




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

  # load pretrained model
  __a			=					load_gpta('gpt2'			).to(UpperCAmelCase__			)
  print('computing perplexity on objective set'			)
  __a			=					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 						SCREAMING_SNAKE_CASE       (      a_	:							Tuple				,					a_	:							Optional[Any]=15				,					a_	:							Optional[Any]=128				,					a_	:							Optional[Any]=100				,					a_	:							List[Any]="igf_model.pt"				,					):
  set_seed(42			)

  # Load pre-trained model
  __a			=					GPTaLMHeadModel.from_pretrained('gpt2'			)

  # Initialize secondary learner to use embedding weights of model
  __a			=					SecondaryLearner(UpperCAmelCase__			)

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

  __a			=					max_steps // (len(UpperCAmelCase__			)) + 1
  __a			=					0
  __a			=					torch.zeros((1, context_len)				,					dtype=torch.long				,					device=UpperCAmelCase__			)
  __a	,       __a	,       __a			=					recopy_model(UpperCAmelCase__				,					UpperCAmelCase__				,					UpperCAmelCase__			)

  model.train()
  if secondary_learner is not None:
    secondary_learner.to(UpperCAmelCase__			)
    secondary_learner.eval()
  __a			=					[]
  __a			=					0

  __a			=					[]
  __a			=					[]

  # Compute the performance of the transformer model at the beginning
  __a			=					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()
      __a			=					random.randint(0				,					example.size(2			) - context_len - 1			)
      __a			=					example[0, 0, start : start + context_len]
      lm_optimizer.zero_grad()
      __a			=					model(UpperCAmelCase__				,					labels=UpperCAmelCase__			)
      __a			=					True

      if secondary_learner is not None:
        __a			=					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:
          __a			=					-1
        if predicted_q < threshold:
          __a			=					False

            # If we passed the filter, add the context to the batch!
      if do_backprop:
        contexts.append(np.array(context.cpu()			)			)
        __a			=					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()
        __a			=					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:
          __a			=					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 						SCREAMING_SNAKE_CASE       (      ):
  __a			=					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
  __a			=					joblib.load('data/IGF_values.jbl'			)

  # Train secondary learner
  __a			=					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
  __a			=					GPTaLMHeadModel.from_pretrained('gpt2'			)
  set_seed(42			)

  # Generate train and test data to train and evaluate gpt2 model
  __a	,       __a			=					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()






'''simple docstring'''


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


UpperCAmelCase_ 		=      logging.get_logger(__name__)

UpperCAmelCase_ 		=      {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase_ 		=      {
    "vocab_file": {
        "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_char_small": (
            "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_chinese_char_base": (
            "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_discriminator": (
            "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_generator": (
            "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"
        ),
    }
}

UpperCAmelCase_ 		=      {
    "junnyu/roformer_chinese_small": 15_36,
    "junnyu/roformer_chinese_base": 15_36,
    "junnyu/roformer_chinese_char_small": 5_12,
    "junnyu/roformer_chinese_char_base": 5_12,
    "junnyu/roformer_small_discriminator": 1_28,
    "junnyu/roformer_small_generator": 1_28,
}


UpperCAmelCase_ 		=      {
    "junnyu/roformer_chinese_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_base": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_base": {"do_lower_case": True},
    "junnyu/roformer_small_discriminator": {"do_lower_case": True},
    "junnyu/roformer_small_generator": {"do_lower_case": True},
}





class    __lowercase						(  __magic_name__ ):
   _a							=      VOCAB_FILES_NAMES
   _a							=      PRETRAINED_VOCAB_FILES_MAP
   _a							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a							=      PRETRAINED_INIT_CONFIGURATION
   _a							=      RoFormerTokenizer







   def __init__(	self      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=True      ,							UpperCamelCase="[UNK]"      ,							UpperCamelCase="[SEP]"      ,							UpperCamelCase="[PAD]"      ,							UpperCamelCase="[CLS]"      ,							UpperCamelCase="[MASK]"      ,							UpperCamelCase=True      ,							UpperCamelCase=None      ,							**UpperCamelCase      ,							)				->    int:
     super().__init__(
         UpperCamelCase      ,							tokenizer_file=UpperCamelCase      ,							do_lower_case=UpperCamelCase      ,							unk_token=UpperCamelCase      ,							sep_token=UpperCamelCase      ,							pad_token=UpperCamelCase      ,							cls_token=UpperCamelCase      ,							mask_token=UpperCamelCase      ,							tokenize_chinese_chars=UpperCamelCase      ,							strip_accents=UpperCamelCase      ,							**UpperCamelCase      ,							)

     __a			=					json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
     if (
         pre_tok_state.get('lowercase'      ,							UpperCamelCase      ) != do_lower_case
         or pre_tok_state.get('strip_accents'      ,							UpperCamelCase      ) != strip_accents
     ):
       __a			=					getattr(UpperCamelCase      ,							pre_tok_state.pop('type'      )      )
       __a			=					do_lower_case
       __a			=					strip_accents
       __a			=					pre_tok_class(**UpperCamelCase      )

     __a			=					do_lower_case







   def __getstate__(	self      )				->    List[Any]:
     __a			=					self.__dict__.copy()
     __a			=					BertPreTokenizer()
     return state







   def __setstate__(	self      ,							UpperCamelCase      )				->    Optional[int]:
     __a			=					d
     __a			=					self.__dict__['_tokenizer'].get_vocab()
     __a			=					PreTokenizer.custom(JiebaPreTokenizer(UpperCamelCase      )      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=None      )				->    int:
     __a			=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

     if token_ids_a:
       output += token_ids_a + [self.sep_token_id]

     return output







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    List[int]:
     __a			=					[self.sep_token_id]
     __a			=					[self.cls_token_id]
     if token_ids_a is None:
       return len(cls + token_ids_a + sep      ) * [0]
     return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    Tuple[str]:
     __a			=					self._tokenizer.model.save(UpperCamelCase      ,							name=UpperCamelCase      )
     return tuple(UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=False      ,							**UpperCamelCase      ,							)				->    List[Any]:
     __a			=					BertPreTokenizer()
     return super().save_pretrained(UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							**UpperCamelCase      )



