

import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


lowercase_             =     re.compile('[^A-Za-z_0-9]')
# parameters used in DuplicationIndex
lowercase_             =     1_0
lowercase_             =     2_5_6
def   a   (    A__			:     List[str]  )					->   Optional[MinHash]:





      """simple docstring"""



      if len(A__  ) < MIN_NUM_TOKENS:
            return None
      _lowercase			 =MinHash(num_perm=A__  )
      for token in set(A__  ):
            min_hash.update(token.encode()  )
      return min_hash
def   a   (    A__			:     str  )					->   Set[str]:





      """simple docstring"""



      return {t for t in NON_ALPHA.split(A__  ) if len(t.strip()  ) > 0}





class 				__lowerCAmelCase  :




  def __init__(       self							,     *,
      lowerCAmelCase = 0.85							,     )						->				Any:
        '''simple docstring'''






        _lowercase			 =duplication_jaccard_threshold
        _lowercase			 =NUM_PERM
        _lowercase			 =MinHashLSH(threshold=self._duplication_jaccard_threshold							,     num_perm=self._num_perm      )

        _lowercase			 =defaultdict(lowerCAmelCase      )




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        _lowercase			 =self._index.query(lowerCAmelCase      )
        if code_key in self._index.keys:
              print(F'''Duplicate key {code_key}'''      )
              return

        self._index.insert(lowerCAmelCase							,     lowerCAmelCase      )
        if len(lowerCAmelCase      ) > 0:
              for base_duplicate in close_duplicates:
                    if base_duplicate in self._duplicate_clusters:
                          self._duplicate_clusters[base_duplicate].add(lowerCAmelCase      )
                          break
              else:
                    self._duplicate_clusters[close_duplicates[0]].add(lowerCAmelCase      )




  def 						A__  (       self      )						->				List[List[Dict]]:
        '''simple docstring'''






        _lowercase			 =[]
        for base, duplicates in self._duplicate_clusters.items():
              _lowercase			 =[base] + list(lowerCAmelCase      )
              # reformat the cluster to be a list of dict
              _lowercase			 =[{'base_index': el[0], 'repo_name': el[1], 'path': el[2]} for el in cluster]
              duplicate_clusters.append(lowerCAmelCase      )
        return duplicate_clusters




  def 						A__  (       self							,     lowerCAmelCase      )						->				None:
        '''simple docstring'''






        _lowercase			 =self.get_duplicate_clusters()
        with open(lowerCAmelCase							,     'w'      ) as f:
              json.dump(lowerCAmelCase							,     lowerCAmelCase      )
def   a   (    A__			:     List[str]  )					->   Optional[Any]:





      """simple docstring"""



      _lowercase , _lowercase			 =element
      _lowercase			 =get_min_hash([t for t in NON_ALPHA.split(data['content']  ) if len(t.strip()  ) > 0]  )
      if min_hash is not None:
            return (index, data["repo_name"], data["path"]), min_hash
def   a   (    A__			:     Type[Dataset]  )					->   Tuple:





      """simple docstring"""



      with mp.Pool() as pool:
            for data in pool.imap_unordered(
                _compute_min_hash   ,     ThreadedIterator(A__   ,     max_queue_size=10000  )   ,     chunksize=100   ,     ):
                  if data is not None:
                        yield data
def   a   (    A__			:     Type[Dataset]   ,     A__			:     float  )					->   List[str]:





      """simple docstring"""



      _lowercase			 =DuplicationIndex(duplication_jaccard_threshold=A__  )

      for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(A__  )  )   ,     max_queue_size=100  )  ):
            di.add(A__   ,     A__  )

      # Returns a List[Cluster] where Cluster is List[str] with the filenames.
      return di.get_duplicate_clusters()
def   a   (    A__			:     str   ,     A__			:     str  )					->   float:





      """simple docstring"""



      _lowercase			 =get_tokens(A__  )
      _lowercase			 =get_tokens(A__  )
      return len(tokensa & tokensa  ) / len(tokensa | tokensa  )


lowercase_             =     None
def   a   (    A__			:     str   ,     A__			:     Union[str, Any]  )					->   Optional[int]:





      """simple docstring"""



      _lowercase			 =[]
      for elementa in cluster:
            _lowercase			 =_shared_dataset[elementa['base_index']]['content']
            for elementa in extremes:
                  _lowercase			 =_shared_dataset[elementa['base_index']]['content']
                  if jaccard_similarity(A__   ,     A__  ) >= jaccard_threshold:
                        elementa["copies"] += 1
                        break
            else:
                  _lowercase			 =1
                  extremes.append(A__  )
      return extremes
def   a   (    A__			:     Any   ,     A__			:     int   ,     A__			:     str  )					->   str:





      """simple docstring"""



      global _shared_dataset
      _lowercase			 =dataset
      _lowercase			 =[]
      _lowercase			 =partial(_find_cluster_extremes_shared   ,     jaccard_threshold=A__  )
      with mp.Pool() as pool:
            for extremes in tqdm(
                pool.imap_unordered(
                    A__   ,     A__   ,     )   ,     total=len(A__  )   ,     ):
                  extremes_list.append(A__  )
      return extremes_list







def   a   (    A__			:     Type[Dataset]   ,     A__			:     float = 0.85  )					->   Tuple[Type[Dataset], List[List[Dict]]]:





      """simple docstring"""



      _lowercase			 =make_duplicate_clusters(A__   ,     A__  )
      _lowercase			 ={x['base_index'] for cluster in duplicate_clusters for x in cluster}
      _lowercase			 ={}
      _lowercase			 =find_extremes(A__   ,     A__   ,     A__  )
      for extremes in extremes_clusters:
            for element in extremes:
                  _lowercase			 =element
      _lowercase			 =duplicate_indices - set(extreme_dict.keys()  )
      _lowercase			 =dataset.filter(lambda A__   ,     A__  : idx not in remove_indices   ,     with_indices=A__  )

      # update duplicate_clusters
      for cluster in duplicate_clusters:
            for element in cluster:
                  _lowercase			 =element['base_index'] in extreme_dict
                  if element["is_extreme"]:
                        _lowercase			 =extreme_dict[element['base_index']]['copies']

      print(F'''Original dataset size: {len(A__  )}'''  )
      print(F'''Number of duplicate clusters: {len(A__  )}'''  )
      print(F'''Files in duplicate cluster: {len(A__  )}'''  )
      print(F'''Unique files in duplicate cluster: {len(A__  )}'''  )
      print(F'''Filtered dataset size: {len(A__  )}'''  )

      return ds_filter, duplicate_clusters





import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel
def   a   (    A__			:     Optional[int]=32   ,     A__			:     str=10   ,     A__			:     Tuple=100   ,     A__			:     Union[str, Any]=1026   ,     A__			:     Tuple=True   ,     A__			:     Dict="data/tokenized_stories_train_wikitext103.jbl"   ,     A__			:     Dict="igf_context_pairs.jbl"   ,     )					->   Optional[int]:





      """simple docstring"""



      set_seed(3  )
      # generate train_data and objective_set
      _lowercase , _lowercase			 =generate_datasets(
          A__   ,     A__   ,     number=A__   ,     min_len=1026   ,     trim=A__  )
      # keeps model same across runs
      set_seed(4  )
      # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
      # can we train on GPU?
      _lowercase			 =torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'  )

      # load pretrained model
      _lowercase			 =load_gpta('gpt2'  ).to(A__  )
      print('computing perplexity on objective set'  )
      _lowercase			 =compute_perplexity(A__   ,     A__   ,     A__  ).item()
      print('perplexity on objective set:'   ,     A__  )

      # collect igf pairs and save to file demo.jbl
      collect_objective_set(A__   ,     A__   ,     A__   ,     A__   ,     A__   ,     A__   ,     A__   ,     A__  )

      # clean up, delete model and data we don't need anymore
      del model, train_data, objective_set
      torch.cuda.empty_cache()
def   a   (    A__			:     str   ,     A__			:     str=15   ,     A__			:     Optional[int]=128   ,     A__			:     Dict=100   ,     A__			:     Optional[Any]="igf_model.pt"   ,     )					->   int:





      """simple docstring"""



      set_seed(42  )

      # Load pre-trained model
      _lowercase			 =GPTaLMHeadModel.from_pretrained('gpt2'  )

      # Initialize secondary learner to use embedding weights of model
      _lowercase			 =SecondaryLearner(A__  )

      # Train secondary learner
      _lowercase			 =train_secondary_learner(
          A__   ,     A__   ,     max_epochs=A__   ,     batch_size=A__   ,     eval_freq=100   ,     igf_model_path=A__   ,     )

      del model, secondary_learner_train_data
      torch.cuda.empty_cache()

      return secondary_learner
def   a   (    A__			:     Dict   ,     A__			:     Optional[Any]   ,     A__			:     str   ,     A__			:     Union[str, Any]=32   ,     A__			:     List[str]=1000   ,     A__			:     int=16   ,     A__			:     List[str]=1.0   ,     A__			:     Tuple=recopy_gpta   ,     A__			:     Any=None   ,     A__			:     Tuple=10   ,     A__			:     Tuple="gpt2_finetuned.pt"   ,     )					->   str:





      """simple docstring"""



      _lowercase			 =torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'  )
      _lowercase			 =RandomSampler(A__  )
      _lowercase			 =DataLoader(A__   ,     sampler=A__  )

      _lowercase			 =max_steps // (len(A__  )) + 1
      _lowercase			 =0
      _lowercase			 =torch.zeros((1, context_len)   ,     dtype=torch.long   ,     device=A__  )
      _lowercase , _lowercase , _lowercase			 =recopy_model(A__   ,     A__   ,     A__  )

      model.train()
      if secondary_learner is not None:
            secondary_learner.to(A__  )
            secondary_learner.eval()
      _lowercase			 =[]
      _lowercase			 =0

      _lowercase			 =[]
      _lowercase			 =[]

      # Compute the performance of the transformer model at the beginning
      _lowercase			 =compute_perplexity(A__   ,     A__   ,     A__  )
      test_perps.append(A__  )
      print('Test perplexity, step'   ,     A__   ,     ':'   ,     A__  )
      for epoch in range(int(A__  )  ):
            for step, example in enumerate(A__  ):
                  torch.cuda.empty_cache()
                  _lowercase			 =random.randint(0   ,     example.size(2  ) - context_len - 1  )
                  _lowercase			 =example[0, 0, start : start + context_len]
                  lm_optimizer.zero_grad()
                  _lowercase			 =model(A__   ,     labels=A__  )
                  _lowercase			 =True

                  if secondary_learner is not None:
                        _lowercase			 =secondary_learner.forward(
                            torch.tensor(A__   ,     dtype=torch.long   ,     device=A__  ).unsqueeze(0  )  )[0].item()
                        observed_qs.append(float(A__  )  )

                        # Here we implement the simple non-constant threshold for the predicted IG(X) value
                        # We will decay the selectivity of our secondary learner filter from
                        # 1 standard deviation above average to 1 below average after 10 batches.

                        if global_step == 10:
                              _lowercase			 =-1
                        if predicted_q < threshold:
                              _lowercase			 =False

            # If we passed the filter, add the context to the batch!
                  if do_backprop:
                        contexts.append(np.array(context.cpu()  )  )
                        _lowercase			 =outputs[0]
                        lm_loss.backward()
                        examples += 1

                  del outputs

                  # Once the batch is filled with enough contexts, backprop on the batch.
                  if examples == batch_size:
                        torch.cuda.empty_cache()
                        _lowercase			 =0
                        # Do LM backprop
                        torch.nn.utils.clip_grad_norm_(model.parameters()   ,     3.0  )
                        lm_optimizer.step()
                        lm_scheduler.step()  # Update learning rate schedule
                        global_step += 1
                        # Compute the performance of the transformer model at this batch
                        if global_step % eval_interval == 0:
                              _lowercase			 =compute_perplexity(A__   ,     A__   ,     A__  )
                              test_perps.append(A__  )

                              print('Test perplexity, step'   ,     A__   ,     ':'   ,     A__  )
            # Break out of the loop after 60 batches
                  if max_steps > 0 and global_step > 60:
                        break
            if max_steps > 0 and global_step > 60:
                  break

    # save finetuned transformer model
      torch.save(model.state_dict()   ,     A__  )
      torch.cuda.empty_cache()
      # Do some cleaning up so we can reinitialize for the next run of this function
      del lm_optimizer
      del lm_scheduler
      return model
def   a   (    )					->   Optional[int]:





      """simple docstring"""



      _lowercase			 =argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task'  )

      # Required parameters
      parser.add_argument(
          '--data_dir'   ,     default=A__   ,     type=A__   ,     required=A__   ,     help='The input data dir. Should contain data files for WikiText.'   ,     )
      parser.add_argument(
          '--model_name_or_path'   ,     default=A__   ,     type=A__   ,     required=A__   ,     help='Path to pretrained model or model identifier from huggingface.co/models'   ,     )
      parser.add_argument(
          '--data_file'   ,     type=A__   ,     default=A__   ,     help=(
              'A jbl file containing tokenized data which can be split as objective dataset, '
              'train_dataset and test_dataset.'
          )   ,     )

      parser.add_argument(
          '--igf_data_file'   ,     type=A__   ,     default=A__   ,     help='A jbl file containing the context and information gain pairs to train secondary learner.'   ,     )

      parser.add_argument(
          '--output_dir'   ,     default=A__   ,     type=A__   ,     required=A__   ,     help='The output directory where the final fine-tuned model is stored.'   ,     )

      parser.add_argument(
          '--tokenizer_name'   ,     default=A__   ,     type=A__   ,     help='Pretrained tokenizer name or path if not the same as model_name'   ,     )
      parser.add_argument('--seed'   ,     type=A__   ,     default=A__   ,     help='A seed for reproducible training.'  )

      parser.add_argument(
          '--context_len'   ,     default=32   ,     type=A__   ,     help=(
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )   ,     )

      parser.add_argument(
          '--size_objective_set'   ,     default=100   ,     type=A__   ,     help='number of articles that are long enough to be used as our objective set'   ,     )
      parser.add_argument(
          '--eval_freq'   ,     default=100   ,     type=A__   ,     help='secondary model evaluation is triggered at eval_freq'  )

      parser.add_argument('--max_steps'   ,     default=1000   ,     type=A__   ,     help='To calculate training epochs'  )

      parser.add_argument(
          '--secondary_learner_batch_size'   ,     default=128   ,     type=A__   ,     help='batch size of training data for secondary learner'   ,     )

      parser.add_argument(
          '--batch_size'   ,     default=16   ,     type=A__   ,     help='batch size of training data of language model(gpt2) '  )

      parser.add_argument(
          '--eval_interval'   ,     default=10   ,     type=A__   ,     help=(
              'decay the selectivity of our secondary learner filter from'
              '1 standard deviation above average to 1 below average after 10 batches'
          )   ,     )

      parser.add_argument(
          '--number'   ,     default=100   ,     type=A__   ,     help='The number of examples split to be used as objective_set/test_data'  )

      parser.add_argument(
          '--min_len'   ,     default=1026   ,     type=A__   ,     help='The minimum length of the article to be used as objective set'  )

      parser.add_argument(
          '--secondary_learner_max_epochs'   ,     default=15   ,     type=A__   ,     help='number of epochs to train secondary learner'  )

      parser.add_argument('--trim'   ,     default=A__   ,     type=A__   ,     help='truncate the example if it exceeds context length'  )

      parser.add_argument(
          '--threshold'   ,     default=1.0   ,     type=A__   ,     help=(
              'The threshold value used by secondary learner to filter the train_data and allow only'
              ' informative data as input to the model'
          )   ,     )

      parser.add_argument('--finetuned_model_name'   ,     default='gpt2_finetuned.pt'   ,     type=A__   ,     help='finetuned_model_name'  )

      parser.add_argument(
          '--recopy_model'   ,     default=A__   ,     type=A__   ,     help='Reset the model to the original pretrained GPT-2 weights after each iteration'   ,     )

      # function calls
      # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
      generate_n_pairs(
          context_len=32   ,     max_steps=10   ,     size_objective_set=100   ,     min_len=1026   ,     trim=A__   ,     data_file='data/tokenized_stories_train_wikitext103.jbl'   ,     igf_data_file='igf_context_pairs.jbl'   ,     )

      # Load train data for secondary learner
      _lowercase			 =joblib.load('data/IGF_values.jbl'  )

      # Train secondary learner
      _lowercase			 =training_secondary_learner(
          A__   ,     secondary_learner_max_epochs=15   ,     secondary_learner_batch_size=128   ,     eval_freq=100   ,     igf_model_path='igf_model.pt'   ,     )

      # load pretrained gpt2 model
      _lowercase			 =GPTaLMHeadModel.from_pretrained('gpt2'  )
      set_seed(42  )

      # Generate train and test data to train and evaluate gpt2 model
      _lowercase , _lowercase			 =generate_datasets(
          context_len=32   ,     file='data/tokenized_stories_train_wikitext103.jbl'   ,     number=100   ,     min_len=1026   ,     trim=A__  )

      # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
      finetune(
          A__   ,     A__   ,     A__   ,     context_len=32   ,     max_steps=1000   ,     batch_size=16   ,     threshold=1.0   ,     recopy_model=A__   ,     secondary_learner=A__   ,     eval_interval=10   ,     finetuned_model_name='gpt2_finetuned.pt'   ,     )


if __name__ == "__main__":
  main()


