





import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer

class 		__a							(	nn.Linear      ):



       def __init__(     self	:   Dict					,			UpperCAmelCase	:   int					,			UpperCAmelCase	:   int					,			UpperCAmelCase	:   bool = True					,			UpperCAmelCase	:   str = "constant"					,			UpperCAmelCase	:   float = 0.0					,			UpperCAmelCase	:   str = "topK"					,			):
           super(UpperCAmelCase					,			self			).__init__(in_features=UpperCAmelCase					,			out_features=UpperCAmelCase					,			bias=UpperCAmelCase			)
           assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
           lowerCAmelCase_					:	int						=   pruning_method

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



       def  A							(     self	:   List[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  A							(     self	:   List[str]					,			UpperCAmelCase	:   torch.tensor					,			UpperCAmelCase	:   float			):
           # Get the mask
           if self.pruning_method == "topK":
               lowerCAmelCase_					:	Optional[Any]						=   TopKBinarizer.apply(self.mask_scores					,			UpperCAmelCase			)
           elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
               lowerCAmelCase_					:	List[Any]						=   """sigmoied""" in self.pruning_method
               lowerCAmelCase_					:	List[str]						=   ThresholdBinarizer.apply(self.mask_scores					,			UpperCAmelCase					,			UpperCAmelCase			)
           elif self.pruning_method == "magnitude":
               lowerCAmelCase_					:	List[Any]						=   MagnitudeBinarizer.apply(self.weight					,			UpperCAmelCase			)
           elif self.pruning_method == "l0":
               lowerCAmelCase_					:	str						=   -0.1, 1.1, 2 / 3
               if self.training:
                   lowerCAmelCase_					:	Tuple						=   torch.zeros_like(self.mask_scores			).uniform_().clamp(0.0001					,			0.9999			)
                   lowerCAmelCase_					:	Optional[int]						=   torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b			)
               else:
                   lowerCAmelCase_					:	Any						=   torch.sigmoid(self.mask_scores			)
               lowerCAmelCase_					:	Union[str, Any]						=   s * (r - l) + l
               lowerCAmelCase_					:	Tuple						=   s_bar.clamp(min=0.0					,			max=1.0			)
           # Mask weights with computed mask
           lowerCAmelCase_					:	Optional[int]						=   mask * self.weight
           # Compute output (linear layer) with masked weights
           return nn.functional.linear(UpperCAmelCase					,			UpperCAmelCase					,			self.bias			)









import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property

class 		__a							(	unittest.TestCase      ):



       @cached_property
       def  A							(     self	:   Optional[int]			):
           return BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""			)



       @cached_property
       def  A							(     self	:   Optional[int]			):
           return BlenderbotTokenizerFast.from_pretrained("""facebook/blenderbot-3B"""			)



       def  A							(     self	:   Optional[Any]			):
           lowerCAmelCase_					:	List[str]						=   self.tokenizer_ab
           lowerCAmelCase_					:	Any						=   """ I am a small frog."""
           lowerCAmelCase_					:	Union[str, Any]						=   tok([src_text]					,			padding=UpperCAmelCase					,			truncation=UpperCAmelCase			)["""input_ids"""]
           lowerCAmelCase_					:	int						=   tok.batch_decode(UpperCAmelCase					,			skip_special_tokens=UpperCAmelCase					,			clean_up_tokenization_spaces=UpperCAmelCase			)[0]
           assert src_text == decoded



       def  A							(     self	:   List[Any]			):
           lowerCAmelCase_					:	Dict						=   self.rust_tokenizer_ab
           lowerCAmelCase_					:	str						=   """ I am a small frog."""
           lowerCAmelCase_					:	Tuple						=   tok([src_text]					,			padding=UpperCAmelCase					,			truncation=UpperCAmelCase			)["""input_ids"""]
           lowerCAmelCase_					:	int						=   tok.batch_decode(UpperCAmelCase					,			skip_special_tokens=UpperCAmelCase					,			clean_up_tokenization_spaces=UpperCAmelCase			)[0]
           assert src_text == decoded



       def  A							(     self	:   str			):
           assert self.tokenizer_ab.add_prefix_space
           assert self.tokenizer_ab([""" Sam""", """Sam"""]			).input_ids == [[55_02, 2], [55_02, 2]]



       def  A							(     self	:   List[str]			):
           assert self.rust_tokenizer_ab.add_prefix_space
           assert self.rust_tokenizer_ab([""" Sam""", """Sam"""]			).input_ids == [[55_02, 2], [55_02, 2]]


