
"""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    _lowerCAmelCase    (     lowerCamelCase__							:							Union[str, Any]=3_2,						lowerCamelCase__							:							List[str]=1_0,						lowerCamelCase__							:							Optional[Any]=1_0_0,						lowerCamelCase__							:							Any=1_0_2_6,						lowerCamelCase__							:							int=True,						lowerCamelCase__							:							int="data/tokenized_stories_train_wikitext103.jbl",						lowerCamelCase__							:							Optional[int]="igf_context_pairs.jbl",						)  ->    Any:
  set_seed(3				)
  # generate train_data and objective_set
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				List[str]							    = generate_datasets(
      lowerCamelCase__,						lowerCamelCase__,						number=lowerCamelCase__,						min_len=1_0_2_6,						trim=lowerCamelCase__				)
  # 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   :				Optional[Any]							    = torch.device("cuda:0" if torch.cuda.is_available() else "cpu"				)

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

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

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





def    _lowerCAmelCase    (     lowerCamelCase__							:							Dict,						lowerCamelCase__							:							List[Any]=1_5,						lowerCamelCase__							:							Optional[int]=1_2_8,						lowerCamelCase__							:							Optional[int]=1_0_0,						lowerCamelCase__							:							Dict="igf_model.pt",						)  ->    List[Any]:
  set_seed(4_2				)

  # Load pre-trained model
  _SCREAMING_SNAKE_CASE   :				Tuple							    = GPTaLMHeadModel.from_pretrained("gpt2"				)

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

  # Train secondary learner
  _SCREAMING_SNAKE_CASE   :				List[str]							    = train_secondary_learner(
      lowerCamelCase__,						lowerCamelCase__,						max_epochs=lowerCamelCase__,						batch_size=lowerCamelCase__,						eval_freq=1_0_0,						igf_model_path=lowerCamelCase__,						)

  del model, secondary_learner_train_data
  torch.cuda.empty_cache()

  return secondary_learner





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[Any],						lowerCamelCase__							:							List[Any],						lowerCamelCase__							:							List[Any],						lowerCamelCase__							:							Optional[Any]=3_2,						lowerCamelCase__							:							str=1_0_0_0,						lowerCamelCase__							:							List[str]=1_6,						lowerCamelCase__							:							List[str]=1.0,						lowerCamelCase__							:							Dict=recopy_gpta,						lowerCamelCase__							:							Optional[Any]=None,						lowerCamelCase__							:							Optional[Any]=1_0,						lowerCamelCase__							:							Optional[Any]="gpt2_finetuned.pt",						)  ->    List[str]:
  _SCREAMING_SNAKE_CASE   :				str							    = torch.device("cuda:0" if torch.cuda.is_available() else "cpu"				)
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = RandomSampler(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				str							    = DataLoader(lowerCamelCase__,						sampler=lowerCamelCase__				)

  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = max_steps // (len(lowerCamelCase__				)) + 1
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = 0
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = torch.zeros((1, context_len),						dtype=torch.long,						device=lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				List[str]							    = recopy_model(lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__				)

  model.train()
  if secondary_learner is not None:
    secondary_learner.to(lowerCamelCase__				)
    secondary_learner.eval()
  _SCREAMING_SNAKE_CASE   :				Tuple							    = []
  _SCREAMING_SNAKE_CASE   :				List[str]							    = 0

  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = []
  _SCREAMING_SNAKE_CASE   :				Tuple							    = []

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

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

        # 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 == 1_0:
          _SCREAMING_SNAKE_CASE   :				Optional[int]							    = -1
        if predicted_q < threshold:
          _SCREAMING_SNAKE_CASE   :				Any							    = False

            # If we passed the filter, add the context to the batch!
      if do_backprop:
        contexts.append(np.array(context.cpu()				)				)
        _SCREAMING_SNAKE_CASE   :				int							    = 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   :				str							    = 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   :				Dict							    = compute_perplexity(lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__				)
          test_perps.append(lowerCamelCase__				)

          print("Test perplexity, step",						lowerCamelCase__,						":",						lowerCamelCase__				)
            # Break out of the loop after 60 batches
      if max_steps > 0 and global_step > 6_0:
        break
    if max_steps > 0 and global_step > 6_0:
      break

    # save finetuned transformer model
  torch.save(model.state_dict(),						lowerCamelCase__				)
  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    _lowerCAmelCase    (     )  ->    Dict:
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task"				)

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

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

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

  parser.add_argument(
      "--context_len",						default=3_2,						type=lowerCamelCase__,						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=1_0_0,						type=lowerCamelCase__,						help="number of articles that are long enough to be used as our objective set",						)
  parser.add_argument(
      "--eval_freq",						default=1_0_0,						type=lowerCamelCase__,						help="secondary model evaluation is triggered at eval_freq"				)

  parser.add_argument("--max_steps",						default=1_0_0_0,						type=lowerCamelCase__,						help="To calculate training epochs"				)

  parser.add_argument(
      "--secondary_learner_batch_size",						default=1_2_8,						type=lowerCamelCase__,						help="batch size of training data for secondary learner",						)

  parser.add_argument(
      "--batch_size",						default=1_6,						type=lowerCamelCase__,						help="batch size of training data of language model(gpt2) "				)

  parser.add_argument(
      "--eval_interval",						default=1_0,						type=lowerCamelCase__,						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=1_0_0,						type=lowerCamelCase__,						help="The number of examples split to be used as objective_set/test_data"				)

  parser.add_argument(
      "--min_len",						default=1_0_2_6,						type=lowerCamelCase__,						help="The minimum length of the article to be used as objective set"				)

  parser.add_argument(
      "--secondary_learner_max_epochs",						default=1_5,						type=lowerCamelCase__,						help="number of epochs to train secondary learner"				)

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

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

  parser.add_argument(
      "--recopy_model",						default=lowerCamelCase__,						type=lowerCamelCase__,						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=3_2,						max_steps=1_0,						size_objective_set=1_0_0,						min_len=1_0_2_6,						trim=lowerCamelCase__,						data_file="data/tokenized_stories_train_wikitext103.jbl",						igf_data_file="igf_context_pairs.jbl",						)

  # Load train data for secondary learner
  _SCREAMING_SNAKE_CASE   :				int							    = joblib.load("data/IGF_values.jbl"				)

  # Train secondary learner
  _SCREAMING_SNAKE_CASE   :				Dict							    = training_secondary_learner(
      lowerCamelCase__,						secondary_learner_max_epochs=1_5,						secondary_learner_batch_size=1_2_8,						eval_freq=1_0_0,						igf_model_path="igf_model.pt",						)

  # load pretrained gpt2 model
  _SCREAMING_SNAKE_CASE   :				Any							    = GPTaLMHeadModel.from_pretrained("gpt2"				)
  set_seed(4_2				)

  # Generate train and test data to train and evaluate gpt2 model
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				List[Any]							    = generate_datasets(
      context_len=3_2,						file="data/tokenized_stories_train_wikitext103.jbl",						number=1_0_0,						min_len=1_0_2_6,						trim=lowerCamelCase__				)

  # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
  finetune(
      lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__,						context_len=3_2,						max_steps=1_0_0_0,						batch_size=1_6,						threshold=1.0,						recopy_model=lowerCamelCase__,						secondary_learner=lowerCamelCase__,						eval_interval=1_0,						finetuned_model_name="gpt2_finetuned.pt",						)


if __name__ == "__main__":
      main()





"""simple docstring"""
import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE      ,       unittest.TestCase  ):
     A__      =			None
     A__      =			BloomTokenizerFast
     A__      =			BloomTokenizerFast
     A__      =			True
     A__      =			False
     A__      =			"""tokenizer_file"""
     A__      =			{"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       super().setUp()
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = BloomTokenizerFast.from_pretrained("bigscience/tokenizer"				)
       tokenizer.save_pretrained(self.tmpdirname				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     **snake_case__				):




       """simple docstring"""


       kwargs.update(self.special_tokens_map				)
       return BloomTokenizerFast.from_pretrained(self.tmpdirname  ,     **snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = self.get_rust_tokenizer()

       _SCREAMING_SNAKE_CASE   :				Tuple							    = ["The quick brown fox</s>", "jumps over the lazy dog</s>"]
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = [[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = tokenizer.batch_encode_plus(snake_case__				)["input_ids"]
       self.assertListEqual(snake_case__  ,     snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Tuple							    = tokenizer.batch_decode(snake_case__				)
       self.assertListEqual(snake_case__  ,     snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__=6				):




       """simple docstring"""


       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''				):
           _SCREAMING_SNAKE_CASE   :				Tuple							    = self.rust_tokenizer_class.from_pretrained(snake_case__  ,     **snake_case__				)
           # tokenizer_r.pad_token = None # Hotfixing padding = None
           # Simple input
           _SCREAMING_SNAKE_CASE   :				str							    = "This is a simple input"
           _SCREAMING_SNAKE_CASE   :				Tuple							    = ["This is a simple input 1", "This is a simple input 2"]
           _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = ("This is a simple input", "This is a pair")
           _SCREAMING_SNAKE_CASE   :				List[str]							    = [
               ("This is a simple input 1", "This is a simple input 2"),
               ("This is a simple pair 1", "This is a simple pair 2"),
           ]

           # Simple input tests
           try:
             tokenizer_r.encode(snake_case__  ,     max_length=snake_case__				)
             tokenizer_r.encode_plus(snake_case__  ,     max_length=snake_case__				)

             tokenizer_r.batch_encode_plus(snake_case__  ,     max_length=snake_case__				)
             tokenizer_r.encode(snake_case__  ,     max_length=snake_case__				)
             tokenizer_r.batch_encode_plus(snake_case__  ,     max_length=snake_case__				)
           except ValueError:
             self.fail("Bloom Tokenizer should be able to deal with padding"				)

           _SCREAMING_SNAKE_CASE   :				Tuple							    = None  # Hotfixing padding = None
           self.assertRaises(snake_case__  ,     tokenizer_r.encode  ,     snake_case__  ,     max_length=snake_case__  ,     padding="max_length"				)

           # Simple input
           self.assertRaises(snake_case__  ,     tokenizer_r.encode_plus  ,     snake_case__  ,     max_length=snake_case__  ,     padding="max_length"				)

           # Simple input
           self.assertRaises(
               snake_case__  ,     tokenizer_r.batch_encode_plus  ,     snake_case__  ,     max_length=snake_case__  ,     padding="max_length"  ,     )

           # Pair input
           self.assertRaises(snake_case__  ,     tokenizer_r.encode  ,     snake_case__  ,     max_length=snake_case__  ,     padding="max_length"				)

           # Pair input
           self.assertRaises(snake_case__  ,     tokenizer_r.encode_plus  ,     snake_case__  ,     max_length=snake_case__  ,     padding="max_length"				)

           # Pair input
           self.assertRaises(
               snake_case__  ,     tokenizer_r.batch_encode_plus  ,     snake_case__  ,     max_length=snake_case__  ,     padding="max_length"  ,     )
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self.get_rust_tokenizer()
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = load_dataset("xnli"  ,     "all_languages"  ,     split="test"  ,     streaming=snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Tuple							    = next(iter(snake_case__				)				)["premise"]  # pick up one data
       _SCREAMING_SNAKE_CASE   :				int							    = list(sample_data.values()				)

       _SCREAMING_SNAKE_CASE   :				List[str]							    = list(map(tokenizer.encode  ,     snake_case__				)				)
       _SCREAMING_SNAKE_CASE   :				int							    = [tokenizer.decode(snake_case__  ,     clean_up_tokenization_spaces=snake_case__				) for x in output_tokens]
       self.assertListEqual(snake_case__  ,     snake_case__				)




     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map				)  ,     1				)
       self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()				)[0]				)  ,     1				)



