

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


lowerCamelCase 	=   logging.get_logger(__name__)

lowerCamelCase 	=   {
    '''facebook/regnet-y-040''': '''https://huggingface.co/facebook/regnet-y-040/blob/main/config.json''',
}







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   lowerCamelCase__					:      int        =  'regnet'
   lowerCamelCase__					:      Optional[int]        =  ['x', 'y']



   def __init__(							self,       lowercase_=3,       lowercase_=32,       lowercase_=[128, 192, 512, 1088],       lowercase_=[2, 6, 12, 2],       lowercase_=64,       lowercase_="y",       lowercase_="relu",       **lowercase_,       )				->       List[Any]:






     """simple docstring"""






     super().__init__(**lowercase_			)
     if layer_type not in self.layer_types:
       raise ValueError(F"""layer_type={layer_type} is not one of {",".join(self.layer_types			)}"""			)
     a__    =num_channels
     a__    =embedding_size
     a__    =hidden_sizes
     a__    =depths
     a__    =groups_width
     a__    =layer_type
     a__    =hidden_act
     # always downsample in the first stage
     a__    =True



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 	=   logging.getLogger(__name__)




def      UpperCAmelCase__							(     _A :					Optional[Any]				):





  '''simple docstring'''







  random.seed(_A				)
  np.random.seed(_A				)
  torch.manual_seed(_A				)
  torch.cuda.manual_seed_all(_A				)




def      UpperCAmelCase__							(     _A :					Optional[Any]      , _A :					Tuple      , _A :					Union[str, Any]				):





  '''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      UpperCAmelCase__							(     _A :					Dict="gpt2"				):





  '''simple docstring'''







  a__    =GPTaLMHeadModel.from_pretrained(_A      , output_hidden_states=_A				)
  torch.save(model.state_dict()      , model_name + '''local.pt'''				)
  return model




def      UpperCAmelCase__							(     _A :					Dict      , _A :					Tuple      , _A :					Dict				):





  '''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      UpperCAmelCase__							(     _A :					List[str]      , _A :					Optional[Any]      , _A :					Tuple      , _A :					List[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      UpperCAmelCase__							(     _A :					Union[str, Any]      , _A :					Dict      , _A :					Optional[Any]      , _A :					Union[str, Any]      , _A :					Dict      , _A :					str      , _A :					List[Any]      , _A :					Optional[Any]="dev.jbl"      , _A :					Optional[int]=recopy_gpta      , ):





  '''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 % 10_00 == 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      UpperCAmelCase__							(     _A :					Any      , _A :					Optional[int]="data/tokenized_stories_train_wikitext103.jbl"      , _A :					Any=1_00      , _A :					Dict=10_26      , _A :					Any=True				):





  '''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      UpperCAmelCase__							(     _A :					str      , _A :					Dict      , _A :					Union[str, Any]      , _A :					Dict      , _A :					Dict=50      , _A :					int="secondary_learner.pt"				):





  '''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_12]
  a__    =train_dataset[5_12:]
  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=0.00_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(_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 		__magic_name__     (				nn.Module				):

   '''simple docstring'''




   def __init__(							self,       lowercase_			)				->       str:






     """simple docstring"""






     super(lowercase_,       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 					_UpperCAmelCase					(							self,       lowercase_			)				->       List[str]:






     """simple docstring"""






     a__    =torch.max(self.conv(self.embeddings(lowercase_			).squeeze(1			).transpose(1,       2			)			),       2			)[0]
     a__    =self.fc(lowercase_			)
     return qs.squeeze(1			)



   @classmethod
   def 					_UpperCAmelCase					(							cls,       lowercase_,       lowercase_			)				->       Union[str, Any]:






     """simple docstring"""






     a__    =cls(lowercase_			)  # this calls __init__
     a__    =torch.load(lowercase_			)
     secondary_learner.load_state_dict(lowercase_			)
     a__    =model.transformer.wte
     a__    =copy.deepcopy(model.transformer.wte.weight			)
     return secondary_learner
