


import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser
def     __lowerCamelCase						(			UpperCAmelCase_						:		Tuple=None			,  UpperCAmelCase_						:		Tuple=None						):
      return field(default_factory=lambda: default			,  metadata=UpperCAmelCase_						)







@dataclass
class 		_snake_case  :
 SCREAMING_SNAKE_CASE__				=	field(
     metadata={'help': 'The csv file to plot.'}						,   )
 SCREAMING_SNAKE_CASE__				=	field(
     default=_snake_case						,   metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'}						,   )
 SCREAMING_SNAKE_CASE__				=	field(
     default=_snake_case						,   metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'}						,   )
 SCREAMING_SNAKE_CASE__				=	field(
     default=_snake_case						,   metadata={'help': 'Disable logarithmic scale when plotting'}						,   )
 SCREAMING_SNAKE_CASE__				=	field(
     default=_snake_case						,   metadata={
         'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
     }						,   )
 SCREAMING_SNAKE_CASE__				=	field(
     default=_snake_case						,   metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'}						,   )
 SCREAMING_SNAKE_CASE__				=	list_field(
     default=_snake_case						,   metadata={'help': 'List of model names that are used instead of the ones in the csv file.'}							)







def     __lowerCamelCase						(			UpperCAmelCase_						:		Optional[int]						):





      try:
            int(UpperCAmelCase_						)
            return True
      except ValueError:
            return False
def     __lowerCamelCase						(			UpperCAmelCase_						:		Optional[Any]						):





      try:
            float(UpperCAmelCase_						)
            return True
      except ValueError:
            return False







class 		_snake_case  :




 def __init__(		self ,  _lowerCamelCase   ):
       a						:Any										=	args
       a						:int										=	defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}   )

       with open(self.args.csv_file ,  newline=''''''   ) as csv_file:
             a						:Union[str, Any]										=	csv.DictReader(_lowerCamelCase   )
             for row in reader:
                   a						:Dict										=	row['''model''']
                   self.result_dict[model_name]["bsz"].append(int(row['''batch_size''']   )   )
                   self.result_dict[model_name]["seq_len"].append(int(row['''sequence_length''']   )   )
                   if can_convert_to_int(row['''result''']   ):
                         # value is not None
                         a						:Dict										=	int(row['''result''']   )
                   elif can_convert_to_float(row['''result''']   ):
                         # value is not None
                         a						:Optional[int]										=	float(row['''result''']   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:str										=	plt.subplots()
       a						:List[Any]										=	'''Time usage''' if self.args.is_time else '''Memory usage'''
       a						:str										=	title_str + ''' for training''' if self.args.is_train else title_str + ''' for inference'''

       if not self.args.no_log_scale:
             # set logarithm scales
             ax.set_xscale('''log'''   )
             ax.set_yscale('''log'''   )

       for axis in [ax.xaxis, ax.yaxis]:
             axis.set_major_formatter(ScalarFormatter()   )

       for model_name_idx, model_name in enumerate(self.result_dict.keys()   ):
             a						:Any										=	sorted(set(self.result_dict[model_name]['''bsz''']   )   )
             a						:Optional[int]										=	sorted(set(self.result_dict[model_name]['''seq_len''']   )   )
             a						:Optional[Any]										=	self.result_dict[model_name]['''result''']

             (a)						:Optional[int]										=	(
                 (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
             )

             a						:Any										=	(
                 model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
             )

             for inner_loop_value in inner_loop_array:
                   if self.args.plot_along_batch:
                         a						:Any										=	np.asarray(
                             [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] ,  dtype=_lowerCamelCase ,  )
                   else:
                         a						:Dict										=	np.asarray(
                             [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] ,  dtype=np.floataa ,  )

                   (a)						:Union[str, Any]										=	(
                       ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
                   )

                   a						:Any										=	np.asarray(_lowerCamelCase ,  _lowerCamelCase   )[: len(_lowerCamelCase   )]
                   plt.scatter(
                       _lowerCamelCase ,  _lowerCamelCase ,  label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''   )
                   plt.plot(_lowerCamelCase ,  _lowerCamelCase ,  '''--'''   )

             title_str += F''' {label_model_name} vs.'''

       a						:int										=	title_str[:-4]
       a						:List[str]										=	'''Time in s''' if self.args.is_time else '''Memory in MB'''

       # plot
       plt.title(_lowerCamelCase   )
       plt.xlabel(_lowerCamelCase   )
       plt.ylabel(_lowerCamelCase   )
       plt.legend()

       if self.args.figure_png_file is not None:
             plt.savefig(self.args.figure_png_file   )
       else:
             plt.show()







def     __lowerCamelCase						(			):
      a						:str										=	HfArgumentParser(UpperCAmelCase_						)
      a						:List[str]										=	parser.parse_args_into_dataclasses()[0]
      a						:Optional[int]										=	Plot(args=UpperCAmelCase_						)
      plot.plot()


if __name__ == "__main__":
     main()









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						(			UpperCAmelCase_						:		Optional[int]=32			,  UpperCAmelCase_						:		Any=10			,  UpperCAmelCase_						:		Any=100			,  UpperCAmelCase_						:		List[str]=1026			,  UpperCAmelCase_						:		Optional[int]=True			,  UpperCAmelCase_						:		str="data/tokenized_stories_train_wikitext103.jbl"			,  UpperCAmelCase_						:		List[Any]="igf_context_pairs.jbl"			,  ):




      """simple docstring"""





      set_seed(3						)
      # generate train_data and objective_set
      a							,    a						:Optional[int]										=	generate_datasets(
          UpperCAmelCase_			,  UpperCAmelCase_			,  number=UpperCAmelCase_			,  min_len=1026			,  trim=UpperCAmelCase_						)
      # 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						:str										=	torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''						)

      # load pretrained model
      a						:str										=	load_gpta('''gpt2'''						).to(UpperCAmelCase_						)
      print('''computing perplexity on objective set'''						)
      a						:Dict										=	compute_perplexity(UpperCAmelCase_			,  UpperCAmelCase_			,  UpperCAmelCase_						).item()
      print('''perplexity on objective set:'''			,  UpperCAmelCase_						)

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

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




      """simple docstring"""





      set_seed(42						)

      # Load pre-trained model
      a						:Tuple										=	GPTaLMHeadModel.from_pretrained('''gpt2'''						)

      # Initialize secondary learner to use embedding weights of model
      a						:List[str]										=	SecondaryLearner(UpperCAmelCase_						)

      # Train secondary learner
      a						:List[str]										=	train_secondary_learner(
          UpperCAmelCase_			,  UpperCAmelCase_			,  max_epochs=UpperCAmelCase_			,  batch_size=UpperCAmelCase_			,  eval_freq=100			,  igf_model_path=UpperCAmelCase_			,  )

      del model, secondary_learner_train_data
      torch.cuda.empty_cache()

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




      """simple docstring"""





      a						:Optional[Any]										=	torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''						)
      a						:Optional[Any]										=	RandomSampler(UpperCAmelCase_						)
      a						:Union[str, Any]										=	DataLoader(UpperCAmelCase_			,  sampler=UpperCAmelCase_						)

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

      model.train()
      if secondary_learner is not None:
            secondary_learner.to(UpperCAmelCase_						)
            secondary_learner.eval()
      a						:Optional[Any]										=	[]
      a						:Union[str, Any]										=	0

      a						:Optional[Any]										=	[]
      a						:Tuple										=	[]

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

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

                        # 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						:List[str]										=	-1
                        if predicted_q < threshold:
                              a						:Tuple										=	False

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

                              print('''Test perplexity, step'''			,  UpperCAmelCase_			,  ''':'''			,  UpperCAmelCase_						)
            # 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()			,  UpperCAmelCase_						)
      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						(			):




      """simple docstring"""





      a						:Union[str, Any]										=	argparse.ArgumentParser(description='''Fine-tune a transformer model with IGF on a language modeling task'''						)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

      # Train secondary learner
      a						:Any										=	training_secondary_learner(
          UpperCAmelCase_			,  secondary_learner_max_epochs=15			,  secondary_learner_batch_size=128			,  eval_freq=100			,  igf_model_path='''igf_model.pt'''			,  )

      # load pretrained gpt2 model
      a						:Any										=	GPTaLMHeadModel.from_pretrained('''gpt2'''						)
      set_seed(42						)

      # Generate train and test data to train and evaluate gpt2 model
      a							,    a						:Union[str, Any]										=	generate_datasets(
          context_len=32			,  file='''data/tokenized_stories_train_wikitext103.jbl'''			,  number=100			,  min_len=1026			,  trim=UpperCAmelCase_						)

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


if __name__ == "__main__":
     main()





