





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       :				List[Any]           =			logging.getLogger(__name__)
def  __UpperCamelCase    (						snake_case      )    ->		str:


   '''simple docstring'''







   random.seed(snake_case      )
   np.random.seed(snake_case      )
   torch.manual_seed(snake_case      )
   torch.cuda.manual_seed_all(snake_case      )
def  __UpperCamelCase    (						snake_case							,			snake_case							,			snake_case      )    ->		int:


   '''simple docstring'''







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


   '''simple docstring'''







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


   '''simple docstring'''







   __A    =  copy.deepcopy(snake_case      )
   model.to(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(snake_case							,			lr=5E-5							,			eps=1E-8      )
   __A    =  get_linear_schedule_with_warmup(snake_case							,			0							,			snake_case      )
   torch.cuda.empty_cache()
   return model, lm_optimizer, lm_scheduler
def  __UpperCamelCase    (						snake_case							,			snake_case							,			snake_case							,			snake_case      )    ->		Dict:


   '''simple docstring'''







   __A    =  np.array(snake_case      ).mean()
   __A    =  np.array(snake_case      ).std()
   __A    =  (real_perps - avg) / std
   __A    =  list(zip(snake_case							,			snake_case							,			snake_case      )      )
   joblib.dump(snake_case							,			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							,			)    ->		Optional[Any]:


   '''simple docstring'''







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

   # Initialize the transformer model
   __A    =  copy.deepcopy(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(snake_case							,			snake_case							,			snake_case      )

   for step in tqdm(range(snake_case      )      ):
      __A    =  torch.zeros((1, context_len)							,			dtype=torch.long							,			device=snake_case      )
      __A    =  random.choice(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(snake_case							,			labels=snake_case      )
      __A    =  outputs[0]
      __A    =  compute_perplexity(snake_case							,			snake_case							,			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(snake_case							,			snake_case							,			snake_case      )

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

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

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

   intermittent_save(snake_case							,			snake_case							,			snake_case							,			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      )    ->		List[Any]:


   '''simple docstring'''







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

   joblib.dump(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"      )    ->		Union[str, 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(snake_case							,			shuffle=snake_case							,			batch_size=snake_case      )
   __A    =  DataLoader(snake_case							,			shuffle=snake_case							,			batch_size=snake_case      )

   # secondary learner model set up
   __A    =  nn.MSELoss()
   __A    =  nn.MSELoss(reduction='''sum'''      )
   secondary_learner.to(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(snake_case      )      ):
      __A    =  0.0
      secondary_learner.train()
      for step, batch in enumerate(snake_case      ):
         __A    =  batch[0].to(snake_case      )
         __A    =  batch[1].to(snake_case      )
         __A    =  secondary_learner(snake_case      )
         q_optimizer.zero_grad()
         __A    =  loss(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(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(snake_case      ):
               __A    =  batcha[0].to(snake_case      )
               __A    =  batcha[1].to(snake_case      )
               __A    =  secondary_learner(snake_case      )
               q_lossa += test_loss(snake_case							,			snake_case      ).item()
               sum_qa += torch.sum(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(snake_case      )
            print(
                '''Epoch: '''							,			snake_case							,			'''step: '''							,			snake_case							,			'''Avg. q:'''							,			sum_qa / len(snake_case      )							,			'''Train Loss: '''							,			snake_case							,			'''Test Loss: '''							,			snake_case							,			)
            if q_lossa < best_test_loss:
               joblib.dump((predicted, actual)							,			'''pred_vs_actual.jbl'''      )
               torch.save(secondary_learner.state_dict()							,			snake_case      )
               __A    =  q_lossa

         secondary_learner.train()
   return secondary_learner







class 		_lowerCAmelCase(		nn.Module):

  """simple docstring"""







  def __init__(   self    ,							UpperCAmelCase      )->   Union[str, 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      )->   Optional[int]:
     __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      )->   Any:
     __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








import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


_UpperCamelCase       :				Optional[Any]           =			logging.get_logger(__name__)


_UpperCamelCase       :				Optional[int]           =			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
_UpperCamelCase       :				int           =			{
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
}

_UpperCamelCase       :				str           =			{
    """RUCAIBox/mvp""": 1_0_2_4,
}
@lru_cache()
def  __UpperCamelCase    (						)    ->		Tuple:


   '''simple docstring'''







   __A    =  (
       list(range(ord('''!'''      )							,			ord('''~'''      ) + 1      )      ) + list(range(ord('''¡'''      )							,			ord('''¬'''      ) + 1      )      ) + list(range(ord('''®'''      )							,			ord('''ÿ'''      ) + 1      )      )
   )
   __A    =  bs[:]
   __A    =  0
   for b in range(2**8      ):
      if b not in bs:
         bs.append(snake_case      )
         cs.append(2**8 + n      )
         n += 1
   __A    =  [chr(snake_case      ) for n in cs]
   return dict(zip(snake_case							,			snake_case      )      )
def  __UpperCamelCase    (						snake_case      )    ->		int:


   '''simple docstring'''







   __A    =  set()
   __A    =  word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)      )
      __A    =  char
   return pairs







class 		_lowerCAmelCase(		_a):

  """simple docstring"""







  lowerCamelCase__     	=       VOCAB_FILES_NAMES
  lowerCamelCase__     	=       PRETRAINED_VOCAB_FILES_MAP
  lowerCamelCase__     	=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCamelCase__     	=       ['''input_ids''', '''attention_mask''']

  def __init__(   self    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase="replace"    ,							UpperCAmelCase="<s>"    ,							UpperCAmelCase="</s>"    ,							UpperCAmelCase="</s>"    ,							UpperCAmelCase="<s>"    ,							UpperCAmelCase="<unk>"    ,							UpperCAmelCase="<pad>"    ,							UpperCAmelCase="<mask>"    ,							UpperCAmelCase=False    ,							**UpperCAmelCase    ,							)->   Optional[int]:
     __A    =  AddedToken(UpperCAmelCase    ,							lstrip=UpperCAmelCase    ,							rstrip=UpperCAmelCase      ) if isinstance(UpperCAmelCase    ,							UpperCAmelCase      ) else bos_token
     __A    =  AddedToken(UpperCAmelCase    ,							lstrip=UpperCAmelCase    ,							rstrip=UpperCAmelCase      ) if isinstance(UpperCAmelCase    ,							UpperCAmelCase      ) else eos_token
     __A    =  AddedToken(UpperCAmelCase    ,							lstrip=UpperCAmelCase    ,							rstrip=UpperCAmelCase      ) if isinstance(UpperCAmelCase    ,							UpperCAmelCase      ) else sep_token
     __A    =  AddedToken(UpperCAmelCase    ,							lstrip=UpperCAmelCase    ,							rstrip=UpperCAmelCase      ) if isinstance(UpperCAmelCase    ,							UpperCAmelCase      ) else cls_token
     __A    =  AddedToken(UpperCAmelCase    ,							lstrip=UpperCAmelCase    ,							rstrip=UpperCAmelCase      ) if isinstance(UpperCAmelCase    ,							UpperCAmelCase      ) else unk_token
     __A    =  AddedToken(UpperCAmelCase    ,							lstrip=UpperCAmelCase    ,							rstrip=UpperCAmelCase      ) if isinstance(UpperCAmelCase    ,							UpperCAmelCase      ) else pad_token

     # Mask token behave like a normal word, i.e. include the space before it
     __A    =  AddedToken(UpperCAmelCase    ,							lstrip=UpperCAmelCase    ,							rstrip=UpperCAmelCase      ) if isinstance(UpperCAmelCase    ,							UpperCAmelCase      ) else mask_token

     super().__init__(
         errors=UpperCAmelCase    ,							bos_token=UpperCAmelCase    ,							eos_token=UpperCAmelCase    ,							unk_token=UpperCAmelCase    ,							sep_token=UpperCAmelCase    ,							cls_token=UpperCAmelCase    ,							pad_token=UpperCAmelCase    ,							mask_token=UpperCAmelCase    ,							add_prefix_space=UpperCAmelCase    ,							**UpperCAmelCase    ,							)
     with open(UpperCAmelCase    ,							encoding='''utf-8'''      ) as vocab_handle:
        __A    =  json.load(UpperCAmelCase      )
     __A    =  {v: k for k, v in self.encoder.items()}
     __A    =  errors  # how to handle errors in decoding
     __A    =  bytes_to_unicode()
     __A    =  {v: k for k, v in self.byte_encoder.items()}
     with open(UpperCAmelCase    ,							encoding='''utf-8'''      ) as merges_handle:
        __A    =  merges_handle.read().split('''\n'''      )[1:-1]
     __A    =  [tuple(merge.split()      ) for merge in bpe_merges]
     __A    =  dict(zip(UpperCAmelCase    ,							range(len(UpperCAmelCase      )      )      )      )
     __A    =  {}
     __A    =  add_prefix_space

     # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
     __A    =  re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''      )

  @property
  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Tuple:
     return len(self.encoder      )

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   str:
     return dict(self.encoder    ,							**self.added_tokens_encoder      )

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   List[str]:
     if token in self.cache:
        return self.cache[token]
     __A    =  tuple(UpperCAmelCase      )
     __A    =  get_pairs(UpperCAmelCase      )

     if not pairs:
        return token

     while True:
        __A    =  min(UpperCAmelCase    ,							key=lambda UpperCAmelCase      : self.bpe_ranks.get(UpperCAmelCase    ,							float('''inf'''      )      )      )
        if bigram not in self.bpe_ranks:
           break
        __A   ,		__A    =  bigram
        __A    =  []
        __A    =  0
        while i < len(UpperCAmelCase      ):
           try:
              __A    =  word.index(UpperCAmelCase    ,							UpperCAmelCase      )
           except ValueError:
              new_word.extend(word[i:]      )
              break
           else:
              new_word.extend(word[i:j]      )
              __A    =  j

           if word[i] == first and i < len(UpperCAmelCase      ) - 1 and word[i + 1] == second:
              new_word.append(first + second      )
              i += 2
           else:
              new_word.append(word[i]      )
              i += 1
        __A    =  tuple(UpperCAmelCase      )
        __A    =  new_word
        if len(UpperCAmelCase      ) == 1:
           break
        else:
           __A    =  get_pairs(UpperCAmelCase      )
     __A    =  ''' '''.join(UpperCAmelCase      )
     __A    =  word
     return word

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   List[Any]:
     __A    =  []
     for token in re.findall(self.pat    ,							UpperCAmelCase      ):
        __A    =  ''''''.join(
            self.byte_encoder[b] for b in token.encode('''utf-8'''      )      )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
        bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCAmelCase      ).split(''' '''      )      )
     return bpe_tokens

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   Any:
     return self.encoder.get(UpperCAmelCase    ,							self.encoder.get(self.unk_token      )      )

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   List[Any]:
     return self.decoder.get(UpperCAmelCase      )

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   Tuple:
     __A    =  ''''''.join(UpperCAmelCase      )
     __A    =  bytearray([self.byte_decoder[c] for c in text]      ).decode('''utf-8'''    ,							errors=self.errors      )
     return text

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase = None      )->   Tuple[str]:
     if not os.path.isdir(UpperCAmelCase      ):
        logger.error(f"Vocabulary path ({save_directory}) should be a directory"      )
        return
     __A    =  os.path.join(
         UpperCAmelCase    ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )
     __A    =  os.path.join(
         UpperCAmelCase    ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']      )

     with open(UpperCAmelCase    ,							'''w'''    ,							encoding='''utf-8'''      ) as f:
        f.write(json.dumps(self.encoder    ,							indent=2    ,							sort_keys=UpperCAmelCase    ,							ensure_ascii=UpperCAmelCase      ) + '''\n'''      )

     __A    =  0
     with open(UpperCAmelCase    ,							'''w'''    ,							encoding='''utf-8'''      ) as writer:
        writer.write('''#version: 0.2\n'''      )
        for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,							key=lambda UpperCAmelCase      : kv[1]      ):
           if index != token_index:
              logger.warning(
                  f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                  ''' Please check that the tokenizer is not corrupted!'''      )
              __A    =  token_index
           writer.write(''' '''.join(UpperCAmelCase      ) + '''\n'''      )
           index += 1

     return vocab_file, merge_file

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase = None      )->   List[int]:
     if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     __A    =  [self.cls_token_id]
     __A    =  [self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase = None    ,							UpperCAmelCase = False      )->   List[int]:
     if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=UpperCAmelCase    ,							token_ids_a=UpperCAmelCase    ,							already_has_special_tokens=UpperCAmelCase      )

     if token_ids_a is None:
        return [1] + ([0] * len(UpperCAmelCase      )) + [1]
     return [1] + ([0] * len(UpperCAmelCase      )) + [1, 1] + ([0] * len(UpperCAmelCase      )) + [1]

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase = None      )->   List[int]:
     __A    =  [self.sep_token_id]
     __A    =  [self.cls_token_id]

     if token_ids_a is None:
        return len(cls + token_ids_a + sep      ) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]





  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase    ,							UpperCAmelCase=False    ,							**UpperCAmelCase      )->   Union[str, Any]:
     __A    =  kwargs.pop('''add_prefix_space'''    ,							self.add_prefix_space      )
     if (is_split_into_words or add_prefix_space) and (len(UpperCAmelCase      ) > 0 and not text[0].isspace()):
        __A    =  ''' ''' + text
     return (text, kwargs)

