
"""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  lowercase     (_lowerCAmelCase=32 ,    _lowerCAmelCase=10 ,    _lowerCAmelCase=100 ,    _lowerCAmelCase=1026 ,    _lowerCAmelCase=True ,    _lowerCAmelCase="data/tokenized_stories_train_wikitext103.jbl" ,    _lowerCAmelCase="igf_context_pairs.jbl" ,    ):
						set_seed(3						)
						# generate train_data and objective_set
						__lowerCAmelCase   ,     __lowerCAmelCase     =  generate_datasets(
						    _lowerCAmelCase ,    _lowerCAmelCase ,    number=_lowerCAmelCase ,    min_len=1026 ,    trim=_lowerCAmelCase						)
						# 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(_lowerCAmelCase						)
						print("""computing perplexity on objective set"""						)
						__lowerCAmelCase     =  compute_perplexity(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						).item()
						print("""perplexity on objective set:""" ,    _lowerCAmelCase						)

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

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

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase=15 ,    _lowerCAmelCase=128 ,    _lowerCAmelCase=100 ,    _lowerCAmelCase="igf_model.pt" ,    ):
						set_seed(42						)

						# Load pre-trained model
						__lowerCAmelCase     =  GPTaLMHeadModel.from_pretrained("""gpt2"""						)

						# Initialize secondary learner to use embedding weights of model
						__lowerCAmelCase     =  SecondaryLearner(_lowerCAmelCase						)

						# Train secondary learner
						__lowerCAmelCase     =  train_secondary_learner(
						    _lowerCAmelCase ,    _lowerCAmelCase ,    max_epochs=_lowerCAmelCase ,    batch_size=_lowerCAmelCase ,    eval_freq=100 ,    igf_model_path=_lowerCAmelCase ,    )

						del model, secondary_learner_train_data
						torch.cuda.empty_cache()

						return secondary_learner

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase=32 ,    _lowerCAmelCase=1000 ,    _lowerCAmelCase=16 ,    _lowerCAmelCase=1.0 ,    _lowerCAmelCase=recopy_gpta ,    _lowerCAmelCase=None ,    _lowerCAmelCase=10 ,    _lowerCAmelCase="gpt2_finetuned.pt" ,    ):
						__lowerCAmelCase     =  torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""						)
						__lowerCAmelCase     =  RandomSampler(_lowerCAmelCase						)
						__lowerCAmelCase     =  DataLoader(_lowerCAmelCase ,    sampler=_lowerCAmelCase						)

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

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

						__lowerCAmelCase     =  []
						__lowerCAmelCase     =  []

						# Compute the performance of the transformer model at the beginning
						__lowerCAmelCase     =  compute_perplexity(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						)
						test_perps.append(_lowerCAmelCase						)
						print("""Test perplexity, step""" ,    _lowerCAmelCase ,    """:""" ,    _lowerCAmelCase						)
						for epoch in range(int(_lowerCAmelCase						)						):
												for step, example in enumerate(_lowerCAmelCase						):
																		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(_lowerCAmelCase ,    labels=_lowerCAmelCase						)
																		__lowerCAmelCase     =  True

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

																								# 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:
																														__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(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						)
																														test_perps.append(_lowerCAmelCase						)

																														print("""Test perplexity, step""" ,    _lowerCAmelCase ,    """:""" ,    _lowerCAmelCase						)
            # 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() ,    _lowerCAmelCase						)
						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  lowercase     ():
						__lowerCAmelCase     =  argparse.ArgumentParser(description="""Fine-tune a transformer model with IGF on a language modeling task"""						)

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

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

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

						parser.add_argument(
						    """--context_len""" ,    default=32 ,    type=_lowerCAmelCase ,    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=_lowerCAmelCase ,    help="""number of articles that are long enough to be used as our objective set""" ,    )
						parser.add_argument(
						    """--eval_freq""" ,    default=100 ,    type=_lowerCAmelCase ,    help="""secondary model evaluation is triggered at eval_freq"""						)

						parser.add_argument("""--max_steps""" ,    default=1000 ,    type=_lowerCAmelCase ,    help="""To calculate training epochs"""						)

						parser.add_argument(
						    """--secondary_learner_batch_size""" ,    default=128 ,    type=_lowerCAmelCase ,    help="""batch size of training data for secondary learner""" ,    )

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

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

						parser.add_argument(
						    """--min_len""" ,    default=1026 ,    type=_lowerCAmelCase ,    help="""The minimum length of the article to be used as objective set"""						)

						parser.add_argument(
						    """--secondary_learner_max_epochs""" ,    default=15 ,    type=_lowerCAmelCase ,    help="""number of epochs to train secondary learner"""						)

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

						parser.add_argument(
						    """--threshold""" ,    default=1.0 ,    type=_lowerCAmelCase ,    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=_lowerCAmelCase ,    help="""finetuned_model_name"""						)

						parser.add_argument(
						    """--recopy_model""" ,    default=_lowerCAmelCase ,    type=_lowerCAmelCase ,    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=_lowerCAmelCase ,    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(
						    _lowerCAmelCase ,    secondary_learner_max_epochs=15 ,    secondary_learner_batch_size=128 ,    eval_freq=100 ,    igf_model_path="""igf_model.pt""" ,    )

						# load pretrained gpt2 model
						__lowerCAmelCase     =  GPTaLMHeadModel.from_pretrained("""gpt2"""						)
						set_seed(42						)

						# Generate train and test data to train and evaluate gpt2 model
						__lowerCAmelCase   ,     __lowerCAmelCase     =  generate_datasets(
						    context_len=32 ,    file="""data/tokenized_stories_train_wikitext103.jbl""" ,    number=100 ,    min_len=1026 ,    trim=_lowerCAmelCase						)

						# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
						finetune(
						    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    context_len=32 ,    max_steps=1000 ,    batch_size=16 ,    threshold=1.0 ,    recopy_model=_lowerCAmelCase ,    secondary_learner=_lowerCAmelCase ,    eval_interval=10 ,    finetuned_model_name="""gpt2_finetuned.pt""" ,    )


if __name__ == "__main__":
			main()


"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					     =	{
    '''alibaba-damo/mgp-str-base''': '''https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json''',
}
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		'''mgp-str'''




							def __init__( self    ,				snake_case_=[32, 128]    ,				snake_case_=4    ,				snake_case_=3    ,				snake_case_=27    ,				snake_case_=38    ,				snake_case_=50_257    ,				snake_case_=30_522    ,				snake_case_=768    ,				snake_case_=12    ,				snake_case_=12    ,				snake_case_=4.0    ,				snake_case_=True    ,				snake_case_=False    ,				snake_case_=1e-5    ,				snake_case_=0.0    ,				snake_case_=0.0    ,				snake_case_=0.0    ,				snake_case_=False    ,				snake_case_=0.02    ,				**snake_case_    ,				)       ->  List[Any]:
													super().__init__(**snake_case_				)

													__lowerCAmelCase     =  image_size
													__lowerCAmelCase     =  patch_size
													__lowerCAmelCase     =  num_channels
													__lowerCAmelCase     =  max_token_length
													__lowerCAmelCase     =  num_character_labels
													__lowerCAmelCase     =  num_bpe_labels
													__lowerCAmelCase     =  num_wordpiece_labels
													__lowerCAmelCase     =  hidden_size
													__lowerCAmelCase     =  num_hidden_layers
													__lowerCAmelCase     =  num_attention_heads
													__lowerCAmelCase     =  mlp_ratio
													__lowerCAmelCase     =  distilled
													__lowerCAmelCase     =  layer_norm_eps
													__lowerCAmelCase     =  drop_rate
													__lowerCAmelCase     =  qkv_bias
													__lowerCAmelCase     =  attn_drop_rate
													__lowerCAmelCase     =  drop_path_rate
													__lowerCAmelCase     =  output_aa_attentions
													__lowerCAmelCase     =  initializer_range
