

import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


UpperCAmelCase				: List[str]         =  logging.getLogger(__name__)







def      a__ (						a__		):




     """simple docstring"""


     random.seed(a__		)
     np.random.seed(a__		)
     torch.manual_seed(a__		)
     torch.cuda.manual_seed_all(a__		)







def      a__ (						a__   ,					a__   ,					a__		):




     """simple docstring"""


     model.eval()
     __SCREAMING_SNAKE_CASE						     =   next(model.parameters()		).device
     __SCREAMING_SNAKE_CASE						     =   1
     __SCREAMING_SNAKE_CASE						     =   torch.zeros((eval_batch_size, context_len)   ,					dtype=torch.long   ,					device=a__		)
     __SCREAMING_SNAKE_CASE						     =   DataLoader(a__   ,					shuffle=a__   ,					batch_size=a__		)
     __SCREAMING_SNAKE_CASE						     =   torch.zeros(1   ,					device=a__		)
     __SCREAMING_SNAKE_CASE						     =   0
     for batch in eval_dataloader:
          batch.to(a__		)
          # pad
          context.zero_()
          for i in range(a__		):
               __SCREAMING_SNAKE_CASE						     =   batch[i]
          __SCREAMING_SNAKE_CASE						     =   model(a__   ,					labels=a__		)
          eval_loss += outputs[0].sum().item()
          nb_eval_examples += batch.size(0		)
     __SCREAMING_SNAKE_CASE						     =   eval_loss / nb_eval_examples
     __SCREAMING_SNAKE_CASE						     =   torch.exp(a__		)
     model.train()
     return perplexity







def      a__ (						a__="gpt2"		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   GPTaLMHeadModel.from_pretrained(a__   ,					output_hidden_states=a__		)
     torch.save(model.state_dict()   ,					model_name + """local.pt"""		)
     return model







def      a__ (						a__   ,					a__   ,					a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   copy.deepcopy(a__		)
     model.to(a__		)

     __SCREAMING_SNAKE_CASE						     =   ["""bias""", """LayerNorm.weight"""]
     __SCREAMING_SNAKE_CASE						     =   [
         {
             """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay		)],
             """weight_decay""": 0.0,
         },
         {"""params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay		)], """weight_decay""": 0.0},
     ]
     __SCREAMING_SNAKE_CASE						     =   AdamW(a__   ,					lr=5E-5   ,					eps=1E-8		)
     __SCREAMING_SNAKE_CASE						     =   get_linear_schedule_with_warmup(a__   ,					0   ,					a__		)
     torch.cuda.empty_cache()
     return model, lm_optimizer, lm_scheduler







def      a__ (						a__   ,					a__   ,					a__   ,					a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   np.array(a__		).mean()
     __SCREAMING_SNAKE_CASE						     =   np.array(a__		).std()
     __SCREAMING_SNAKE_CASE						     =   (real_perps - avg) / std
     __SCREAMING_SNAKE_CASE						     =   list(zip(a__   ,					a__   ,					a__		)		)
     joblib.dump(a__   ,					a__		)







def      a__ (						a__   ,					a__   ,					a__   ,					a__   ,					a__   ,					a__   ,					a__   ,					a__="dev.jbl"   ,					a__=recopy_gpta   ,					):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   []
     __SCREAMING_SNAKE_CASE						     =   []
     __SCREAMING_SNAKE_CASE						     =   []

     # Initialize the transformer model
     __SCREAMING_SNAKE_CASE						     =   copy.deepcopy(a__		)
     orig_model.to(device="""cpu"""		)
     torch.cuda.empty_cache()

     # Compute perplexity of initial transformer model for comparison
     model.train()
     __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   recopy_model(a__   ,					a__   ,					a__		)

     for step in tqdm(range(a__		)		):
          __SCREAMING_SNAKE_CASE						     =   torch.zeros((1, context_len)   ,					dtype=torch.long   ,					device=a__		)
          __SCREAMING_SNAKE_CASE						     =   random.choice(a__		)
          __SCREAMING_SNAKE_CASE						     =   random.randint(0   ,					len(story[0]		) - context_len - 1		)
          __SCREAMING_SNAKE_CASE						     =   story[0][start : start + context_len]
          lm_optimizer.zero_grad()
          __SCREAMING_SNAKE_CASE						     =   model(a__   ,					labels=a__		)
          __SCREAMING_SNAKE_CASE						     =   outputs[0]
          __SCREAMING_SNAKE_CASE						     =   compute_perplexity(a__   ,					a__   ,					a__		)
          model.train()
          lm_loss.backward()
          # Do LM backprop
          torch.nn.utils.clip_grad_norm_(model.parameters()   ,					3.0		)
          lm_optimizer.step()
          lm_scheduler.step()  # Update learning rate schedule

          # Compute perplexity after back-propagating on the selected context
          __SCREAMING_SNAKE_CASE						     =   compute_perplexity(a__   ,					a__   ,					a__		)

          # Periodically save the stored (X, IG(X)) pairs
          if step % 10_00 == 0 and step > 1:
               intermittent_save(a__   ,					a__   ,					a__   ,					a__		)

          # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
          __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   recopy_model(a__   ,					a__   ,					a__		)

          past_perps.append(past_perp.item()		)
          real_perps.append(orig_perp - real_perp.item()		)
          contexts.append(np.array(context.cpu()		)		)

     intermittent_save(a__   ,					a__   ,					a__   ,					a__		)







def      a__ (						a__   ,					a__="data/tokenized_stories_train_wikitext103.jbl"   ,					a__=1_00   ,					a__=10_26   ,					a__=True		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   joblib.load(a__		)
     print("""data loaded"""		)
     __SCREAMING_SNAKE_CASE						     =   []
     if trim:
          for i, example in enumerate(a__		):
               if len(example[0]		) > min_len:
                    __SCREAMING_SNAKE_CASE						     =   random.randint(0   ,					len(example[0]		) - context_len - 1		)
                    objective_set.append(example[0, start : start + context_len]		)
               if len(a__		) >= number:
                    break
          __SCREAMING_SNAKE_CASE						     =   []
          for j in range(i + 1   ,					len(a__		)		):
               if len(data[j][0]		) > min_len:
                    train_data.append(data[j]		)
     else:
          __SCREAMING_SNAKE_CASE						     =   data[0:number]
          __SCREAMING_SNAKE_CASE						     =   data[number:]

     joblib.dump(a__   ,					"""objective_set.jbl"""		)
     print("""objective set saved"""		)
     return train_data, objective_set







def      a__ (						a__   ,					a__   ,					a__   ,					a__   ,					a__=50   ,					a__="secondary_learner.pt"		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""		)
     # We will use the first 512 pairs from our dataset as a test set for
     # our secondary learner and the rest to train
     __SCREAMING_SNAKE_CASE						     =   train_dataset[:5_12]
     __SCREAMING_SNAKE_CASE						     =   train_dataset[5_12:]
     __SCREAMING_SNAKE_CASE						     =   DataLoader(a__   ,					shuffle=a__   ,					batch_size=a__		)
     __SCREAMING_SNAKE_CASE						     =   DataLoader(a__   ,					shuffle=a__   ,					batch_size=a__		)

     # secondary learner model set up
     __SCREAMING_SNAKE_CASE						     =   nn.MSELoss()
     __SCREAMING_SNAKE_CASE						     =   nn.MSELoss(reduction="""sum"""		)
     secondary_learner.to(a__		)
     __SCREAMING_SNAKE_CASE						     =   torch.optim.Adam(secondary_learner.parameters()   ,					lr=0.00_001		)
     secondary_learner.train()

     # TODO in original code this is written as number of actual batches seen
     # not number of items seen but other places it is number of items instead.
     # improve consistency! changed this to epochs for clarity
     __SCREAMING_SNAKE_CASE						     =   float("""inf"""		)
     # Iterate through batches until we've used max_steps batches
     for epoch in range(int(a__		)		):
          __SCREAMING_SNAKE_CASE						     =   0.0
          secondary_learner.train()
          for step, batch in enumerate(a__		):
               __SCREAMING_SNAKE_CASE						     =   batch[0].to(a__		)
               __SCREAMING_SNAKE_CASE						     =   batch[1].to(a__		)
               __SCREAMING_SNAKE_CASE						     =   secondary_learner(a__		)
               q_optimizer.zero_grad()
               __SCREAMING_SNAKE_CASE						     =   loss(a__   ,					real_q.float()		)
               q_loss.backward()
               q_optimizer.step()
               tr_q_loss += q_loss.item()

               # model trains fairly quickly so we won't wait for a full epoch
               # eval is triggered at eval_freq and end of epochs
               if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(a__		)):
                    __SCREAMING_SNAKE_CASE						     =   tr_q_loss / (step + 1)

                    secondary_learner.eval()
                    __SCREAMING_SNAKE_CASE						     =   0.0
                    __SCREAMING_SNAKE_CASE						     =   0.0
                    __SCREAMING_SNAKE_CASE						     =   []
                    __SCREAMING_SNAKE_CASE						     =   []
                    # Compute performance of the secondary learner after this batch
                    for stepa, batcha in enumerate(a__		):
                         __SCREAMING_SNAKE_CASE						     =   batcha[0].to(a__		)
                         __SCREAMING_SNAKE_CASE						     =   batcha[1].to(a__		)
                         __SCREAMING_SNAKE_CASE						     =   secondary_learner(a__		)
                         q_lossa += test_loss(a__   ,					a__		).item()
                         sum_qa += torch.sum(a__		).item()
                         for ei, i in enumerate(predicted_qa.cpu().detach().numpy()		):
                              predicted.append(i.item()		)
                         for ei, i in enumerate(real_qa.cpu().detach().numpy()		):
                              actual.append(i.item()		)

                    q_lossa /= len(a__		)
                    print(
                        """Epoch: """   ,					a__   ,					"""step: """   ,					a__   ,					"""Avg. q:"""   ,					sum_qa / len(a__		)   ,					"""Train Loss: """   ,					a__   ,					"""Test Loss: """   ,					a__   ,					)
                    if q_lossa < best_test_loss:
                         joblib.dump((predicted, actual)   ,					"""pred_vs_actual.jbl"""		)
                         torch.save(secondary_learner.state_dict()   ,					a__		)
                         __SCREAMING_SNAKE_CASE						     =   q_lossa

               secondary_learner.train()
     return secondary_learner







class 		lowerCAmelCase__     (   nn.Module      ):



 """simple docstring"""



 def __init__(	self      : str      ,							__SCREAMING_SNAKE_CASE      : List[Any]    )						->				Union[str, Any]:


      """simple docstring"""




      super(__SCREAMING_SNAKE_CASE      ,							self    ).__init__()
      __SCREAMING_SNAKE_CASE						     =   model.transformer.wte
      __SCREAMING_SNAKE_CASE						     =   copy.deepcopy(model.transformer.wte.weight    )
      __SCREAMING_SNAKE_CASE						     =   nn.Convad(self.embeddings.weight.size(1    )      ,							256      ,							3      ,							padding=1    )
      __SCREAMING_SNAKE_CASE						     =   nn.Sequential(nn.Linear(256      ,							32    )      ,							nn.Dropout(p=0.1    )      ,							nn.Linear(32      ,							32    )      ,							nn.Linear(32      ,							1    )    )
 def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]    )						->				List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   torch.max(self.conv(self.embeddings(__SCREAMING_SNAKE_CASE    ).squeeze(1    ).transpose(1      ,							2    )    )      ,							2    )[0]
      __SCREAMING_SNAKE_CASE						     =   self.fc(__SCREAMING_SNAKE_CASE    )
      return qs.squeeze(1    )


 @classmethod
 def    UpperCAmelCase__	(	cls      : int      ,							__SCREAMING_SNAKE_CASE      : Dict      ,							__SCREAMING_SNAKE_CASE      : int    )						->				str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   cls(__SCREAMING_SNAKE_CASE    )  # this calls __init__
      __SCREAMING_SNAKE_CASE						     =   torch.load(__SCREAMING_SNAKE_CASE    )
      secondary_learner.load_state_dict(__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   model.transformer.wte
      __SCREAMING_SNAKE_CASE						     =   copy.deepcopy(model.transformer.wte.weight    )
      return secondary_learner









'''simple docstring'''



import os
import re
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


UpperCAmelCase				: Union[str, Any]         =  logging.get_logger(__name__)
UpperCAmelCase				: List[Any]         =  {'vocab_file': 'spiece.model'}

UpperCAmelCase				: Any         =  {
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
}

UpperCAmelCase				: Tuple         =  {
    'google/fnet-base': 5_1_2,
    'google/fnet-large': 5_1_2,
}

UpperCAmelCase				: Union[str, Any]         =  '▁'







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   VOCAB_FILES_NAMES
 lowerCAmelCase__						      =   PRETRAINED_VOCAB_FILES_MAP
 lowerCAmelCase__						      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCAmelCase__						      =   ["input_ids", "token_type_ids"]
 def __init__(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]      ,							__SCREAMING_SNAKE_CASE      : str=False      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=True      ,							__SCREAMING_SNAKE_CASE      : List[Any]=True      ,							__SCREAMING_SNAKE_CASE      : int="<unk>"      ,							__SCREAMING_SNAKE_CASE      : List[str]="[SEP]"      ,							__SCREAMING_SNAKE_CASE      : List[str]="<pad>"      ,							__SCREAMING_SNAKE_CASE      : List[Any]="[CLS]"      ,							__SCREAMING_SNAKE_CASE      : Dict="[MASK]"      ,							__SCREAMING_SNAKE_CASE      : Optional[Dict[str, Any]] = None      ,							**__SCREAMING_SNAKE_CASE      : Dict      ,							)						->				None:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   (
          AddedToken(__SCREAMING_SNAKE_CASE      ,							lstrip=__SCREAMING_SNAKE_CASE      ,							rstrip=__SCREAMING_SNAKE_CASE      ,							normalized=__SCREAMING_SNAKE_CASE    )
          if isinstance(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )
          else mask_token
      )

      __SCREAMING_SNAKE_CASE						     =   {} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          do_lower_case=__SCREAMING_SNAKE_CASE      ,							remove_space=__SCREAMING_SNAKE_CASE      ,							keep_accents=__SCREAMING_SNAKE_CASE      ,							unk_token=__SCREAMING_SNAKE_CASE      ,							sep_token=__SCREAMING_SNAKE_CASE      ,							pad_token=__SCREAMING_SNAKE_CASE      ,							cls_token=__SCREAMING_SNAKE_CASE      ,							mask_token=__SCREAMING_SNAKE_CASE      ,							sp_model_kwargs=self.sp_model_kwargs      ,							**__SCREAMING_SNAKE_CASE      ,							)

      __SCREAMING_SNAKE_CASE						     =   do_lower_case
      __SCREAMING_SNAKE_CASE						     =   remove_space
      __SCREAMING_SNAKE_CASE						     =   keep_accents
      __SCREAMING_SNAKE_CASE						     =   vocab_file

      __SCREAMING_SNAKE_CASE						     =   spm.SentencePieceProcessor(**self.sp_model_kwargs    )
      self.sp_model.Load(__SCREAMING_SNAKE_CASE    )
 @property
 def    UpperCAmelCase__	(	self      : int    )						->				Optional[Any]:


      """simple docstring"""




      return len(self.sp_model    )
 def    UpperCAmelCase__	(	self      : Dict    )						->				Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE    ): i for i in range(self.vocab_size    )}
      vocab.update(self.added_tokens_encoder    )
      return vocab
 def __getstate__(	self      : Any    )						->				str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.__dict__.copy()
      __SCREAMING_SNAKE_CASE						     =   None
      return state
 def __setstate__(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : Dict    )						->				Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   d

      # for backward compatibility
      if not hasattr(self      ,							"""sp_model_kwargs"""    ):
           __SCREAMING_SNAKE_CASE						     =   {}

      __SCREAMING_SNAKE_CASE						     =   spm.SentencePieceProcessor(**self.sp_model_kwargs    )
      self.sp_model.Load(self.vocab_file    )
 def    UpperCAmelCase__	(	self      : Tuple      ,							__SCREAMING_SNAKE_CASE      : Optional[int]    )						->				Optional[int]:


      """simple docstring"""




      if self.remove_space:
           __SCREAMING_SNAKE_CASE						     =   """ """.join(inputs.strip().split()    )
      else:
           __SCREAMING_SNAKE_CASE						     =   inputs
      __SCREAMING_SNAKE_CASE						     =   outputs.replace("""``"""      ,							"""\""""    ).replace("""''"""      ,							"""\""""    )

      if not self.keep_accents:
           __SCREAMING_SNAKE_CASE						     =   unicodedata.normalize("""NFKD"""      ,							__SCREAMING_SNAKE_CASE    )
           __SCREAMING_SNAKE_CASE						     =   """""".join([c for c in outputs if not unicodedata.combining(__SCREAMING_SNAKE_CASE    )]    )
      if self.do_lower_case:
           __SCREAMING_SNAKE_CASE						     =   outputs.lower()

      return outputs
 def    UpperCAmelCase__	(	self      : str      ,							__SCREAMING_SNAKE_CASE      : str    )						->				List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.preprocess_text(__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   self.sp_model.encode(__SCREAMING_SNAKE_CASE      ,							out_type=__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   []
      for piece in pieces:
           if len(__SCREAMING_SNAKE_CASE    ) > 1 and piece[-1] == str(""","""    ) and piece[-2].isdigit():
                __SCREAMING_SNAKE_CASE						     =   self.sp_model.EncodeAsPieces(piece[:-1].replace(__SCREAMING_SNAKE_CASE      ,							""""""    )    )
                if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                     if len(cur_pieces[0]    ) == 1:
                          __SCREAMING_SNAKE_CASE						     =   cur_pieces[1:]
                     else:
                          __SCREAMING_SNAKE_CASE						     =   cur_pieces[0][1:]
                cur_pieces.append(piece[-1]    )
                new_pieces.extend(__SCREAMING_SNAKE_CASE    )
           else:
                new_pieces.append(__SCREAMING_SNAKE_CASE    )

      return new_pieces
 def    UpperCAmelCase__	(	self      : Any      ,							__SCREAMING_SNAKE_CASE      : str    )						->				Optional[Any]:


      """simple docstring"""




      return self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : Optional[Any]      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]    )						->				Optional[Any]:


      """simple docstring"""




      return self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : Dict      ,							__SCREAMING_SNAKE_CASE      : List[str]    )						->				Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   []
      __SCREAMING_SNAKE_CASE						     =   """"""
      __SCREAMING_SNAKE_CASE						     =   False
      for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
                if not prev_is_special:
                     out_string += " "
                out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE    ) + token
                __SCREAMING_SNAKE_CASE						     =   True
                __SCREAMING_SNAKE_CASE						     =   []
           else:
                current_sub_tokens.append(__SCREAMING_SNAKE_CASE    )
                __SCREAMING_SNAKE_CASE						     =   False
      out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE    )
      return out_string.strip()
 def    UpperCAmelCase__	(	self      : List[Any]      ,							__SCREAMING_SNAKE_CASE      : List[int]      ,							__SCREAMING_SNAKE_CASE      : bool = False      ,							__SCREAMING_SNAKE_CASE      : bool = None      ,							__SCREAMING_SNAKE_CASE      : bool = True      ,							**__SCREAMING_SNAKE_CASE      : List[str]      ,							)						->				str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   kwargs.pop("""use_source_tokenizer"""      ,							__SCREAMING_SNAKE_CASE    )

      __SCREAMING_SNAKE_CASE						     =   self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE      ,							skip_special_tokens=__SCREAMING_SNAKE_CASE    )

      # To avoid mixing byte-level and unicode for byte-level BPT
      # we need to build string separately for added tokens and byte-level tokens
      # cf. https://github.com/huggingface/transformers/issues/1133
      __SCREAMING_SNAKE_CASE						     =   []
      __SCREAMING_SNAKE_CASE						     =   []
      for token in filtered_tokens:
           if skip_special_tokens and token in self.all_special_ids:
                continue
           if token in self.added_tokens_encoder:
                if current_sub_text:
                     sub_texts.append(self.convert_tokens_to_string(__SCREAMING_SNAKE_CASE    )    )
                     __SCREAMING_SNAKE_CASE						     =   []
                sub_texts.append(__SCREAMING_SNAKE_CASE    )
           else:
                current_sub_text.append(__SCREAMING_SNAKE_CASE    )
      if current_sub_text:
           sub_texts.append(self.convert_tokens_to_string(__SCREAMING_SNAKE_CASE    )    )

      # Mimic the behavior of the Rust tokenizer:
      # No space after <unk>
      if spaces_between_special_tokens:
           __SCREAMING_SNAKE_CASE						     =   re.sub(r"""(<unk>) """      ,							r"""\1"""      ,							""" """.join(__SCREAMING_SNAKE_CASE    )    )
      else:
           __SCREAMING_SNAKE_CASE						     =   """""".join(__SCREAMING_SNAKE_CASE    )

      __SCREAMING_SNAKE_CASE						     =   (
          clean_up_tokenization_spaces
          if clean_up_tokenization_spaces is not None
          else self.clean_up_tokenization_spaces
      )
      if clean_up_tokenization_spaces:
           __SCREAMING_SNAKE_CASE						     =   self.clean_up_tokenization(__SCREAMING_SNAKE_CASE    )
           return clean_text
      else:
           return text
 def    UpperCAmelCase__	(	self      : str      ,							__SCREAMING_SNAKE_CASE      : List[int]      ,							__SCREAMING_SNAKE_CASE      : Optional[List[int]] = None    )						->				List[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   [self.sep_token_id]
      __SCREAMING_SNAKE_CASE						     =   [self.cls_token_id]
      if token_ids_a is None:
           return cls + token_ids_a + sep
      return cls + token_ids_a + sep + token_ids_a + sep
 def    UpperCAmelCase__	(	self      : Dict      ,							__SCREAMING_SNAKE_CASE      : List[int]      ,							__SCREAMING_SNAKE_CASE      : Optional[List[int]] = None      ,							__SCREAMING_SNAKE_CASE      : bool = False    )						->				List[int]:


      """simple docstring"""




      if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=__SCREAMING_SNAKE_CASE      ,							token_ids_a=__SCREAMING_SNAKE_CASE      ,							already_has_special_tokens=__SCREAMING_SNAKE_CASE    )

      if token_ids_a is not None:
           return [1] + ([0] * len(__SCREAMING_SNAKE_CASE    )) + [1] + ([0] * len(__SCREAMING_SNAKE_CASE    )) + [1]
      return [1] + ([0] * len(__SCREAMING_SNAKE_CASE    )) + [1]
 def    UpperCAmelCase__	(	self      : List[Any]      ,							__SCREAMING_SNAKE_CASE      : List[int]      ,							__SCREAMING_SNAKE_CASE      : Optional[List[int]] = None    )						->				List[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   [self.sep_token_id]
      __SCREAMING_SNAKE_CASE						     =   [self.cls_token_id]

      if token_ids_a is None:
           return len(cls + token_ids_a + sep    ) * [0]
      return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]


 def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : str      ,							__SCREAMING_SNAKE_CASE      : Optional[str] = None    )						->				Tuple[str]:


      """simple docstring"""




      if not os.path.isdir(__SCREAMING_SNAKE_CASE    ):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory'    )
           return
      __SCREAMING_SNAKE_CASE						     =   os.path.join(
          __SCREAMING_SNAKE_CASE      ,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )

      if os.path.abspath(self.vocab_file    ) != os.path.abspath(__SCREAMING_SNAKE_CASE    ) and os.path.isfile(self.vocab_file    ):
           copyfile(self.vocab_file      ,							__SCREAMING_SNAKE_CASE    )
      elif not os.path.isfile(self.vocab_file    ):
           with open(__SCREAMING_SNAKE_CASE      ,							"""wb"""    ) as fi:
                __SCREAMING_SNAKE_CASE						     =   self.sp_model.serialized_model_proto()
                fi.write(__SCREAMING_SNAKE_CASE    )

      return (out_vocab_file,)






