
import torch
from torch import autograd







class 	lowerCamelCase						(    autograd.Function    ):





     @staticmethod
     def 						UpperCAmelCase(_A :       Union[str, Any]							,       _A :       torch.tensor							,       _A :       float							,       _A :       bool  )		->		Tuple:
         snake_case						=   inputs.numel()
         snake_case						=   int(0.0_05 * nb_elems  ) + 1
         if sigmoid:
             snake_case						=   (torch.sigmoid(_A  ) > threshold).type(inputs.type()  )
         else:
             snake_case						=   (inputs > threshold).type(inputs.type()  )
         if mask.sum() < nb_min:
             # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
             snake_case						=   inputs.flatten().kthvalue(max(nb_elems - nb_min							,       1  )  ).values
             snake_case						=   (inputs > k_threshold).type(inputs.type()  )
         return mask





     @staticmethod
     def 						UpperCAmelCase(_A :       int							,       _A :       List[Any]  )		->		List[str]:
         return gradOutput, None, None







class 	lowerCamelCase						(    autograd.Function    ):





     @staticmethod
     def 						UpperCAmelCase(_A :       Dict							,       _A :       torch.tensor							,       _A :       float  )		->		List[str]:
         snake_case						=   inputs.clone()
         snake_case	,				snake_case						=   inputs.flatten().sort(descending=_A  )
         snake_case						=   int(threshold * inputs.numel()  )

         # flat_out and mask access the same memory.
         snake_case						=   mask.flatten()
         snake_case						=   0
         snake_case						=   1
         return mask





     @staticmethod
     def 						UpperCAmelCase(_A :       Optional[Any]							,       _A :       Any  )		->		str:
         return gradOutput, None







class 	lowerCamelCase						(    A_    ):





     @staticmethod
     def 						UpperCAmelCase(_A :       torch.tensor							,       _A :       float  )		->		Any:
         snake_case						=   inputs.clone()
         snake_case	,				snake_case						=   inputs.abs().flatten().sort(descending=_A  )
         snake_case						=   int(threshold * inputs.numel()  )

         # flat_out and mask access the same memory.
         snake_case						=   mask.flatten()
         snake_case						=   0
         snake_case						=   1
         return mask



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 	lowerCamelCase						(    A_    ):





     def 						UpperCAmelCase(self :       Optional[Any]							,       _A :       Tuple							,       _A :       Tuple							,       _A :       str							,       _A :       Tuple							,       _A :       Any  )		->		Optional[int]:
         return True





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

         snake_case						=   tokenizer.tokenize(_A  )
         self.assertListEqual(_A							,       _A  )

         snake_case						=   tokens + [tokenizer.unk_token]

         snake_case						=   [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(_A  )							,       _A  )

         snake_case						=   tokenizer.decode(_A  )
         self.assertEqual(_A							,       _A  )

