"""
This example loads the pre-trained SentenceTransformer model 'nli-distilroberta-base-v2' from the server.
It then fine-tunes this model for some epochs on the STS benchmark dataset.

Note: In this example, you must specify a SentenceTransformer model.
If you want to fine-tune a huggingface/transformers model like bert-base-uncased, see training_nli.py and training_stsbenchmark.py
"""

from torch.utils.data import DataLoader
import math
from sentence_transformers import SentenceTransformer, LoggingHandler, losses, util, InputExample
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
import logging
from datetime import datetime
import os
import gzip
import csv

#### 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()]
)
#### /print debug information to stdout

# Check if dataset exists. If not, download and extract  it
sts_dataset_path = "datasets/stsbenchmark.tsv.gz"

if not os.path.exists(sts_dataset_path):
    util.http_get("https://sbert.net/datasets/stsbenchmark.tsv.gz", sts_dataset_path)


# Read the dataset
model_name = "nli-distilroberta-base-v2"
train_batch_size = 16
num_epochs = 4
model_save_path = (
    "output/training_stsbenchmark_continue_training-" + model_name + "-" + datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
)


# Load a pre-trained sentence transformer model
model = SentenceTransformer(model_name)

# Convert the dataset to a DataLoader ready for training
logging.info("Read STSbenchmark train dataset")

train_samples = []
dev_samples = []
test_samples = []
with gzip.open(sts_dataset_path, "rt", encoding="utf8") as fIn:
    reader = csv.DictReader(fIn, delimiter="\t", quoting=csv.QUOTE_NONE)
    for row in reader:
        score = float(row["score"]) / 5.0  # Normalize score to range 0 ... 1
        inp_example = InputExample(texts=[row["sentence1"], row["sentence2"]], label=score)

        if row["split"] == "dev":
            dev_samples.append(inp_example)
        elif row["split"] == "test":
            test_samples.append(inp_example)
        else:
            train_samples.append(inp_example)


train_dataloader = DataLoader(train_samples, shuffle=True, batch_size=train_batch_size)
train_loss = losses.CosineSimilarityLoss(model=model)


# Development set: Measure correlation between cosine score and gold labels
logging.info("Read STSbenchmark dev dataset")
evaluator = EmbeddingSimilarityEvaluator.from_input_examples(dev_samples, name="sts-dev")


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


# Train the model
model.fit(
    train_objectives=[(train_dataloader, train_loss)],
    evaluator=evaluator,
    epochs=num_epochs,
    evaluation_steps=1000,
    warmup_steps=warmup_steps,
    output_path=model_save_path,
)


##############################################################################
#
# Load the stored model and evaluate its performance on STS benchmark dataset
#
##############################################################################

model = SentenceTransformer(model_save_path)
test_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(test_samples, name="sts-test")
test_evaluator(model, output_path=model_save_path)

"""
This example loads the pre-trained SentenceTransformer model 'nli-distilroberta-base-v2' from the server.
It then fine-tunes this model for some epochs on the STS benchmark dataset.

Note: In this example, you must specify a SentenceTransformer model.
If you want to fine-tune a huggingface/transformers model like bert-base-uncased, see training_nli.py and training_stsbenchmark.py
"""
from torch.utils.data import DataLoader
import math
from sentence_transformers import SentenceTransformer, LoggingHandler, losses, util, InputExample
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
import logging
from datetime import datetime
import os
import gzip
import csv

#### 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()]
)
#### /print debug information to stdout

# Check if dataset exists. If not, download and extract  it
sts_dataset_path = "datasets/stsbenchmark.tsv.gz"

if not os.path.exists(sts_dataset_path):
    util.http_get("https://sbert.net/datasets/stsbenchmark.tsv.gz", sts_dataset_path)


# Read the dataset
model_name = "nli-distilroberta-base-v2"
train_batch_size = 16
num_epochs = 4
model_save_path = (
    "output/training_stsbenchmark_continue_training-" + model_name + "-" + datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
)


# Load a pre-trained sentence transformer model
model = SentenceTransformer(model_name)

# Convert the dataset to a DataLoader ready for training
logging.info("Read STSbenchmark train dataset")

train_samples = []
dev_samples = []
test_samples = []
with gzip.open(sts_dataset_path, "rt", encoding="utf8") as fIn:
    reader = csv.DictReader(fIn, delimiter="\t", quoting=csv.QUOTE_NONE)
    for row in reader:
        score = float(row["score"]) / 5.0  # Normalize score to range 0 ... 1
        inp_example = InputExample(texts=[row["sentence1"], row["sentence2"]], label=score)

        if row["split"] == "dev":
            dev_samples.append(inp_example)
        elif row["split"] == "test":
            test_samples.append(inp_example)
        else:
            train_samples.append(inp_example)


train_dataloader = DataLoader(train_samples, shuffle=True, batch_size=train_batch_size)
train_loss = losses.CosineSimilarityLoss(model=model)


# Development set: Measure correlation between cosine score and gold labels
logging.info("Read STSbenchmark dev dataset")
evaluator = EmbeddingSimilarityEvaluator.from_input_examples(dev_samples, name="sts-dev")


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


# Train the model
model.fit(
    train_objectives=[(train_dataloader, train_loss)],
    evaluator=evaluator,
    epochs=num_epochs,
    evaluation_steps=1000,
    warmup_steps=warmup_steps,
    output_path=model_save_path,
)


##############################################################################
#
# Load the stored model and evaluate its performance on STS benchmark dataset
#
##############################################################################

model = SentenceTransformer(model_save_path)
test_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(test_samples, name="sts-test")
test_evaluator(model, output_path=model_save_path)
