code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  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
  
  
  _A							    =	logging.getLogger(__name__)
  def 						lowerCAmelCase_       (	__a )       ->			Optional[Any]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							random.seed(__a )
  							np.random.seed(__a )
  							torch.manual_seed(__a )
  							torch.cuda.manual_seed_all(__a )
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			Tuple:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							model.eval()
  							SCREAMING_SNAKE_CASE			: str	     =next(model.parameters() ).device
  							SCREAMING_SNAKE_CASE			: str	     =1
  							SCREAMING_SNAKE_CASE			: str	     =torch.zeros((eval_batch_size, context_len)	,	dtype=torch.long	,	device=__a )
  							SCREAMING_SNAKE_CASE			: Tuple	     =DataLoader(__a	,	shuffle=__a	,	batch_size=__a )
  							SCREAMING_SNAKE_CASE			: List[Any]	     =torch.zeros(1	,	device=__a )
  							SCREAMING_SNAKE_CASE			: List[str]	     =0
  							for batch in eval_dataloader:
  														batch.to(__a )
  														# pad
  														context.zero_()
  														for i in range(__a ):
  																					SCREAMING_SNAKE_CASE			: List[Any]	     =batch[i]
  														SCREAMING_SNAKE_CASE			: Tuple	     =model(__a	,	labels=__a )
  														eval_loss += outputs[0].sum().item()
  														nb_eval_examples += batch.size(0 )
  							SCREAMING_SNAKE_CASE			: int	     =eval_loss / nb_eval_examples
  							SCREAMING_SNAKE_CASE			: Tuple	     =torch.exp(__a )
  							model.train()
  							return perplexity
  def 						lowerCAmelCase_       (	__a="gpt2" )       ->			Any:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Any	     =GPTaLMHeadModel.from_pretrained(__a	,	output_hidden_states=__a )
  							torch.save(model.state_dict()	,	model_name + '''local.pt''' )
  							return model
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			int:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Any	     =copy.deepcopy(__a )
  							model.to(__a )
  
  							SCREAMING_SNAKE_CASE			: str	     =['''bias''', '''LayerNorm.weight''']
  							SCREAMING_SNAKE_CASE			: List[str]	     =[
  							    {
  							        '''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			: int	     =AdamW(__a	,	lr=5E-5	,	eps=1E-8 )
  							SCREAMING_SNAKE_CASE			: Tuple	     =get_linear_schedule_with_warmup(__a	,	0	,	__a )
  							torch.cuda.empty_cache()
  							return model, lm_optimizer, lm_scheduler
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a	,	__a )       ->			Optional[Any]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Any	     =np.array(__a ).mean()
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =np.array(__a ).std()
  							SCREAMING_SNAKE_CASE			: List[Any]	     =(real_perps - avg) / std
  							SCREAMING_SNAKE_CASE			: List[Any]	     =list(zip(__a	,	__a	,	__a ) )
  							joblib.dump(__a	,	__a )
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a	,	__a	,	__a	,	__a	,	__a	,	__a="dev.jbl"	,	__a=recopy_gpta	,	)       ->			Optional[Any]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Dict	     =[]
  							SCREAMING_SNAKE_CASE			: str	     =[]
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =[]
  
  							# Initialize the transformer model
  							SCREAMING_SNAKE_CASE			: int	     =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			: List[Any]	     =recopy_model(__a	,	__a	,	__a )
  
  							for step in tqdm(range(__a ) ):
  														SCREAMING_SNAKE_CASE			: Any	     =torch.zeros((1, context_len)	,	dtype=torch.long	,	device=__a )
  														SCREAMING_SNAKE_CASE			: Any	     =random.choice(__a )
  														SCREAMING_SNAKE_CASE			: Optional[Any]	     =random.randint(0	,	len(story[0] ) - context_len - 1 )
  														SCREAMING_SNAKE_CASE			: List[Any]	     =story[0][start : start + context_len]
  														lm_optimizer.zero_grad()
  														SCREAMING_SNAKE_CASE			: Dict	     =model(__a	,	labels=__a )
  														SCREAMING_SNAKE_CASE			: Optional[Any]	     =outputs[0]
  														SCREAMING_SNAKE_CASE			: Dict	     =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			: Tuple	     =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
  														SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Dict	     =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 						lowerCAmelCase_       (	__a	,	__a="data/tokenized_stories_train_wikitext103.jbl"	,	__a=100	,	__a=1026	,	__a=True )       ->			List[str]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: str	     =joblib.load(__a )
  							print('''data loaded''' )
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =[]
  							if trim:
  														for i, example in enumerate(__a ):
  																					if len(example[0] ) > min_len:
  																												SCREAMING_SNAKE_CASE			: int	     =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			: Any	     =[]
  														for j in range(i + 1	,	len(__a ) ):
  																					if len(data[j][0] ) > min_len:
  																												train_data.append(data[j] )
  							else:
  														SCREAMING_SNAKE_CASE			: Dict	     =data[0:number]
  														SCREAMING_SNAKE_CASE			: Dict	     =data[number:]
  
  							joblib.dump(__a	,	'''objective_set.jbl''' )
  							print('''objective set saved''' )
  							return train_data, objective_set
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a	,	__a	,	__a=50	,	__a="secondary_learner.pt" )       ->			Union[str, Any]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =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			: Any	     =train_dataset[:512]
  							SCREAMING_SNAKE_CASE			: List[Any]	     =train_dataset[512:]
  							SCREAMING_SNAKE_CASE			: List[Any]	     =DataLoader(__a	,	shuffle=__a	,	batch_size=__a )
  							SCREAMING_SNAKE_CASE			: int	     =DataLoader(__a	,	shuffle=__a	,	batch_size=__a )
  
  							# secondary learner model set up
  							SCREAMING_SNAKE_CASE			: Dict	     =nn.MSELoss()
  							SCREAMING_SNAKE_CASE			: int	     =nn.MSELoss(reduction='''sum''' )
  							secondary_learner.to(__a )
  							SCREAMING_SNAKE_CASE			: Tuple	     =torch.optim.Adam(secondary_learner.parameters()	,	lr=0.00001 )
  							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			: Dict	     =float('''inf''' )
  							# Iterate through batches until we've used max_steps batches
  							for epoch in range(int(__a ) ):
  														SCREAMING_SNAKE_CASE			: Tuple	     =0.0
  														secondary_learner.train()
  														for step, batch in enumerate(__a ):
  																					SCREAMING_SNAKE_CASE			: Union[str, Any]	     =batch[0].to(__a )
  																					SCREAMING_SNAKE_CASE			: Optional[Any]	     =batch[1].to(__a )
  																					SCREAMING_SNAKE_CASE			: Tuple	     =secondary_learner(__a )
  																					q_optimizer.zero_grad()
  																					SCREAMING_SNAKE_CASE			: Optional[int]	     =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			: List[Any]	     =tr_q_loss / (step + 1)
  
  																												secondary_learner.eval()
  																												SCREAMING_SNAKE_CASE			: Optional[int]	     =0.0
  																												SCREAMING_SNAKE_CASE			: Union[str, Any]	     =0.0
  																												SCREAMING_SNAKE_CASE			: str	     =[]
  																												SCREAMING_SNAKE_CASE			: str	     =[]
  																												# Compute performance of the secondary learner after this batch
  																												for stepa, batcha in enumerate(__a ):
  																																			SCREAMING_SNAKE_CASE			: Tuple	     =batcha[0].to(__a )
  																																			SCREAMING_SNAKE_CASE			: Any	     =batcha[1].to(__a )
  																																			SCREAMING_SNAKE_CASE			: str	     =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			: Dict	     =q_lossa
  
  																					secondary_learner.train()
  							return secondary_learner
  
  
  class 							_lowerCAmelCase		(     nn.Module	):
  
  
  
  
  				def __init__( self		,		snake_case_							)    ->      Optional[int]:
  											super(UpperCamelCase__		,		self							).__init__()
  											SCREAMING_SNAKE_CASE			: List[Any]	     =model.transformer.wte
  											SCREAMING_SNAKE_CASE			: List[Any]	     =copy.deepcopy(model.transformer.wte.weight							)
  											SCREAMING_SNAKE_CASE			: List[str]	     =nn.Convad(self.embeddings.weight.size(1							)		,		256		,		3		,		padding=1							)
  											SCREAMING_SNAKE_CASE			: Any	     =nn.Sequential(nn.Linear(256		,		32							)		,		nn.Dropout(p=0.1							)		,		nn.Linear(32		,		32							)		,		nn.Linear(32		,		1							)							)
  
  
  
  
  				def     __a		( self		,		snake_case_							)    ->      Optional[int]:
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =torch.max(self.conv(self.embeddings(UpperCamelCase__							).squeeze(1							).transpose(1		,		2							)							)		,		2							)[0]
  											SCREAMING_SNAKE_CASE			: Tuple	     =self.fc(UpperCamelCase__							)
  											return qs.squeeze(1							)
  
  
  
  
  
  
  
  				@classmethod
  				def     __a		( cls		,		snake_case_		,		snake_case_							)    ->      Any:
  											SCREAMING_SNAKE_CASE			: Tuple	     =cls(UpperCamelCase__							)  # this calls __init__
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =torch.load(UpperCamelCase__							)
  											secondary_learner.load_state_dict(UpperCamelCase__							)
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =model.transformer.wte
  											SCREAMING_SNAKE_CASE			: str	     =copy.deepcopy(model.transformer.wte.weight							)
  											return secondary_learner
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
  from ...utils import logging
  
  
  _A							    =	logging.get_logger(__name__)
  
  _A							    =	{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
  
  _A							    =	{
      """vocab_file""": {
          """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
      },
      """tokenizer_file""": {
          """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json""",
      },
  }
  
  
  class 							_lowerCAmelCase		(     UpperCamelCase__	):
  
  
  
  
  				def __init__( self		,		snake_case_=None		,		snake_case_=None		,		snake_case_=False		,		snake_case_=True		,		snake_case_=False		,		snake_case_="<s>"		,		snake_case_="</s>"		,		snake_case_="<unk>"		,		snake_case_="<sep>"		,		snake_case_="<pad>"		,		snake_case_="<cls>"		,		snake_case_="<mask>"		,		snake_case_=["<eop>", "<eod>"]		,		**snake_case_		,		)    ->      str:
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =AddedToken(snake_case_		,		lstrip=snake_case_		,		rstrip=snake_case_							) if isinstance(snake_case_		,		snake_case_							) else mask_token
  
  											super().__init__(
  											    vocab_file=snake_case_		,		tokenizer_file=snake_case_		,		do_lower_case=snake_case_		,		remove_space=snake_case_		,		keep_accents=snake_case_		,		bos_token=snake_case_		,		eos_token=snake_case_		,		unk_token=snake_case_		,		sep_token=snake_case_		,		pad_token=snake_case_		,		cls_token=snake_case_		,		mask_token=snake_case_		,		additional_special_tokens=snake_case_		,		**snake_case_		,		)
  
  											SCREAMING_SNAKE_CASE			: Dict	     =3
  											SCREAMING_SNAKE_CASE			: Dict	     =do_lower_case
  											SCREAMING_SNAKE_CASE			: int	     =remove_space
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =keep_accents
  											SCREAMING_SNAKE_CASE			: Tuple	     =vocab_file
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =False if not self.vocab_file else True
  
  											try:
  																		import jieba
  											except ModuleNotFoundError as error:
  																		raise error.__class__(
  																		    '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
  																		    '''See https://pypi.org/project/jieba/ for installation.'''							)
  											SCREAMING_SNAKE_CASE			: str	     =jieba
  											SCREAMING_SNAKE_CASE			: Dict	     =str.maketrans(''' \n'''		,		'''\u2582\u2583'''							)
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_ = None							)    ->      List[int]:
  											SCREAMING_SNAKE_CASE			: Dict	     =[self.sep_token_id]
  											SCREAMING_SNAKE_CASE			: Any	     =[self.cls_token_id]
  											if token_ids_a is None:
  																		return token_ids_a + sep + cls
  											return token_ids_a + sep + token_ids_a + sep + cls
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_ = None							)    ->      List[int]:
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[self.sep_token_id]
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[2]
  
  											if token_ids_a is None:
  																		return len(token_ids_a + sep							) * [0] + cls_segment_id
  											return len(token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1] + cls_segment_id
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_ = None							)    ->      Tuple[str]:
  											if not self.can_save_slow_tokenizer:
  																		raise ValueError(
  																		    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
  																		    '''tokenizer.'''							)
  
  											if not os.path.isdir(snake_case_							):
  																		logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
  																		return
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =os.path.join(
  											    snake_case_		,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)
  
  											if os.path.abspath(self.vocab_file							) != os.path.abspath(snake_case_							):
  																		copyfile(self.vocab_file		,		snake_case_							)
  
  											return (out_vocab_file,)
  
  
  
  
  				def     __a		( self		,		snake_case_		,		*snake_case_		,		**snake_case_							)    ->      Optional[Any]:
  											SCREAMING_SNAKE_CASE			: int	     =[
  											    ''' '''.join([x.translate(self.translator							) for x in self.jieba.cut(snake_case_		,		cut_all=snake_case_							)]							)
  											    for text in batch_text_or_text_pairs
  											]
  											return super()._batch_encode_plus(snake_case_		,		*snake_case_		,		**snake_case_							)
  
  
  
  
  
  
  
  				def     __a		( self		,		*snake_case_		,		**snake_case_							)    ->      str:
  											SCREAMING_SNAKE_CASE			: List[Any]	     =super()._decode(*snake_case_		,		**snake_case_							)
  											SCREAMING_SNAKE_CASE			: List[Any]	     =text.replace(''' '''		,		''''''							).replace('''\u2582'''		,		''' '''							).replace('''\u2583'''		,		'''\n'''							)
  											return text
  
  
  
  
  
  
  