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_     (						snake_case__=3_2   ,  snake_case__=1_0   ,  snake_case__=1_0_0   ,  snake_case__=1_0_2_6   ,  snake_case__=True   ,  snake_case__="data/tokenized_stories_train_wikitext103.jbl"   ,  snake_case__="igf_context_pairs.jbl"   ,  )     ->	List[str]:
							set_seed(3							)
							# generate train_data and objective_set
							lowerCAmelCase		,      lowerCAmelCase			=		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?
							lowerCAmelCase			=		torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''							)

							# load pretrained model
							lowerCAmelCase			=		load_gpta('''gpt2'''							).to(__A							)
							print('''computing perplexity on objective set'''							)
							lowerCAmelCase			=		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_     (						snake_case__   ,  snake_case__=1_5   ,  snake_case__=1_2_8   ,  snake_case__=1_0_0   ,  snake_case__="igf_model.pt"   ,  )     ->	Dict:
							set_seed(4_2							)

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

							# Initialize secondary learner to use embedding weights of model
							lowerCAmelCase			=		SecondaryLearner(__A							)

							# Train secondary learner
							lowerCAmelCase			=		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    SCREAMING_SNAKE_CASE_     (						snake_case__   ,  snake_case__   ,  snake_case__   ,  snake_case__=3_2   ,  snake_case__=1_0_0_0   ,  snake_case__=1_6   ,  snake_case__=1.0   ,  snake_case__=recopy_gpta   ,  snake_case__=None   ,  snake_case__=1_0   ,  snake_case__="gpt2_finetuned.pt"   ,  )     ->	Tuple:
							lowerCAmelCase			=		torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''							)
							lowerCAmelCase			=		RandomSampler(__A							)
							lowerCAmelCase			=		DataLoader(__A   ,  sampler=__A							)

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

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

							lowerCAmelCase			=		[]
							lowerCAmelCase			=		[]

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

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

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

							# Train secondary learner
							lowerCAmelCase			=		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
							lowerCAmelCase			=		GPTaLMHeadModel.from_pretrained('''gpt2'''							)
							set_seed(4_2							)

							# Generate train and test data to train and evaluate gpt2 model
							lowerCAmelCase		,      lowerCAmelCase			=		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'''





from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Any          =       'gptj'
 __lowerCamelCase    :       List[str]          =       {
     'max_position_embeddings': 'n_positions',
     'hidden_size': 'n_embd',
     'num_attention_heads': 'n_head',
     'num_hidden_layers': 'n_layer',
 }



 def __init__(self ,   A=50_400 ,   A=2_048 ,   A=4_096 ,   A=28 ,   A=16 ,   A=64 ,   A=None ,   A="gelu_new" ,   A=0.0 ,   A=0.0 ,   A=0.0 ,   A=1E-5 ,   A=0.02 ,   A=True ,   A=50_256 ,   A=50_256 ,   A=False ,   **A ,   )    ->     Tuple:


      """simple docstring"""



      _a      =  vocab_size
      _a      =  n_positions
      _a      =  n_embd
      _a      =  n_layer
      _a      =  n_head
      _a      =  n_inner
      _a      =  rotary_dim
      _a      =  activation_function
      _a      =  resid_pdrop
      _a      =  embd_pdrop
      _a      =  attn_pdrop
      _a      =  layer_norm_epsilon
      _a      =  initializer_range
      _a      =  use_cache

      _a      =  bos_token_id
      _a      =  eos_token_id

      super().__init__(
          bos_token_id=A ,   eos_token_id=A ,   tie_word_embeddings=A ,   **A       )






class __A			(  A						):



 '''simple docstring'''







 def __init__(self ,   A ,   A = "default" ,   A = None ,   A = False ,   )    ->     List[str]:


      """simple docstring"""



      super().__init__(A ,   task=A ,   patching_specs=A ,   use_past=A       )
      if not getattr(self._config ,   '''pad_token_id''' ,   A       ):
           # TODO: how to do that better?
           _a      =  0



 @property
 def 					a__  (self       )    ->     Mapping[str, Mapping[int, str]]:


      """simple docstring"""



      _a      =  OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}       )
      if self.use_past:
           self.fill_with_past_key_values_(A ,   direction='''inputs'''       )
           _a      =  {0: '''batch''', 1: '''past_sequence + sequence'''}
      else:
           _a      =  {0: '''batch''', 1: '''sequence'''}

      return common_inputs



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return self._config.n_layer



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return self._config.n_head



 def 					a__  (self ,   A ,   A = -1 ,   A = -1 ,   A = False ,   A = None ,   )    ->     Mapping[str, Any]:


      """simple docstring"""



      _a      =  super(A ,   self       ).generate_dummy_inputs(
          A ,   batch_size=A ,   seq_length=A ,   is_pair=A ,   framework=A       )

      # We need to order the input in the way they appears in the forward()
      _a      =  OrderedDict({'''input_ids''': common_inputs['''input_ids''']}       )

      # Need to add the past_keys
      if self.use_past:
           if not is_torch_available():
                raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''       )
           else:
                import torch

                _a  ,    _a      =  common_inputs['''input_ids'''].shape
                # Not using the same length for past_key_values
                _a      =  seqlen + 2
                _a      =  (
                    batch,
                    self.num_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // self.num_attention_heads,
                )
                _a      =  [
                    (torch.zeros(A       ), torch.zeros(A       )) for _ in range(self.num_layers       )
                ]

      _a      =  common_inputs['''attention_mask''']
      if self.use_past:
           _a      =  ordered_inputs['''attention_mask'''].dtype
           _a      =  torch.cat(
               [ordered_inputs['''attention_mask'''], torch.ones(A ,   A ,   dtype=A       )] ,   dim=1       )

      return ordered_inputs



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return 13



