


import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







class   __lowercase						(nn.Linear     ):





							"""simple docstring"""







							def __init__(			self ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ = True ,	lowerCAmelCase__ = "constant" ,	lowerCAmelCase__ = 0.0 ,	lowerCAmelCase__ = "topK" ,	):





										"""simple docstring"""




										super(lowerCAmelCase__ ,	self					).__init__(in_features=lowerCAmelCase__ ,	out_features=lowerCAmelCase__ ,	bias=lowerCAmelCase__					)
										assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
										SCREAMING_SNAKE_CASE_		:    Any 		=				pruning_method

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






							def 				UpperCamelCase__    (			self					):





										"""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 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




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









from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest







@custom_tokenizers
class   __lowercase						(__SCREAMING_SNAKE_CASE     ):





							"""simple docstring"""







							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										return True






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[str] 		=				CpmTokenizer.from_pretrained('TsinghuaAI/CPM-Generate'					)
										SCREAMING_SNAKE_CASE_		:    int 		=				'Hugging Face大法好，谁用谁知道。'
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				'Hugging Face大法好,谁用谁知道。<unk>'
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				'▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'.split()

										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				tokenizer.tokenize(lowerCAmelCase__					)
										self.assertListEqual(lowerCAmelCase__ ,	lowerCAmelCase__					)

										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				tokens + [tokenizer.unk_token]

										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				[1_3_7_8_9, 1_3_2_8_3, 1_4_2_1, 8, 1_0, 1_1_6_4, 1_3_6_0_8, 1_6_5_2_8, 6_3, 8, 9, 4_4_0, 1_0_8, 4_4_0, 1_2_1, 9_0, 8, 1_2, 0]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__					) ,	lowerCAmelCase__					)

										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				tokenizer.decode(lowerCAmelCase__					)
										self.assertEqual(lowerCAmelCase__ ,	lowerCAmelCase__					)





