


'''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 					a__       (					lowerCAmelCase__=32  ,					lowerCAmelCase__=10  ,					lowerCAmelCase__=1_00  ,					lowerCAmelCase__=10_26  ,					lowerCAmelCase__=True  ,					lowerCAmelCase__="data/tokenized_stories_train_wikitext103.jbl"  ,					lowerCAmelCase__="igf_context_pairs.jbl"  ,					)    ->	str:
    set_seed(3						)
    # generate train_data and objective_set
    UpperCAmelCase__						,						UpperCAmelCase__			:		str													=				generate_datasets(
        lowerCAmelCase__  ,					lowerCAmelCase__  ,					number=lowerCAmelCase__  ,					min_len=10_26  ,					trim=lowerCAmelCase__						)
    # 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__			:		int													=				torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''						)

    # load pretrained model
    UpperCAmelCase__			:		Tuple													=				load_gpta('''gpt2'''						).to(lowerCAmelCase__						)
    print('''computing perplexity on objective set'''						)
    UpperCAmelCase__			:		List[str]													=				compute_perplexity(lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						).item()
    print('''perplexity on objective set:'''  ,					lowerCAmelCase__						)

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

    # clean up, delete model and data we don't need anymore
    del model, train_data, objective_set
    torch.cuda.empty_cache()





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__=15  ,					lowerCAmelCase__=1_28  ,					lowerCAmelCase__=1_00  ,					lowerCAmelCase__="igf_model.pt"  ,					)    ->	List[Any]:
    set_seed(42						)

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

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

    # Train secondary learner
    UpperCAmelCase__			:		Tuple													=				train_secondary_learner(
        lowerCAmelCase__  ,					lowerCAmelCase__  ,					max_epochs=lowerCAmelCase__  ,					batch_size=lowerCAmelCase__  ,					eval_freq=1_00  ,					igf_model_path=lowerCAmelCase__  ,					)

    del model, secondary_learner_train_data
    torch.cuda.empty_cache()

    return secondary_learner





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__=32  ,					lowerCAmelCase__=10_00  ,					lowerCAmelCase__=16  ,					lowerCAmelCase__=1.0  ,					lowerCAmelCase__=recopy_gpta  ,					lowerCAmelCase__=None  ,					lowerCAmelCase__=10  ,					lowerCAmelCase__="gpt2_finetuned.pt"  ,					)    ->	Optional[Any]:
    UpperCAmelCase__			:		int													=				torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''						)
    UpperCAmelCase__			:		Any													=				RandomSampler(lowerCAmelCase__						)
    UpperCAmelCase__			:		List[str]													=				DataLoader(lowerCAmelCase__  ,					sampler=lowerCAmelCase__						)

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

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

    UpperCAmelCase__			:		Optional[int]													=				[]
    UpperCAmelCase__			:		Optional[int]													=				[]

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

            if secondary_learner is not None:
                UpperCAmelCase__			:		List[Any]													=				secondary_learner.forward(
                    torch.tensor(lowerCAmelCase__  ,					dtype=torch.long  ,					device=lowerCAmelCase__						).unsqueeze(0						)						)[0].item()
                observed_qs.append(float(lowerCAmelCase__						)						)

                # 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__			:		Dict													=				-1
                if predicted_q < threshold:
                    UpperCAmelCase__			:		str													=				False

            # If we passed the filter, add the context to the batch!
            if do_backprop:
                contexts.append(np.array(context.cpu()						)						)
                UpperCAmelCase__			:		List[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__			:		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[int]													=				compute_perplexity(lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)
                    test_perps.append(lowerCAmelCase__						)

                    print('''Test perplexity, step'''  ,					lowerCAmelCase__  ,					''':'''  ,					lowerCAmelCase__						)
            # 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()  ,					lowerCAmelCase__						)
    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__       (					)    ->	str:
    UpperCAmelCase__			:		Optional[int]													=				argparse.ArgumentParser(description='''Fine-tune a transformer model with IGF on a language modeling task'''						)

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

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

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

    parser.add_argument(
        '''--context_len'''  ,					default=32  ,					type=lowerCAmelCase__  ,					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=lowerCAmelCase__  ,					help='''number of articles that are long enough to be used as our objective set'''  ,					)
    parser.add_argument(
        '''--eval_freq'''  ,					default=1_00  ,					type=lowerCAmelCase__  ,					help='''secondary model evaluation is triggered at eval_freq'''						)

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

    parser.add_argument(
        '''--secondary_learner_batch_size'''  ,					default=1_28  ,					type=lowerCAmelCase__  ,					help='''batch size of training data for secondary learner'''  ,					)

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

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

    parser.add_argument(
        '''--min_len'''  ,					default=10_26  ,					type=lowerCAmelCase__  ,					help='''The minimum length of the article to be used as objective set'''						)

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

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

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

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

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

    # Train secondary learner
    UpperCAmelCase__			:		int													=				training_secondary_learner(
        lowerCAmelCase__  ,					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__			:		Union[str, Any]													=				GPTaLMHeadModel.from_pretrained('''gpt2'''						)
    set_seed(42						)

    # Generate train and test data to train and evaluate gpt2 model
    UpperCAmelCase__						,						UpperCAmelCase__			:		Optional[Any]													=				generate_datasets(
        context_len=32  ,					file='''data/tokenized_stories_train_wikitext103.jbl'''  ,					number=1_00  ,					min_len=10_26  ,					trim=lowerCAmelCase__						)

    # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
    finetune(
        lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					context_len=32  ,					max_steps=10_00  ,					batch_size=16  ,					threshold=1.0  ,					recopy_model=lowerCAmelCase__  ,					secondary_learner=lowerCAmelCase__  ,					eval_interval=10  ,					finetuned_model_name='''gpt2_finetuned.pt'''  ,					)


if __name__ == "__main__":
  main()




'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class 			lowerCamelCase_   (	__a      ):






      def __init__(					self				:    List[str]						,						_A				:    NestedDataStructureLike[PathLike]						,						_A				:    Optional[NamedSplit] = None						,						_A				:    Optional[Features] = None						,						_A				:    str = None						,						_A				:    bool = False						,						_A				:    bool = False						,						_A				:    Optional[str] = None						,						_A				:    Optional[int] = None						,						**_A				:    Union[str, Any]						,						):


          '''simple docstring'''


          super().__init__(
              _A						,						split=_A						,						features=_A						,						cache_dir=_A						,						keep_in_memory=_A						,						streaming=_A						,						num_proc=_A						,						**_A						,						)
          UpperCAmelCase__			:		List[str]													=				field
          UpperCAmelCase__			:		Optional[Any]													=				path_or_paths if isinstance(_A						,						_A ) else {self.split: path_or_paths}
          UpperCAmelCase__			:		Any													=				Json(
              cache_dir=_A						,						data_files=_A						,						features=_A						,						field=_A						,						**_A						,						)






      def lowercase_							(					self				:    Dict ):


          '''simple docstring'''


          if self.streaming:
              UpperCAmelCase__			:		int													=				self.builder.as_streaming_dataset(split=self.split )
          # Build regular (map-style) dataset
          else:
              UpperCAmelCase__			:		List[str]													=				None
              UpperCAmelCase__			:		Dict													=				None
              UpperCAmelCase__			:		Optional[int]													=				None
              UpperCAmelCase__			:		List[Any]													=				None

              self.builder.download_and_prepare(
                  download_config=_A						,						download_mode=_A						,						verification_mode=_A						,						base_path=_A						,						num_proc=self.num_proc						,						)
              UpperCAmelCase__			:		str													=				self.builder.as_dataset(
                  split=self.split						,						verification_mode=_A						,						in_memory=self.keep_in_memory )
          return dataset




class 			lowerCamelCase_   :






      def __init__(					self				:    Optional[Any]						,						_A				:    Dataset						,						_A				:    Union[PathLike, BinaryIO]						,						_A				:    Optional[int] = None						,						_A				:    Optional[int] = None						,						**_A				:    List[str]						,						):


          '''simple docstring'''


          if num_proc is not None and num_proc <= 0:
              raise ValueError(f"""num_proc {num_proc} must be an integer > 0.""" )

          UpperCAmelCase__			:		Dict													=				dataset
          UpperCAmelCase__			:		Any													=				path_or_buf
          UpperCAmelCase__			:		List[str]													=				batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
          UpperCAmelCase__			:		Tuple													=				num_proc
          UpperCAmelCase__			:		Any													=				'''utf-8'''
          UpperCAmelCase__			:		Optional[int]													=				to_json_kwargs






      def lowercase_							(					self				:    int ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				self.to_json_kwargs.pop('''path_or_buf'''						,						_A )
          UpperCAmelCase__			:		Optional[int]													=				self.to_json_kwargs.pop('''orient'''						,						'''records''' )
          UpperCAmelCase__			:		Tuple													=				self.to_json_kwargs.pop('''lines'''						,						True if orient == '''records''' else False )
          UpperCAmelCase__			:		str													=				self.to_json_kwargs.pop('''index'''						,						False if orient in ['''split''', '''table'''] else True )
          UpperCAmelCase__			:		Optional[Any]													=				self.to_json_kwargs.pop('''compression'''						,						_A )

          if compression not in [None, "infer", "gzip", "bz2", "xz"]:
              raise NotImplementedError(f"""`datasets` currently does not support {compression} compression""" )

          if isinstance(self.path_or_buf						,						(str, bytes, os.PathLike) ):
              with fsspec.open(self.path_or_buf						,						'''wb'''						,						compression=_A ) as buffer:
                  UpperCAmelCase__			:		Union[str, Any]													=				self._write(file_obj=_A						,						orient=_A						,						lines=_A						,						index=_A						,						**self.to_json_kwargs )
          else:
              if compression:
                  raise NotImplementedError(
                      f"""The compression parameter is not supported when writing to a buffer, but compression={compression}"""
                      ''' was passed. Please provide a local path instead.''' )
              UpperCAmelCase__			:		Union[str, Any]													=				self._write(
                  file_obj=self.path_or_buf						,						orient=_A						,						lines=_A						,						index=_A						,						**self.to_json_kwargs )
          return written






      def lowercase_							(					self				:    Optional[int]						,						_A				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__			:		Dict													=				args

          UpperCAmelCase__			:		Dict													=				query_table(
              table=self.dataset.data						,						key=slice(_A						,						offset + self.batch_size )						,						indices=self.dataset._indices						,						)
          UpperCAmelCase__			:		str													=				batch.to_pandas().to_json(
              path_or_buf=_A						,						orient=_A						,						lines=_A						,						index=_A						,						**_A )
          if not json_str.endswith('''\n''' ):
              json_str += "\n"
          return json_str.encode(self.encoding )






      def lowercase_							(					self				:    Union[str, Any]						,						_A				:    BinaryIO						,						_A				:    Optional[int]						,						_A				:    int						,						_A				:    Any						,						**_A				:    Any						,						):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				0

          if self.num_proc is None or self.num_proc == 1:
              for offset in logging.tqdm(
                  range(0						,						len(self.dataset )						,						self.batch_size )						,						unit='''ba'''						,						disable=not logging.is_progress_bar_enabled()						,						desc='''Creating json from Arrow format'''						,						):
                  UpperCAmelCase__			:		Optional[int]													=				self._batch_json((offset, orient, lines, index, to_json_kwargs) )
                  written += file_obj.write(_A )
          else:
              UpperCAmelCase__						,						UpperCAmelCase__			:		str													=				len(self.dataset ), self.batch_size
              with multiprocessing.Pool(self.num_proc ) as pool:
                  for json_str in logging.tqdm(
                      pool.imap(
                          self._batch_json						,						[(offset, orient, lines, index, to_json_kwargs) for offset in range(0						,						_A						,						_A )]						,						)						,						total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size						,						unit='''ba'''						,						disable=not logging.is_progress_bar_enabled()						,						desc='''Creating json from Arrow format'''						,						):
                      written += file_obj.write(_A )

          return written
