




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


_UpperCamelCase   :							int     =	logging.getLogger(__name__)
def 				__UpperCamelCase  (		snake_case     )  ->						Optional[Any]:


   '''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 				__UpperCamelCase  (		snake_case    ,	snake_case    ,	snake_case     )  ->						Optional[Any]:


   '''simple docstring'''







   model.eval()
   __A				 =       next(model.parameters()     ).device
   __A				 =       1
   __A				 =       torch.zeros((eval_batch_size, context_len)    ,	dtype=torch.long    ,	device=SCREAMING_SNAKE_CASE_     )
   __A				 =       DataLoader(SCREAMING_SNAKE_CASE_    ,	shuffle=SCREAMING_SNAKE_CASE_    ,	batch_size=SCREAMING_SNAKE_CASE_     )
   __A				 =       torch.zeros(1    ,	device=SCREAMING_SNAKE_CASE_     )
   __A				 =       0
   for batch in eval_dataloader:
      batch.to(SCREAMING_SNAKE_CASE_     )
      # pad
      context.zero_()
      for i in range(SCREAMING_SNAKE_CASE_     ):
         __A				 =       batch[i]
      __A				 =       model(SCREAMING_SNAKE_CASE_    ,	labels=SCREAMING_SNAKE_CASE_     )
      eval_loss += outputs[0].sum().item()
      nb_eval_examples += batch.size(0     )
   __A				 =       eval_loss / nb_eval_examples
   __A				 =       torch.exp(SCREAMING_SNAKE_CASE_     )
   model.train()
   return perplexity
def 				__UpperCamelCase  (		snake_case="gpt2"     )  ->						Dict:


   '''simple docstring'''







   __A				 =       GPTaLMHeadModel.from_pretrained(SCREAMING_SNAKE_CASE_    ,	output_hidden_states=SCREAMING_SNAKE_CASE_     )
   torch.save(model.state_dict()    ,	model_name + '''local.pt'''     )
   return model
def 				__UpperCamelCase  (		snake_case    ,	snake_case    ,	snake_case     )  ->						List[Any]:


   '''simple docstring'''







   __A				 =       copy.deepcopy(SCREAMING_SNAKE_CASE_     )
   model.to(SCREAMING_SNAKE_CASE_     )

   __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(SCREAMING_SNAKE_CASE_    ,	lr=5E-5    ,	eps=1E-8     )
   __A				 =       get_linear_schedule_with_warmup(SCREAMING_SNAKE_CASE_    ,	0    ,	SCREAMING_SNAKE_CASE_     )
   torch.cuda.empty_cache()
   return model, lm_optimizer, lm_scheduler
def 				__UpperCamelCase  (		snake_case    ,	snake_case    ,	snake_case    ,	snake_case     )  ->						int:


   '''simple docstring'''







   __A				 =       np.array(SCREAMING_SNAKE_CASE_     ).mean()
   __A				 =       np.array(SCREAMING_SNAKE_CASE_     ).std()
   __A				 =       (real_perps - avg) / std
   __A				 =       list(zip(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_     )     )
   joblib.dump(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_     )
def 				__UpperCamelCase  (		snake_case    ,	snake_case    ,	snake_case    ,	snake_case    ,	snake_case    ,	snake_case    ,	snake_case    ,	snake_case="dev.jbl"    ,	snake_case=recopy_gpta    ,	)  ->						Dict:


   '''simple docstring'''







   __A				 =       []
   __A				 =       []
   __A				 =       []

   # Initialize the transformer model
   __A				 =       copy.deepcopy(SCREAMING_SNAKE_CASE_     )
   orig_model.to(device='''cpu'''     )
   torch.cuda.empty_cache()

   # Compute perplexity of initial transformer model for comparison
   model.train()
   __A ,						__A ,						__A				 =       recopy_model(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_     )

   for step in tqdm(range(SCREAMING_SNAKE_CASE_     )     ):
      __A				 =       torch.zeros((1, context_len)    ,	dtype=torch.long    ,	device=SCREAMING_SNAKE_CASE_     )
      __A				 =       random.choice(SCREAMING_SNAKE_CASE_     )
      __A				 =       random.randint(0    ,	len(story[0]     ) - context_len - 1     )
      __A				 =       story[0][start : start + context_len]
      lm_optimizer.zero_grad()
      __A				 =       model(SCREAMING_SNAKE_CASE_    ,	labels=SCREAMING_SNAKE_CASE_     )
      __A				 =       outputs[0]
      __A				 =       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
      __A				 =       compute_perplexity(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_     )

      # Periodically save the stored (X, IG(X)) pairs
      if step % 1_0_0_0 == 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
      __A ,						__A ,						__A				 =       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 				__UpperCamelCase  (		snake_case    ,	snake_case="data/tokenized_stories_train_wikitext103.jbl"    ,	snake_case=1_0_0    ,	snake_case=1_0_2_6    ,	snake_case=True     )  ->						Optional[Any]:


   '''simple docstring'''







   __A				 =       joblib.load(SCREAMING_SNAKE_CASE_     )
   print('''data loaded'''     )
   __A				 =       []
   if trim:
      for i, example in enumerate(SCREAMING_SNAKE_CASE_     ):
         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(SCREAMING_SNAKE_CASE_     ) >= number:
            break
      __A				 =       []
      for j in range(i + 1    ,	len(SCREAMING_SNAKE_CASE_     )     ):
         if len(data[j][0]     ) > min_len:
            train_data.append(data[j]     )
   else:
      __A				 =       data[0:number]
      __A				 =       data[number:]

   joblib.dump(SCREAMING_SNAKE_CASE_    ,	'''objective_set.jbl'''     )
   print('''objective set saved'''     )
   return train_data, objective_set
def 				__UpperCamelCase  (		snake_case    ,	snake_case    ,	snake_case    ,	snake_case    ,	snake_case=5_0    ,	snake_case="secondary_learner.pt"     )  ->						Optional[Any]:


   '''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(SCREAMING_SNAKE_CASE_    ,	shuffle=SCREAMING_SNAKE_CASE_    ,	batch_size=SCREAMING_SNAKE_CASE_     )
   __A				 =       DataLoader(SCREAMING_SNAKE_CASE_    ,	shuffle=SCREAMING_SNAKE_CASE_    ,	batch_size=SCREAMING_SNAKE_CASE_     )

   # secondary learner model set up
   __A				 =       nn.MSELoss()
   __A				 =       nn.MSELoss(reduction='''sum'''     )
   secondary_learner.to(SCREAMING_SNAKE_CASE_     )
   __A				 =       torch.optim.Adam(secondary_learner.parameters()    ,	lr=0.00_001     )
   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(SCREAMING_SNAKE_CASE_     )     ):
      __A				 =       0.0
      secondary_learner.train()
      for step, batch in enumerate(SCREAMING_SNAKE_CASE_     ):
         __A				 =       batch[0].to(SCREAMING_SNAKE_CASE_     )
         __A				 =       batch[1].to(SCREAMING_SNAKE_CASE_     )
         __A				 =       secondary_learner(SCREAMING_SNAKE_CASE_     )
         q_optimizer.zero_grad()
         __A				 =       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_     )):
            __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(SCREAMING_SNAKE_CASE_     ):
               __A				 =       batcha[0].to(SCREAMING_SNAKE_CASE_     )
               __A				 =       batcha[1].to(SCREAMING_SNAKE_CASE_     )
               __A				 =       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_     )
               __A				 =       q_lossa

         secondary_learner.train()
   return secondary_learner







class 						_lowerCAmelCase(						nn.Module):

  """simple docstring"""







  def __init__( self  ,    UpperCAmelCase     )-> List[Any]:
     super(UpperCAmelCase  ,    self     ).__init__()
     __A				 =       model.transformer.wte
     __A				 =       copy.deepcopy(model.transformer.wte.weight     )
     __A				 =       nn.Convad(self.embeddings.weight.size(1     )  ,    2_56  ,    3  ,    padding=1     )
     __A				 =       nn.Sequential(nn.Linear(2_56  ,    32     )  ,    nn.Dropout(p=0.1     )  ,    nn.Linear(32  ,    32     )  ,    nn.Linear(32  ,    1     )     )

  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase     )-> List[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  ,    UpperCAmelCase  ,    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








from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase   :							Dict     =	logging.get_logger(__name__)

_UpperCamelCase   :							Optional[int]     =	{
    """MCG-NJU/videomae-base""": """https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json""",
}







class 						_lowerCAmelCase(						_a):

  """simple docstring"""







  lowerCamelCase__      					=   '''videomae'''

  def __init__( self  ,    UpperCAmelCase=2_24  ,    UpperCAmelCase=16  ,    UpperCAmelCase=3  ,    UpperCAmelCase=16  ,    UpperCAmelCase=2  ,    UpperCAmelCase=7_68  ,    UpperCAmelCase=12  ,    UpperCAmelCase=12  ,    UpperCAmelCase=30_72  ,    UpperCAmelCase="gelu"  ,    UpperCAmelCase=0.0  ,    UpperCAmelCase=0.0  ,    UpperCAmelCase=0.02  ,    UpperCAmelCase=1e-12  ,    UpperCAmelCase=True  ,    UpperCAmelCase=True  ,    UpperCAmelCase=6  ,    UpperCAmelCase=3_84  ,    UpperCAmelCase=4  ,    UpperCAmelCase=15_36  ,    UpperCAmelCase=True  ,    **UpperCAmelCase  ,    )-> Any:
     super().__init__(**UpperCAmelCase     )

     __A				 =       image_size
     __A				 =       patch_size
     __A				 =       num_channels
     __A				 =       num_frames
     __A				 =       tubelet_size

     __A				 =       hidden_size
     __A				 =       num_hidden_layers
     __A				 =       num_attention_heads
     __A				 =       intermediate_size
     __A				 =       hidden_act
     __A				 =       hidden_dropout_prob
     __A				 =       attention_probs_dropout_prob
     __A				 =       initializer_range
     __A				 =       layer_norm_eps
     __A				 =       qkv_bias
     __A				 =       use_mean_pooling

     __A				 =       decoder_num_attention_heads
     __A				 =       decoder_hidden_size
     __A				 =       decoder_num_hidden_layers
     __A				 =       decoder_intermediate_size
     __A				 =       norm_pix_loss

