


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



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}		)