
import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







class 	lowerCamelCase						(    nn.Linear    ):





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

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





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



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A       =					logging.get_logger(__name__)

_A       =					{
    "openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json"
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}







class 	lowerCamelCase						(    A_    ):
     UpperCAmelCase__							:				List[Any] 	=						"cpmant"





     def __init__(self :       List[Any]							,       _A :       int = 3_0_7_2_0							,       _A :       int = 4_0_9_6							,       _A :       int = 3_2							,       _A :       int = 1_2_8							,       _A :       int = 1_0_2_4_0							,       _A :       int = 4_8							,       _A :       int = 0.0							,       _A :       int = 5_1_2							,       _A :       int = 2_0_4_8							,       _A :       int = 1E-6							,       _A :       float = 1.0							,       _A :       int = 3_2							,       _A :       int = 3_2							,       _A :       int = 3_2							,       _A :       bool = True							,       **_A :       Dict							,       )		->		Dict:
         super().__init__(**_A  )
         snake_case						=   prompt_types
         snake_case						=   prompt_length
         snake_case						=   segment_types
         snake_case						=   hidden_size
         snake_case						=   num_attention_heads
         snake_case						=   dim_head
         snake_case						=   dim_ff
         snake_case						=   num_hidden_layers
         snake_case						=   position_bias_num_buckets
         snake_case						=   position_bias_max_distance
         snake_case						=   dropout_p
         snake_case						=   eps
         snake_case						=   use_cache
         snake_case						=   vocab_size
         snake_case						=   init_std

