


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  _lowerCamelCase(			lowercase__			)		->			Tuple:

  '''simple docstring'''







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







def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__			)		->			List[str]:

  '''simple docstring'''







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







def  _lowerCamelCase(			lowercase__="gpt2"			)		->			str:

  '''simple docstring'''







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







def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__			)		->			List[str]:

  '''simple docstring'''







  __lowercase=    copy.deepcopy(lowercase__			)
  model.to(lowercase__			)

  __lowercase=    ['bias', 'LayerNorm.weight']
  __lowercase=    [
      {
          '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=    AdamW(lowercase__     ,				lr=5E-5     ,				eps=1E-8			)
  __lowercase=    get_linear_schedule_with_warmup(lowercase__     ,				0     ,				lowercase__			)
  torch.cuda.empty_cache()
  return model, lm_optimizer, lm_scheduler







def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__			)		->			Optional[Any]:

  '''simple docstring'''







  __lowercase=    np.array(lowercase__			).mean()
  __lowercase=    np.array(lowercase__			).std()
  __lowercase=    (real_perps - avg) / std
  __lowercase=    list(zip(lowercase__     ,				lowercase__     ,				lowercase__			)			)
  joblib.dump(lowercase__     ,				lowercase__			)







def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__="dev.jbl"     ,				lowercase__=recopy_gpta     ,				)		->			int:

  '''simple docstring'''







  __lowercase=    []
  __lowercase=    []
  __lowercase=    []

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

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

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

    # Periodically save the stored (X, IG(X)) pairs
    if step % 1_0_0_0 == 0 and step > 1:
      intermittent_save(lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__			)

    # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
    __lowercase,							__lowercase,							__lowercase=    recopy_model(lowercase__     ,				lowercase__     ,				lowercase__			)

    past_perps.append(past_perp.item()			)
    real_perps.append(orig_perp - real_perp.item()			)
    contexts.append(np.array(context.cpu()			)			)

  intermittent_save(lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__			)







def  _lowerCamelCase(			lowercase__     ,				lowercase__="data/tokenized_stories_train_wikitext103.jbl"     ,				lowercase__=1_0_0     ,				lowercase__=1_0_2_6     ,				lowercase__=True			)		->			str:

  '''simple docstring'''







  __lowercase=    joblib.load(lowercase__			)
  print('data loaded'			)
  __lowercase=    []
  if trim:
    for i, example in enumerate(lowercase__			):
      if len(example[0]			) > min_len:
        __lowercase=    random.randint(0     ,				len(example[0]			) - context_len - 1			)
        objective_set.append(example[0, start : start + context_len]			)
      if len(lowercase__			) >= number:
        break
    __lowercase=    []
    for j in range(i + 1     ,				len(lowercase__			)			):
      if len(data[j][0]			) > min_len:
        train_data.append(data[j]			)
  else:
    __lowercase=    data[0:number]
    __lowercase=    data[number:]

  joblib.dump(lowercase__     ,				'objective_set.jbl'			)
  print('objective set saved'			)
  return train_data, objective_set







def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__=5_0     ,				lowercase__="secondary_learner.pt"			)		->			Tuple:

  '''simple docstring'''







  __lowercase=    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=    train_dataset[:5_1_2]
  __lowercase=    train_dataset[5_1_2:]
  __lowercase=    DataLoader(lowercase__     ,				shuffle=lowercase__     ,				batch_size=lowercase__			)
  __lowercase=    DataLoader(lowercase__     ,				shuffle=lowercase__     ,				batch_size=lowercase__			)

  # secondary learner model set up
  __lowercase=    nn.MSELoss()
  __lowercase=    nn.MSELoss(reduction='sum'			)
  secondary_learner.to(lowercase__			)
  __lowercase=    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=    float('inf'			)
  # Iterate through batches until we've used max_steps batches
  for epoch in range(int(lowercase__			)			):
    __lowercase=    0.0
    secondary_learner.train()
    for step, batch in enumerate(lowercase__			):
      __lowercase=    batch[0].to(lowercase__			)
      __lowercase=    batch[1].to(lowercase__			)
      __lowercase=    secondary_learner(lowercase__			)
      q_optimizer.zero_grad()
      __lowercase=    loss(lowercase__     ,				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(lowercase__			)):
        __lowercase=    tr_q_loss / (step + 1)

        secondary_learner.eval()
        __lowercase=    0.0
        __lowercase=    0.0
        __lowercase=    []
        __lowercase=    []
        # Compute performance of the secondary learner after this batch
        for stepa, batcha in enumerate(lowercase__			):
          __lowercase=    batcha[0].to(lowercase__			)
          __lowercase=    batcha[1].to(lowercase__			)
          __lowercase=    secondary_learner(lowercase__			)
          q_lossa += test_loss(lowercase__     ,				lowercase__			).item()
          sum_qa += torch.sum(lowercase__			).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(lowercase__			)
        print(
            'Epoch: '     ,				lowercase__     ,				'step: '     ,				lowercase__     ,				'Avg. q:'     ,				sum_qa / len(lowercase__			)     ,				'Train Loss: '     ,				lowercase__     ,				'Test Loss: '     ,				lowercase__     ,				)
        if q_lossa < best_test_loss:
          joblib.dump((predicted, actual)     ,				'pred_vs_actual.jbl'			)
          torch.save(secondary_learner.state_dict()     ,				lowercase__			)
          __lowercase=    q_lossa

      secondary_learner.train()
  return secondary_learner





class    A					(     nn.Module				):


       def __init__(self   ,       lowerCAmelCase      ):
         super(lowerCAmelCase   ,       self      ).__init__()
         __lowercase=    model.transformer.wte
         __lowercase=    copy.deepcopy(model.transformer.wte.weight      )
         __lowercase=    nn.Convad(self.embeddings.weight.size(1      )   ,       2_5_6   ,       3   ,       padding=1      )
         __lowercase=    nn.Sequential(nn.Linear(2_5_6   ,       3_2      )   ,       nn.Dropout(p=0.1      )   ,       nn.Linear(3_2   ,       3_2      )   ,       nn.Linear(3_2   ,       1      )      )


       def     _A (self   ,       lowerCAmelCase      ):
         __lowercase=    torch.max(self.conv(self.embeddings(lowerCAmelCase      ).squeeze(1      ).transpose(1   ,       2      )      )   ,       2      )[0]
         __lowercase=    self.fc(lowerCAmelCase      )
         return qs.squeeze(1      )







       @classmethod
       def     _A (cls   ,       lowerCAmelCase   ,       lowerCAmelCase      ):
         __lowercase=    cls(lowerCAmelCase      )  # this calls __init__
         __lowercase=    torch.load(lowerCAmelCase      )
         secondary_learner.load_state_dict(lowerCAmelCase      )
         __lowercase=    model.transformer.wte
         __lowercase=    copy.deepcopy(model.transformer.wte.weight      )
         return secondary_learner










from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase					 =	logging.get_logger(__name__)





class    A					(     A_				):
							UpperCamelCase_     :							Any =['''input_ids''', '''attention_mask''']


							def __init__(self   ,       lowerCAmelCase="[PAD]"   ,       lowerCAmelCase="[BOS]"   ,       lowerCAmelCase="[EOS]"   ,       lowerCAmelCase="[MASK]"   ,       lowerCAmelCase="[CLS]"   ,       lowerCAmelCase="[SEP]"   ,       lowerCAmelCase=2_0_4_8   ,       **lowerCAmelCase   ,       ):
									__lowercase=    AddedToken(lowerCAmelCase   ,       lstrip=lowerCAmelCase   ,       rstrip=lowerCAmelCase      ) if isinstance(lowerCAmelCase   ,       lowerCAmelCase      ) else pad_token
									__lowercase=    AddedToken(lowerCAmelCase   ,       lstrip=lowerCAmelCase   ,       rstrip=lowerCAmelCase      ) if isinstance(lowerCAmelCase   ,       lowerCAmelCase      ) else bos_token
									__lowercase=    AddedToken(lowerCAmelCase   ,       lstrip=lowerCAmelCase   ,       rstrip=lowerCAmelCase      ) if isinstance(lowerCAmelCase   ,       lowerCAmelCase      ) else eos_token
									__lowercase=    AddedToken(lowerCAmelCase   ,       lstrip=lowerCAmelCase   ,       rstrip=lowerCAmelCase      ) if isinstance(lowerCAmelCase   ,       lowerCAmelCase      ) else mask_token
									__lowercase=    AddedToken(lowerCAmelCase   ,       lstrip=lowerCAmelCase   ,       rstrip=lowerCAmelCase      ) if isinstance(lowerCAmelCase   ,       lowerCAmelCase      ) else cls_token
									__lowercase=    AddedToken(lowerCAmelCase   ,       lstrip=lowerCAmelCase   ,       rstrip=lowerCAmelCase      ) if isinstance(lowerCAmelCase   ,       lowerCAmelCase      ) else sep_token

									super().__init__(
									    pad_token=lowerCAmelCase   ,       bos_token=lowerCAmelCase   ,       eos_token=lowerCAmelCase   ,       mask_token=lowerCAmelCase   ,       cls_token=lowerCAmelCase   ,       sep_token=lowerCAmelCase   ,       model_max_length=lowerCAmelCase   ,       **lowerCAmelCase   ,       )

									__lowercase=    2**8  # utf is 8 bits

									# define special tokens dict
									__lowercase=    {
									    self.pad_token: 0,
									    self.bos_token: 1,
									    self.eos_token: 2,
									    self.mask_token: 3,
									    self.cls_token: 4,
									    self.sep_token: 5,
									}
									__lowercase=    len(self.special_tokens_encoder      )
									__lowercase=    {v: k for k, v in self.special_tokens_encoder.items()}


							def     _A (self      ):
									__lowercase=    self.special_tokens_encoder.copy()
									vocab.update(self.added_tokens_encoder      )
									for i in range(self._utf_vocab_size      ):
											__lowercase=    chr(lowerCAmelCase      )
											__lowercase=    i + len(self.special_tokens_encoder      )
									return vocab


							@property
							def     _A (self      ):
									return self._utf_vocab_size + self._num_special_tokens


							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase = None   ,       lowerCAmelCase = False      ):
									if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=lowerCAmelCase   ,       token_ids_a=lowerCAmelCase   ,       already_has_special_tokens=lowerCAmelCase      )

									# normal case: some special tokens
									if token_ids_a is None:
											return [1] + [0] * len(lowerCAmelCase      ) + [1]
									return [1] + ([0] * len(lowerCAmelCase      )) + [1] + ([0] * len(lowerCAmelCase      )) + [1]


							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase = None      ):
									if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									else:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]


							def     _A (self   ,       lowerCAmelCase      ):
									__lowercase=    [chr(lowerCAmelCase      ) for i in text.encode('utf-8'      )]
									return tokens


							def     _A (self   ,       lowerCAmelCase      ):
									if token in self.special_tokens_encoder:
											__lowercase=    self.special_tokens_encoder[token]
									elif token in self.added_tokens_encoder:
											__lowercase=    self.added_tokens_encoder[token]
									elif len(lowerCAmelCase      ) != 1:
											__lowercase=    self.unk_token_id
									else:
											__lowercase=    ord(lowerCAmelCase      ) + self._num_special_tokens
									return token_id


							def     _A (self   ,       lowerCAmelCase      ):
									if index in self.special_tokens_decoder:
											__lowercase=    self.special_tokens_decoder[index]
									elif index in self.added_tokens_decoder:
											__lowercase=    self.added_tokens_decoder[index]
									else:
											__lowercase=    chr(index - self._num_special_tokens      )
									return token


							def     _A (self   ,       lowerCAmelCase      ):
									__lowercase=    B''
									for token in tokens:
											if token in self.special_tokens_decoder:
													__lowercase=    self.special_tokens_decoder[token].encode('utf-8'      )
											elif token in self.added_tokens_decoder:
													__lowercase=    self.special_tokens_decoder[token].encode('utf-8'      )
											elif token in self.special_tokens_encoder:
													__lowercase=    token.encode('utf-8'      )
											elif token in self.added_tokens_encoder:
													__lowercase=    token.encode('utf-8'      )
											else:
													__lowercase=    bytes([ord(lowerCAmelCase      )]      )
											bstring += tok_string
									__lowercase=    bstring.decode('utf-8'   ,       errors='replace'      )
									return string







							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase = None      ):
									return ()






