  							@property
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return Features(**self.input_schema			,    **self.label_schema		)
  
  
  							@property
  							@abc.abstractmethod
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													raise NotImplementedError
  
  
  
  
  
  
  							@classmethod
  							def 						lowercase__     (   cls			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		{f.name for f in dataclasses.fields(cls		)}
  													return cls(**{k: v for k, v in template_dict.items() if k in field_names}		)