

'''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  :       Tuple=32  ,		A  :       Optional[int]=10  ,		A  :       Optional[int]=1_00  ,		A  :       List[str]=10_26  ,		A  :       List[str]=True  ,		A  :       List[str]="data/tokenized_stories_train_wikitext103.jbl"  ,		A  :       List[Any]="igf_context_pairs.jbl"  ,		)    -> Optional[Any]:







   """simple docstring"""
   set_seed(3  )
   # generate train_data and objective_set
   __snake_case				:      str     							= generate_datasets(
       A  ,		A  ,		number=A  ,		min_len=10_26  ,		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?
   __snake_case				:      int     							= torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'  )

   # load pretrained model
   __snake_case				:      str     							= load_gpta('gpt2'  ).to(A  )
   print('computing perplexity on objective set'  )
   __snake_case				:      List[str]     							= 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        _SCREAMING_SNAKE_CASE  (				A  :       Tuple  ,		A  :       Optional[int]=15  ,		A  :       List[Any]=1_28  ,		A  :       Tuple=1_00  ,		A  :       Tuple="igf_model.pt"  ,		)    -> Dict:







   """simple docstring"""
   set_seed(42  )

   # Load pre-trained model
   __snake_case				:      Dict     							= GPTaLMHeadModel.from_pretrained('gpt2'  )

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

   # Train secondary learner
   __snake_case				:      str     							= train_secondary_learner(
       A  ,		A  ,		max_epochs=A  ,		batch_size=A  ,		eval_freq=1_00  ,		igf_model_path=A  ,		)

   del model, secondary_learner_train_data
   torch.cuda.empty_cache()

   return secondary_learner




def        _SCREAMING_SNAKE_CASE  (				A  :       List[Any]  ,		A  :       List[str]  ,		A  :       Dict  ,		A  :       Any=32  ,		A  :       Dict=10_00  ,		A  :       List[Any]=16  ,		A  :       Dict=1.0  ,		A  :       Union[str, Any]=recopy_gpta  ,		A  :       int=None  ,		A  :       Optional[int]=10  ,		A  :       Tuple="gpt2_finetuned.pt"  ,		)    -> Dict:







   """simple docstring"""
   __snake_case				:      Optional[int]     							= torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'  )
   __snake_case				:      Any     							= RandomSampler(A  )
   __snake_case				:      List[Any]     							= DataLoader(A  ,		sampler=A  )

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

   model.train()
   if secondary_learner is not None:
      secondary_learner.to(A  )
      secondary_learner.eval()
   __snake_case				:      Dict     							= []
   __snake_case				:      Dict     							= 0

   __snake_case				:      Any     							= []
   __snake_case				:      Optional[int]     							= []

   # Compute the performance of the transformer model at the beginning
   __snake_case				:      List[str]     							= 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()
         __snake_case				:      Optional[int]     							= random.randint(0  ,		example.size(2  ) - context_len - 1  )
         __snake_case				:      int     							= example[0, 0, start : start + context_len]
         lm_optimizer.zero_grad()
         __snake_case				:      Any     							= model(A  ,		labels=A  )
         __snake_case				:      Tuple     							= True

         if secondary_learner is not None:
            __snake_case				:      Optional[int]     							= 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 == 10:
               __snake_case				:      Dict     							= -1
            if predicted_q < threshold:
               __snake_case				:      Tuple     							= False

            # If we passed the filter, add the context to the batch!
         if do_backprop:
            contexts.append(np.array(context.cpu()  )  )
            __snake_case				:      Optional[int]     							= 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()
            __snake_case				:      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:
               __snake_case				:      Tuple     							= 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 > 60:
            break
      if max_steps > 0 and global_step > 60:
         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        _SCREAMING_SNAKE_CASE  (				)    -> Dict:







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

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

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

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

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

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

   parser.add_argument(
       '--secondary_learner_max_epochs'  ,		default=15  ,		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=32  ,		max_steps=10  ,		size_objective_set=1_00  ,		min_len=10_26  ,		trim=A  ,		data_file='data/tokenized_stories_train_wikitext103.jbl'  ,		igf_data_file='igf_context_pairs.jbl'  ,		)

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

   # Train secondary learner
   __snake_case				:      Tuple     							= training_secondary_learner(
       A  ,		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
   __snake_case				:      List[Any]     							= GPTaLMHeadModel.from_pretrained('gpt2'  )
   set_seed(42  )

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

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


if __name__ == "__main__":
      main()







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A    	=   {
    '''configuration_distilbert''': [
        '''DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DistilBertConfig''',
        '''DistilBertOnnxConfig''',
    ],
    '''tokenization_distilbert''': ['''DistilBertTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   ['''DistilBertTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   [
          '''DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''DistilBertForMaskedLM''',
          '''DistilBertForMultipleChoice''',
          '''DistilBertForQuestionAnswering''',
          '''DistilBertForSequenceClassification''',
          '''DistilBertForTokenClassification''',
          '''DistilBertModel''',
          '''DistilBertPreTrainedModel''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   [
          '''TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFDistilBertForMaskedLM''',
          '''TFDistilBertForMultipleChoice''',
          '''TFDistilBertForQuestionAnswering''',
          '''TFDistilBertForSequenceClassification''',
          '''TFDistilBertForTokenClassification''',
          '''TFDistilBertMainLayer''',
          '''TFDistilBertModel''',
          '''TFDistilBertPreTrainedModel''',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   [
          '''FlaxDistilBertForMaskedLM''',
          '''FlaxDistilBertForMultipleChoice''',
          '''FlaxDistilBertForQuestionAnswering''',
          '''FlaxDistilBertForSequenceClassification''',
          '''FlaxDistilBertForTokenClassification''',
          '''FlaxDistilBertModel''',
          '''FlaxDistilBertPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_distilbert import (
          DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DistilBertConfig,
          DistilBertOnnxConfig,
      )
      from .tokenization_distilbert import DistilBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_distilbert_fast import DistilBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_distilbert import (
                DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DistilBertForMaskedLM,
                DistilBertForMultipleChoice,
                DistilBertForQuestionAnswering,
                DistilBertForSequenceClassification,
                DistilBertForTokenClassification,
                DistilBertModel,
                DistilBertPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_distilbert import (
                TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFDistilBertForMaskedLM,
                TFDistilBertForMultipleChoice,
                TFDistilBertForQuestionAnswering,
                TFDistilBertForSequenceClassification,
                TFDistilBertForTokenClassification,
                TFDistilBertMainLayer,
                TFDistilBertModel,
                TFDistilBertPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_distilbert import (
                FlaxDistilBertForMaskedLM,
                FlaxDistilBertForMultipleChoice,
                FlaxDistilBertForQuestionAnswering,
                FlaxDistilBertForSequenceClassification,
                FlaxDistilBertForTokenClassification,
                FlaxDistilBertModel,
                FlaxDistilBertPreTrainedModel,
            )

else:
      import sys

      __A    	=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)