





"""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_       (   UpperCAmelCase_=32  ,			UpperCAmelCase_=10  ,			UpperCAmelCase_=1_00  ,			UpperCAmelCase_=10_26  ,			UpperCAmelCase_=True  ,			UpperCAmelCase_="data/tokenized_stories_train_wikitext103.jbl"  ,			UpperCAmelCase_="igf_context_pairs.jbl"  ,			)						->Optional[int]:





		"""simple docstring"""


		set_seed(3  )
		# generate train_data and objective_set
		__UpperCAmelCase       ,     __UpperCAmelCase    :						List[str]       =						generate_datasets(
		    UpperCAmelCase_  ,			UpperCAmelCase_  ,			number=UpperCAmelCase_  ,			min_len=10_26  ,			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?
		__UpperCAmelCase    :						List[Any]       =						torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''  )

		# load pretrained model
		__UpperCAmelCase    :						Optional[Any]       =						load_gpta('''gpt2'''  ).to(UpperCAmelCase_  )
		print('''computing perplexity on objective set'''  )
		__UpperCAmelCase    :						Any       =						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_  ,			UpperCAmelCase_=15  ,			UpperCAmelCase_=1_28  ,			UpperCAmelCase_=1_00  ,			UpperCAmelCase_="igf_model.pt"  ,			)						->Optional[int]:





		"""simple docstring"""


		set_seed(42  )

		# Load pre-trained model
		__UpperCAmelCase    :						Optional[int]       =						GPTaLMHeadModel.from_pretrained('''gpt2'''  )

		# Initialize secondary learner to use embedding weights of model
		__UpperCAmelCase    :						int       =						SecondaryLearner(UpperCAmelCase_  )

		# Train secondary learner
		__UpperCAmelCase    :						Optional[int]       =						train_secondary_learner(
		    UpperCAmelCase_  ,			UpperCAmelCase_  ,			max_epochs=UpperCAmelCase_  ,			batch_size=UpperCAmelCase_  ,			eval_freq=1_00  ,			igf_model_path=UpperCAmelCase_  ,			)

		del model, secondary_learner_train_data
		torch.cuda.empty_cache()

		return secondary_learner


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=32  ,			UpperCAmelCase_=10_00  ,			UpperCAmelCase_=16  ,			UpperCAmelCase_=1.0  ,			UpperCAmelCase_=recopy_gpta  ,			UpperCAmelCase_=None  ,			UpperCAmelCase_=10  ,			UpperCAmelCase_="gpt2_finetuned.pt"  ,			)						->Optional[Any]:





		"""simple docstring"""


		__UpperCAmelCase    :						List[str]       =						torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''  )
		__UpperCAmelCase    :						Optional[int]       =						RandomSampler(UpperCAmelCase_  )
		__UpperCAmelCase    :						Dict       =						DataLoader(UpperCAmelCase_  ,			sampler=UpperCAmelCase_  )

		__UpperCAmelCase    :						int       =						max_steps // (len(UpperCAmelCase_  )) + 1
		__UpperCAmelCase    :						Union[str, Any]       =						0
		__UpperCAmelCase    :						Dict       =						torch.zeros((1, context_len)  ,			dtype=torch.long  ,			device=UpperCAmelCase_  )
		__UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase    :						int       =						recopy_model(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )

		model.train()
		if secondary_learner is not None:
				secondary_learner.to(UpperCAmelCase_  )
				secondary_learner.eval()
		__UpperCAmelCase    :						int       =						[]
		__UpperCAmelCase    :						int       =						0

		__UpperCAmelCase    :						Tuple       =						[]
		__UpperCAmelCase    :						List[Any]       =						[]

		# Compute the performance of the transformer model at the beginning
		__UpperCAmelCase    :						Optional[int]       =						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()
						__UpperCAmelCase    :						Union[str, Any]       =						random.randint(0  ,			example.size(2  ) - context_len - 1  )
						__UpperCAmelCase    :						str       =						example[0, 0, start : start + context_len]
						lm_optimizer.zero_grad()
						__UpperCAmelCase    :						Optional[Any]       =						model(UpperCAmelCase_  ,			labels=UpperCAmelCase_  )
						__UpperCAmelCase    :						Union[str, Any]       =						True

						if secondary_learner is not None:
								__UpperCAmelCase    :						List[Any]       =						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:
										__UpperCAmelCase    :						Union[str, Any]       =						-1
								if predicted_q < threshold:
										__UpperCAmelCase    :						Tuple       =						False

            # If we passed the filter, add the context to the batch!
						if do_backprop:
								contexts.append(np.array(context.cpu()  )  )
								__UpperCAmelCase    :						List[str]       =						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    :						Union[str, Any]       =						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    :						Optional[Any]       =						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_       (   )						->List[str]:





		"""simple docstring"""


		__UpperCAmelCase    :						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=1_00  ,			type=UpperCAmelCase_  ,			help='''number of articles that are long enough to be used as our objective set'''  ,			)
		parser.add_argument(
		    '''--eval_freq'''  ,			default=1_00  ,			type=UpperCAmelCase_  ,			help='''secondary model evaluation is triggered at eval_freq'''  )

		parser.add_argument('''--max_steps'''  ,			default=10_00  ,			type=UpperCAmelCase_  ,			help='''To calculate training epochs'''  )

		parser.add_argument(
		    '''--secondary_learner_batch_size'''  ,			default=1_28  ,			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=1_00  ,			type=UpperCAmelCase_  ,			help='''The number of examples split to be used as objective_set/test_data'''  )

		parser.add_argument(
		    '''--min_len'''  ,			default=10_26  ,			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=1_00  ,			min_len=10_26  ,			trim=UpperCAmelCase_  ,			data_file='''data/tokenized_stories_train_wikitext103.jbl'''  ,			igf_data_file='''igf_context_pairs.jbl'''  ,			)

		# Load train data for secondary learner
		__UpperCAmelCase    :						Optional[Any]       =						joblib.load('''data/IGF_values.jbl'''  )

		# Train secondary learner
		__UpperCAmelCase    :						Any       =						training_secondary_learner(
		    UpperCAmelCase_  ,			secondary_learner_max_epochs=15  ,			secondary_learner_batch_size=1_28  ,			eval_freq=1_00  ,			igf_model_path='''igf_model.pt'''  ,			)

		# load pretrained gpt2 model
		__UpperCAmelCase    :						Optional[int]       =						GPTaLMHeadModel.from_pretrained('''gpt2'''  )
		set_seed(42  )

		# Generate train and test data to train and evaluate gpt2 model
		__UpperCAmelCase       ,     __UpperCAmelCase    :						int       =						generate_datasets(
		    context_len=32  ,			file='''data/tokenized_stories_train_wikitext103.jbl'''  ,			number=1_00  ,			min_len=10_26  ,			trim=UpperCAmelCase_  )

		# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
		finetune(
		    UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			context_len=32  ,			max_steps=10_00  ,			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()






"""simple docstring"""





from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

lowercase__				:int				   =       TypeVar('T')




class 							snake_case							(			Generic[T] ):

      '''simple docstring'''



      def __init__(							self       :      Optional[Any]						,				__lowercase       :      list[T]						,				__lowercase       :      Callable[[T, T], T] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Any | T       =						None

        __UpperCAmelCase    :						int       =						len(__lowercase )
        __UpperCAmelCase    :						list[T]       =						[any_type for _ in range(self.N )] + arr
        __UpperCAmelCase    :						List[Any]       =						fnc
        self.build()

      def       A_					(							self       :      str ):


        '''simple docstring'''




        for p in range(self.N - 1						,				0						,				-1 ):
          __UpperCAmelCase    :						Any       =						self.fn(self.st[p * 2]						,				self.st[p * 2 + 1] )

      def       A_					(							self       :      Union[str, Any]						,				__lowercase       :      int						,				__lowercase       :      T ):


        '''simple docstring'''




        p += self.N
        __UpperCAmelCase    :						Tuple       =						v
        while p > 1:
          __UpperCAmelCase    :						Union[str, Any]       =						p // 2
          __UpperCAmelCase    :						Any       =						self.fn(self.st[p * 2]						,				self.st[p * 2 + 1] )







      def       A_					(							self       :      str						,				__lowercase       :      int						,				__lowercase       :      int ):  # noqa: E741


        '''simple docstring'''




        __UpperCAmelCase       ,     __UpperCAmelCase    :						int       =						l + self.N, r + self.N

        __UpperCAmelCase    :						T | None       =						None
        while l <= r:
          if l % 2 == 1:
            __UpperCAmelCase    :						Any       =						self.st[l] if res is None else self.fn(__lowercase						,				self.st[l] )
          if r % 2 == 0:
            __UpperCAmelCase    :						Any       =						self.st[r] if res is None else self.fn(__lowercase						,				self.st[r] )
          __UpperCAmelCase       ,     __UpperCAmelCase    :						Any       =						(l + 1) // 2, (r - 1) // 2
        return res


if __name__ == "__main__":
  from functools import reduce

  lowercase__				:Any				   =       [1, 1_0, -2, 9, -3, 8, 4, -7, 5, 6, 1_1, -1_2]

  lowercase__				:List[Any]				   =       {
      0: 7,
      1: 2,
      2: 6,
      3: -1_4,
      4: 5,
      5: 4,
      6: 7,
      7: -1_0,
      8: 9,
      9: 1_0,
      1_0: 1_2,
      1_1: 1,
  }

  lowercase__				:List[str]				   =       SegmentTree(test_array, min)
  lowercase__				:List[str]				   =       SegmentTree(test_array, max)
  lowercase__				:Tuple				   =       SegmentTree(test_array, lambda a, b: a + b)


  def       lowerCamelCase_       (   )						->None:





    """simple docstring"""


    for i in range(len(UpperCAmelCase_  )  ):
      for j in range(UpperCAmelCase_  ,			len(UpperCAmelCase_  )  ):
        __UpperCAmelCase    :						Optional[Any]       =						reduce(UpperCAmelCase_  ,			test_array[i : j + 1]  )
        __UpperCAmelCase    :						Optional[int]       =						reduce(UpperCAmelCase_  ,			test_array[i : j + 1]  )
        __UpperCAmelCase    :						Union[str, Any]       =						reduce(lambda UpperCAmelCase_  ,			UpperCAmelCase_  : a + b  ,			test_array[i : j + 1]  )
        assert min_range == min_segment_tree.query(UpperCAmelCase_  ,			UpperCAmelCase_  )
        assert max_range == max_segment_tree.query(UpperCAmelCase_  ,			UpperCAmelCase_  )
        assert sum_range == sum_segment_tree.query(UpperCAmelCase_  ,			UpperCAmelCase_  )

  test_all_segments()

  for index, value in test_updates.items():
    lowercase__				:Optional[Any]				   =       value
    min_segment_tree.update(index, value)
    max_segment_tree.update(index, value)
    sum_segment_tree.update(index, value)
    test_all_segments()