





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:			Union[str, Any]=32		,   __UpperCAmelCase:			Optional[int]=10		,   __UpperCAmelCase:			Any=100		,   __UpperCAmelCase:			Optional[int]=1026		,   __UpperCAmelCase:			Union[str, Any]=True		,   __UpperCAmelCase:			Optional[int]="data/tokenized_stories_train_wikitext103.jbl"		,   __UpperCAmelCase:			str="igf_context_pairs.jbl"		,   )      ->	int:
    set_seed(3      )
    # generate train_data and objective_set
    UpperCamelCase__   ,UpperCamelCase__							:				Dict          =      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?
    UpperCamelCase__							:				Tuple          =      torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''      )

    # load pretrained model
    UpperCamelCase__							:				Union[str, Any]          =      load_gpta('''gpt2'''      ).to(__UpperCAmelCase      )
    print('''computing perplexity on objective set'''      )
    UpperCamelCase__							:				str          =      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:			Tuple		,   __UpperCAmelCase:			Optional[Any]=15		,   __UpperCAmelCase:			Union[str, Any]=128		,   __UpperCAmelCase:			List[str]=100		,   __UpperCAmelCase:			str="igf_model.pt"		,   )      ->	Optional[int]:
    set_seed(42      )

    # Load pre-trained model
    UpperCamelCase__							:				int          =      GPTaLMHeadModel.from_pretrained('''gpt2'''      )

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

    # Train secondary learner
    UpperCamelCase__							:				List[Any]          =      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:			Tuple		,   __UpperCAmelCase:			List[Any]		,   __UpperCAmelCase:			Optional[Any]		,   __UpperCAmelCase:			str=32		,   __UpperCAmelCase:			List[Any]=1000		,   __UpperCAmelCase:			Dict=16		,   __UpperCAmelCase:			Tuple=1.0		,   __UpperCAmelCase:			Dict=recopy_gpta		,   __UpperCAmelCase:			List[str]=None		,   __UpperCAmelCase:			Dict=10		,   __UpperCAmelCase:			int="gpt2_finetuned.pt"		,   )      ->	List[str]:
    UpperCamelCase__							:				Union[str, Any]          =      torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''      )
    UpperCamelCase__							:				int          =      RandomSampler(__UpperCAmelCase      )
    UpperCamelCase__							:				List[Any]          =      DataLoader(__UpperCAmelCase		,   sampler=__UpperCAmelCase      )

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

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

    UpperCamelCase__							:				List[Any]          =      []
    UpperCamelCase__							:				Optional[Any]          =      []

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

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

            # If we passed the filter, add the context to the batch!
            if do_backprop:
                contexts.append(np.array(context.cpu()      )      )
                UpperCamelCase__							:				Optional[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()
                UpperCamelCase__							:				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:
                    UpperCamelCase__							:				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_    (  )      ->	Union[str, Any]:
    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=__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
    UpperCamelCase__							:				List[str]          =      joblib.load('''data/IGF_values.jbl'''      )

    # Train secondary learner
    UpperCamelCase__							:				Optional[int]          =      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
    UpperCamelCase__							:				Any          =      GPTaLMHeadModel.from_pretrained('''gpt2'''      )
    set_seed(42      )

    # Generate train and test data to train and evaluate gpt2 model
    UpperCamelCase__   ,UpperCamelCase__							:				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()








from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_     							= logging.get_logger(__name__)






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							str   		=		["pixel_values"]




   def __init__(    self,		__magic_name__ = True,		__magic_name__ = 32,		__magic_name__=PILImageResampling.BILINEAR,		__magic_name__ = True,		**__magic_name__,		) ->      None:






       """simple docstring"""
       UpperCamelCase__							:				int          =      do_resize
       UpperCamelCase__							:				Tuple          =      do_rescale
       UpperCamelCase__							:				Any          =      size_divisor
       UpperCamelCase__							:				List[Any]          =      resample
       super().__init__(**__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__ = None,		**__magic_name__							) ->      np.ndarray:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__							:				List[Any]          =      get_image_size(__magic_name__							)
       # Rounds the height and width down to the closest multiple of size_divisor
       UpperCamelCase__							:				Any          =      height // size_divisor * size_divisor
       UpperCamelCase__							:				Optional[int]          =      width // size_divisor * size_divisor
       UpperCamelCase__							:				str          =      resize(__magic_name__,		(new_h, new_w),		resample=__magic_name__,		data_format=__magic_name__,		**__magic_name__							)
       return image




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__ = None,		**__magic_name__							) ->      np.ndarray:






       """simple docstring"""
       return rescale(image=__magic_name__,		scale=__magic_name__,		data_format=__magic_name__,		**__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__=None,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = ChannelDimension.FIRST,		**__magic_name__,		) ->      BatchFeature:






       """simple docstring"""
       UpperCamelCase__							:				str          =      do_resize if do_resize is not None else self.do_resize
       UpperCamelCase__							:				Union[str, Any]          =      do_rescale if do_rescale is not None else self.do_rescale
       UpperCamelCase__							:				Any          =      size_divisor if size_divisor is not None else self.size_divisor
       UpperCamelCase__							:				str          =      resample if resample is not None else self.resample

       if do_resize and size_divisor is None:
           raise ValueError('''size_divisor is required for resizing'''							)

       UpperCamelCase__							:				Union[str, Any]          =      make_list_of_images(__magic_name__							)

       if not valid_images(__magic_name__							):
           raise ValueError('''Invalid image(s)'''							)

       # All transformations expect numpy arrays.
       UpperCamelCase__							:				Optional[Any]          =      [to_numpy_array(__magic_name__							) for img in images]

       if do_resize:
           UpperCamelCase__							:				str          =      [self.resize(__magic_name__,		size_divisor=__magic_name__,		resample=__magic_name__							) for image in images]

       if do_rescale:
           UpperCamelCase__							:				List[Any]          =      [self.rescale(__magic_name__,		scale=1 / 255							) for image in images]

       UpperCamelCase__							:				Optional[Any]          =      [to_channel_dimension_format(__magic_name__,		__magic_name__							) for image in images]

       UpperCamelCase__							:				Tuple          =      {'''pixel_values''': images}
       return BatchFeature(data=__magic_name__,		tensor_type=__magic_name__							)

