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
  
  
  __lowercase:		Optional[int]							=     logging.getLogger(__name__)
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Union[str, Any]   )		->					Union[str, Any]:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     random.seed(_UpperCamelCase   )
     np.random.seed(_UpperCamelCase   )
     torch.manual_seed(_UpperCamelCase   )
     torch.cuda.manual_seed_all(_UpperCamelCase   )
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Any    ,		_UpperCamelCase      :				Union[str, Any]    ,		_UpperCamelCase      :				int   )		->					Any:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     model.eval()
     UpperCamelCase__ 						=						next(model.parameters()   ).device
     UpperCamelCase__ 						=						1
     UpperCamelCase__ 						=						torch.zeros((eval_batch_size, context_len)    ,		dtype=torch.long    ,		device=_UpperCamelCase   )
     UpperCamelCase__ 						=						DataLoader(_UpperCamelCase    ,		shuffle=_UpperCamelCase    ,		batch_size=_UpperCamelCase   )
     UpperCamelCase__ 						=						torch.zeros(1    ,		device=_UpperCamelCase   )
     UpperCamelCase__ 						=						0
     for batch in eval_dataloader:
        batch.to(_UpperCamelCase   )
        # pad
        context.zero_()
        for i in range(_UpperCamelCase   ):
           UpperCamelCase__ 						=						batch[i]
        UpperCamelCase__ 						=						model(_UpperCamelCase    ,		labels=_UpperCamelCase   )
        eval_loss += outputs[0].sum().item()
        nb_eval_examples += batch.size(0   )
     UpperCamelCase__ 						=						eval_loss / nb_eval_examples
     UpperCamelCase__ 						=						torch.exp(_UpperCamelCase   )
     model.train()
     return perplexity
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				int="gpt2"   )		->					str:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						GPTaLMHeadModel.from_pretrained(_UpperCamelCase    ,		output_hidden_states=_UpperCamelCase   )
     torch.save(model.state_dict()    ,		model_name + "local.pt"   )
     return model
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				int    ,		_UpperCamelCase      :				Dict    ,		_UpperCamelCase      :				str   )		->					int:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						copy.deepcopy(_UpperCamelCase   )
     model.to(_UpperCamelCase   )
  
     UpperCamelCase__ 						=						["bias", "LayerNorm.weight"]
     UpperCamelCase__ 						=						[
         {
             "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},
     ]
     UpperCamelCase__ 						=						AdamW(_UpperCamelCase    ,		lr=5e-5    ,		eps=1e-8   )
     UpperCamelCase__ 						=						get_linear_schedule_with_warmup(_UpperCamelCase    ,		0    ,		_UpperCamelCase   )
     torch.cuda.empty_cache()
     return model, lm_optimizer, lm_scheduler
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Optional[Any]    ,		_UpperCamelCase      :				Any    ,		_UpperCamelCase      :				Optional[int]    ,		_UpperCamelCase      :				int   )		->					List[str]:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						np.array(_UpperCamelCase   ).mean()
     UpperCamelCase__ 						=						np.array(_UpperCamelCase   ).std()
     UpperCamelCase__ 						=						(real_perps - avg) / std
     UpperCamelCase__ 						=						list(zip(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase   )   )
     joblib.dump(_UpperCamelCase    ,		_UpperCamelCase   )
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				str    ,		_UpperCamelCase      :				Union[str, Any]    ,		_UpperCamelCase      :				Any    ,		_UpperCamelCase      :				str    ,		_UpperCamelCase      :				List[str]    ,		_UpperCamelCase      :				int    ,		_UpperCamelCase      :				List[str]    ,		_UpperCamelCase      :				Optional[Any]="dev.jbl"    ,		_UpperCamelCase      :				int=recopy_gpta    ,		)		->					Any:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						[]
     UpperCamelCase__ 						=						[]
     UpperCamelCase__ 						=						[]
  
     # Initialize the transformer model
     UpperCamelCase__ 						=						copy.deepcopy(_UpperCamelCase   )
     orig_model.to(device="cpu"   )
     torch.cuda.empty_cache()
  
     # Compute perplexity of initial transformer model for comparison
     model.train()
     UpperCamelCase__						,		UpperCamelCase__						,		UpperCamelCase__ 						=						recopy_model(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase   )
  
     for step in tqdm(range(_UpperCamelCase   )   ):
        UpperCamelCase__ 						=						torch.zeros((1, context_len)    ,		dtype=torch.long    ,		device=_UpperCamelCase   )
        UpperCamelCase__ 						=						random.choice(_UpperCamelCase   )
        UpperCamelCase__ 						=						random.randint(0    ,		len(story[0]   ) - context_len - 1   )
        UpperCamelCase__ 						=						story[0][start : start + context_len]
        lm_optimizer.zero_grad()
        UpperCamelCase__ 						=						model(_UpperCamelCase    ,		labels=_UpperCamelCase   )
        UpperCamelCase__ 						=						outputs[0]
        UpperCamelCase__ 						=						compute_perplexity(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase   )
        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
        UpperCamelCase__ 						=						compute_perplexity(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase   )
  
        # Periodically save the stored (X, IG(X)) pairs
        if step % 10_00 == 0 and step > 1:
           intermittent_save(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase   )
  
        # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
        UpperCamelCase__						,		UpperCamelCase__						,		UpperCamelCase__ 						=						recopy_model(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase   )
  
        past_perps.append(past_perp.item()   )
        real_perps.append(orig_perp - real_perp.item()   )
        contexts.append(np.array(context.cpu()   )   )
  
     intermittent_save(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase   )
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				List[str]    ,		_UpperCamelCase      :				int="data/tokenized_stories_train_wikitext103.jbl"    ,		_UpperCamelCase      :				Dict=1_00    ,		_UpperCamelCase      :				Dict=10_26    ,		_UpperCamelCase      :				str=True   )		->					str:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						joblib.load(_UpperCamelCase   )
     print("data loaded"   )
     UpperCamelCase__ 						=						[]
     if trim:
        for i, example in enumerate(_UpperCamelCase   ):
           if len(example[0]   ) > min_len:
              UpperCamelCase__ 						=						random.randint(0    ,		len(example[0]   ) - context_len - 1   )
              objective_set.append(example[0, start : start + context_len]   )
           if len(_UpperCamelCase   ) >= number:
              break
        UpperCamelCase__ 						=						[]
        for j in range(i + 1    ,		len(_UpperCamelCase   )   ):
           if len(data[j][0]   ) > min_len:
              train_data.append(data[j]   )
     else:
        UpperCamelCase__ 						=						data[0:number]
        UpperCamelCase__ 						=						data[number:]
  
     joblib.dump(_UpperCamelCase    ,		"objective_set.jbl"   )
     print("objective set saved"   )
     return train_data, objective_set
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Tuple    ,		_UpperCamelCase      :				Optional[Any]    ,		_UpperCamelCase      :				int    ,		_UpperCamelCase      :				List[str]    ,		_UpperCamelCase      :				Union[str, Any]=50    ,		_UpperCamelCase      :				int="secondary_learner.pt"   )		->					List[str]:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						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
     UpperCamelCase__ 						=						train_dataset[:5_12]
     UpperCamelCase__ 						=						train_dataset[5_12:]
     UpperCamelCase__ 						=						DataLoader(_UpperCamelCase    ,		shuffle=_UpperCamelCase    ,		batch_size=_UpperCamelCase   )
     UpperCamelCase__ 						=						DataLoader(_UpperCamelCase    ,		shuffle=_UpperCamelCase    ,		batch_size=_UpperCamelCase   )
  
     # secondary learner model set up
     UpperCamelCase__ 						=						nn.MSELoss()
     UpperCamelCase__ 						=						nn.MSELoss(reduction="sum"   )
     secondary_learner.to(_UpperCamelCase   )
     UpperCamelCase__ 						=						torch.optim.Adam(secondary_learner.parameters()    ,		lr=0.0_0_0_0_1   )
     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
     UpperCamelCase__ 						=						float("inf"   )
     # Iterate through batches until we've used max_steps batches
     for epoch in range(int(_UpperCamelCase   )   ):
        UpperCamelCase__ 						=						0.0
        secondary_learner.train()
        for step, batch in enumerate(_UpperCamelCase   ):
           UpperCamelCase__ 						=						batch[0].to(_UpperCamelCase   )
           UpperCamelCase__ 						=						batch[1].to(_UpperCamelCase   )
           UpperCamelCase__ 						=						secondary_learner(_UpperCamelCase   )
           q_optimizer.zero_grad()
           UpperCamelCase__ 						=						loss(_UpperCamelCase    ,		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(_UpperCamelCase   )):
              UpperCamelCase__ 						=						tr_q_loss / (step + 1)
  
              secondary_learner.eval()
              UpperCamelCase__ 						=						0.0
              UpperCamelCase__ 						=						0.0
              UpperCamelCase__ 						=						[]
              UpperCamelCase__ 						=						[]
              # Compute performance of the secondary learner after this batch
              for stepa, batcha in enumerate(_UpperCamelCase   ):
                 UpperCamelCase__ 						=						batcha[0].to(_UpperCamelCase   )
                 UpperCamelCase__ 						=						batcha[1].to(_UpperCamelCase   )
                 UpperCamelCase__ 						=						secondary_learner(_UpperCamelCase   )
                 q_lossa += test_loss(_UpperCamelCase    ,		_UpperCamelCase   ).item()
                 sum_qa += torch.sum(_UpperCamelCase   ).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(_UpperCamelCase   )
              print(
                  "Epoch: "    ,		_UpperCamelCase    ,		"step: "    ,		_UpperCamelCase    ,		"Avg. q:"    ,		sum_qa / len(_UpperCamelCase   )    ,		"Train Loss: "    ,		_UpperCamelCase    ,		"Test Loss: "    ,		_UpperCamelCase    ,		)
              if q_lossa < best_test_loss:
                 joblib.dump((predicted, actual)    ,		"pred_vs_actual.jbl"   )
                 torch.save(secondary_learner.state_dict()    ,		_UpperCamelCase   )
                 UpperCamelCase__ 						=						q_lossa
  
           secondary_learner.train()
     return secondary_learner
  
  
  
  
  
  
  
  class  UpperCAmelCase (       nn.Module):
  
   def __init__( self :	int,					a_ :	Dict					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      super(a_,					self					).__init__()
      UpperCamelCase__ 						=						model.transformer.wte
      UpperCamelCase__ 						=						copy.deepcopy(model.transformer.wte.weight					)
      UpperCamelCase__ 						=						nn.Convad(self.embeddings.weight.size(1					),					256,					3,					padding=1					)
      UpperCamelCase__ 						=						nn.Sequential(nn.Linear(256,					32					),					nn.Dropout(p=0.1					),					nn.Linear(32,					32					),					nn.Linear(32,					1					)					)
  
   def 		lowercase_						( self :	Optional[Any],					a_ :	Optional[int]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						torch.max(self.conv(self.embeddings(a_					).squeeze(1					).transpose(1,					2					)					),					2					)[0]
      UpperCamelCase__ 						=						self.fc(a_					)
      return qs.squeeze(1					)
  
   @classmethod
   def 		lowercase_						( cls :	Tuple,					a_ :	List[str],					a_ :	List[str]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						cls(a_					)  # this calls __init__
      UpperCamelCase__ 						=						torch.load(a_					)
      secondary_learner.load_state_dict(a_					)
      UpperCamelCase__ 						=						model.transformer.wte
      UpperCamelCase__ 						=						copy.deepcopy(model.transformer.wte.weight					)
      return secondary_learner
style_context: 
  
  
  '''simple docstring'''
  
  from typing import List
  
  from ...processing_utils import ProcessorMixin
  from ...utils import is_torch_available
  
  
  if is_torch_available():
         import torch
  
  
  
  
  
  
  
  class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):
   _lowerCamelCase :					Tuple    					=					['image_processor', 'tokenizer']
   _lowerCamelCase :					Tuple    					=					'OneFormerImageProcessor'
   _lowerCamelCase :					List[Any]    					=					('CLIPTokenizer', 'CLIPTokenizerFast')
  
   def __init__( self :	Any,					a_ :	Dict=None,					a_ :	int=None,					a_ :	int = 77,					a_ :	int = 77,					**a_ :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if image_processor is None:
         raise ValueError("You need to specify an `image_processor`."					)
      if tokenizer is None:
         raise ValueError("You need to specify a `tokenizer`."					)
  
      UpperCamelCase__ 						=						max_seq_length
      UpperCamelCase__ 						=						task_seq_length
  
      super().__init__(a_,					a_					)
  
   def 		lowercase_						( self :	Any,					a_ :	Optional[Any]=None,					a_ :	Optional[int]=77					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if text_list is None:
         raise ValueError("tokens cannot be None."					)
  
      UpperCamelCase__ 						=						self.tokenizer(a_,					padding="max_length",					max_length=a_,					truncation=a_					)
  
      UpperCamelCase__						,		UpperCamelCase__ 						=						tokens["attention_mask"], tokens["input_ids"]
  
      UpperCamelCase__ 						=						[]
      for attn_mask, input_id in zip(a_,					a_					):
         UpperCamelCase__ 						=						torch.tensor(a_					) * torch.tensor(a_					)
         token_inputs.append(token.unsqueeze(0					)					)
  
      UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)
      return token_inputs
  
   def __call__( self :	str,					a_ :	Dict=None,					a_ :	str=None,					a_ :	Tuple=None,					**a_ :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if task_inputs is None:
         raise ValueError("You have to specify the task_input. Found None."					)
      elif images is None:
         raise ValueError("You have to specify the image. Found None."					)
  
      if not all(task in ["semantic", "instance", "panoptic"] for task in task_inputs					):
         raise ValueError("task_inputs must be semantic, instance, or panoptic."					)
  
      UpperCamelCase__ 						=						self.image_processor(a_,					a_,					a_,					**a_					)
  
      if isinstance(a_,					a_					):
         UpperCamelCase__ 						=						[task_inputs]
  
      if isinstance(a_,					a_					) and all(isinstance(a_,					a_					) for task_input in task_inputs					):
         UpperCamelCase__ 						=						[]
         for task in task_inputs:
            UpperCamelCase__ 						=						f'the task is {task}'
            task_token_inputs.append(a_					)
         UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.task_seq_length					)
      else:
         raise TypeError("Task Inputs should be a string or a list of strings."					)
  
      if hasattr(a_,					"text_inputs"					):
         UpperCamelCase__ 						=						encoded_inputs.text_inputs
  
         UpperCamelCase__ 						=						[]
         for texts in texts_list:
            UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.max_seq_length					)
            text_inputs.append(text_input_list.unsqueeze(0					)					)
  
         UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)
  
      return encoded_inputs
  
   def 		lowercase_						( self :	Union[str, Any],					a_ :	Tuple=None,					a_ :	List[str]=None,					a_ :	Optional[Any]=None,					**a_ :	Tuple					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if task_inputs is None:
         raise ValueError("You have to specify the task_input. Found None."					)
      elif images is None:
         raise ValueError("You have to specify the image. Found None."					)
  
      if not all(task in ["semantic", "instance", "panoptic"] for task in task_inputs					):
         raise ValueError("task_inputs must be semantic, instance, or panoptic."					)
  
      UpperCamelCase__ 						=						self.image_processor.encode_inputs(a_,					a_,					a_,					**a_					)
  
      if isinstance(a_,					a_					):
         UpperCamelCase__ 						=						[task_inputs]
  
      if isinstance(a_,					a_					) and all(isinstance(a_,					a_					) for task_input in task_inputs					):
         UpperCamelCase__ 						=						[]
         for task in task_inputs:
            UpperCamelCase__ 						=						f'the task is {task}'
            task_token_inputs.append(a_					)
         UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.task_seq_length					)
      else:
         raise TypeError("Task Inputs should be a string or a list of strings."					)
  
      if hasattr(a_,					"text_inputs"					):
         UpperCamelCase__ 						=						encoded_inputs.text_inputs
  
         UpperCamelCase__ 						=						[]
         for texts in texts_list:
            UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.max_seq_length					)
            text_inputs.append(text_input_list.unsqueeze(0					)					)
  
         UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)
  
      return encoded_inputs
  
   def 		lowercase_						( self :	Any,					*a_ :	int,					**a_ :	Optional[int]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self.image_processor.post_process_semantic_segmentation(*a_,					**a_					)
  
   def 		lowercase_						( self :	str,					*a_ :	Tuple,					**a_ :	Optional[int]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self.image_processor.post_process_instance_segmentation(*a_,					**a_					)
  
   def 		lowercase_						( self :	Dict,					*a_ :	str,					**a_ :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self.image_processor.post_process_panoptic_segmentation(*a_,					**a_					)