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  (   SCREAMING_SNAKE_CASE=32  ,			SCREAMING_SNAKE_CASE=10  ,			SCREAMING_SNAKE_CASE=100  ,			SCREAMING_SNAKE_CASE=1_026  ,			SCREAMING_SNAKE_CASE=True  ,			SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl"  ,			SCREAMING_SNAKE_CASE="igf_context_pairs.jbl"  ,			):
    '''simple docstring'''







    set_seed(3	)
    # generate train_data and objective_set
    __UpperCamelCase  ,       __UpperCamelCase     :Optional[Any]			  =		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?
    __UpperCamelCase     :List[Any]			  =		torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''	)

    # load pretrained model
    __UpperCamelCase     :str			  =		load_gpta('''gpt2'''	).to(SCREAMING_SNAKE_CASE	)
    print('''computing perplexity on objective set'''	)
    __UpperCamelCase     :List[str]			  =		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 lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=15  ,			SCREAMING_SNAKE_CASE=128  ,			SCREAMING_SNAKE_CASE=100  ,			SCREAMING_SNAKE_CASE="igf_model.pt"  ,			):
    '''simple docstring'''







    set_seed(42	)

    # Load pre-trained model
    __UpperCamelCase     :str			  =		GPTaLMHeadModel.from_pretrained('''gpt2'''	)

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

    # Train secondary learner
    __UpperCamelCase     :Tuple			  =		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 lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=32  ,			SCREAMING_SNAKE_CASE=1_000  ,			SCREAMING_SNAKE_CASE=16  ,			SCREAMING_SNAKE_CASE=1.0  ,			SCREAMING_SNAKE_CASE=recopy_gpta  ,			SCREAMING_SNAKE_CASE=None  ,			SCREAMING_SNAKE_CASE=10  ,			SCREAMING_SNAKE_CASE="gpt2_finetuned.pt"  ,			):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''	)
    __UpperCamelCase     :Tuple			  =		RandomSampler(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Union[str, Any]			  =		DataLoader(SCREAMING_SNAKE_CASE  ,			sampler=SCREAMING_SNAKE_CASE	)

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

    __UpperCamelCase     :int			  =		[]
    __UpperCamelCase     :int			  =		[]

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

            if secondary_learner is not None:
                __UpperCamelCase     :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:
                    __UpperCamelCase     :List[Any]			  =		-1
                if predicted_q < threshold:
                    __UpperCamelCase     :List[str]			  =		False

            # If we passed the filter, add the context to the batch!
            if do_backprop:
                contexts.append(np.array(context.cpu()	)	)
                __UpperCamelCase     :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()
                __UpperCamelCase     :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     :Tuple			  =		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 lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :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
    __UpperCamelCase     :Optional[Any]			  =		joblib.load('''data/IGF_values.jbl'''	)

    # Train secondary learner
    __UpperCamelCase     :str			  =		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
    __UpperCamelCase     :Union[str, Any]			  =		GPTaLMHeadModel.from_pretrained('''gpt2'''	)
    set_seed(42	)

    # Generate train and test data to train and evaluate gpt2 model
    __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		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()







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import MobileNetVaImageProcessor

class __snake_case		(      unittest.TestCase     ):
    def __init__(  self		,snake_case		,snake_case=7		,snake_case=3		,snake_case=18		,snake_case=30		,snake_case=400		,snake_case=True		,snake_case=None		,snake_case=True		,snake_case=None		,):



           '''simple docstring'''







           lowercase							:     Dict       	=      size if size is not None else {"""shortest_edge""": 20}
           lowercase							:     Union[str, Any]       	=      crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
           lowercase							:     str       	=      parent
           lowercase							:     int       	=      batch_size
           lowercase							:     str       	=      num_channels
           lowercase							:     int       	=      image_size
           lowercase							:     List[str]       	=      min_resolution
           lowercase							:     str       	=      max_resolution
           lowercase							:     Dict       	=      do_resize
           lowercase							:     Dict       	=      size
           lowercase							:     Dict       	=      do_center_crop
           lowercase							:     str       	=      crop_size
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_center_crop": self.do_center_crop,
               "crop_size": self.crop_size,
           }






@require_torch
@require_vision
class __snake_case		(      lowerCAmelCase					,	unittest.TestCase     ):
    _a		:						Any=				MobileNetVaImageProcessor if is_vision_available() else None
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      MobileNetVaImageProcessingTester(self      )
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return self.image_processor_tester.prepare_image_processor_dict()
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      self.image_processing_class(**self.image_processor_dict      )
           self.assertTrue(hasattr(snake_case		,"""do_resize"""      )      )
           self.assertTrue(hasattr(snake_case		,"""size"""      )      )
           self.assertTrue(hasattr(snake_case		,"""do_center_crop"""      )      )
           self.assertTrue(hasattr(snake_case		,"""crop_size"""      )      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.image_processing_class.from_dict(self.image_processor_dict      )
           self.assertEqual(image_processor.size		,{"""shortest_edge""": 20}      )
           self.assertEqual(image_processor.crop_size		,{"""height""": 18, """width""": 18}      )

           lowercase							:     int       	=      self.image_processing_class.from_dict(self.image_processor_dict		,size=42		,crop_size=84      )
           self.assertEqual(image_processor.size		,{"""shortest_edge""": 42}      )
           self.assertEqual(image_processor.crop_size		,{"""height""": 84, """width""": 84}      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           pass
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      self.image_processing_class(**self.image_processor_dict      )
           # create random PIL images
           lowercase							:     str       	=      prepare_image_inputs(self.image_processor_tester		,equal_resolution=snake_case      )
           for image in image_inputs:
                  self.assertIsInstance(snake_case		,Image.Image      )

           # Test not batched input
           lowercase							:     Dict       	=      image_processing(image_inputs[0]		,return_tensors="""pt"""      ).pixel_values
           self.assertEqual(
               encoded_images.shape		,(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,)

           # Test batched
           lowercase							:     Tuple       	=      image_processing(snake_case		,return_tensors="""pt"""      ).pixel_values
           self.assertEqual(
               encoded_images.shape		,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,)
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      self.image_processing_class(**self.image_processor_dict      )
           # create random numpy tensors
           lowercase							:     Union[str, Any]       	=      prepare_image_inputs(self.image_processor_tester		,equal_resolution=snake_case		,numpify=snake_case      )
           for image in image_inputs:
                  self.assertIsInstance(snake_case		,np.ndarray      )

           # Test not batched input
           lowercase							:     Optional[Any]       	=      image_processing(image_inputs[0]		,return_tensors="""pt"""      ).pixel_values
           self.assertEqual(
               encoded_images.shape		,(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,)

           # Test batched
           lowercase							:     List[str]       	=      image_processing(snake_case		,return_tensors="""pt"""      ).pixel_values
           self.assertEqual(
               encoded_images.shape		,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,)
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      self.image_processing_class(**self.image_processor_dict      )
           # create random PyTorch tensors
           lowercase							:     Tuple       	=      prepare_image_inputs(self.image_processor_tester		,equal_resolution=snake_case		,torchify=snake_case      )
           for image in image_inputs:
                  self.assertIsInstance(snake_case		,torch.Tensor      )

           # Test not batched input
           lowercase							:     Optional[int]       	=      image_processing(image_inputs[0]		,return_tensors="""pt"""      ).pixel_values
           self.assertEqual(
               encoded_images.shape		,(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,)

           # Test batched
           lowercase							:     List[str]       	=      image_processing(snake_case		,return_tensors="""pt"""      ).pixel_values
           self.assertEqual(
               encoded_images.shape		,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )		,)
