


import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
 import torch


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


__lowerCAmelCase		       =							logging.get_logger(__name__)
__lowerCAmelCase		       =							{'''vocab_file''': '''spiece.model'''}

__lowerCAmelCase		       =							{
    '''vocab_file''': {
        '''AI-Sweden/gpt-sw3-126m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-350m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-1.6b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-6.7b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-20b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model''',
    }
}

__lowerCAmelCase		       =							{
    '''AI-Sweden/gpt-sw3-126m''': 20_48,
    '''AI-Sweden/gpt-sw3-350m''': 20_48,
    '''AI-Sweden/gpt-sw3-1.6b''': 20_48,
    '''AI-Sweden/gpt-sw3-6.7b''': 20_48,
    '''AI-Sweden/gpt-sw3-20b''': 20_48,
}





class   __a			(    __UpperCamelCase      ):
 __lowercase :							int        = VOCAB_FILES_NAMES
 __lowercase :							Dict        = PRETRAINED_VOCAB_FILES_MAP
 __lowercase :							Any        = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase :							str        = ['input_ids', 'attention_mask']
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__=False				, lowerCAmelCase__=False				, lowerCAmelCase__=False				, lowerCAmelCase__=None				, lowerCAmelCase__=None				, lowerCAmelCase__=None				, lowerCAmelCase__=None				, lowerCAmelCase__ = None				, **lowerCAmelCase__				, )					->				None:




        '''simple docstring'''
        lowercase__:   Union[str, Any]  			=   {} if sp_model_kwargs is None else sp_model_kwargs

        lowercase__:   Optional[int]  			=   kwargs.get('name_or_path' )
        if name_or_path is None:
               logger.warning(
                   'name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'
                   ' you are testing the model, this can safely be ignored' )
               lowercase__:   Optional[Any]  			=   'None'

        # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
        lowercase__:   Union[str, Any]  			=   '<|endoftext|>' if eos_token is None else eos_token
        lowercase__:   Optional[int]  			=   '<unk>' if unk_token is None else unk_token
        if "gpt-sw3-7b" in name_or_path:
               lowercase__:   List[str]  			=   unk_token if pad_token is None else pad_token
               lowercase__:   Optional[int]  			=   eos_token if bos_token is None else bos_token
        else:
               lowercase__:   Optional[int]  			=   '<pad>' if pad_token is None else pad_token
               lowercase__:   Tuple  			=   '<s>' if bos_token is None else bos_token

        super().__init__(
            do_lower_case=lowerCAmelCase__				, remove_space=lowerCAmelCase__				, keep_accents=lowerCAmelCase__				, bos_token=lowerCAmelCase__				, eos_token=lowerCAmelCase__				, unk_token=lowerCAmelCase__				, pad_token=lowerCAmelCase__				, sp_model_kwargs=self.sp_model_kwargs				, **lowerCAmelCase__				, )

        lowercase__:   int  			=   do_lower_case
        lowercase__:   Optional[int]  			=   remove_space
        lowercase__:   Optional[Any]  			=   keep_accents
        lowercase__:   str  			=   vocab_file

        lowercase__:   str  			=   spm.SentencePieceProcessor(**self.sp_model_kwargs )
        self.sp_model.Load(lowerCAmelCase__ )

        # Used for whitespace normalization in input texts
        # fmt : off
        lowercase__:   str  			=   {' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
        # fmt : on

        # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
        lowercase__:   Tuple  			=   re.compile(
            F'[{"".join(map(lowerCAmelCase__				, list(range(0				, 9 ) ) + list(range(11				, 32 ) ) + list(range(127				, 160 ) ) + [160, 173, 8_203] ) )}]' )
 def __getstate__(   self )					->				Optional[Any]:




        '''simple docstring'''
        lowercase__:   Any  			=   self.__dict__.copy()
        lowercase__:   List[str]  			=   None
        return state
 def __setstate__(   self				, lowerCAmelCase__ )					->				int:




        '''simple docstring'''
        lowercase__:   Optional[int]  			=   d

        # for backward compatibility
        if not hasattr(self				, 'sp_model_kwargs' ):
               lowercase__:   str  			=   {}

        lowercase__:   Tuple  			=   spm.SentencePieceProcessor(**self.sp_model_kwargs )
        self.sp_model.Load(self.vocab_file )
 @property
 # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				int:




        '''simple docstring'''
        return len(self.sp_model )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				str:




        '''simple docstring'''
        lowercase__:   Optional[int]  			=   self.non_printing_characters_re.sub(''				, lowerCAmelCase__ )

        # Normalize whitespaces
        lowercase__:   Tuple  			=   ''.join([char if char not in self.whitespaces else ' ' for char in text] )

        # NFC Unicode normalization
        lowercase__:   Union[str, Any]  			=   unicodedata.normalize('NFC'				, lowerCAmelCase__ )
        return text
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, **lowerCAmelCase__ )					->				List[str]:




        '''simple docstring'''
        lowercase__:   str  			=   self.preprocess_text(lowerCAmelCase__ )
        return self.sp_model.encode(lowerCAmelCase__				, out_type=lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				int:




        '''simple docstring'''
        return self.sp_model.PieceToId(lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				str:




        '''simple docstring'''
        return self.sp_model.IdToPiece(lowerCAmelCase__ )
 @staticmethod
 def 		SCREAMING_SNAKE_CASE__   (   lowerCAmelCase__ )					->				str:




        '''simple docstring'''
        return out_string
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				str:




        '''simple docstring'''
        lowercase__:   Dict  			=   []
        lowercase__:   Tuple  			=   ''
        lowercase__:   Tuple  			=   False
        for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                      # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
                      if not prev_is_special:
                             out_string += " "

                      out_string += self.sp_model.decode(lowerCAmelCase__ ) + token
                      lowercase__:   Optional[int]  			=   True
                      lowercase__:   List[Any]  			=   []
               else:
                      current_sub_tokens.append(lowerCAmelCase__ )
                      lowercase__:   int  			=   False
        out_string += self.sp_model.decode(lowerCAmelCase__ )

        return out_string
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Dict[str, int]:




        '''simple docstring'''
        lowercase__:   List[Any]  			=   {self.convert_ids_to_tokens(lowerCAmelCase__ ): i for i in range(self.vocab_size )}
        vocab.update(self.added_tokens_encoder )
        return vocab
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__ = None )					->				Tuple[str]:




        '''simple docstring'''
        if not os.path.isdir(lowerCAmelCase__ ):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
               return
        lowercase__:   Union[str, Any]  			=   os.path.join(
            lowerCAmelCase__				, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

        if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCAmelCase__ ) and os.path.isfile(self.vocab_file ):
               copyfile(self.vocab_file				, lowerCAmelCase__ )
        elif not os.path.isfile(self.vocab_file ):
               with open(lowerCAmelCase__				, 'wb' ) as fi:
                      lowercase__:   Union[str, Any]  			=   self.sp_model.serialized_model_proto()
                      fi.write(lowerCAmelCase__ )

        return (out_vocab_file,)
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__ = False )					->				Union[List[int], List[List[int]], "torch.Tensor"]:




        '''simple docstring'''

        if isinstance(lowerCAmelCase__				, lowerCAmelCase__ ):
               lowercase__:   Optional[int]  			=   self.preprocess_text(lowerCAmelCase__ )
               lowercase__:   Optional[int]  			=   self.sp_model.encode(lowerCAmelCase__ )
        else:
               lowercase__:   Optional[Any]  			=   [self.preprocess_text(lowerCAmelCase__ ) for t in text]
               lowercase__:   List[Any]  			=   self.sp_model.encode(lowerCAmelCase__ )

        if return_tensors is True or return_tensors == "pt":
               lowercase__:   str  			=   torch.tensor(lowerCAmelCase__ )

        return token_ids
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				str:




        '''simple docstring'''
        return self.sp_model.decode(lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				List[int]:




        '''simple docstring'''
        lowercase__:   Dict  			=   [F'User: {text}' if is_user else F'Bot: {text}' for is_user, text in conversation.iter_texts()]
        lowercase__:   List[Any]  			=   (
            F'{self.eos_token}{self.bos_token}' + F'{self.bos_token}'.join(lowerCAmelCase__ ) + F'{self.bos_token}Bot:'
        )
        return self.encode(text=lowerCAmelCase__ )






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    snake_case_     (  snake_case=32			,							snake_case=10			,							snake_case=1_00			,							snake_case=10_26			,							snake_case=True			,							snake_case="data/tokenized_stories_train_wikitext103.jbl"			,							snake_case="igf_context_pairs.jbl"			,							)  ->				Union[str, Any]:
       set_seed(3     )
       # generate train_data and objective_set
       lowercase__			,						lowercase__:   List[str]  			=   generate_datasets(
           snake_case			,							snake_case			,							number=snake_case			,							min_len=10_26			,							trim=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?
       lowercase__:   Optional[Any]  			=   torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'     )

       # load pretrained model
       lowercase__:   str  			=   load_gpta('gpt2'     ).to(snake_case     )
       print('computing perplexity on objective set'     )
       lowercase__:   int  			=   compute_perplexity(snake_case			,							snake_case			,							snake_case     ).item()
       print('perplexity on objective set:'			,							snake_case     )

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

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




def    snake_case_     (  snake_case			,							snake_case=15			,							snake_case=1_28			,							snake_case=1_00			,							snake_case="igf_model.pt"			,							)  ->				Optional[Any]:
       set_seed(42     )

       # Load pre-trained model
       lowercase__:   Any  			=   GPTaLMHeadModel.from_pretrained('gpt2'     )

       # Initialize secondary learner to use embedding weights of model
       lowercase__:   Any  			=   SecondaryLearner(snake_case     )

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

       del model, secondary_learner_train_data
       torch.cuda.empty_cache()

       return secondary_learner




def    snake_case_     (  snake_case			,							snake_case			,							snake_case			,							snake_case=32			,							snake_case=10_00			,							snake_case=16			,							snake_case=1.0			,							snake_case=recopy_gpta			,							snake_case=None			,							snake_case=10			,							snake_case="gpt2_finetuned.pt"			,							)  ->				Tuple:
       lowercase__:   Dict  			=   torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'     )
       lowercase__:   Optional[int]  			=   RandomSampler(snake_case     )
       lowercase__:   Optional[int]  			=   DataLoader(snake_case			,							sampler=snake_case     )

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

       model.train()
       if secondary_learner is not None:
              secondary_learner.to(snake_case     )
              secondary_learner.eval()
       lowercase__:   List[Any]  			=   []
       lowercase__:   str  			=   0

       lowercase__:   Tuple  			=   []
       lowercase__:   Dict  			=   []

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

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

            # If we passed the filter, add the context to the batch!
                     if do_backprop:
                            contexts.append(np.array(context.cpu()     )     )
                            lowercase__:   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()
                            lowercase__:   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:
                                   lowercase__:   int  			=   compute_perplexity(snake_case			,							snake_case			,							snake_case     )
                                   test_perps.append(snake_case     )

                                   print('Test perplexity, step'			,							snake_case			,							':'			,							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()			,							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    snake_case_     (  )  ->				str:
       lowercase__:   Tuple  			=   argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task'     )

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

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

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

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

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

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

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

       parser.add_argument(
           '--eval_interval'			,							default=10			,							type=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=1_00			,							type=snake_case			,							help='The number of examples split to be used as objective_set/test_data'     )

       parser.add_argument(
           '--min_len'			,							default=10_26			,							type=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=snake_case			,							help='number of epochs to train secondary learner'     )

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

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

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

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

       # Train secondary learner
       lowercase__:   List[str]  			=   training_secondary_learner(
           snake_case			,							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
       lowercase__:   Dict  			=   GPTaLMHeadModel.from_pretrained('gpt2'     )
       set_seed(42     )

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

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


if __name__ == "__main__":
 main()


