


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   (lowerCAmelCase__=32		,				lowerCAmelCase__=10		,				lowerCAmelCase__=100		,				lowerCAmelCase__=1_026		,				lowerCAmelCase__=True		,				lowerCAmelCase__="data/tokenized_stories_train_wikitext103.jbl"		,				lowerCAmelCase__="igf_context_pairs.jbl"		,				):
   set_seed(3   )
   # generate train_data and objective_set
   __a	,    __a					=  generate_datasets(
       lowerCAmelCase__		,				lowerCAmelCase__		,				number=lowerCAmelCase__		,				min_len=1_026		,				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?
   __a					=  torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""   )

   # load pretrained model
   __a					=  load_gpta("""gpt2"""   ).to(lowerCAmelCase__   )
   print("""computing perplexity on objective set"""   )
   __a					=  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  a   (lowerCAmelCase__		,				lowerCAmelCase__=15		,				lowerCAmelCase__=128		,				lowerCAmelCase__=100		,				lowerCAmelCase__="igf_model.pt"		,				):
   set_seed(42   )

   # Load pre-trained model
   __a					=  GPTaLMHeadModel.from_pretrained("""gpt2"""   )

   # Initialize secondary learner to use embedding weights of model
   __a					=  SecondaryLearner(lowerCAmelCase__   )

   # Train secondary learner
   __a					=  train_secondary_learner(
       lowerCAmelCase__		,				lowerCAmelCase__		,				max_epochs=lowerCAmelCase__		,				batch_size=lowerCAmelCase__		,				eval_freq=100		,				igf_model_path=lowerCAmelCase__		,				)

   del model, secondary_learner_train_data
   torch.cuda.empty_cache()

   return secondary_learner




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__=32		,				lowerCAmelCase__=1_000		,				lowerCAmelCase__=16		,				lowerCAmelCase__=1.0		,				lowerCAmelCase__=recopy_gpta		,				lowerCAmelCase__=None		,				lowerCAmelCase__=10		,				lowerCAmelCase__="gpt2_finetuned.pt"		,				):
   __a					=  torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""   )
   __a					=  RandomSampler(lowerCAmelCase__   )
   __a					=  DataLoader(lowerCAmelCase__		,				sampler=lowerCAmelCase__   )

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

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

   __a					=  []
   __a					=  []

   # Compute the performance of the transformer model at the beginning
   __a					=  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()
         __a					=  random.randint(0		,				example.size(2   ) - context_len - 1   )
         __a					=  example[0, 0, start : start + context_len]
         lm_optimizer.zero_grad()
         __a					=  model(lowerCAmelCase__		,				labels=lowerCAmelCase__   )
         __a					=  True

         if secondary_learner is not None:
            __a					=  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 == 10:
               __a					=  -1
            if predicted_q < threshold:
               __a					=  False

            # If we passed the filter, add the context to the batch!
         if do_backprop:
            contexts.append(np.array(context.cpu()   )   )
            __a					=  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()
            __a					=  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:
               __a					=  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 > 60:
            break
      if max_steps > 0 and global_step > 60:
         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  a   ():
   __a					=  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=32		,				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=100		,				type=lowerCAmelCase__		,				help="""number of articles that are long enough to be used as our objective set"""		,				)
   parser.add_argument(
       """--eval_freq"""		,				default=100		,				type=lowerCAmelCase__		,				help="""secondary model evaluation is triggered at eval_freq"""   )

   parser.add_argument("""--max_steps"""		,				default=1_000		,				type=lowerCAmelCase__		,				help="""To calculate training epochs"""   )

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

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

   parser.add_argument(
       """--eval_interval"""		,				default=10		,				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=100		,				type=lowerCAmelCase__		,				help="""The number of examples split to be used as objective_set/test_data"""   )

   parser.add_argument(
       """--min_len"""		,				default=1_026		,				type=lowerCAmelCase__		,				help="""The minimum length of the article to be used as objective set"""   )

   parser.add_argument(
       """--secondary_learner_max_epochs"""		,				default=15		,				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=32		,				max_steps=10		,				size_objective_set=100		,				min_len=1_026		,				trim=lowerCAmelCase__		,				data_file="""data/tokenized_stories_train_wikitext103.jbl"""		,				igf_data_file="""igf_context_pairs.jbl"""		,				)

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

   # Train secondary learner
   __a					=  training_secondary_learner(
       lowerCAmelCase__		,				secondary_learner_max_epochs=15		,				secondary_learner_batch_size=128		,				eval_freq=100		,				igf_model_path="""igf_model.pt"""		,				)

   # load pretrained gpt2 model
   __a					=  GPTaLMHeadModel.from_pretrained("""gpt2"""   )
   set_seed(42   )

   # Generate train and test data to train and evaluate gpt2 model
   __a	,    __a					=  generate_datasets(
       context_len=32		,				file="""data/tokenized_stories_train_wikitext103.jbl"""		,				number=100		,				min_len=1_026		,				trim=lowerCAmelCase__   )

   # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
   finetune(
       lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				context_len=32		,				max_steps=1_000		,				batch_size=16		,				threshold=1.0		,				recopy_model=lowerCAmelCase__		,				secondary_learner=lowerCAmelCase__		,				eval_interval=10		,				finetuned_model_name="""gpt2_finetuned.pt"""		,				)


if __name__ == "__main__":
  main()







'''simple docstring'''







import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       unittest.TestCase						):



     """simple docstring"""






     __magic_name__           =     RoFormerTokenizer
     __magic_name__           =     RoFormerTokenizerFast
     __magic_name__           =     True
     __magic_name__           =     True







     def a						(			self ):

            '''simple docstring'''

            super().setUp()







     def a						(			self   ,						**snake_case__ ):

            '''simple docstring'''

            return self.tokenizer_class.from_pretrained('junnyu/roformer_chinese_base'   ,						**snake_case__ )







     def a						(			self   ,						**snake_case__ ):

            '''simple docstring'''

            return self.rust_tokenizer_class.from_pretrained('junnyu/roformer_chinese_base'   ,						**snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							'永和服装饰品有限公司,今天天气非常好'
            _lowerCAmelCase     :				str       =							'永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'
            return input_text, output_text







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							self.get_tokenizer()
            _lowerCAmelCase      ,  _lowerCAmelCase     :				int       =							self.get_chinese_input_output_texts()
            _lowerCAmelCase     :				List[Any]       =							tokenizer.tokenize(snake_case__ )

            self.assertListEqual(snake_case__   ,						output_text.split() )

            _lowerCAmelCase     :				Optional[Any]       =							tokens + [tokenizer.unk_token]
            _lowerCAmelCase     :				List[str]       =							[2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ )   ,						snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							self.get_rust_tokenizer()
            _lowerCAmelCase      ,  _lowerCAmelCase     :				Any       =							self.get_chinese_input_output_texts()
            _lowerCAmelCase     :				Optional[int]       =							tokenizer.tokenize(snake_case__ )
            self.assertListEqual(snake_case__   ,						output_text.split() )
            _lowerCAmelCase     :				Tuple       =							tokens + [tokenizer.unk_token]
            _lowerCAmelCase     :				List[Any]       =							[2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ )   ,						snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            pass







     def a						(			self ):

            '''simple docstring'''

            pass







     def a						(			self ):

            '''simple docstring'''

            pass

