


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_      (    SCREAMING_SNAKE_CASE__					:      Union[str, Any]=32		,			SCREAMING_SNAKE_CASE__					:      Optional[Any]=10		,			SCREAMING_SNAKE_CASE__					:      Optional[Any]=100		,			SCREAMING_SNAKE_CASE__					:      Tuple=1_026		,			SCREAMING_SNAKE_CASE__					:      Any=True		,			SCREAMING_SNAKE_CASE__					:      Optional[int]="data/tokenized_stories_train_wikitext103.jbl"		,			SCREAMING_SNAKE_CASE__					:      List[Any]="igf_context_pairs.jbl"		,			):
       '''simple docstring'''





       set_seed(3     )
       # generate train_data and objective_set
       _lowerCamelCase	,     _lowerCamelCase       :		int		=generate_datasets(
           SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			number=SCREAMING_SNAKE_CASE__		,			min_len=1_026		,			trim=SCREAMING_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?
       _lowerCamelCase       :		Tuple		=torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'     )

       # load pretrained model
       _lowerCamelCase       :		Dict		=load_gpta('gpt2'     ).to(SCREAMING_SNAKE_CASE__     )
       print('computing perplexity on objective set'     )
       _lowerCamelCase       :		Optional[int]		=compute_perplexity(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     ).item()
       print('perplexity on objective set:'		,			SCREAMING_SNAKE_CASE__     )

       # collect igf pairs and save to file demo.jbl
       collect_objective_set(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_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_      (    SCREAMING_SNAKE_CASE__					:      Optional[int]		,			SCREAMING_SNAKE_CASE__					:      List[str]=15		,			SCREAMING_SNAKE_CASE__					:      List[Any]=128		,			SCREAMING_SNAKE_CASE__					:      Any=100		,			SCREAMING_SNAKE_CASE__					:      Union[str, Any]="igf_model.pt"		,			):
       '''simple docstring'''





       set_seed(42     )

       # Load pre-trained model
       _lowerCamelCase       :		Dict		=GPTaLMHeadModel.from_pretrained('gpt2'     )

       # Initialize secondary learner to use embedding weights of model
       _lowerCamelCase       :		List[Any]		=SecondaryLearner(SCREAMING_SNAKE_CASE__     )

       # Train secondary learner
       _lowerCamelCase       :		str		=train_secondary_learner(
           SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			max_epochs=SCREAMING_SNAKE_CASE__		,			batch_size=SCREAMING_SNAKE_CASE__		,			eval_freq=100		,			igf_model_path=SCREAMING_SNAKE_CASE__		,			)

       del model, secondary_learner_train_data
       torch.cuda.empty_cache()

       return secondary_learner




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Optional[Any]		,			SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      Optional[Any]		,			SCREAMING_SNAKE_CASE__					:      List[str]=32		,			SCREAMING_SNAKE_CASE__					:      str=1_000		,			SCREAMING_SNAKE_CASE__					:      str=16		,			SCREAMING_SNAKE_CASE__					:      List[str]=1.0		,			SCREAMING_SNAKE_CASE__					:      Tuple=recopy_gpta		,			SCREAMING_SNAKE_CASE__					:      Dict=None		,			SCREAMING_SNAKE_CASE__					:      List[str]=10		,			SCREAMING_SNAKE_CASE__					:      Optional[int]="gpt2_finetuned.pt"		,			):
       '''simple docstring'''





       _lowerCamelCase       :		str		=torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'     )
       _lowerCamelCase       :		Optional[Any]		=RandomSampler(SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		List[Any]		=DataLoader(SCREAMING_SNAKE_CASE__		,			sampler=SCREAMING_SNAKE_CASE__     )

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

       model.train()
       if secondary_learner is not None:
              secondary_learner.to(SCREAMING_SNAKE_CASE__     )
              secondary_learner.eval()
       _lowerCamelCase       :		Optional[int]		=[]
       _lowerCamelCase       :		Dict		=0

       _lowerCamelCase       :		List[Any]		=[]
       _lowerCamelCase       :		Optional[Any]		=[]

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

                     if secondary_learner is not None:
                            _lowerCamelCase       :		List[Any]		=secondary_learner.forward(
                                torch.tensor(SCREAMING_SNAKE_CASE__		,			dtype=torch.long		,			device=SCREAMING_SNAKE_CASE__     ).unsqueeze(0     )     )[0].item()
                            observed_qs.append(float(SCREAMING_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:
                                   _lowerCamelCase       :		Any		=-1
                            if predicted_q < threshold:
                                   _lowerCamelCase       :		List[str]		=False

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

                                   print('Test perplexity, step'		,			SCREAMING_SNAKE_CASE__		,			':'		,			SCREAMING_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()		,			SCREAMING_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_      (    ):
       '''simple docstring'''





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

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

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

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

       parser.add_argument(
           '--context_len'		,			default=32		,			type=SCREAMING_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=SCREAMING_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=SCREAMING_SNAKE_CASE__		,			help='secondary model evaluation is triggered at eval_freq'     )

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

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

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

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

       parser.add_argument(
           '--min_len'		,			default=1_026		,			type=SCREAMING_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=SCREAMING_SNAKE_CASE__		,			help='number of epochs to train secondary learner'     )

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

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

       parser.add_argument(
           '--recopy_model'		,			default=SCREAMING_SNAKE_CASE__		,			type=SCREAMING_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=1_026		,			trim=SCREAMING_SNAKE_CASE__		,			data_file='data/tokenized_stories_train_wikitext103.jbl'		,			igf_data_file='igf_context_pairs.jbl'		,			)

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

       # Train secondary learner
       _lowerCamelCase       :		Optional[Any]		=training_secondary_learner(
           SCREAMING_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
       _lowerCamelCase       :		str		=GPTaLMHeadModel.from_pretrained('gpt2'     )
       set_seed(42     )

       # Generate train and test data to train and evaluate gpt2 model
       _lowerCamelCase	,     _lowerCamelCase       :		Optional[Any]		=generate_datasets(
           context_len=32		,			file='data/tokenized_stories_train_wikitext103.jbl'		,			number=100		,			min_len=1_026		,			trim=SCREAMING_SNAKE_CASE__     )

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


if __name__ == "__main__":
 main()






def 	a_      (    SCREAMING_SNAKE_CASE__					:      str		,			SCREAMING_SNAKE_CASE__					:      str     ):
       '''simple docstring'''





       assert x is not None
       assert y is not None

       _lowerCamelCase       :		Dict		=len(SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		Optional[Any]		=len(SCREAMING_SNAKE_CASE__     )

       # declaring the array for storing the dp values
       _lowerCamelCase       :		Optional[int]		=[[0] * (n + 1) for _ in range(m + 1     )]  # noqa: E741

       for i in range(1		,			m + 1     ):
              for j in range(1		,			n + 1     ):
                     _lowerCamelCase       :		int		=1 if x[i - 1] == y[j - 1] else 0

                     _lowerCamelCase       :		Union[str, Any]		=max(l[i - 1][j]		,			l[i][j - 1]		,			l[i - 1][j - 1] + match     )

       _lowerCamelCase       :		Optional[int]		=''
       _lowerCamelCase	,     _lowerCamelCase       :		Dict		=m, n
       while i > 0 and j > 0:
              _lowerCamelCase       :		int		=1 if x[i - 1] == y[j - 1] else 0

              if l[i][j] == l[i - 1][j - 1] + match:
                     if match == 1:
                            _lowerCamelCase       :		Union[str, Any]		=x[i - 1] + seq
                     i -= 1
                     j -= 1
              elif l[i][j] == l[i - 1][j]:
                     i -= 1
              else:
                     j -= 1

       return l[m][n], seq


if __name__ == "__main__":
 lowerCamelCase											=       'AGGTAB'
 lowerCamelCase											=       'GXTXAYB'
 lowerCamelCase											=       4
 lowerCamelCase											=       'GTAB'

 lowerCamelCase					,				lowerCamelCase											=       longest_common_subsequence(a, b)
 print('len =', ln, ', sub-sequence =', subseq)
 import doctest

 doctest.testmod()


