


'''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    UpperCAmelCase_  (       A=3_2		,	A=1_0		,	A=1_0_0		,	A=1_0_2_6		,	A=True		,	A="data/tokenized_stories_train_wikitext103.jbl"		,	A="igf_context_pairs.jbl"		,	):
   '''simple docstring'''





   set_seed(3      )
   # generate train_data and objective_set
   _a		,						_a					:  Union[str, Any]						=  generate_datasets(
       A		,	A		,	number=A		,	min_len=1_0_2_6		,	trim=A      )
   # 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					:  Optional[int]						=  torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'      )

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

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

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


def    UpperCAmelCase_  (       A		,	A=1_5		,	A=1_2_8		,	A=1_0_0		,	A="igf_model.pt"		,	):
   '''simple docstring'''





   set_seed(4_2      )

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

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

   # Train secondary learner
   _a					:  List[str]						=  train_secondary_learner(
       A		,	A		,	max_epochs=A		,	batch_size=A		,	eval_freq=1_0_0		,	igf_model_path=A		,	)

   del model, secondary_learner_train_data
   torch.cuda.empty_cache()

   return secondary_learner


def    UpperCAmelCase_  (       A		,	A		,	A		,	A=3_2		,	A=1_0_0_0		,	A=1_6		,	A=1.0		,	A=recopy_gpta		,	A=None		,	A=1_0		,	A="gpt2_finetuned.pt"		,	):
   '''simple docstring'''





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

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

   model.train()
   if secondary_learner is not None:
      secondary_learner.to(A      )
      secondary_learner.eval()
   _a					:  Tuple						=  []
   _a					:  Optional[int]						=  0

   _a					:  str						=  []
   _a					:  Union[str, Any]						=  []

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

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

            # 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 == 1_0:
               _a					:  List[Any]						=  -1
            if predicted_q < threshold:
               _a					:  List[str]						=  False

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

               print('Test perplexity, step'		,	A		,	':'		,	A      )
            # Break out of the loop after 60 batches
         if max_steps > 0 and global_step > 6_0:
            break
      if max_steps > 0 and global_step > 6_0:
         break

    # save finetuned transformer model
   torch.save(model.state_dict()		,	A      )
   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    UpperCAmelCase_  (       ):
   '''simple docstring'''





   _a					:  Optional[Any]						=  argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task'      )

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

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

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

   parser.add_argument(
       '--context_len'		,	default=3_2		,	type=A		,	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_0_0		,	type=A		,	help='number of articles that are long enough to be used as our objective set'		,	)
   parser.add_argument(
       '--eval_freq'		,	default=1_0_0		,	type=A		,	help='secondary model evaluation is triggered at eval_freq'      )

   parser.add_argument('--max_steps'		,	default=1_0_0_0		,	type=A		,	help='To calculate training epochs'      )

   parser.add_argument(
       '--secondary_learner_batch_size'		,	default=1_2_8		,	type=A		,	help='batch size of training data for secondary learner'		,	)

   parser.add_argument(
       '--batch_size'		,	default=1_6		,	type=A		,	help='batch size of training data of language model(gpt2) '      )

   parser.add_argument(
       '--eval_interval'		,	default=1_0		,	type=A		,	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_0_0		,	type=A		,	help='The number of examples split to be used as objective_set/test_data'      )

   parser.add_argument(
       '--min_len'		,	default=1_0_2_6		,	type=A		,	help='The minimum length of the article to be used as objective set'      )

   parser.add_argument(
       '--secondary_learner_max_epochs'		,	default=1_5		,	type=A		,	help='number of epochs to train secondary learner'      )

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

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

   parser.add_argument(
       '--recopy_model'		,	default=A		,	type=A		,	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=3_2		,	max_steps=1_0		,	size_objective_set=1_0_0		,	min_len=1_0_2_6		,	trim=A		,	data_file='data/tokenized_stories_train_wikitext103.jbl'		,	igf_data_file='igf_context_pairs.jbl'		,	)

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

   # Train secondary learner
   _a					:  int						=  training_secondary_learner(
       A		,	secondary_learner_max_epochs=1_5		,	secondary_learner_batch_size=1_2_8		,	eval_freq=1_0_0		,	igf_model_path='igf_model.pt'		,	)

   # load pretrained gpt2 model
   _a					:  Optional[int]						=  GPTaLMHeadModel.from_pretrained('gpt2'      )
   set_seed(4_2      )

   # Generate train and test data to train and evaluate gpt2 model
   _a		,						_a					:  Optional[int]						=  generate_datasets(
       context_len=3_2		,	file='data/tokenized_stories_train_wikitext103.jbl'		,	number=1_0_0		,	min_len=1_0_2_6		,	trim=A      )

   # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
   finetune(
       A		,	A		,	A		,	context_len=3_2		,	max_steps=1_0_0_0		,	batch_size=1_6		,	threshold=1.0		,	recopy_model=A		,	secondary_learner=A		,	eval_interval=1_0		,	finetuned_model_name='gpt2_finetuned.pt'		,	)


if __name__ == "__main__":
 main()






'''simple docstring'''




import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_      :		Any          =		logging.get_logger(__name__)

UpperCAmelCase_      :		Any          =		{
    "asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
    # See all SEW models at https://huggingface.co/models?filter=sew
}







class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: Tuple							  =		"""sew"""




 def __init__(    self		,      lowerCamelCase_=3_2		,      lowerCamelCase_=7_6_8		,      lowerCamelCase_=1_2		,      lowerCamelCase_=1_2		,      lowerCamelCase_=3_0_7_2		,      lowerCamelCase_=2		,      lowerCamelCase_="gelu"		,      lowerCamelCase_=0.1		,      lowerCamelCase_=0.1		,      lowerCamelCase_=0.1		,      lowerCamelCase_=0.0		,      lowerCamelCase_=0.1		,      lowerCamelCase_=0.1		,      lowerCamelCase_=0.02		,      lowerCamelCase_=1e-5		,      lowerCamelCase_="group"		,      lowerCamelCase_="gelu"		,      lowerCamelCase_=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2)		,      lowerCamelCase_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)		,      lowerCamelCase_=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)		,      lowerCamelCase_=False		,      lowerCamelCase_=1_2_8		,      lowerCamelCase_=1_6		,      lowerCamelCase_=True		,      lowerCamelCase_=0.05		,      lowerCamelCase_=1_0		,      lowerCamelCase_=2		,      lowerCamelCase_=0.0		,      lowerCamelCase_=1_0		,      lowerCamelCase_=0		,      lowerCamelCase_="mean"		,      lowerCamelCase_=False		,      lowerCamelCase_=False		,      lowerCamelCase_=2_5_6		,      lowerCamelCase_=0		,      lowerCamelCase_=1		,      lowerCamelCase_=2		,      **lowerCamelCase_		,      )      ->    Tuple:
    super().__init__(**lowerCamelCase_		,      pad_token_id=lowerCamelCase_		,      bos_token_id=lowerCamelCase_		,      eos_token_id=lowerCamelCase_      )
    _a					:  Optional[int]						=  hidden_size
    _a					:  int						=  feat_extract_norm
    _a					:  Optional[int]						=  feat_extract_activation
    _a					:  str						=  list(lowerCamelCase_      )
    _a					:  Union[str, Any]						=  list(lowerCamelCase_      )
    _a					:  List[Any]						=  list(lowerCamelCase_      )
    _a					:  Union[str, Any]						=  conv_bias
    _a					:  Optional[int]						=  num_conv_pos_embeddings
    _a					:  Dict						=  num_conv_pos_embedding_groups
    _a					:  str						=  len(self.conv_dim      )
    _a					:  Any						=  num_hidden_layers
    _a					:  List[Any]						=  intermediate_size
    _a					:  Tuple						=  squeeze_factor
    _a					:  Tuple						=  hidden_act
    _a					:  Any						=  num_attention_heads
    _a					:  Optional[int]						=  hidden_dropout
    _a					:  List[str]						=  attention_dropout
    _a					:  Optional[Any]						=  activation_dropout
    _a					:  str						=  feat_proj_dropout
    _a					:  str						=  final_dropout
    _a					:  str						=  layerdrop
    _a					:  Optional[Any]						=  layer_norm_eps
    _a					:  Optional[Any]						=  initializer_range
    _a					:  Any						=  vocab_size

    if (
        (len(self.conv_stride      ) != self.num_feat_extract_layers)
        or (len(self.conv_kernel      ) != self.num_feat_extract_layers)
        or (len(self.conv_dim      ) != self.num_feat_extract_layers)
    ):
       raise ValueError(
           'Configuration for convolutional layers is incorrect.'
           'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
           F'''but is `len(config.conv_dim) = {len(self.conv_dim      )}`, `len(config.conv_stride)'''
           F'''= {len(self.conv_stride      )}`, `len(config.conv_kernel) = {len(self.conv_kernel      )}`.'''      )

    # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
    _a					:  str						=  apply_spec_augment
    _a					:  List[Any]						=  mask_time_prob
    _a					:  Optional[Any]						=  mask_time_length
    _a					:  Union[str, Any]						=  mask_time_min_masks
    _a					:  List[str]						=  mask_feature_prob
    _a					:  List[str]						=  mask_feature_length
    _a					:  str						=  mask_feature_min_masks

    # ctc loss
    _a					:  Any						=  ctc_loss_reduction
    _a					:  Optional[Any]						=  ctc_zero_infinity

    # sequence classification
    _a					:  List[Any]						=  use_weighted_layer_sum
    _a					:  Tuple						=  classifier_proj_size




 @property
 def   __UpperCamelCase   (    self      )      ->    Optional[int]:
    return functools.reduce(operator.mul		,      self.conv_stride		,      1      )


