

from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase   					=  logging.get_logger(__name__)





class      snake_case_      (					__UpperCamelCase				):


							"""simple docstring"""





							__UpperCAmelCase      =["input_ids", "attention_mask"]




							def __init__(			self       ,    _A="[PAD]"       ,    _A="[BOS]"       ,    _A="[EOS]"       ,    _A="[MASK]"       ,    _A="[CLS]"       ,    _A="[SEP]"       ,    _A=2_0_4_8       ,    **_A       ,    ):
												__lowerCAmelCase       =				AddedToken(_lowerCAmelCase       ,    lstrip=_lowerCAmelCase       ,    rstrip=_lowerCAmelCase				) if isinstance(_lowerCAmelCase       ,    _lowerCAmelCase				) else pad_token
												__lowerCAmelCase       =				AddedToken(_lowerCAmelCase       ,    lstrip=_lowerCAmelCase       ,    rstrip=_lowerCAmelCase				) if isinstance(_lowerCAmelCase       ,    _lowerCAmelCase				) else bos_token
												__lowerCAmelCase       =				AddedToken(_lowerCAmelCase       ,    lstrip=_lowerCAmelCase       ,    rstrip=_lowerCAmelCase				) if isinstance(_lowerCAmelCase       ,    _lowerCAmelCase				) else eos_token
												__lowerCAmelCase       =				AddedToken(_lowerCAmelCase       ,    lstrip=_lowerCAmelCase       ,    rstrip=_lowerCAmelCase				) if isinstance(_lowerCAmelCase       ,    _lowerCAmelCase				) else mask_token
												__lowerCAmelCase       =				AddedToken(_lowerCAmelCase       ,    lstrip=_lowerCAmelCase       ,    rstrip=_lowerCAmelCase				) if isinstance(_lowerCAmelCase       ,    _lowerCAmelCase				) else cls_token
												__lowerCAmelCase       =				AddedToken(_lowerCAmelCase       ,    lstrip=_lowerCAmelCase       ,    rstrip=_lowerCAmelCase				) if isinstance(_lowerCAmelCase       ,    _lowerCAmelCase				) else sep_token

												super().__init__(
												    pad_token=_lowerCAmelCase       ,    bos_token=_lowerCAmelCase       ,    eos_token=_lowerCAmelCase       ,    mask_token=_lowerCAmelCase       ,    cls_token=_lowerCAmelCase       ,    sep_token=_lowerCAmelCase       ,    model_max_length=_lowerCAmelCase       ,    **_lowerCAmelCase       ,    )

												__lowerCAmelCase       =				2**8  # utf is 8 bits

												# define special tokens dict
												__lowerCAmelCase       =				{
												    self.pad_token: 0,
												    self.bos_token: 1,
												    self.eos_token: 2,
												    self.mask_token: 3,
												    self.cls_token: 4,
												    self.sep_token: 5,
												}
												__lowerCAmelCase       =				len(self.special_tokens_encoder				)
												__lowerCAmelCase       =				{v: k for k, v in self.special_tokens_encoder.items()}




							def 							A__	(			self				):
												__lowerCAmelCase       =				self.special_tokens_encoder.copy()
												vocab.update(self.added_tokens_encoder				)
												for i in range(self._utf_vocab_size				):
																	__lowerCAmelCase       =				chr(_lowerCAmelCase				)
																	__lowerCAmelCase       =				i + len(self.special_tokens_encoder				)
												return vocab




							@property
							def 							A__	(			self				):
												return self._utf_vocab_size + self._num_special_tokens




							def 							A__	(			self       ,    _A       ,    _A = None       ,    _A = False				):
												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=_lowerCAmelCase       ,    token_ids_a=_lowerCAmelCase       ,    already_has_special_tokens=_lowerCAmelCase				)

												# normal case: some special tokens
												if token_ids_a is None:
																	return [1] + [0] * len(_lowerCAmelCase				) + [1]
												return [1] + ([0] * len(_lowerCAmelCase				)) + [1] + ([0] * len(_lowerCAmelCase				)) + [1]




							def 							A__	(			self       ,    _A       ,    _A = None				):
												if token_ids_a is None:
																	return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												else:
																	return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]




							def 							A__	(			self       ,    _A				):
												__lowerCAmelCase       =				[chr(_lowerCAmelCase				) for i in text.encode('utf-8'				)]
												return tokens




							def 							A__	(			self       ,    _A				):
												if token in self.special_tokens_encoder:
																	__lowerCAmelCase       =				self.special_tokens_encoder[token]
												elif token in self.added_tokens_encoder:
																	__lowerCAmelCase       =				self.added_tokens_encoder[token]
												elif len(_lowerCAmelCase				) != 1:
																	__lowerCAmelCase       =				self.unk_token_id
												else:
																	__lowerCAmelCase       =				ord(_lowerCAmelCase				) + self._num_special_tokens
												return token_id




							def 							A__	(			self       ,    _A				):
												if index in self.special_tokens_decoder:
																	__lowerCAmelCase       =				self.special_tokens_decoder[index]
												elif index in self.added_tokens_decoder:
																	__lowerCAmelCase       =				self.added_tokens_decoder[index]
												else:
																	__lowerCAmelCase       =				chr(index - self._num_special_tokens				)
												return token




							def 							A__	(			self       ,    _A				):
												__lowerCAmelCase       =				b""""""
												for token in tokens:
																	if token in self.special_tokens_decoder:
																						__lowerCAmelCase       =				self.special_tokens_decoder[token].encode('utf-8'				)
																	elif token in self.added_tokens_decoder:
																						__lowerCAmelCase       =				self.special_tokens_decoder[token].encode('utf-8'				)
																	elif token in self.special_tokens_encoder:
																						__lowerCAmelCase       =				token.encode('utf-8'				)
																	elif token in self.added_tokens_encoder:
																						__lowerCAmelCase       =				token.encode('utf-8'				)
																	else:
																						__lowerCAmelCase       =				bytes([ord(_lowerCAmelCase				)]				)
																	bstring += tok_string
												__lowerCAmelCase       =				bstring.decode('utf-8'       ,    errors='replace'				)
												return string




							def 							A__	(			self       ,    _A       ,    _A = None				):
												return ()







import torch
from torch import autograd







class      SCREAMING_SNAKE_CASE__					(  autograd.Function    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Any			,			_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			,			_lowerCAmelCase					:     bool			):
           __snake_case       :				Optional[int] 			=    inputs.numel()
           __snake_case       :				str 			=    int(0.005 * nb_elems			) + 1
           if sigmoid:
               __snake_case       :				Union[str, Any] 			=    (torch.sigmoid(_lowerCAmelCase			) > threshold).type(inputs.type()			)
           else:
               __snake_case       :				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
               __snake_case       :				Dict 			=    inputs.flatten().kthvalue(max(nb_elems - nb_min			,			1			)			).values
               __snake_case       :				List[str] 			=    (inputs > k_threshold).type(inputs.type()			)
           return mask





       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     str			,			_lowerCAmelCase					:     Tuple			):
           return gradOutput, None, None







class      SCREAMING_SNAKE_CASE__					(  autograd.Function    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Optional[int]			,			_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			):
           __snake_case       :				str 			=    inputs.clone()
           __snake_case			, __snake_case       :				Dict 			=    inputs.flatten().sort(descending=_lowerCAmelCase			)
           __snake_case       :				Optional[Any] 			=    int(threshold * inputs.numel()			)

           # flat_out and mask access the same memory.
           __snake_case       :				Optional[int] 			=    mask.flatten()
           __snake_case       :				Dict 			=    0
           __snake_case       :				Optional[Any] 			=    1
           return mask





       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     Any			,			_lowerCAmelCase					:     Optional[Any]			):
           return gradOutput, None







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):

       @staticmethod
       def 			snake_case__    (							_lowerCAmelCase					:     torch.tensor			,			_lowerCAmelCase					:     float			):
           __snake_case       :				List[str] 			=    inputs.clone()
           __snake_case			, __snake_case       :				str 			=    inputs.abs().flatten().sort(descending=_lowerCAmelCase			)
           __snake_case       :				List[Any] 			=    int(threshold * inputs.numel()			)

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



