






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


A_	:	Any				=				logging.getLogger(__name__)

def 						snake_case      (UpperCAmelCase__						) ->       str:

  '''simple docstring'''





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

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__						) ->       int:

  '''simple docstring'''





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

def 						snake_case      (UpperCAmelCase__="gpt2"						) ->       Dict:

  '''simple docstring'''





  UpperCamelCase_:      Optional[Any]					   =       GPTaLMHeadModel.from_pretrained(UpperCAmelCase__			,					output_hidden_states=UpperCAmelCase__						)
  torch.save(model.state_dict()			,					model_name + 'local.pt'						)
  return model

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__						) ->       int:

  '''simple docstring'''





  UpperCamelCase_:      Optional[int]					   =       copy.deepcopy(UpperCAmelCase__						)
  model.to(UpperCAmelCase__						)

  UpperCamelCase_:      List[Any]					   =       ['bias', 'LayerNorm.weight']
  UpperCamelCase_:      List[str]					   =       [
      {
          '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_:      Dict					   =       AdamW(UpperCAmelCase__			,					lr=5E-5			,					eps=1E-8						)
  UpperCamelCase_:      Optional[int]					   =       get_linear_schedule_with_warmup(UpperCAmelCase__			,					0			,					UpperCAmelCase__						)
  torch.cuda.empty_cache()
  return model, lm_optimizer, lm_scheduler

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__						) ->       Union[str, Any]:

  '''simple docstring'''





  UpperCamelCase_:      Any					   =       np.array(UpperCAmelCase__						).mean()
  UpperCamelCase_:      Optional[Any]					   =       np.array(UpperCAmelCase__						).std()
  UpperCamelCase_:      Optional[int]					   =       (real_perps - avg) / std
  UpperCamelCase_:      int					   =       list(zip(UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__						)						)
  joblib.dump(UpperCAmelCase__			,					UpperCAmelCase__						)

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__="dev.jbl"			,					UpperCAmelCase__=recopy_gpta			,					) ->       List[Any]:

  '''simple docstring'''





  UpperCamelCase_:      List[Any]					   =       []
  UpperCamelCase_:      Optional[Any]					   =       []
  UpperCamelCase_:      Dict					   =       []

  # Initialize the transformer model
  UpperCamelCase_:      List[str]					   =       copy.deepcopy(UpperCAmelCase__						)
  orig_model.to(device='cpu'						)
  torch.cuda.empty_cache()

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

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

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

    # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
    UpperCamelCase_   ,UpperCamelCase_   ,UpperCamelCase_:      Optional[int]					   =       recopy_model(UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__						)

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

  intermittent_save(UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__						)

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__="data/tokenized_stories_train_wikitext103.jbl"			,					UpperCAmelCase__=1_0_0			,					UpperCAmelCase__=1_0_2_6			,					UpperCAmelCase__=True						) ->       Union[str, Any]:

  '''simple docstring'''





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

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

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__=5_0			,					UpperCAmelCase__="secondary_learner.pt"						) ->       Tuple:

  '''simple docstring'''





  UpperCamelCase_:      Optional[Any]					   =       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_:      int					   =       train_dataset[:5_1_2]
  UpperCamelCase_:      Tuple					   =       train_dataset[5_1_2:]
  UpperCamelCase_:      Dict					   =       DataLoader(UpperCAmelCase__			,					shuffle=UpperCAmelCase__			,					batch_size=UpperCAmelCase__						)
  UpperCamelCase_:      Optional[int]					   =       DataLoader(UpperCAmelCase__			,					shuffle=UpperCAmelCase__			,					batch_size=UpperCAmelCase__						)

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

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

      secondary_learner.train()
  return secondary_learner




class        _lowerCAmelCase( nn.Module      ):





       """simple docstring"""







       def __init__(      self						,    _lowerCamelCase							):
         super(UpperCamelCase__						,    self							).__init__()
         UpperCamelCase_:      Union[str, Any]					   =       model.transformer.wte
         UpperCamelCase_:      Union[str, Any]					   =       copy.deepcopy(model.transformer.wte.weight							)
         UpperCamelCase_:      str					   =       nn.Convad(self.embeddings.weight.size(1							)						,    2_5_6						,    3						,    padding=1							)
         UpperCamelCase_:      Union[str, Any]					   =       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							):
         UpperCamelCase_:      str					   =       torch.max(self.conv(self.embeddings(UpperCamelCase__							).squeeze(1							).transpose(1						,    2							)							)						,    2							)[0]
         UpperCamelCase_:      Tuple					   =       self.fc(UpperCamelCase__							)
         return qs.squeeze(1							)





       @classmethod
       def     _a					(      cls						,    _lowerCamelCase						,    _lowerCamelCase							):
         UpperCamelCase_:      Union[str, Any]					   =       cls(UpperCamelCase__							)  # this calls __init__
         UpperCamelCase_:      int					   =       torch.load(UpperCamelCase__							)
         secondary_learner.load_state_dict(UpperCamelCase__							)
         UpperCamelCase_:      str					   =       model.transformer.wte
         UpperCamelCase_:      Optional[Any]					   =       copy.deepcopy(model.transformer.wte.weight							)
         return secondary_learner







import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_reformer import ReformerTokenizer
else:
   A_	:	Union[str, Any]				=				None


A_	:	List[Any]				=				logging.get_logger(__name__)


A_	:	List[str]				=				'▁'

A_	:	int				=				{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

A_	:	Optional[Any]				=				{
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    },
    'tokenizer_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'
        )
    },
}

A_	:	List[Any]				=				{
    'google/reformer-crime-and-punishment': 524288,
}




class        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       a       :	int								=VOCAB_FILES_NAMES
       a       :	Optional[Any]								=PRETRAINED_VOCAB_FILES_MAP
       a       :	List[Any]								=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a       :	Tuple								=['''input_ids''', '''attention_mask''']
       a       :	Optional[Any]								=ReformerTokenizer





       def __init__(      self						,    _lowerCamelCase=None						,    _lowerCamelCase=None						,    _lowerCamelCase="</s>"						,    _lowerCamelCase="<unk>"						,    _lowerCamelCase=[]						,    **_lowerCamelCase						,    ):
         super().__init__(
             _lowerCamelCase						,    tokenizer_file=_lowerCamelCase						,    eos_token=_lowerCamelCase						,    unk_token=_lowerCamelCase						,    additional_special_tokens=_lowerCamelCase						,    **_lowerCamelCase						,    )

         UpperCamelCase_:      Any					   =       vocab_file
         UpperCamelCase_:      str					   =       False if not self.vocab_file else True





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase = None							):
         if not self.can_save_slow_tokenizer:
           raise ValueError(
               'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
               'tokenizer.'							)

         if not os.path.isdir(_lowerCamelCase							):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
           return
         UpperCamelCase_:      str					   =       os.path.join(
             _lowerCamelCase						,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)

         if os.path.abspath(self.vocab_file							) != os.path.abspath(_lowerCamelCase							):
           copyfile(self.vocab_file						,    _lowerCamelCase							)

         return (out_vocab_file,)