

import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







class 							a__							( nn.Linear							):







  """simple docstring"""




  def __init__(      self     :			Optional[int]					,					UpperCAmelCase__     :			int					,					UpperCAmelCase__     :			int					,					UpperCAmelCase__     :			bool = True					,					UpperCAmelCase__     :			str = "constant"					,					UpperCAmelCase__     :			float = 0.0					,					UpperCAmelCase__     :			str = "topK"					,					)       ->Optional[Any]:
        """simple docstring"""







        super(UpperCAmelCase__					,					self		).__init__(in_features=UpperCAmelCase__					,					out_features=UpperCAmelCase__					,					bias=UpperCAmelCase__		)
        assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
        SCREAMING_SNAKE_CASE			:   List[str]	  = pruning_method

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


  def 			_lowercase   (      self     :			Tuple		)       ->Optional[int]:
        """simple docstring"""







        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 			_lowercase   (      self     :			Union[str, Any]					,					UpperCAmelCase__     :			torch.tensor					,					UpperCAmelCase__     :			float		)       ->List[str]:
        """simple docstring"""







        if self.pruning_method == "topK":
              SCREAMING_SNAKE_CASE			:   List[str]	  = TopKBinarizer.apply(self.mask_scores					,					UpperCAmelCase__		)
        elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
              SCREAMING_SNAKE_CASE			:   List[str]	  = """sigmoied""" in self.pruning_method
              SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = ThresholdBinarizer.apply(self.mask_scores					,					UpperCAmelCase__					,					UpperCAmelCase__		)
        elif self.pruning_method == "magnitude":
              SCREAMING_SNAKE_CASE			:   Dict	  = MagnitudeBinarizer.apply(self.weight					,					UpperCAmelCase__		)
        elif self.pruning_method == "l0":
              SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   List[Any]	  = -0.1, 1.1, 2 / 3
              if self.training:
                    SCREAMING_SNAKE_CASE			:   List[str]	  = torch.zeros_like(self.mask_scores		).uniform_().clamp(0.00_01					,					0.99_99		)
                    SCREAMING_SNAKE_CASE			:   Any	  = torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b		)
              else:
                    SCREAMING_SNAKE_CASE			:   List[str]	  = torch.sigmoid(self.mask_scores		)
              SCREAMING_SNAKE_CASE			:   int	  = s * (r - l) + l
              SCREAMING_SNAKE_CASE			:   List[Any]	  = s_bar.clamp(min=0.0					,					max=1.0		)
        # Mask weights with computed mask
        SCREAMING_SNAKE_CASE			:   Dict	  = mask * self.weight
        # Compute output (linear layer) with masked weights
        return nn.functional.linear(UpperCAmelCase__					,					UpperCAmelCase__					,					self.bias		)







from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
  import torch

UpperCAmelCase__			:  int					     =     logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
  from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):






  def        __lowercase   (  cls      )  ->    Any:
        return cls






def        __lowercase   (  _A    ,     _A = None    ,     _A = None    ,     _A = None    ,     _A = None    ,     )  ->    Tuple:
      SCREAMING_SNAKE_CASE			:   Optional[Any]	  = device
      SCREAMING_SNAKE_CASE			:   Tuple	  = shape[0]

      SCREAMING_SNAKE_CASE			:   Optional[int]	  = layout or torch.strided
      SCREAMING_SNAKE_CASE			:   Dict	  = device or torch.device("""cpu"""      )

      if generator is not None:
            SCREAMING_SNAKE_CASE			:   Optional[Any]	  = generator.device.type if not isinstance(_A    ,     _A      ) else generator[0].device.type
            if gen_device_type != device.type and gen_device_type == "cpu":
                  SCREAMING_SNAKE_CASE			:   Tuple	  = """cpu"""
                  if device != "mps":
                        logger.info(
                            F"The passed generator was created on 'cpu' even though a tensor on {device} was expected."
                            F" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"
                            F" slighly speed up this function by passing a generator that was created on the {device} device."      )
            elif gen_device_type != device.type and gen_device_type == "cuda":
                  raise ValueError(F"Cannot generate a {device} tensor from a generator of type {gen_device_type}."      )

      if isinstance(_A    ,     _A      ):
            SCREAMING_SNAKE_CASE			:   int	  = (1,) + shape[1:]
            SCREAMING_SNAKE_CASE			:   Any	  = [
                torch.randn(_A    ,     generator=generator[i]    ,     device=_A    ,     dtype=_A    ,     layout=_A      )
                for i in range(_A      )
            ]
            SCREAMING_SNAKE_CASE			:   Any	  = torch.cat(_A    ,     dim=0      ).to(_A      )
      else:
            SCREAMING_SNAKE_CASE			:   int	  = torch.randn(_A    ,     generator=_A    ,     device=_A    ,     dtype=_A    ,     layout=_A      ).to(_A      )

      return latents







def        __lowercase   (  _A      )  ->    Dict:
      if is_torch_version("""<"""    ,     """2.0.0"""      ) or not hasattr(_A    ,     """_dynamo"""      ):
            return False
      return isinstance(_A    ,     torch._dynamo.eval_frame.OptimizedModule      )




