"""
This examples trains a CrossEncoder for the Quora Duplicate Questions Detection task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it output a continuous labels 0...1 to indicate the similarity between the input pair.

It does NOT produce a sentence embedding and does NOT work for individual sentences.

Usage:
python training_quora_duplicate_questions.py

"""

import csv
import logging
import math
import os
from datetime import datetime
from zipfile import ZipFile

from torch.utils.data import DataLoader

from sentence_transformers import LoggingHandler, util
from sentence_transformers.cross_encoder import CrossEncoder
from sentence_transformers.cross_encoder.evaluation import CEBinaryClassificationEvaluator
from sentence_transformers.readers import InputExample

#### Just some code to print debug information to stdout
logging.basicConfig(
    format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO, handlers=[LoggingHandler()]
)
logger = logging.getLogger(__name__)
#### /print debug information to stdout


# Check if dataset exists. If not, download and extract  it
dataset_path = "quora-dataset/"

if not os.path.exists(dataset_path):
    logger.info("Dataset not found. Download")
    zip_save_path = "quora-IR-dataset.zip"
    util.http_get(url="https://sbert.net/datasets/quora-IR-dataset.zip", path=zip_save_path)
    with ZipFile(zip_save_path, "r") as zip:
        zip.extractall(dataset_path)


# Read the quora dataset split for classification
logger.info("Read train dataset")
train_samples = []
with open(os.path.join(dataset_path, "classification", "train_pairs.tsv"), encoding="utf8") as fIn:
    reader = csv.DictReader(fIn, delimiter="\t", quoting=csv.QUOTE_NONE)
    for row in reader:
        train_samples.append(InputExample(texts=[row["question1"], row["question2"]], label=int(row["is_duplicate"])))
        train_samples.append(InputExample(texts=[row["question2"], row["question1"]], label=int(row["is_duplicate"])))


logger.info("Read dev dataset")
dev_samples = []
with open(os.path.join(dataset_path, "classification", "dev_pairs.tsv"), encoding="utf8") as fIn:
    reader = csv.DictReader(fIn, delimiter="\t", quoting=csv.QUOTE_NONE)
    for row in reader:
        dev_samples.append(InputExample(texts=[row["question1"], row["question2"]], label=int(row["is_duplicate"])))


# Configuration
train_batch_size = 16
num_epochs = 4
model_save_path = "output/training_quora-" + datetime.now().strftime("%Y-%m-%d_%H-%M-%S")


# We use distilroberta-base with a single label, i.e., it will output a value between 0 and 1 indicating the similarity of the two questions
model = CrossEncoder("distilroberta-base", num_labels=1)

# We wrap train_samples (which is a List[InputExample]) into a pytorch DataLoader
train_dataloader = DataLoader(train_samples, shuffle=True, batch_size=train_batch_size)


# We add an evaluator, which evaluates the performance during training
evaluator = CEBinaryClassificationEvaluator.from_input_examples(dev_samples, name="Quora-dev")


# Configure the training
warmup_steps = math.ceil(len(train_dataloader) * num_epochs * 0.1)  # 10% of train data for warm-up
logger.info(f"Warmup-steps: {warmup_steps}")


# Train the model
model.fit(
    train_dataloader=train_dataloader,
    evaluator=evaluator,
    epochs=num_epochs,
    evaluation_steps=5000,
    warmup_steps=warmup_steps,
    output_path=model_save_path,
)

"""
This examples trains a CrossEncoder for the Quora Duplicate Questions Detection task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it output a continuous labels 0...1 to indicate the similarity between the input pair.

It does NOT produce a sentence embedding and does NOT work for individual sentences.

Usage:
python training_quora_duplicate_questions.py

"""

import csv
import logging
import math
import os
from datetime import datetime
from zipfile import ZipFile

from torch.utils.data import DataLoader

from sentence_transformers import LoggingHandler, util
from sentence_transformers.cross_encoder import CrossEncoder
from sentence_transformers.cross_encoder.evaluation import CEBinaryClassificationEvaluator
from sentence_transformers.readers import InputExample

#### Just some code to print debug information to stdout
logging.basicConfig(
    format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO, handlers=[LoggingHandler()]
)
logger = logging.getLogger(__name__)
#### /print debug information to stdout


# Check if dataset exists. If not, download and extract  it
dataset_path = "quora-dataset/"

if not os.path.exists(dataset_path):
    logger.info("Dataset not found. Download")
    zip_save_path = "quora-IR-dataset.zip"
    util.http_get(url="https://sbert.net/datasets/quora-IR-dataset.zip", path=zip_save_path)
    with ZipFile(zip_save_path, "r") as zip:
        zip.extractall(dataset_path)


# Read the quora dataset split for classification
logger.info("Read train dataset")
train_samples = []
with open(os.path.join(dataset_path, "classification", "train_pairs.tsv"), "r", encoding="utf8") as fIn:
    reader = csv.DictReader(fIn, delimiter="\t", quoting=csv.QUOTE_NONE)
    for row in reader:
        train_samples.append(InputExample(texts=[row["question1"], row["question2"]], label=int(row["is_duplicate"])))
        train_samples.append(InputExample(texts=[row["question2"], row["question1"]], label=int(row["is_duplicate"])))


logger.info("Read dev dataset")
dev_samples = []
with open(os.path.join(dataset_path, "classification", "dev_pairs.tsv"), "r", encoding="utf8") as fIn:
    reader = csv.DictReader(fIn, delimiter="\t", quoting=csv.QUOTE_NONE)
    for row in reader:
        dev_samples.append(InputExample(texts=[row["question1"], row["question2"]], label=int(row["is_duplicate"])))


# Configuration
train_batch_size = 16
num_epochs = 4
model_save_path = "output/training_quora-" + datetime.now().strftime("%Y-%m-%d_%H-%M-%S")


# We use distilroberta-base with a single label, i.e., it will output a value between 0 and 1 indicating the similarity of the two questions
model = CrossEncoder("distilroberta-base", num_labels=1)

# We wrap train_samples (which is a List[InputExample]) into a pytorch DataLoader
train_dataloader = DataLoader(train_samples, shuffle=True, batch_size=train_batch_size)


# We add an evaluator, which evaluates the performance during training
evaluator = CEBinaryClassificationEvaluator.from_input_examples(dev_samples, name="Quora-dev")


# Configure the training
warmup_steps = math.ceil(len(train_dataloader) * num_epochs * 0.1)  # 10% of train data for warm-up
logger.info("Warmup-steps: {}".format(warmup_steps))


# Train the model
model.fit(
    train_dataloader=train_dataloader,
    evaluator=evaluator,
    epochs=num_epochs,
    evaluation_steps=5000,
    warmup_steps=warmup_steps,
    output_path=model_save_path,
)
