

'''simple docstring'''

import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
    from sagemaker import Session, TrainingJobAnalytics
    from sagemaker.huggingface import HuggingFace





@pytest.mark.skipif(
    literal_eval(os.getenv('''TEST_SAGEMAKER'''	,  '''False'''						)						) is not True	,  reason='''Skipping test because should only be run when releasing minor transformers version'''	,  )
@pytest.mark.usefixtures('''sm_env'''						)
@parameterized_class(
    [
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 6_5_0, '''eval_accuracy''': 0.6, '''eval_loss''': 0.9},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]						)
class 							_snake_case (       unittest.TestCase						):
   def     __UpperCamelCase						( self			:      int							):
     if self.framework == "pytorch":
       subprocess.run(
           F'''cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'''.split()     ,encoding="utf-8"     ,check=SCREAMING_SNAKE_CASE__     ,)
     assert hasattr(self     ,"env"							)
   def     __UpperCamelCase						( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      Dict=1							):
     # creates estimator
     return HuggingFace(
         entry_point=self.script     ,source_dir=self.env.test_path     ,role=self.env.role     ,image_uri=self.env.image_uri     ,base_job_name=F'''{self.env.base_job_name}-single'''     ,instance_count=SCREAMING_SNAKE_CASE__     ,instance_type=self.instance_type     ,debugger_hook_config=SCREAMING_SNAKE_CASE__     ,hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}     ,metric_definitions=self.env.metric_definitions     ,py_version="py36"     ,)
   def     __UpperCamelCase						( self			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]							):
     TrainingJobAnalytics(SCREAMING_SNAKE_CASE__							).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''							)







   def     __UpperCamelCase						( self			:      str							):
     # create estimator
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.create_estimator()

     # run training
     estimator.fit()

     # result dataframe
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					TrainingJobAnalytics(estimator.latest_training_job.name							).dataframe()

     # extract kpis
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]							)
     SCREAMING_SNAKE_CASE:Tuple						      =					list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]							)
     # get train time from SageMaker job, this includes starting, preprocessing, stopping
     SCREAMING_SNAKE_CASE:int						      =					(
         Session().describe_training_job(estimator.latest_training_job.name							).get("TrainingTimeInSeconds"     ,999_999							)
     )

     # assert kpis
     assert train_runtime <= self.results["train_runtime"]
     assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy							)
     assert all(t <= self.results["eval_loss"] for t in eval_loss							)

     # dump tests result into json file to share in PR
     with open(F'''{estimator.latest_training_job.name}.json'''     ,"w"							) as outfile:
       json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}     ,SCREAMING_SNAKE_CASE__							)









'''simple docstring'''

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_  ( snake_case=32      ,       snake_case=10      ,       snake_case=100      ,       snake_case=1026      ,       snake_case=True      ,       snake_case="data/tokenized_stories_train_wikitext103.jbl"      ,       snake_case="igf_context_pairs.jbl"      ,       ):
  set_seed(3 )
  # generate train_data and objective_set
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[int]						      =					generate_datasets(
      snake_case      ,       snake_case      ,       number=snake_case      ,       min_len=1026      ,       trim=snake_case )
  # 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?
  SCREAMING_SNAKE_CASE:Tuple						      =					torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )

  # load pretrained model
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					load_gpta("gpt2" ).to(snake_case )
  print("computing perplexity on objective set" )
  SCREAMING_SNAKE_CASE:str						      =					compute_perplexity(snake_case      ,       snake_case      ,       snake_case ).item()
  print("perplexity on objective set:"      ,       snake_case )

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

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





def    A_  ( snake_case      ,       snake_case=15      ,       snake_case=128      ,       snake_case=100      ,       snake_case="igf_model.pt"      ,       ):
  set_seed(42 )

  # Load pre-trained model
  SCREAMING_SNAKE_CASE:int						      =					GPTaLMHeadModel.from_pretrained("gpt2" )

  # Initialize secondary learner to use embedding weights of model
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					SecondaryLearner(snake_case )

  # Train secondary learner
  SCREAMING_SNAKE_CASE:int						      =					train_secondary_learner(
      snake_case      ,       snake_case      ,       max_epochs=snake_case      ,       batch_size=snake_case      ,       eval_freq=100      ,       igf_model_path=snake_case      ,       )

  del model, secondary_learner_train_data
  torch.cuda.empty_cache()

  return secondary_learner





def    A_  ( snake_case      ,       snake_case      ,       snake_case      ,       snake_case=32      ,       snake_case=1000      ,       snake_case=16      ,       snake_case=1.0      ,       snake_case=recopy_gpta      ,       snake_case=None      ,       snake_case=10      ,       snake_case="gpt2_finetuned.pt"      ,       ):
  SCREAMING_SNAKE_CASE:str						      =					torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )
  SCREAMING_SNAKE_CASE:Dict						      =					RandomSampler(snake_case )
  SCREAMING_SNAKE_CASE:str						      =					DataLoader(snake_case      ,       sampler=snake_case )

  SCREAMING_SNAKE_CASE:str						      =					max_steps // (len(snake_case )) + 1
  SCREAMING_SNAKE_CASE:Tuple						      =					0
  SCREAMING_SNAKE_CASE:List[Any]						      =					torch.zeros((1, context_len)      ,       dtype=torch.long      ,       device=snake_case )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:int						      =					recopy_model(snake_case      ,       snake_case      ,       snake_case )

  model.train()
  if secondary_learner is not None:
    secondary_learner.to(snake_case )
    secondary_learner.eval()
  SCREAMING_SNAKE_CASE:List[Any]						      =					[]
  SCREAMING_SNAKE_CASE:Tuple						      =					0

  SCREAMING_SNAKE_CASE:Tuple						      =					[]
  SCREAMING_SNAKE_CASE:Tuple						      =					[]

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

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

        # 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:
          SCREAMING_SNAKE_CASE:int						      =					-1
        if predicted_q < threshold:
          SCREAMING_SNAKE_CASE:Optional[int]						      =					False

            # If we passed the filter, add the context to the batch!
      if do_backprop:
        contexts.append(np.array(context.cpu() ) )
        SCREAMING_SNAKE_CASE:Union[str, Any]						      =					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()
        SCREAMING_SNAKE_CASE:int						      =					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:
          SCREAMING_SNAKE_CASE:Optional[Any]						      =					compute_perplexity(snake_case      ,       snake_case      ,       snake_case )
          test_perps.append(snake_case )

          print("Test perplexity, step"      ,       snake_case      ,       ":"      ,       snake_case )
            # 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()      ,       snake_case )
  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_  ( ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task" )

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

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

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

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

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

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

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

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

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

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

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

  parser.add_argument(
      "--threshold"      ,       default=1.0      ,       type=snake_case      ,       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=snake_case      ,       help="finetuned_model_name" )

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

  # Load train data for secondary learner
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					joblib.load("data/IGF_values.jbl" )

  # Train secondary learner
  SCREAMING_SNAKE_CASE:Optional[int]						      =					training_secondary_learner(
      snake_case      ,       secondary_learner_max_epochs=15      ,       secondary_learner_batch_size=128      ,       eval_freq=100      ,       igf_model_path="igf_model.pt"      ,       )

  # load pretrained gpt2 model
  SCREAMING_SNAKE_CASE:Dict						      =					GPTaLMHeadModel.from_pretrained("gpt2" )
  set_seed(42 )

  # Generate train and test data to train and evaluate gpt2 model
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[Any]						      =					generate_datasets(
      context_len=32      ,       file="data/tokenized_stories_train_wikitext103.jbl"      ,       number=100      ,       min_len=1026      ,       trim=snake_case )

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


if __name__ == "__main__":
    main()






