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
  
  
  __SCREAMING_SNAKE_CASE					=						logging.getLogger(__name__)
  
  
  
  def 						__a      (  a					)     ->   Any:
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         random.seed(a					)
         np.random.seed(a					)
         torch.manual_seed(a					)
         torch.cuda.manual_seed_all(a					)
  
  
  
  def 						__a      (  a,		a,		a					)     ->   Dict:
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         model.eval()
         _a          =				next(model.parameters()					).device
         _a          =				1
         _a          =				torch.zeros((eval_batch_size, context_len),		dtype=torch.long,		device=a					)
         _a          =				DataLoader(a,		shuffle=a,		batch_size=a					)
         _a          =				torch.zeros(1,		device=a					)
         _a          =				0
         for batch in eval_dataloader:
                batch.to(a					)
                # pad
                context.zero_()
                for i in range(a					):
                       _a          =				batch[i]
                _a          =				model(a,		labels=a					)
                eval_loss += outputs[0].sum().item()
                nb_eval_examples += batch.size(0					)
         _a          =				eval_loss / nb_eval_examples
         _a          =				torch.exp(a					)
         model.train()
         return perplexity
  
  
  
  def 						__a      (  a="gpt2"					)     ->   int:
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				GPTaLMHeadModel.from_pretrained(a,		output_hidden_states=a					)
         torch.save(model.state_dict(),		model_name + "local.pt"					)
         return model
  
  
  
  def 						__a      (  a,		a,		a					)     ->   List[Any]:
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				copy.deepcopy(a					)
         model.to(a					)
  
         _a          =				["bias", "LayerNorm.weight"]
         _a          =				[
             {
                 "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},
         ]
         _a          =				AdamW(a,		lr=5e-5,		eps=1e-8					)
         _a          =				get_linear_schedule_with_warmup(a,		0,		a					)
         torch.cuda.empty_cache()
         return model, lm_optimizer, lm_scheduler
  
  
  
  def 						__a      (  a,		a,		a,		a					)     ->   str:
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				np.array(a					).mean()
         _a          =				np.array(a					).std()
         _a          =				(real_perps - avg) / std
         _a          =				list(zip(a,		a,		a					)					)
         joblib.dump(a,		a					)
  
  
  
  def 						__a      (  a,		a,		a,		a,		a,		a,		a,		a="dev.jbl",		a=recopy_gpta,		)     ->   List[str]:
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				[]
         _a          =				[]
         _a          =				[]
  
         # Initialize the transformer model
         _a          =				copy.deepcopy(a					)
         orig_model.to(device="cpu"					)
         torch.cuda.empty_cache()
  
         # Compute perplexity of initial transformer model for comparison
         model.train()
         _a   , _a   , _a          =				recopy_model(a,		a,		a					)
  
         for step in tqdm(range(a					)					):
                _a          =				torch.zeros((1, context_len),		dtype=torch.long,		device=a					)
                _a          =				random.choice(a					)
                _a          =				random.randint(0,		len(story[0]					) - context_len - 1					)
                _a          =				story[0][start : start + context_len]
                lm_optimizer.zero_grad()
                _a          =				model(a,		labels=a					)
                _a          =				outputs[0]
                _a          =				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
                _a          =				compute_perplexity(a,		a,		a					)
  
                # Periodically save the stored (X, IG(X)) pairs
                if step % 1_0_0_0 == 0 and step > 1:
                       intermittent_save(a,		a,		a,		a					)
  
                # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
                _a   , _a   , _a          =				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_0_0,		a=1_0_2_6,		a=True					)     ->   Optional[int]:
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				joblib.load(a					)
         print("data loaded"					)
         _a          =				[]
         if trim:
                for i, example in enumerate(a					):
                       if len(example[0]					) > min_len:
                              _a          =				random.randint(0,		len(example[0]					) - context_len - 1					)
                              objective_set.append(example[0, start : start + context_len]					)
                       if len(a					) >= number:
                              break
                _a          =				[]
                for j in range(i + 1,		len(a					)					):
                       if len(data[j][0]					) > min_len:
                              train_data.append(data[j]					)
         else:
                _a          =				data[0:number]
                _a          =				data[number:]
  
         joblib.dump(a,		"objective_set.jbl"					)
         print("objective set saved"					)
         return train_data, objective_set
  
  
  
  def 						__a      (  a,		a,		a,		a,		a=5_0,		a="secondary_learner.pt"					)     ->   Optional[int]:
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				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
         _a          =				train_dataset[:5_1_2]
         _a          =				train_dataset[5_1_2:]
         _a          =				DataLoader(a,		shuffle=a,		batch_size=a					)
         _a          =				DataLoader(a,		shuffle=a,		batch_size=a					)
  
         # secondary learner model set up
         _a          =				nn.MSELoss()
         _a          =				nn.MSELoss(reduction="sum"					)
         secondary_learner.to(a					)
         _a          =				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
         _a          =				float("inf"					)
         # Iterate through batches until we've used max_steps batches
         for epoch in range(int(a					)					):
                _a          =				0.0
                secondary_learner.train()
                for step, batch in enumerate(a					):
                       _a          =				batch[0].to(a					)
                       _a          =				batch[1].to(a					)
                       _a          =				secondary_learner(a					)
                       q_optimizer.zero_grad()
                       _a          =				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					)):
                              _a          =				tr_q_loss / (step + 1)
  
                              secondary_learner.eval()
                              _a          =				0.0
                              _a          =				0.0
                              _a          =				[]
                              _a          =				[]
                              # Compute performance of the secondary learner after this batch
                              for stepa, batcha in enumerate(a					):
                                     _a          =				batcha[0].to(a					)
                                     _a          =				batcha[1].to(a					)
                                     _a          =				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					)
                                     _a          =				q_lossa
  
                       secondary_learner.train()
         return secondary_learner
  
  
  
  
  
  class __snake_case		(					nn.Module      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:List[Any]				,  UpperCamelCase__							:Optional[Any]       ):
            super(UpperCamelCase__				,  self       ).__init__()
            _a          =				model.transformer.wte
            _a          =				copy.deepcopy(model.transformer.wte.weight       )
            _a          =				nn.Convad(self.embeddings.weight.size(1       )				,  256				,  3				,  padding=1       )
            _a          =				nn.Sequential(nn.Linear(256				,  32       )				,  nn.Dropout(p=0.1       )				,  nn.Linear(32				,  32       )				,  nn.Linear(32				,  1       )       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Optional[Any]				,  UpperCamelCase__							:str       ):
            _a          =				torch.max(self.conv(self.embeddings(UpperCamelCase__       ).squeeze(1       ).transpose(1				,  2       )       )				,  2       )[0]
            _a          =				self.fc(UpperCamelCase__       )
            return qs.squeeze(1       )
  
  
  
  
     @classmethod
     def SCREAMING_SNAKE_CASE_      (		cls							:Optional[int]				,  UpperCamelCase__							:Optional[int]				,  UpperCamelCase__							:Dict       ):
            _a          =				cls(UpperCamelCase__       )  # this calls __init__
            _a          =				torch.load(UpperCamelCase__       )
            secondary_learner.load_state_dict(UpperCamelCase__       )
            _a          =				model.transformer.wte
            _a          =				copy.deepcopy(model.transformer.wte.weight       )
            return secondary_learner
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import warnings
  
  from ...utils import logging
  from .image_processing_vit import ViTImageProcessor
  
  
  __SCREAMING_SNAKE_CASE					=						logging.get_logger(__name__)
  
  
  
  
  
  class __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:Tuple				,  *UpperCamelCase__							:Tuple				,  **UpperCamelCase__							:Any       ):
            warnings.warn(
                "The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
                " use ViTImageProcessor instead."				,  UpperCamelCase__				,  )
            super().__init__(*UpperCamelCase__				,  **UpperCamelCase__       )
  