




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__		=						logging.getLogger(__name__)






def  snake_case (							A      )							->							Tuple:




	'''simple docstring'''


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






def  snake_case (							A						,       A						,       A      )							->							Tuple:




	'''simple docstring'''


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






def  snake_case (							A="gpt2"      )							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Dict							       =							GPTaLMHeadModel.from_pretrained(A						,       output_hidden_states=A      )
	torch.save(model.state_dict()						,       model_name + 'local.pt'      )
	return model






def  snake_case (							A						,       A						,       A      )							->							Tuple:




	'''simple docstring'''


	lowerCamelCase_							:		Optional[Any]							       =							copy.deepcopy(A      )
	model.to(A      )

	lowerCamelCase_							:		Tuple							       =							['bias', 'LayerNorm.weight']
	lowerCamelCase_							:		Dict							       =							[
	    {
	        '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},
	]
	lowerCamelCase_							:		Optional[Any]							       =							AdamW(A						,       lr=5e-5						,       eps=1e-8      )
	lowerCamelCase_							:		str							       =							get_linear_schedule_with_warmup(A						,       0						,       A      )
	torch.cuda.empty_cache()
	return model, lm_optimizer, lm_scheduler






def  snake_case (							A						,       A						,       A						,       A      )							->							Dict:




	'''simple docstring'''


	lowerCamelCase_							:		List[str]							       =							np.array(A      ).mean()
	lowerCamelCase_							:		str							       =							np.array(A      ).std()
	lowerCamelCase_							:		str							       =							(real_perps - avg) / std
	lowerCamelCase_							:		int							       =							list(zip(A						,       A						,       A      )      )
	joblib.dump(A						,       A      )






def  snake_case (							A						,       A						,       A						,       A						,       A						,       A						,       A						,       A="dev.jbl"						,       A=recopy_gpta						,       )							->							Optional[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Optional[int]							       =							[]
	lowerCamelCase_							:		Optional[Any]							       =							[]
	lowerCamelCase_							:		List[Any]							       =							[]

	# Initialize the transformer model
	lowerCamelCase_							:		List[str]							       =							copy.deepcopy(A      )
	orig_model.to(device='cpu'      )
	torch.cuda.empty_cache()

	# Compute perplexity of initial transformer model for comparison
	model.train()
	lowerCamelCase_       ,      lowerCamelCase_       ,      lowerCamelCase_							:		Optional[Any]							       =							recopy_model(A						,       A						,       A      )

	for step in tqdm(range(A      )      ):
		lowerCamelCase_							:		Tuple							       =							torch.zeros((1, context_len)						,       dtype=torch.long						,       device=A      )
		lowerCamelCase_							:		str							       =							random.choice(A      )
		lowerCamelCase_							:		Optional[int]							       =							random.randint(0						,       len(story[0]      ) - context_len - 1      )
		lowerCamelCase_							:		Any							       =							story[0][start : start + context_len]
		lm_optimizer.zero_grad()
		lowerCamelCase_							:		Optional[Any]							       =							model(A						,       labels=A      )
		lowerCamelCase_							:		int							       =							outputs[0]
		lowerCamelCase_							:		List[str]							       =							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
		lowerCamelCase_							:		List[str]							       =							compute_perplexity(A						,       A						,       A      )

		# Periodically save the stored (X, IG(X)) pairs
		if step % 1000 == 0 and step > 1:
			intermittent_save(A						,       A						,       A						,       A      )

		# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
		lowerCamelCase_       ,      lowerCamelCase_       ,      lowerCamelCase_							:		Optional[Any]							       =							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  snake_case (							A						,       A="data/tokenized_stories_train_wikitext103.jbl"						,       A=100						,       A=1026						,       A=True      )							->							Union[str, Any]:




	'''simple docstring'''


	lowerCamelCase_							:		str							       =							joblib.load(A      )
	print('data loaded'      )
	lowerCamelCase_							:		Dict							       =							[]
	if trim:
		for i, example in enumerate(A      ):
			if len(example[0]      ) > min_len:
				lowerCamelCase_							:		str							       =							random.randint(0						,       len(example[0]      ) - context_len - 1      )
				objective_set.append(example[0, start : start + context_len]      )
			if len(A      ) >= number:
				break
		lowerCamelCase_							:		List[Any]							       =							[]
		for j in range(i + 1						,       len(A      )      ):
			if len(data[j][0]      ) > min_len:
				train_data.append(data[j]      )
	else:
		lowerCamelCase_							:		List[Any]							       =							data[0:number]
		lowerCamelCase_							:		Any							       =							data[number:]

	joblib.dump(A						,       'objective_set.jbl'      )
	print('objective set saved'      )
	return train_data, objective_set






def  snake_case (							A						,       A						,       A						,       A						,       A=50						,       A="secondary_learner.pt"      )							->							Union[str, Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							       =							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
	lowerCamelCase_							:		int							       =							train_dataset[:512]
	lowerCamelCase_							:		str							       =							train_dataset[512:]
	lowerCamelCase_							:		List[Any]							       =							DataLoader(A						,       shuffle=A						,       batch_size=A      )
	lowerCamelCase_							:		Any							       =							DataLoader(A						,       shuffle=A						,       batch_size=A      )

	# secondary learner model set up
	lowerCamelCase_							:		List[Any]							       =							nn.MSELoss()
	lowerCamelCase_							:		Dict							       =							nn.MSELoss(reduction='sum'      )
	secondary_learner.to(A      )
	lowerCamelCase_							:		Union[str, Any]							       =							torch.optim.Adam(secondary_learner.parameters()						,       lr=0.0_0001      )
	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
	lowerCamelCase_							:		Optional[Any]							       =							float('inf'      )
	# Iterate through batches until we've used max_steps batches
	for epoch in range(int(A      )      ):
		lowerCamelCase_							:		Union[str, Any]							       =							0.0
		secondary_learner.train()
		for step, batch in enumerate(A      ):
			lowerCamelCase_							:		List[Any]							       =							batch[0].to(A      )
			lowerCamelCase_							:		List[Any]							       =							batch[1].to(A      )
			lowerCamelCase_							:		Optional[Any]							       =							secondary_learner(A      )
			q_optimizer.zero_grad()
			lowerCamelCase_							:		str							       =							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      )):
				lowerCamelCase_							:		Dict							       =							tr_q_loss / (step + 1)

				secondary_learner.eval()
				lowerCamelCase_							:		Optional[int]							       =							0.0
				lowerCamelCase_							:		Union[str, Any]							       =							0.0
				lowerCamelCase_							:		Union[str, Any]							       =							[]
				lowerCamelCase_							:		List[Any]							       =							[]
				# Compute performance of the secondary learner after this batch
				for stepa, batcha in enumerate(A      ):
					lowerCamelCase_							:		Any							       =							batcha[0].to(A      )
					lowerCamelCase_							:		Union[str, Any]							       =							batcha[1].to(A      )
					lowerCamelCase_							:		Dict							       =							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      )
					lowerCamelCase_							:		Tuple							       =							q_lossa

			secondary_learner.train()
	return secondary_learner



class     __SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(    self			,	__lowercase ):







						'''simple docstring'''




						super(__lowercase			,	self ).__init__()
						lowerCamelCase_							:		Optional[int]							       =							model.transformer.wte
						lowerCamelCase_							:		Union[str, Any]							       =							copy.deepcopy(model.transformer.wte.weight )
						lowerCamelCase_							:		List[str]							       =							nn.Convad(self.embeddings.weight.size(1 )			,	256			,	3			,	padding=1 )
						lowerCamelCase_							:		List[str]							       =							nn.Sequential(nn.Linear(256			,	32 )			,	nn.Dropout(p=0.1 )			,	nn.Linear(32			,	32 )			,	nn.Linear(32			,	1 ) )

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							       =							torch.max(self.conv(self.embeddings(__lowercase ).squeeze(1 ).transpose(1			,	2 ) )			,	2 )[0]
						lowerCamelCase_							:		Dict							       =							self.fc(__lowercase )
						return qs.squeeze(1 )





					@classmethod
					def      lowercase_							(    cls			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		str							       =							cls(__lowercase )  # this calls __init__
						lowerCamelCase_							:		List[str]							       =							torch.load(__lowercase )
						secondary_learner.load_state_dict(__lowercase )
						lowerCamelCase_							:		List[Any]							       =							model.transformer.wte
						lowerCamelCase_							:		Dict							       =							copy.deepcopy(model.transformer.wte.weight )
						return secondary_learner






import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


UpperCamelCase__		=						logging.get_logger(__name__)

UpperCamelCase__		=						{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

UpperCamelCase__		=						{
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
}

UpperCamelCase__		=						{
    'roberta-base': 5_1_2,
    'roberta-large': 5_1_2,
    'roberta-large-mnli': 5_1_2,
    'distilroberta-base': 5_1_2,
    'roberta-base-openai-detector': 5_1_2,
    'roberta-large-openai-detector': 5_1_2,
}






@lru_cache()
def  snake_case (							)							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							       =							(
	    list(range(ord('!'      )						,       ord('~'      ) + 1      )      ) + list(range(ord('¡'      )						,       ord('¬'      ) + 1      )      ) + list(range(ord('®'      )						,       ord('ÿ'      ) + 1      )      )
	)
	lowerCamelCase_							:		Dict							       =							bs[:]
	lowerCamelCase_							:		Dict							       =							0
	for b in range(2**8      ):
		if b not in bs:
			bs.append(A      )
			cs.append(2**8 + n      )
			n += 1
	lowerCamelCase_							:		List[str]							       =							[chr(A      ) for n in cs]
	return dict(zip(A						,       A      )      )






def  snake_case (							A      )							->							int:




	'''simple docstring'''


	lowerCamelCase_							:		Union[str, Any]							       =							set()
	lowerCamelCase_							:		Optional[int]							       =							word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)      )
		lowerCamelCase_							:		Optional[Any]							       =							char
	return pairs



class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):
					_lowerCamelCase		  =			VOCAB_FILES_NAMES
					_lowerCamelCase		  =			PRETRAINED_VOCAB_FILES_MAP
					_lowerCamelCase		  =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_lowerCamelCase		  =			["""input_ids""", """attention_mask"""]

					def __init__(    self			,	__lowercase			,	__lowercase			,	__lowercase="replace"			,	__lowercase="<s>"			,	__lowercase="</s>"			,	__lowercase="</s>"			,	__lowercase="<s>"			,	__lowercase="<unk>"			,	__lowercase="<pad>"			,	__lowercase="<mask>"			,	__lowercase=False			,	**__lowercase			,	):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else bos_token
						lowerCamelCase_							:		Dict							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else eos_token
						lowerCamelCase_							:		Union[str, Any]							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else sep_token
						lowerCamelCase_							:		Union[str, Any]							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else cls_token
						lowerCamelCase_							:		Any							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else unk_token
						lowerCamelCase_							:		Tuple							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else pad_token

						# Mask token behave like a normal word, i.e. include the space before it
						lowerCamelCase_							:		Dict							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else mask_token

						super().__init__(
						    errors=__lowercase			,	bos_token=__lowercase			,	eos_token=__lowercase			,	unk_token=__lowercase			,	sep_token=__lowercase			,	cls_token=__lowercase			,	pad_token=__lowercase			,	mask_token=__lowercase			,	add_prefix_space=__lowercase			,	**__lowercase			,	)

						with open(__lowercase			,	encoding='utf-8' ) as vocab_handle:
							lowerCamelCase_							:		Tuple							       =							json.load(__lowercase )
						lowerCamelCase_							:		Dict							       =							{v: k for k, v in self.encoder.items()}
						lowerCamelCase_							:		Any							       =							errors  # how to handle errors in decoding
						lowerCamelCase_							:		Dict							       =							bytes_to_unicode()
						lowerCamelCase_							:		Any							       =							{v: k for k, v in self.byte_encoder.items()}
						with open(__lowercase			,	encoding='utf-8' ) as merges_handle:
							lowerCamelCase_							:		Dict							       =							merges_handle.read().split('\n' )[1:-1]
						lowerCamelCase_							:		Tuple							       =							[tuple(merge.split() ) for merge in bpe_merges]
						lowerCamelCase_							:		Tuple							       =							dict(zip(__lowercase			,	range(len(__lowercase ) ) ) )
						lowerCamelCase_							:		List[str]							       =							{}
						lowerCamelCase_							:		Union[str, Any]							       =							add_prefix_space

						# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
						lowerCamelCase_							:		Dict							       =							re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )

					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




						return len(self.encoder )

					def      lowercase_							(    self ):







						'''simple docstring'''




						return dict(self.encoder			,	**self.added_tokens_encoder )

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						if token in self.cache:
							return self.cache[token]
						lowerCamelCase_							:		Any							       =							tuple(__lowercase )
						lowerCamelCase_							:		Tuple							       =							get_pairs(__lowercase )

						if not pairs:
							return token

						while True:
							lowerCamelCase_							:		str							       =							min(__lowercase			,	key=lambda __lowercase : self.bpe_ranks.get(__lowercase			,	float('inf' ) ) )
							if bigram not in self.bpe_ranks:
								break
							lowerCamelCase_       ,      lowerCamelCase_							:		Dict							       =							bigram
							lowerCamelCase_							:		str							       =							[]
							lowerCamelCase_							:		int							       =							0
							while i < len(__lowercase ):
								try:
									lowerCamelCase_							:		Tuple							       =							word.index(__lowercase			,	__lowercase )
								except ValueError:
									new_word.extend(word[i:] )
									break
								else:
									new_word.extend(word[i:j] )
									lowerCamelCase_							:		Any							       =							j

								if word[i] == first and i < len(__lowercase ) - 1 and word[i + 1] == second:
									new_word.append(first + second )
									i += 2
								else:
									new_word.append(word[i] )
									i += 1
							lowerCamelCase_							:		List[Any]							       =							tuple(__lowercase )
							lowerCamelCase_							:		Optional[Any]							       =							new_word
							if len(__lowercase ) == 1:
								break
							else:
								lowerCamelCase_							:		Optional[Any]							       =							get_pairs(__lowercase )
						lowerCamelCase_							:		Optional[int]							       =							' '.join(__lowercase )
						lowerCamelCase_							:		Union[str, Any]							       =							word
						return word

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							[]
						for token in re.findall(self.pat			,	__lowercase ):
							lowerCamelCase_							:		int							       =							''.join(
							    self.byte_encoder[b] for b in token.encode('utf-8' ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
							bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowercase ).split(' ' ) )
						return bpe_tokens

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						return self.encoder.get(__lowercase			,	self.encoder.get(self.unk_token ) )

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						return self.decoder.get(__lowercase )

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		int							       =							''.join(__lowercase )
						lowerCamelCase_							:		Tuple							       =							bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8'			,	errors=self.errors )
						return text

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = None ):







						'''simple docstring'''




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

						with open(__lowercase			,	'w'			,	encoding='utf-8' ) as f:
							f.write(json.dumps(self.encoder			,	indent=2			,	sort_keys=__lowercase			,	ensure_ascii=__lowercase ) + '\n' )

						lowerCamelCase_							:		Dict							       =							0
						with open(__lowercase			,	'w'			,	encoding='utf-8' ) as writer:
							writer.write('#version: 0.2\n' )
							for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,	key=lambda __lowercase : kv[1] ):
								if index != token_index:
									logger.warning(
									    f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
									    ' Please check that the tokenizer is not corrupted!' )
									lowerCamelCase_							:		Tuple							       =							token_index
								writer.write(' '.join(__lowercase ) + '\n' )
								index += 1

						return vocab_file, merge_file

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = None ):







						'''simple docstring'''




						if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						lowerCamelCase_							:		Optional[Any]							       =							[self.cls_token_id]
						lowerCamelCase_							:		Optional[Any]							       =							[self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = None			,	__lowercase = False ):







						'''simple docstring'''




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

						if token_ids_a is None:
							return [1] + ([0] * len(__lowercase )) + [1]
						return [1] + ([0] * len(__lowercase )) + [1, 1] + ([0] * len(__lowercase )) + [1]

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = None ):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							       =							[self.sep_token_id]
						lowerCamelCase_							:		Any							       =							[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 + sep + token_ids_a + sep ) * [0]





					def      lowercase_							(    self			,	__lowercase			,	__lowercase=False			,	**__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							kwargs.pop('add_prefix_space'			,	self.add_prefix_space )
						if (is_split_into_words or add_prefix_space) and (len(__lowercase ) > 0 and not text[0].isspace()):
							lowerCamelCase_							:		List[Any]							       =							' ' + text
						return (text, kwargs)
