
'''simple docstring'''
import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer






class 				UpperCAmelCase_			(		nn.Linear   ):





 def __init__(      self	,    lowercase_	,    lowercase_	,    lowercase_ = True	,    lowercase_ = "constant"	,    lowercase_ = 0.0	,    lowercase_ = "topK"	,    ):
     super(lowercase_	,    self).__init__(in_features=lowercase_	,    out_features=lowercase_	,    bias=lowercase_)
     assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
     snake_case_	:      Any            =  pruning_method

     if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
         snake_case_	:      int            =  mask_scale
         snake_case_	:      int            =  mask_init
         snake_case_	:      Dict            =  nn.Parameter(torch.empty(self.weight.size()))
         self.init_mask()





 def     snake_case__						(      self):
     if self.mask_init == "constant":
         init.constant_(self.mask_scores	,    val=self.mask_scale)
     elif self.mask_init == "uniform":
         init.uniform_(self.mask_scores	,    a=-self.mask_scale	,    b=self.mask_scale)
     elif self.mask_init == "kaiming":
         init.kaiming_uniform_(self.mask_scores	,    a=math.sqrt(5))





 def     snake_case__						(      self	,    lowercase_	,    lowercase_):
     # Get the mask
     if self.pruning_method == "topK":
         snake_case_	:      Any            =  TopKBinarizer.apply(self.mask_scores	,    lowercase_)
     elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
         snake_case_	:      Union[str, Any]            =  "sigmoied" in self.pruning_method
         snake_case_	:      int            =  ThresholdBinarizer.apply(self.mask_scores	,    lowercase_	,    lowercase_)
     elif self.pruning_method == "magnitude":
         snake_case_	:      Dict            =  MagnitudeBinarizer.apply(self.weight	,    lowercase_)
     elif self.pruning_method == "l0":
         snake_case_     ,	snake_case_     ,	snake_case_	:      Any            =  -0.1, 1.1, 2 / 3
         if self.training:
             snake_case_	:      Dict            =  torch.zeros_like(self.mask_scores).uniform_().clamp(0.0_001	,    0.9_999)
             snake_case_	:      Optional[int]            =  torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b)
         else:
             snake_case_	:      Union[str, Any]            =  torch.sigmoid(self.mask_scores)
         snake_case_	:      Any            =  s * (r - l) + l
         snake_case_	:      Dict            =  s_bar.clamp(min=0.0	,    max=1.0)
     # Mask weights with computed mask
     snake_case_	:      Optional[Any]            =  mask * self.weight
     # Compute output (linear layer) with masked weights
     return nn.functional.linear(lowercase_	,    lowercase_	,    self.bias)






'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a_     =  {
    "configuration_splinter": ["SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SplinterConfig"],
    "tokenization_splinter": ["SplinterTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_     =  ["SplinterTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_     =  [
      "SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "SplinterForQuestionAnswering",
      "SplinterForPreTraining",
      "SplinterLayer",
      "SplinterModel",
      "SplinterPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
  from .tokenization_splinter import SplinterTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_splinter_fast import SplinterTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_splinter import (
        SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
        SplinterForPreTraining,
        SplinterForQuestionAnswering,
        SplinterLayer,
        SplinterModel,
        SplinterPreTrainedModel,
    )


else:
  import sys

  a_     =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




