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
  
  
  lowerCAmelCase   :List[str]								=    logging.getLogger(__name__)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			Tuple	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	random.seed(lowerCAmelCase	)
  	np.random.seed(lowerCAmelCase	)
  	torch.manual_seed(lowerCAmelCase	)
  	torch.cuda.manual_seed_all(lowerCAmelCase	)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			List[Any]	,	lowerCAmelCase       :			List[Any]	,	lowerCAmelCase       :			List[str]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	model.eval()
  	__magic_name__ :					Tuple  			=  next(model.parameters()	).device
  	__magic_name__ :					int  			=  1
  	__magic_name__ :					List[Any]  			=  torch.zeros((eval_batch_size, context_len)	,	dtype=torch.long	,	device=lowerCAmelCase	)
  	__magic_name__ :					int  			=  DataLoader(lowerCAmelCase	,	shuffle=lowerCAmelCase	,	batch_size=lowerCAmelCase	)
  	__magic_name__ :					Any  			=  torch.zeros(1	,	device=lowerCAmelCase	)
  	__magic_name__ :					Optional[Any]  			=  0
  	for batch in eval_dataloader:
  		batch.to(lowerCAmelCase	)
  		# pad
  		context.zero_()
  		for i in range(lowerCAmelCase	):
  			__magic_name__ :					List[Any]  			=  batch[i]
  		__magic_name__ :					str  			=  model(lowerCAmelCase	,	labels=lowerCAmelCase	)
  		eval_loss += outputs[0].sum().item()
  		nb_eval_examples += batch.size(0	)
  	__magic_name__ :					str  			=  eval_loss / nb_eval_examples
  	__magic_name__ :					str  			=  torch.exp(lowerCAmelCase	)
  	model.train()
  	return perplexity
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			List[Any]="gpt2"	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					Optional[Any]  			=  GPTaLMHeadModel.from_pretrained(lowerCAmelCase	,	output_hidden_states=lowerCAmelCase	)
  	torch.save(model.state_dict()	,	model_name + 'local.pt'	)
  	return model
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			List[str]	,	lowerCAmelCase       :			Optional[Any]	,	lowerCAmelCase       :			str	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					List[str]  			=  copy.deepcopy(lowerCAmelCase	)
  	model.to(lowerCAmelCase	)
  
  	__magic_name__ :					List[Any]  			=  ['bias', 'LayerNorm.weight']
  	__magic_name__ :					List[Any]  			=  [
  	    {
  	        '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},
  	]
  	__magic_name__ :					Tuple  			=  AdamW(lowerCAmelCase	,	lr=5e-5	,	eps=1e-8	)
  	__magic_name__ :					int  			=  get_linear_schedule_with_warmup(lowerCAmelCase	,	0	,	lowerCAmelCase	)
  	torch.cuda.empty_cache()
  	return model, lm_optimizer, lm_scheduler
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			Union[str, Any]	,	lowerCAmelCase       :			str	,	lowerCAmelCase       :			Dict	,	lowerCAmelCase       :			List[Any]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					int  			=  np.array(lowerCAmelCase	).mean()
  	__magic_name__ :					Tuple  			=  np.array(lowerCAmelCase	).std()
  	__magic_name__ :					str  			=  (real_perps - avg) / std
  	__magic_name__ :					List[str]  			=  list(zip(lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	)	)
  	joblib.dump(lowerCAmelCase	,	lowerCAmelCase	)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			Optional[Any]	,	lowerCAmelCase       :			int	,	lowerCAmelCase       :			Tuple	,	lowerCAmelCase       :			Any	,	lowerCAmelCase       :			Optional[int]	,	lowerCAmelCase       :			Union[str, Any]	,	lowerCAmelCase       :			Union[str, Any]	,	lowerCAmelCase       :			Union[str, Any]="dev.jbl"	,	lowerCAmelCase       :			Dict=recopy_gpta	,	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					str  			=  []
  	__magic_name__ :					Tuple  			=  []
  	__magic_name__ :					List[str]  			=  []
  
  	# Initialize the transformer model
  	__magic_name__ :					List[Any]  			=  copy.deepcopy(lowerCAmelCase	)
  	orig_model.to(device='cpu'	)
  	torch.cuda.empty_cache()
  
  	# Compute perplexity of initial transformer model for comparison
  	model.train()
  	__magic_name__ :					int  			=  recopy_model(lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	)
  
  	for step in tqdm(range(lowerCAmelCase	)	):
  		__magic_name__ :					Any  			=  torch.zeros((1, context_len)	,	dtype=torch.long	,	device=lowerCAmelCase	)
  		__magic_name__ :					Optional[Any]  			=  random.choice(lowerCAmelCase	)
  		__magic_name__ :					Tuple  			=  random.randint(0	,	len(story[0]	) - context_len - 1	)
  		__magic_name__ :					int  			=  story[0][start : start + context_len]
  		lm_optimizer.zero_grad()
  		__magic_name__ :					Dict  			=  model(lowerCAmelCase	,	labels=lowerCAmelCase	)
  		__magic_name__ :					str  			=  outputs[0]
  		__magic_name__ :					Tuple  			=  compute_perplexity(lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	)
  		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
  		__magic_name__ :					Dict  			=  compute_perplexity(lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	)
  
  		# Periodically save the stored (X, IG(X)) pairs
  		if step % 1000 == 0 and step > 1:
  			intermittent_save(lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	)
  
  		# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
  		__magic_name__ :					Union[str, Any]  			=  recopy_model(lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	)
  
  		past_perps.append(past_perp.item()	)
  		real_perps.append(orig_perp - real_perp.item()	)
  		contexts.append(np.array(context.cpu()	)	)
  
  	intermittent_save(lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			int	,	lowerCAmelCase       :			Any="data/tokenized_stories_train_wikitext103.jbl"	,	lowerCAmelCase       :			str=100	,	lowerCAmelCase       :			Any=1026	,	lowerCAmelCase       :			List[str]=True	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					Dict  			=  joblib.load(lowerCAmelCase	)
  	print('data loaded'	)
  	__magic_name__ :					List[str]  			=  []
  	if trim:
  		for i, example in enumerate(lowerCAmelCase	):
  			if len(example[0]	) > min_len:
  				__magic_name__ :					Optional[Any]  			=  random.randint(0	,	len(example[0]	) - context_len - 1	)
  				objective_set.append(example[0, start : start + context_len]	)
  			if len(lowerCAmelCase	) >= number:
  				break
  		__magic_name__ :					Optional[int]  			=  []
  		for j in range(i + 1	,	len(lowerCAmelCase	)	):
  			if len(data[j][0]	) > min_len:
  				train_data.append(data[j]	)
  	else:
  		__magic_name__ :					int  			=  data[0:number]
  		__magic_name__ :					Any  			=  data[number:]
  
  	joblib.dump(lowerCAmelCase	,	'objective_set.jbl'	)
  	print('objective set saved'	)
  	return train_data, objective_set
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			List[str]	,	lowerCAmelCase       :			Union[str, Any]	,	lowerCAmelCase       :			Tuple	,	lowerCAmelCase       :			Union[str, Any]	,	lowerCAmelCase       :			Optional[Any]=50	,	lowerCAmelCase       :			Dict="secondary_learner.pt"	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					int  			=  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
  	__magic_name__ :					int  			=  train_dataset[:512]
  	__magic_name__ :					Optional[int]  			=  train_dataset[512:]
  	__magic_name__ :					Dict  			=  DataLoader(lowerCAmelCase	,	shuffle=lowerCAmelCase	,	batch_size=lowerCAmelCase	)
  	__magic_name__ :					Optional[int]  			=  DataLoader(lowerCAmelCase	,	shuffle=lowerCAmelCase	,	batch_size=lowerCAmelCase	)
  
  	# secondary learner model set up
  	__magic_name__ :					List[Any]  			=  nn.MSELoss()
  	__magic_name__ :					Any  			=  nn.MSELoss(reduction='sum'	)
  	secondary_learner.to(lowerCAmelCase	)
  	__magic_name__ :					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
  	__magic_name__ :					Any  			=  float('inf'	)
  	# Iterate through batches until we've used max_steps batches
  	for epoch in range(int(lowerCAmelCase	)	):
  		__magic_name__ :					int  			=  0.0
  		secondary_learner.train()
  		for step, batch in enumerate(lowerCAmelCase	):
  			__magic_name__ :					List[str]  			=  batch[0].to(lowerCAmelCase	)
  			__magic_name__ :					Optional[Any]  			=  batch[1].to(lowerCAmelCase	)
  			__magic_name__ :					Dict  			=  secondary_learner(lowerCAmelCase	)
  			q_optimizer.zero_grad()
  			__magic_name__ :					List[Any]  			=  loss(lowerCAmelCase	,	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(lowerCAmelCase	)):
  				__magic_name__ :					Optional[int]  			=  tr_q_loss / (step + 1)
  
  				secondary_learner.eval()
  				__magic_name__ :					Optional[Any]  			=  0.0
  				__magic_name__ :					str  			=  0.0
  				__magic_name__ :					int  			=  []
  				__magic_name__ :					Optional[int]  			=  []
  				# Compute performance of the secondary learner after this batch
  				for stepa, batcha in enumerate(lowerCAmelCase	):
  					__magic_name__ :					int  			=  batcha[0].to(lowerCAmelCase	)
  					__magic_name__ :					Tuple  			=  batcha[1].to(lowerCAmelCase	)
  					__magic_name__ :					int  			=  secondary_learner(lowerCAmelCase	)
  					q_lossa += test_loss(lowerCAmelCase	,	lowerCAmelCase	).item()
  					sum_qa += torch.sum(lowerCAmelCase	).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(lowerCAmelCase	)
  				print(
  				    'Epoch: '	,	lowerCAmelCase	,	'step: '	,	lowerCAmelCase	,	'Avg. q:'	,	sum_qa / len(lowerCAmelCase	)	,	'Train Loss: '	,	lowerCAmelCase	,	'Test Loss: '	,	lowerCAmelCase	,	)
  				if q_lossa < best_test_loss:
  					joblib.dump((predicted, actual)	,	'pred_vs_actual.jbl'	)
  					torch.save(secondary_learner.state_dict()	,	lowerCAmelCase	)
  					__magic_name__ :					Any  			=  q_lossa
  
  			secondary_learner.train()
  	return secondary_learner
  
  
  
  
  
  class _lowerCamelCase (		nn.Module							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(      self	:		List[Any]						,  _A	:		Dict    )						->				str:
  			super(_A						,  self    ).__init__()
  			__magic_name__ :					Union[str, Any]  			=  model.transformer.wte
  			__magic_name__ :					List[Any]  			=  copy.deepcopy(model.transformer.wte.weight    )
  			__magic_name__ :					int  			=  nn.Convad(self.embeddings.weight.size(1    )						,  256						,  3						,  padding=1    )
  			__magic_name__ :					List[Any]  			=  nn.Sequential(nn.Linear(256						,  32    )						,  nn.Dropout(p=0.1    )						,  nn.Linear(32						,  32    )						,  nn.Linear(32						,  1    )    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[int]						,  _A	:		Union[str, Any]    )						->				Optional[int]:
  			__magic_name__ :					Optional[int]  			=  torch.max(self.conv(self.embeddings(_A    ).squeeze(1    ).transpose(1						,  2    )    )						,  2    )[0]
  			__magic_name__ :					Any  			=  self.fc(_A    )
  			return qs.squeeze(1    )
  
  
  
  
  
  
  
  		@classmethod
  		def      __lowerCAmelCase	(      cls	:		List[Any]						,  _A	:		List[Any]						,  _A	:		str    )						->				int:
  			__magic_name__ :					List[str]  			=  cls(_A    )  # this calls __init__
  			__magic_name__ :					List[Any]  			=  torch.load(_A    )
  			secondary_learner.load_state_dict(_A    )
  			__magic_name__ :					List[Any]  			=  model.transformer.wte
  			__magic_name__ :					Any  			=  copy.deepcopy(model.transformer.wte.weight    )
  			return secondary_learner
style_context: 
  
  
  '''simple docstring'''
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowerCAmelCase   :List[Any]								=    logging.get_logger(__name__)
  
  lowerCAmelCase   :Dict								=    {
      '''uw-madison/yoso-4096''': '''https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json''',
      # See all YOSO models at https://huggingface.co/models?filter=yoso
  }
  class _lowerCamelCase (		lowercase__							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    Optional[Any]   					=       """yoso"""
  
  
  
  
  
  
  
  		def __init__(      self	:		Any						,  _A	:		List[Any]=50265						,  _A	:		Any=768						,  _A	:		List[str]=12						,  _A	:		Dict=12						,  _A	:		Union[str, Any]=3072						,  _A	:		List[Any]="gelu"						,  _A	:		Dict=0.1						,  _A	:		Union[str, Any]=0.1						,  _A	:		Union[str, Any]=4096						,  _A	:		Dict=1						,  _A	:		Tuple=0.02						,  _A	:		Any=1E-12						,  _A	:		Optional[int]="absolute"						,  _A	:		Tuple=True						,  _A	:		Any=9						,  _A	:		Tuple=64						,  _A	:		str=None						,  _A	:		Dict=True						,  _A	:		str=True						,  _A	:		int=1						,  _A	:		Optional[int]=0						,  _A	:		Tuple=2						,  **_A	:		Optional[Any]						,  )						->				Any:
  			super().__init__(pad_token_id=_A						,  bos_token_id=_A						,  eos_token_id=_A						,  **_A    )
  
  			__magic_name__ :					List[Any]  			=  vocab_size
  			__magic_name__ :					int  			=  max_position_embeddings
  			__magic_name__ :					int  			=  hidden_size
  			__magic_name__ :					Any  			=  num_hidden_layers
  			__magic_name__ :					List[str]  			=  num_attention_heads
  			__magic_name__ :					str  			=  intermediate_size
  			__magic_name__ :					Any  			=  hidden_act
  			__magic_name__ :					Union[str, Any]  			=  hidden_dropout_prob
  			__magic_name__ :					str  			=  attention_probs_dropout_prob
  			__magic_name__ :					int  			=  initializer_range
  			__magic_name__ :					List[Any]  			=  type_vocab_size
  			__magic_name__ :					Tuple  			=  layer_norm_eps
  			__magic_name__ :					Tuple  			=  position_embedding_type
  			__magic_name__ :					Optional[Any]  			=  use_expectation
  			__magic_name__ :					Union[str, Any]  			=  hash_code_len
  			__magic_name__ :					int  			=  num_hash
  			__magic_name__ :					List[Any]  			=  conv_window
  			__magic_name__ :					Optional[Any]  			=  use_fast_hash
  			__magic_name__ :					int  			=  lsh_backward