

import torch
from torch import autograd






class _UpperCamelCase							(							autograd.Function				):
			'''simple docstring'''







			@staticmethod
			def 							__UpperCamelCase	(     a							:	Any , a							:	List[str] , a							:	Tuple , a							:	Dict		)							->    List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    inputs.numel()
				SCREAMING_SNAKE_CASE				: str			       =    int(0.005 * nb_elems		) + 1
				if sigmoid:
					SCREAMING_SNAKE_CASE				: int			       =    (torch.sigmoid(lowerCAmelCase__		) > threshold).type(inputs.type()		)
				else:
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    (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
					SCREAMING_SNAKE_CASE				: List[Any]			       =    inputs.flatten().kthvalue(max(nb_elems - nb_min , 1		)		).values
					SCREAMING_SNAKE_CASE				: int			       =    (inputs > k_threshold).type(inputs.type()		)
				return mask






			@staticmethod
			def 							__UpperCamelCase	(     a							:	Tuple , a							:	Tuple		)							->    Optional[int]:

				"""simple docstring"""

				return gradOutput, None, None






class _UpperCamelCase							(							autograd.Function				):
			'''simple docstring'''







			@staticmethod
			def 							__UpperCamelCase	(     a							:	int , a							:	Optional[int] , a							:	Optional[int]		)							->    str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[str]			       =    inputs.clone()
				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: Tuple			       =    inputs.flatten().sort(descending=lowerCAmelCase__		)
				SCREAMING_SNAKE_CASE				: Tuple			       =    int(threshold * inputs.numel()		)

				# flat_out and mask access the same memory.
				SCREAMING_SNAKE_CASE				: str			       =    mask.flatten()
				SCREAMING_SNAKE_CASE				: int			       =    0
				SCREAMING_SNAKE_CASE				: int			       =    1
				return mask






			@staticmethod
			def 							__UpperCamelCase	(     a							:	Dict , a							:	int		)							->    str:

				"""simple docstring"""

				return gradOutput, None






class _UpperCamelCase							(							_UpperCAmelCase				):
			'''simple docstring'''







			@staticmethod
			def 							__UpperCamelCase	(     a							:	int , a							:	List[Any]		)							->    List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[Any]			       =    inputs.clone()
				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: Optional[Any]			       =    inputs.abs().flatten().sort(descending=lowerCAmelCase__		)
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    int(threshold * inputs.numel()		)

				# flat_out and mask access the same memory.
				SCREAMING_SNAKE_CASE				: List[str]			       =    mask.flatten()
				SCREAMING_SNAKE_CASE				: int			       =    0
				SCREAMING_SNAKE_CASE				: List[Any]			       =    1
				return mask


from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(     self							:	Optional[int] , a							:	Any		)							->    None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[str]			       =    vocab
				SCREAMING_SNAKE_CASE				: str			       =    normalizers.BertNormalizer(
				    clean_text=a , handle_chinese_chars=a , strip_accents=a , lowercase=a , )
				try:
					import rjieba
				except ImportError:
					raise ImportError(
					    "You need to install rjieba to use RoFormerTokenizer. "
					    "See https://pypi.org/project/rjieba/ for installation."		)
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    rjieba
			def 							__UpperCamelCase	(     self							:	Optional[int] , a							:	int , a							:	NormalizedString		)							->    List[NormalizedString]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Dict			       =    []

				# this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
				for token, start, end in self.jieba.tokenize(str(a		) , hmm=a		):
					if token in self.vocab:
						splits.append(normalized_string[start:end]		)
					else:
						SCREAMING_SNAKE_CASE				: Any			       =    self.normalizers.normalize_str(a		).split()
						for token in token_list:
							if token:
								SCREAMING_SNAKE_CASE				: Optional[int]			       =    start + len(a		)
								splits.append(normalized_string[start:end]		)
								SCREAMING_SNAKE_CASE				: List[Any]			       =    end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

				return splits






			def 							__UpperCamelCase	(     self							:	Optional[int] , a							:	PreTokenizedString		)							->    Tuple:

				"""simple docstring"""

				pretok.split(self.jieba_split		)