code: 
  
  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
  
  
  snake_case_    						=				logging.getLogger(__name__)
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					List[str]    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       random.seed(SCREAMING_SNAKE_CASE_    )
       np.random.seed(SCREAMING_SNAKE_CASE_    )
       torch.manual_seed(SCREAMING_SNAKE_CASE_    )
       torch.cuda.manual_seed_all(SCREAMING_SNAKE_CASE_    )
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					int			,  SCREAMING_SNAKE_CASE_	:					Any			,  SCREAMING_SNAKE_CASE_	:					Optional[Any]    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       model.eval()
       lowercase__					:     List[Any]   = next(model.parameters()    ).device
       lowercase__					:     Any   = 1
       lowercase__					:     str   = torch.zeros((eval_batch_size, context_len)			,  dtype=torch.long			,  device=SCREAMING_SNAKE_CASE_    )
       lowercase__					:     Union[str, Any]   = DataLoader(SCREAMING_SNAKE_CASE_			,  shuffle=SCREAMING_SNAKE_CASE_			,  batch_size=SCREAMING_SNAKE_CASE_    )
       lowercase__					:     int   = torch.zeros(1			,  device=SCREAMING_SNAKE_CASE_    )
       lowercase__					:     Dict   = 0
       for batch in eval_dataloader:
            batch.to(SCREAMING_SNAKE_CASE_    )
            # pad
            context.zero_()
            for i in range(SCREAMING_SNAKE_CASE_    ):
                 lowercase__					:     str   = batch[i]
            lowercase__					:     Tuple   = model(SCREAMING_SNAKE_CASE_			,  labels=SCREAMING_SNAKE_CASE_    )
            eval_loss += outputs[0].sum().item()
            nb_eval_examples += batch.size(0    )
       lowercase__					:     int   = eval_loss / nb_eval_examples
       lowercase__					:     str   = torch.exp(SCREAMING_SNAKE_CASE_    )
       model.train()
       return perplexity
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Optional[Any]="gpt2"    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Tuple   = GPTaLMHeadModel.from_pretrained(SCREAMING_SNAKE_CASE_			,  output_hidden_states=SCREAMING_SNAKE_CASE_    )
       torch.save(model.state_dict()			,  model_name + 'local.pt'    )
       return model
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Optional[Any]			,  SCREAMING_SNAKE_CASE_	:					Optional[Any]			,  SCREAMING_SNAKE_CASE_	:					Tuple    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     str   = copy.deepcopy(SCREAMING_SNAKE_CASE_    )
       model.to(SCREAMING_SNAKE_CASE_    )
  
       lowercase__					:     List[Any]   = ['bias', 'LayerNorm.weight']
       lowercase__					:     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},
       ]
       lowercase__					:     List[Any]   = AdamW(SCREAMING_SNAKE_CASE_			,  lr=5E-5			,  eps=1E-8    )
       lowercase__					:     List[Any]   = get_linear_schedule_with_warmup(SCREAMING_SNAKE_CASE_			,  0			,  SCREAMING_SNAKE_CASE_    )
       torch.cuda.empty_cache()
       return model, lm_optimizer, lm_scheduler
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Any			,  SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]			,  SCREAMING_SNAKE_CASE_	:					Optional[Any]    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Optional[Any]   = np.array(SCREAMING_SNAKE_CASE_    ).mean()
       lowercase__					:     int   = np.array(SCREAMING_SNAKE_CASE_    ).std()
       lowercase__					:     List[str]   = (real_perps - avg) / std
       lowercase__					:     Optional[Any]   = list(zip(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )    )
       joblib.dump(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Any			,  SCREAMING_SNAKE_CASE_	:					List[str]			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]			,  SCREAMING_SNAKE_CASE_	:					Optional[Any]			,  SCREAMING_SNAKE_CASE_	:					Any			,  SCREAMING_SNAKE_CASE_	:					int			,  SCREAMING_SNAKE_CASE_	:					Tuple			,  SCREAMING_SNAKE_CASE_	:					List[Any]="dev.jbl"			,  SCREAMING_SNAKE_CASE_	:					List[Any]=recopy_gpta			,  ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Optional[Any]   = []
       lowercase__					:     Union[str, Any]   = []
       lowercase__					:     Tuple   = []
  
       # Initialize the transformer model
       lowercase__					:     int   = copy.deepcopy(SCREAMING_SNAKE_CASE_    )
       orig_model.to(device='cpu'    )
       torch.cuda.empty_cache()
  
       # Compute perplexity of initial transformer model for comparison
       model.train()
       lowercase__   ,				lowercase__   ,				lowercase__					:     Dict   = recopy_model(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
  
       for step in tqdm(range(SCREAMING_SNAKE_CASE_    )    ):
            lowercase__					:     str   = torch.zeros((1, context_len)			,  dtype=torch.long			,  device=SCREAMING_SNAKE_CASE_    )
            lowercase__					:     int   = random.choice(SCREAMING_SNAKE_CASE_    )
            lowercase__					:     Optional[int]   = random.randint(0			,  len(story[0]    ) - context_len - 1    )
            lowercase__					:     Optional[int]   = story[0][start : start + context_len]
            lm_optimizer.zero_grad()
            lowercase__					:     Tuple   = model(SCREAMING_SNAKE_CASE_			,  labels=SCREAMING_SNAKE_CASE_    )
            lowercase__					:     Tuple   = outputs[0]
            lowercase__					:     Optional[int]   = compute_perplexity(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
            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
            lowercase__					:     Union[str, Any]   = compute_perplexity(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
  
            # Periodically save the stored (X, IG(X)) pairs
            if step % 1_000 == 0 and step > 1:
                 intermittent_save(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
  
            # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
            lowercase__   ,				lowercase__   ,				lowercase__					:     Optional[Any]   = recopy_model(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
  
            past_perps.append(past_perp.item()    )
            real_perps.append(orig_perp - real_perp.item()    )
            contexts.append(np.array(context.cpu()    )    )
  
       intermittent_save(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					List[str]="data/tokenized_stories_train_wikitext103.jbl"			,  SCREAMING_SNAKE_CASE_	:					List[Any]=100			,  SCREAMING_SNAKE_CASE_	:					str=1_026			,  SCREAMING_SNAKE_CASE_	:					int=True    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     int   = joblib.load(SCREAMING_SNAKE_CASE_    )
       print('data loaded'    )
       lowercase__					:     Optional[Any]   = []
       if trim:
            for i, example in enumerate(SCREAMING_SNAKE_CASE_    ):
                 if len(example[0]    ) > min_len:
                      lowercase__					:     List[str]   = random.randint(0			,  len(example[0]    ) - context_len - 1    )
                      objective_set.append(example[0, start : start + context_len]    )
                 if len(SCREAMING_SNAKE_CASE_    ) >= number:
                      break
            lowercase__					:     Optional[int]   = []
            for j in range(i + 1			,  len(SCREAMING_SNAKE_CASE_    )    ):
                 if len(data[j][0]    ) > min_len:
                      train_data.append(data[j]    )
       else:
            lowercase__					:     Union[str, Any]   = data[0:number]
            lowercase__					:     int   = data[number:]
  
       joblib.dump(SCREAMING_SNAKE_CASE_			,  'objective_set.jbl'    )
       print('objective set saved'    )
       return train_data, objective_set
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Tuple			,  SCREAMING_SNAKE_CASE_	:					List[str]			,  SCREAMING_SNAKE_CASE_	:					Tuple			,  SCREAMING_SNAKE_CASE_	:					Tuple			,  SCREAMING_SNAKE_CASE_	:					Optional[Any]=50			,  SCREAMING_SNAKE_CASE_	:					List[str]="secondary_learner.pt"    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     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
       lowercase__					:     List[Any]   = train_dataset[:512]
       lowercase__					:     str   = train_dataset[512:]
       lowercase__					:     int   = DataLoader(SCREAMING_SNAKE_CASE_			,  shuffle=SCREAMING_SNAKE_CASE_			,  batch_size=SCREAMING_SNAKE_CASE_    )
       lowercase__					:     Optional[int]   = DataLoader(SCREAMING_SNAKE_CASE_			,  shuffle=SCREAMING_SNAKE_CASE_			,  batch_size=SCREAMING_SNAKE_CASE_    )
  
       # secondary learner model set up
       lowercase__					:     Union[str, Any]   = nn.MSELoss()
       lowercase__					:     List[str]   = nn.MSELoss(reduction='sum'    )
       secondary_learner.to(SCREAMING_SNAKE_CASE_    )
       lowercase__					:     Tuple   = 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
       lowercase__					:     List[str]   = float('inf'    )
       # Iterate through batches until we've used max_steps batches
       for epoch in range(int(SCREAMING_SNAKE_CASE_    )    ):
            lowercase__					:     Optional[Any]   = 0.0
            secondary_learner.train()
            for step, batch in enumerate(SCREAMING_SNAKE_CASE_    ):
                 lowercase__					:     List[str]   = batch[0].to(SCREAMING_SNAKE_CASE_    )
                 lowercase__					:     List[str]   = batch[1].to(SCREAMING_SNAKE_CASE_    )
                 lowercase__					:     List[str]   = secondary_learner(SCREAMING_SNAKE_CASE_    )
                 q_optimizer.zero_grad()
                 lowercase__					:     List[Any]   = loss(SCREAMING_SNAKE_CASE_			,  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(SCREAMING_SNAKE_CASE_    )):
                      lowercase__					:     Tuple   = tr_q_loss / (step + 1)
  
                      secondary_learner.eval()
                      lowercase__					:     Any   = 0.0
                      lowercase__					:     Dict   = 0.0
                      lowercase__					:     List[Any]   = []
                      lowercase__					:     Optional[Any]   = []
                      # Compute performance of the secondary learner after this batch
                      for stepa, batcha in enumerate(SCREAMING_SNAKE_CASE_    ):
                           lowercase__					:     List[str]   = batcha[0].to(SCREAMING_SNAKE_CASE_    )
                           lowercase__					:     List[str]   = batcha[1].to(SCREAMING_SNAKE_CASE_    )
                           lowercase__					:     str   = secondary_learner(SCREAMING_SNAKE_CASE_    )
                           q_lossa += test_loss(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    ).item()
                           sum_qa += torch.sum(SCREAMING_SNAKE_CASE_    ).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(SCREAMING_SNAKE_CASE_    )
                      print(
                          'Epoch: '			,  SCREAMING_SNAKE_CASE_			,  'step: '			,  SCREAMING_SNAKE_CASE_			,  'Avg. q:'			,  sum_qa / len(SCREAMING_SNAKE_CASE_    )			,  'Train Loss: '			,  SCREAMING_SNAKE_CASE_			,  'Test Loss: '			,  SCREAMING_SNAKE_CASE_			,  )
                      if q_lossa < best_test_loss:
                           joblib.dump((predicted, actual)			,  'pred_vs_actual.jbl'    )
                           torch.save(secondary_learner.state_dict()			,  SCREAMING_SNAKE_CASE_    )
                           lowercase__					:     List[Any]   = q_lossa
  
                 secondary_learner.train()
       return secondary_learner
  
  
  class    SCREAMING_SNAKE_CASE__      (nn.Module  ):
  
  
  
  
  
      def __init__(   self		,     a):
           super(a		,     self).__init__()
           lowercase__					:     str   = model.transformer.wte
           lowercase__					:     str   = copy.deepcopy(model.transformer.wte.weight)
           lowercase__					:     Dict   = nn.Convad(self.embeddings.weight.size(1)		,     256		,     3		,     padding=1)
           lowercase__					:     Any   = nn.Sequential(nn.Linear(256		,     32)		,     nn.Dropout(p=0.1)		,     nn.Linear(32		,     32)		,     nn.Linear(32		,     1))
  
  
  
  
  
      def    snake_case_       (   self		,     a):
           lowercase__					:     Dict   = torch.max(self.conv(self.embeddings(a).squeeze(1).transpose(1		,     2))		,     2)[0]
           lowercase__					:     List[Any]   = self.fc(a)
           return qs.squeeze(1)
  
  
  
  
  
      @classmethod
      def    snake_case_       (   cls		,     a		,     a):
           lowercase__					:     int   = cls(a)  # this calls __init__
           lowercase__					:     Dict   = torch.load(a)
           secondary_learner.load_state_dict(a)
           lowercase__					:     Tuple   = model.transformer.wte
           lowercase__					:     Dict   = copy.deepcopy(model.transformer.wte.weight)
           return secondary_learner
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  snake_case_    						=				{
      '''configuration_donut_swin''': ['''DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DonutSwinConfig'''],
      '''processing_donut''': ['''DonutProcessor'''],
  }
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''DonutSwinModel''',
             '''DonutSwinPreTrainedModel''',
         ]
  
  try:
         if not is_vision_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				['''DonutFeatureExtractor''']
         snake_case_    						=				['''DonutImageProcessor''']
  
  
  if TYPE_CHECKING:
         from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
         from .processing_donut import DonutProcessor
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_donut_swin import (
                    DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
                    DonutSwinModel,
                    DonutSwinPreTrainedModel,
                )
  
         try:
                if not is_vision_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .feature_extraction_donut import DonutFeatureExtractor
                from .image_processing_donut import DonutImageProcessor
  
  else:
         import sys
  
         snake_case_    						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  