




'''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


UpperCAmelCase_       				=      logging.getLogger(__name__)

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      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    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Any       ,						SCREAMING_SNAKE_CASE__			:      Optional[int]       ,						SCREAMING_SNAKE_CASE__			:      Any       ):
 '''simple docstring'''





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

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Union[str, Any]="gpt2"       ):
 '''simple docstring'''





 UpperCAmelCase__										=    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      (				SCREAMING_SNAKE_CASE__			:      Union[str, Any]       ,						SCREAMING_SNAKE_CASE__			:      Tuple       ,						SCREAMING_SNAKE_CASE__			:      Tuple       ):
 '''simple docstring'''





 UpperCAmelCase__										=    copy.deepcopy(SCREAMING_SNAKE_CASE__       )
 model.to(SCREAMING_SNAKE_CASE__       )

 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(SCREAMING_SNAKE_CASE__       ,						lr=5e-5       ,						eps=1e-8       )
 UpperCAmelCase__										=    get_linear_schedule_with_warmup(SCREAMING_SNAKE_CASE__       ,						0       ,						SCREAMING_SNAKE_CASE__       )
 torch.cuda.empty_cache()
 return model, lm_optimizer, lm_scheduler

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      str       ,						SCREAMING_SNAKE_CASE__			:      Optional[Any]       ,						SCREAMING_SNAKE_CASE__			:      List[Any]       ,						SCREAMING_SNAKE_CASE__			:      List[str]       ):
 '''simple docstring'''





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

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      str       ,						SCREAMING_SNAKE_CASE__			:      List[Any]       ,						SCREAMING_SNAKE_CASE__			:      Union[str, Any]       ,						SCREAMING_SNAKE_CASE__			:      Dict       ,						SCREAMING_SNAKE_CASE__			:      Dict       ,						SCREAMING_SNAKE_CASE__			:      Tuple       ,						SCREAMING_SNAKE_CASE__			:      List[str]       ,						SCREAMING_SNAKE_CASE__			:      Optional[int]="dev.jbl"       ,						SCREAMING_SNAKE_CASE__			:      Optional[Any]=recopy_gpta       ,						):
 '''simple docstring'''





 UpperCAmelCase__										=    []
 UpperCAmelCase__										=    []
 UpperCAmelCase__										=    []

 # Initialize the transformer model
 UpperCAmelCase__										=    copy.deepcopy(SCREAMING_SNAKE_CASE__       )
 orig_model.to(device="""cpu"""       )
 torch.cuda.empty_cache()

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

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

  # Periodically save the stored (X, IG(X)) pairs
  if step % 1000 == 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
  UpperCAmelCase__							,			UpperCAmelCase__							,			UpperCAmelCase__										=    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      (				SCREAMING_SNAKE_CASE__			:      List[str]       ,						SCREAMING_SNAKE_CASE__			:      int="data/tokenized_stories_train_wikitext103.jbl"       ,						SCREAMING_SNAKE_CASE__			:      str=100       ,						SCREAMING_SNAKE_CASE__			:      Union[str, Any]=1026       ,						SCREAMING_SNAKE_CASE__			:      str=True       ):
 '''simple docstring'''





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

 joblib.dump(SCREAMING_SNAKE_CASE__       ,						"""objective_set.jbl"""       )
 print("""objective set saved"""       )
 return train_data, objective_set

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Tuple       ,						SCREAMING_SNAKE_CASE__			:      Any       ,						SCREAMING_SNAKE_CASE__			:      Union[str, Any]       ,						SCREAMING_SNAKE_CASE__			:      List[str]       ,						SCREAMING_SNAKE_CASE__			:      List[str]=50       ,						SCREAMING_SNAKE_CASE__			:      Optional[Any]="secondary_learner.pt"       ):
 '''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[:512]
 UpperCAmelCase__										=    train_dataset[512:]
 UpperCAmelCase__										=    DataLoader(SCREAMING_SNAKE_CASE__       ,						shuffle=SCREAMING_SNAKE_CASE__       ,						batch_size=SCREAMING_SNAKE_CASE__       )
 UpperCAmelCase__										=    DataLoader(SCREAMING_SNAKE_CASE__       ,						shuffle=SCREAMING_SNAKE_CASE__       ,						batch_size=SCREAMING_SNAKE_CASE__       )

 # secondary learner model set up
 UpperCAmelCase__										=    nn.MSELoss()
 UpperCAmelCase__										=    nn.MSELoss(reduction="""sum"""       )
 secondary_learner.to(SCREAMING_SNAKE_CASE__       )
 UpperCAmelCase__										=    torch.optim.Adam(secondary_learner.parameters()       ,						lr=0.0_00_01       )
 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(SCREAMING_SNAKE_CASE__       )       ):
  UpperCAmelCase__										=    0.0
  secondary_learner.train()
  for step, batch in enumerate(SCREAMING_SNAKE_CASE__       ):
   UpperCAmelCase__										=    batch[0].to(SCREAMING_SNAKE_CASE__       )
   UpperCAmelCase__										=    batch[1].to(SCREAMING_SNAKE_CASE__       )
   UpperCAmelCase__										=    secondary_learner(SCREAMING_SNAKE_CASE__       )
   q_optimizer.zero_grad()
   UpperCAmelCase__										=    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__       )):
    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(SCREAMING_SNAKE_CASE__       ):
     UpperCAmelCase__										=    batcha[0].to(SCREAMING_SNAKE_CASE__       )
     UpperCAmelCase__										=    batcha[1].to(SCREAMING_SNAKE_CASE__       )
     UpperCAmelCase__										=    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__       )
     UpperCAmelCase__										=    q_lossa

   secondary_learner.train()
 return secondary_learner







class 						lowerCAmelCase_   ( nn.Module   ):







  '''simple docstring'''







  def __init__(						self    :	Optional[int]						,		_UpperCAmelCase    :	str ):






   """simple docstring"""






   super(_UpperCAmelCase						,		self ).__init__()
   UpperCAmelCase__										=    model.transformer.wte
   UpperCAmelCase__										=    copy.deepcopy(model.transformer.wte.weight )
   UpperCAmelCase__										=    nn.Convad(self.embeddings.weight.size(1 )						,		2_56						,		3						,		padding=1 )
   UpperCAmelCase__										=    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    :	Union[str, Any]						,		_UpperCAmelCase    :	Optional[int] ):






   """simple docstring"""






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



  @classmethod
  def 							SCREAMING_SNAKE_CASE__			(						cls    :	List[str]						,		_UpperCAmelCase    :	Dict						,		_UpperCAmelCase    :	Union[str, Any] ):






   """simple docstring"""






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






'''simple docstring'''




from __future__ import annotations

import random
import string

class 						lowerCAmelCase_   :







  '''simple docstring'''







  def __init__(						self    :	int						,		_UpperCAmelCase    :	str | None = None ):






   """simple docstring"""






   UpperCAmelCase__										=    passcode or self.__passcode_creator()
   UpperCAmelCase__										=    self.__make_key_list()
   UpperCAmelCase__										=    self.__make_shift_key()


  def __str__(						self    :	int ):






   """simple docstring"""






   return "".join(self.__passcode )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str]						,		_UpperCAmelCase    :	list[int] ):






   """simple docstring"""






   for i in range(1						,		len(_UpperCAmelCase )						,		2 ):
    iterlist[i] *= -1
   return iterlist


  def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






   UpperCAmelCase__										=    string.ascii_letters + string.digits
   UpperCAmelCase__										=    [random.choice(_UpperCAmelCase ) for _ in range(random.randint(10						,		20 ) )]
   return password


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple ):






   """simple docstring"""






   UpperCAmelCase__										=    (
       string.ascii_letters + string.digits + string.punctuation + """ \t\n"""
   )

   UpperCAmelCase__										=    []

   # creates points known as breakpoints to break the key_list_options at those
   # points and pivot each substring
   UpperCAmelCase__										=    sorted(set(self.__passcode ) )
   UpperCAmelCase__										=    []

   # algorithm for creating a new shuffled list, keys_l, out of key_list_options
   for i in key_list_options:
    temp_list.extend(_UpperCAmelCase )

    # checking breakpoints at which to pivot temporary sublist and add it into
    # keys_l
    if i in breakpoints or i == key_list_options[-1]:
     keys_l.extend(temp_list[::-1] )
     temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
   return keys_l


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple ):






   """simple docstring"""






   UpperCAmelCase__										=    sum(self.__neg_pos([ord(_UpperCAmelCase ) for x in self.__passcode] ) )
   return num if num > 0 else len(self.__passcode )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[Any]						,		_UpperCAmelCase    :	str ):






   """simple docstring"""






   UpperCAmelCase__										=    """"""

   # decoding shift like Caesar cipher algorithm implementing negative shift or
   # reverse shift or left shift
   for i in encoded_message:
    UpperCAmelCase__										=    self.__key_list.index(_UpperCAmelCase )
    decoded_message += self.__key_list[
        (position - self.__shift_key) % -len(self.__key_list )
    ]

   return decoded_message



  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str]						,		_UpperCAmelCase    :	str ):






   """simple docstring"""






   UpperCAmelCase__										=    """"""

   # encoding shift like Caesar cipher algorithm implementing positive shift or
   # forward shift or right shift
   for i in plaintext:
    UpperCAmelCase__										=    self.__key_list.index(_UpperCAmelCase )
    encoded_message += self.__key_list[
        (position + self.__shift_key) % len(self.__key_list )
    ]

   return encoded_message

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      str = "Hello, this is a modified Caesar cipher"       ):
 '''simple docstring'''





 UpperCAmelCase__										=    ShuffledShiftCipher()
 return cipa.decrypt(cipa.encrypt(SCREAMING_SNAKE_CASE__       )       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
