






import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


snake_case__   :						List[Any]													=   getLogger(__name__)


snake_case__   :						Optional[Any]													=   """cuda""" if torch.cuda.is_available() else """cpu"""






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase = 8     ,   __lowercase = DEFAULT_DEVICE     ,   __lowercase=False     ,   __lowercase="summarization"     ,   __lowercase=None     ,   **__lowercase     ,   ):
   UpperCamelCase_						=  Path(__lowercase).open('w'     ,   encoding='utf-8')
   UpperCamelCase_						=  str(__lowercase)
   UpperCamelCase_						=  AutoModelForSeqaSeqLM.from_pretrained(__lowercase).to(__lowercase)
   if fpaa:
      UpperCamelCase_						=  model.half()

   UpperCamelCase_						=  AutoTokenizer.from_pretrained(__lowercase)
   logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}""")  # if this is wrong, check config.model_type.

   UpperCamelCase_						=  time.time()
   # update config with task specific params
   use_task_specific_params(__lowercase     ,   __lowercase)
   if prefix is None:
      UpperCamelCase_						=  prefix or getattr(model.config     ,   'prefix'     ,   '') or ''
   for examples_chunk in tqdm(list(chunks(__lowercase     ,   __lowercase))):
      UpperCamelCase_						=  [prefix + text for text in examples_chunk]
      UpperCamelCase_						=  tokenizer(__lowercase     ,   return_tensors='pt'     ,   truncation=__lowercase     ,   padding='longest').to(__lowercase)
      UpperCamelCase_						=  model.generate(
          input_ids=batch.input_ids     ,   attention_mask=batch.attention_mask     ,   **__lowercase     ,   )
      UpperCamelCase_						=  tokenizer.batch_decode(__lowercase     ,   skip_special_tokens=__lowercase     ,   clean_up_tokenization_spaces=__lowercase)
      for hypothesis in dec:
         fout.write(hypothesis + '\n')
         fout.flush()
   fout.close()
   UpperCamelCase_						=  int(time.time() - start_time)  # seconds
   UpperCamelCase_						=  len(__lowercase)
   return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs     ,   4)}






def      _snake_case     ():
   return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')






def      _snake_case     (__lowercase=True):
   UpperCamelCase_						=  argparse.ArgumentParser()
   parser.add_argument('model_name'     ,   type=__lowercase     ,   help='like facebook/bart-large-cnn,t5-base, etc.')
   parser.add_argument('input_path'     ,   type=__lowercase     ,   help='like cnn_dm/test.source')
   parser.add_argument('save_path'     ,   type=__lowercase     ,   help='where to save summaries')
   parser.add_argument('--reference_path'     ,   type=__lowercase     ,   required=__lowercase     ,   help='like cnn_dm/test.target')
   parser.add_argument('--score_path'     ,   type=__lowercase     ,   required=__lowercase     ,   default='metrics.json'     ,   help='where to save metrics')
   parser.add_argument('--device'     ,   type=__lowercase     ,   required=__lowercase     ,   default=__lowercase     ,   help='cuda, cuda:1, cpu etc.')
   parser.add_argument(
       '--prefix'     ,   type=__lowercase     ,   required=__lowercase     ,   default=__lowercase     ,   help='will be added to the begininng of src examples')
   parser.add_argument('--task'     ,   type=__lowercase     ,   default='summarization'     ,   help='used for task_specific_params + metrics')
   parser.add_argument('--bs'     ,   type=__lowercase     ,   default=8     ,   required=__lowercase     ,   help='batch size')
   parser.add_argument(
       '--n_obs'     ,   type=__lowercase     ,   default=-1     ,   required=__lowercase     ,   help='How many observations. Defaults to all.')
   parser.add_argument('--fp16'     ,   action='store_true')
   parser.add_argument('--dump-args'     ,   action='store_true'     ,   help='print the custom hparams with the results')
   parser.add_argument(
       '--info'     ,   nargs='?'     ,   type=__lowercase     ,   const=datetime_now()     ,   help=(
           'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
           ' lang=en-ru. If no value is passed, the current datetime string will be used.'
       )     ,   )
   # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
   UpperCamelCase_		,   UpperCamelCase_						=  parser.parse_known_args()
   UpperCamelCase_						=  parse_numeric_n_bool_cl_kwargs(__lowercase)
   if parsed_args and verbose:
      print(f"""parsed the following generate kwargs: {parsed_args}""")
   UpperCamelCase_						=  [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path).readlines()]
   if args.n_obs > 0:
      UpperCamelCase_						=  examples[: args.n_obs]
   Path(args.save_path).parent.mkdir(exist_ok=__lowercase)

   if args.reference_path is None and Path(args.score_path).exists():
      warnings.warn(f"""score_path {args.score_path} will be overwritten unless you type ctrl-c.""")

   if args.device == "cpu" and args.fpaa:
      # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
      raise ValueError('Can\'t mix --fp16 and --device cpu')

   UpperCamelCase_						=  generate_summaries_or_translations(
       __lowercase     ,   args.save_path     ,   args.model_name     ,   batch_size=args.bs     ,   device=args.device     ,   fpaa=args.fpaa     ,   task=args.task     ,   prefix=args.prefix     ,   **__lowercase     ,   )

   if args.reference_path is None:
      return {}

   # Compute scores
   UpperCamelCase_						=  calculate_bleu if 'translation' in args.task else calculate_rouge
   UpperCamelCase_						=  [x.rstrip() for x in open(args.save_path).readlines()]
   UpperCamelCase_						=  [x.rstrip() for x in open(args.reference_path).readlines()][: len(__lowercase)]
   UpperCamelCase_						=  score_fn(__lowercase     ,   __lowercase)
   scores.update(__lowercase)

   if args.dump_args:
      scores.update(__lowercase)
   if args.info:
      UpperCamelCase_						=  args.info

   if verbose:
      print(__lowercase)

   if args.score_path is not None:
      json.dump(__lowercase     ,   open(args.score_path     ,   'w'))

   return scores


if __name__ == "__main__":
 # Usage for MT:
 # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
 run_generate(verbose=True)













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      _snake_case     (__lowercase=32     ,   __lowercase=10     ,   __lowercase=100     ,   __lowercase=1026     ,   __lowercase=True     ,   __lowercase="data/tokenized_stories_train_wikitext103.jbl"     ,   __lowercase="igf_context_pairs.jbl"     ,   ):
   set_seed(3)
   # generate train_data and objective_set
   UpperCamelCase_		,   UpperCamelCase_						=  generate_datasets(
       __lowercase     ,   __lowercase     ,   number=__lowercase     ,   min_len=1026     ,   trim=__lowercase)
   # 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?
   UpperCamelCase_						=  torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

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

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

   # clean up, delete model and data we don't need anymore
   del model, train_data, objective_set
   torch.cuda.empty_cache()






def      _snake_case     (__lowercase     ,   __lowercase=15     ,   __lowercase=128     ,   __lowercase=100     ,   __lowercase="igf_model.pt"     ,   ):
   set_seed(42)

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

   # Initialize secondary learner to use embedding weights of model
   UpperCamelCase_						=  SecondaryLearner(__lowercase)

   # Train secondary learner
   UpperCamelCase_						=  train_secondary_learner(
       __lowercase     ,   __lowercase     ,   max_epochs=__lowercase     ,   batch_size=__lowercase     ,   eval_freq=100     ,   igf_model_path=__lowercase     ,   )

   del model, secondary_learner_train_data
   torch.cuda.empty_cache()

   return secondary_learner






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase=32     ,   __lowercase=1000     ,   __lowercase=16     ,   __lowercase=1.0     ,   __lowercase=recopy_gpta     ,   __lowercase=None     ,   __lowercase=10     ,   __lowercase="gpt2_finetuned.pt"     ,   ):
   UpperCamelCase_						=  torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
   UpperCamelCase_						=  RandomSampler(__lowercase)
   UpperCamelCase_						=  DataLoader(__lowercase     ,   sampler=__lowercase)

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

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

   UpperCamelCase_						=  []
   UpperCamelCase_						=  []

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

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

            # 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:
               UpperCamelCase_						=  -1
            if predicted_q < threshold:
               UpperCamelCase_						=  False

            # If we passed the filter, add the context to the batch!
         if do_backprop:
            contexts.append(np.array(context.cpu()))
            UpperCamelCase_						=  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()
            UpperCamelCase_						=  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:
               UpperCamelCase_						=  compute_perplexity(__lowercase     ,   __lowercase     ,   __lowercase)
               test_perps.append(__lowercase)

               print('Test perplexity, step'     ,   __lowercase     ,   ':'     ,   __lowercase)
            # 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()     ,   __lowercase)
   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      _snake_case     ():
   UpperCamelCase_						=  argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task')

   # Required parameters
   parser.add_argument(
       '--data_dir'     ,   default=__lowercase     ,   type=__lowercase     ,   required=__lowercase     ,   help='The input data dir. Should contain data files for WikiText.'     ,   )
   parser.add_argument(
       '--model_name_or_path'     ,   default=__lowercase     ,   type=__lowercase     ,   required=__lowercase     ,   help='Path to pretrained model or model identifier from huggingface.co/models'     ,   )
   parser.add_argument(
       '--data_file'     ,   type=__lowercase     ,   default=__lowercase     ,   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=__lowercase     ,   default=__lowercase     ,   help='A jbl file containing the context and information gain pairs to train secondary learner.'     ,   )

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

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

   parser.add_argument(
       '--context_len'     ,   default=32     ,   type=__lowercase     ,   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=__lowercase     ,   help='number of articles that are long enough to be used as our objective set'     ,   )
   parser.add_argument(
       '--eval_freq'     ,   default=100     ,   type=__lowercase     ,   help='secondary model evaluation is triggered at eval_freq')

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

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

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

   parser.add_argument(
       '--eval_interval'     ,   default=10     ,   type=__lowercase     ,   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=__lowercase     ,   help='The number of examples split to be used as objective_set/test_data')

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

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

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

   parser.add_argument(
       '--threshold'     ,   default=1.0     ,   type=__lowercase     ,   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=__lowercase     ,   help='finetuned_model_name')

   parser.add_argument(
       '--recopy_model'     ,   default=__lowercase     ,   type=__lowercase     ,   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=__lowercase     ,   data_file='data/tokenized_stories_train_wikitext103.jbl'     ,   igf_data_file='igf_context_pairs.jbl'     ,   )

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

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

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

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

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


if __name__ == "__main__":
 main()





