
"""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   (					SCREAMING_SNAKE_CASE=32							,    SCREAMING_SNAKE_CASE=10							,    SCREAMING_SNAKE_CASE=1_00							,    SCREAMING_SNAKE_CASE=10_26							,    SCREAMING_SNAKE_CASE=True							,    SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl"							,    SCREAMING_SNAKE_CASE="igf_context_pairs.jbl"							,    )  ->     Optional[Any]:







							"""simple docstring"""


							set_seed(3 )
							# generate train_data and objective_set
							__snake_case			,   __snake_case     			=       generate_datasets(
							    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    number=SCREAMING_SNAKE_CASE							,    min_len=10_26							,    trim=SCREAMING_SNAKE_CASE )
							# 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     			=       torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )

							# load pretrained model
							__snake_case     			=       load_gpta("gpt2" ).to(SCREAMING_SNAKE_CASE )
							print("computing perplexity on objective set" )
							__snake_case     			=       compute_perplexity(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ).item()
							print("perplexity on objective set:"							,    SCREAMING_SNAKE_CASE )

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

							# clean up, delete model and data we don't need anymore
							del model, train_data, objective_set
							torch.cuda.empty_cache()
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=15							,    SCREAMING_SNAKE_CASE=1_28							,    SCREAMING_SNAKE_CASE=1_00							,    SCREAMING_SNAKE_CASE="igf_model.pt"							,    )  ->     int:







							"""simple docstring"""


							set_seed(42 )

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

							# Initialize secondary learner to use embedding weights of model
							__snake_case     			=       SecondaryLearner(SCREAMING_SNAKE_CASE )

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

							del model, secondary_learner_train_data
							torch.cuda.empty_cache()

							return secondary_learner
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=32							,    SCREAMING_SNAKE_CASE=10_00							,    SCREAMING_SNAKE_CASE=16							,    SCREAMING_SNAKE_CASE=1.0							,    SCREAMING_SNAKE_CASE=recopy_gpta							,    SCREAMING_SNAKE_CASE=None							,    SCREAMING_SNAKE_CASE=10							,    SCREAMING_SNAKE_CASE="gpt2_finetuned.pt"							,    )  ->     Union[str, Any]:







							"""simple docstring"""


							__snake_case     			=       torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )
							__snake_case     			=       RandomSampler(SCREAMING_SNAKE_CASE )
							__snake_case     			=       DataLoader(SCREAMING_SNAKE_CASE							,    sampler=SCREAMING_SNAKE_CASE )

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

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

							__snake_case     			=       []
							__snake_case     			=       []

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

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

																												# 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     			=       -1
																												if predicted_q < threshold:
																																			__snake_case     			=       False

            # If we passed the filter, add the context to the batch!
																					if do_backprop:
																												contexts.append(np.array(context.cpu() ) )
																												__snake_case     			=       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     			=       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     			=       compute_perplexity(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
																																			test_perps.append(SCREAMING_SNAKE_CASE )

																																			print("Test perplexity, step"							,    SCREAMING_SNAKE_CASE							,    ":"							,    SCREAMING_SNAKE_CASE )
            # 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()							,    SCREAMING_SNAKE_CASE )
							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   (					)  ->     Any:







							"""simple docstring"""


							__snake_case     			=       argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task" )

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

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

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

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

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

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

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

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

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

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

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

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

							parser.add_argument(
							    "--recopy_model"							,    default=SCREAMING_SNAKE_CASE							,    type=SCREAMING_SNAKE_CASE							,    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=SCREAMING_SNAKE_CASE							,    data_file="data/tokenized_stories_train_wikitext103.jbl"							,    igf_data_file="igf_context_pairs.jbl"							,    )

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

							# Train secondary learner
							__snake_case     			=       training_secondary_learner(
							    SCREAMING_SNAKE_CASE							,    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     			=       GPTaLMHeadModel.from_pretrained("gpt2" )
							set_seed(42 )

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

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


if __name__ == "__main__":
		main()


"""simple docstring"""







import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     str:







							"""simple docstring"""


							__snake_case     			=       []
							for line in lines:
														__snake_case     			=       re.sub(r"#.*"							,    ""							,    SCREAMING_SNAKE_CASE )  # remove comments
														if line:
																					filtered_lines.append(SCREAMING_SNAKE_CASE )
							__snake_case     			=       "\n".join(SCREAMING_SNAKE_CASE )

							# Make a hash from all this code
							__snake_case     			=       full_str.encode("utf-8" )
							return shaaaa(SCREAMING_SNAKE_CASE ).hexdigest()


# get importable module names and hash for caching
_SCREAMING_SNAKE_CASE	       =      {
    """csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    """json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    """pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    """parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    """arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    """text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    """imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    """audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
_SCREAMING_SNAKE_CASE	       =      {
    """.csv""": ("""csv""", {}),
    """.tsv""": ("""csv""", {"""sep""": """\t"""}),
    """.json""": ("""json""", {}),
    """.jsonl""": ("""json""", {}),
    """.parquet""": ("""parquet""", {}),
    """.arrow""": ("""arrow""", {}),
    """.txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_SCREAMING_SNAKE_CASE	       =      {"""imagefolder""", """audiofolder"""}

# Used to filter data files based on extensions given a module name
_SCREAMING_SNAKE_CASE	       =      {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
		_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")



_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")
