import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer




class  __lowerCAmelCase	(							nn.Linear    ):



 '''simple docstring'''

 def __init__(      self							:		Optional[int]							,      lowerCamelCase							:		int							,      lowerCamelCase							:		int							,      lowerCamelCase							:		bool = True							,      lowerCamelCase							:		str = "constant"							,      lowerCamelCase							:		float = 0.0							,      lowerCamelCase							:		str = "topK"							,      )			->				Optional[int]:
        super(lowerCamelCase							,      self	).__init__(in_features=lowerCamelCase							,      out_features=lowerCamelCase							,      bias=lowerCamelCase	)
        assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
        _UpperCamelCase  :							Optional[Any]	    =	pruning_method

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

 def        __snake_case       (      self							:		Tuple	)			->				Optional[int]:
        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							:		Union[str, Any]							,      lowerCamelCase							:		torch.tensor							,      lowerCamelCase							:		float	)			->				List[Any]:
        # Get the mask
        if self.pruning_method == "topK":
               _UpperCamelCase  :							List[str]	    =	TopKBinarizer.apply(self.mask_scores							,      lowerCamelCase	)
        elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
               _UpperCamelCase  :							Optional[Any]	    =	'sigmoied' in self.pruning_method
               _UpperCamelCase  :							Optional[Any]	    =	ThresholdBinarizer.apply(self.mask_scores							,      lowerCamelCase							,      lowerCamelCase	)
        elif self.pruning_method == "magnitude":
               _UpperCamelCase  :							Union[str, Any]	    =	MagnitudeBinarizer.apply(self.weight							,      lowerCamelCase	)
        elif self.pruning_method == "l0":
               _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							Union[str, Any]	    =	-0.1, 1.1, 2 / 3
               if self.training:
                      _UpperCamelCase  :							List[str]	    =	torch.zeros_like(self.mask_scores	).uniform_().clamp(0.0_0_0_1							,      0.9_9_9_9	)
                      _UpperCamelCase  :							Optional[Any]	    =	torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b	)
               else:
                      _UpperCamelCase  :							List[Any]	    =	torch.sigmoid(self.mask_scores	)
               _UpperCamelCase  :							List[Any]	    =	s * (r - l) + l
               _UpperCamelCase  :							Tuple	    =	s_bar.clamp(min=0.0							,      max=1.0	)
        # Mask weights with computed mask
        _UpperCamelCase  :							Optional[int]	    =	mask * self.weight
        # Compute output (linear layer) with masked weights
        return nn.functional.linear(lowerCamelCase							,      lowerCamelCase							,      self.bias	)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__						:	Any	     = {
    'configuration_decision_transformer': [
        'DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'DecisionTransformerConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:	Optional[Any]	     = [
       'DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'DecisionTransformerGPT2Model',
       'DecisionTransformerGPT2PreTrainedModel',
       'DecisionTransformerModel',
       'DecisionTransformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_decision_transformer import (
       DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DecisionTransformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_decision_transformer import (
          DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DecisionTransformerGPTaModel,
          DecisionTransformerGPTaPreTrainedModel,
          DecisionTransformerModel,
          DecisionTransformerPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase__						:	Optional[Any]	     = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



