"""
This is an example how to train SentenceTransformers in a multi-task setup.

The system trains BERT on the AllNLI and on the STSbenchmark dataset.
"""

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

#### 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

# Read the dataset
model_name = "bert-base-uncased"
batch_size = 16
model_save_path = "output/training_multi-task_" + model_name + "-" + datetime.now().strftime("%Y-%m-%d_%H-%M-%S")


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

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

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


# Use BERT for mapping tokens to embeddings
word_embedding_model = models.Transformer(model_name)

# Apply mean pooling to get one fixed sized sentence vector
pooling_model = models.Pooling(
    word_embedding_model.get_word_embedding_dimension(),
    pooling_mode_mean_tokens=True,
    pooling_mode_cls_token=False,
    pooling_mode_max_tokens=False,
)

model = SentenceTransformer(modules=[word_embedding_model, pooling_model])


# Convert the dataset to a DataLoader ready for training
logging.info("Read AllNLI train dataset")
label2int = {"contradiction": 0, "entailment": 1, "neutral": 2}
train_nli_samples = []
with gzip.open(nli_dataset_path, "rt", encoding="utf8") as fIn:
    reader = csv.DictReader(fIn, delimiter="\t", quoting=csv.QUOTE_NONE)
    for row in reader:
        if row["split"] == "train":
            label_id = label2int[row["label"]]
            train_nli_samples.append(InputExample(texts=[row["sentence1"], row["sentence2"]], label=label_id))


train_dataloader_nli = DataLoader(train_nli_samples, shuffle=True, batch_size=batch_size)
train_loss_nli = losses.SoftmaxLoss(
    model=model, sentence_embedding_dimension=model.get_sentence_embedding_dimension(), num_labels=len(label2int)
)

logging.info("Read STSbenchmark train dataset")
train_sts_samples = []
dev_sts_samples = []
test_sts_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_sts_samples.append(inp_example)
        elif row["split"] == "test":
            test_sts_samples.append(inp_example)
        else:
            train_sts_samples.append(inp_example)


train_dataloader_sts = DataLoader(train_sts_samples, shuffle=True, batch_size=batch_size)
train_loss_sts = losses.CosineSimilarityLoss(model=model)


logging.info("Read STSbenchmark dev dataset")
evaluator = EmbeddingSimilarityEvaluator.from_input_examples(dev_sts_samples, name="sts-dev")

# Configure the training
num_epochs = 4

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


# Here we define the two train objectives: train_dataloader_nli with train_loss_nli (i.e., SoftmaxLoss for NLI data)
# and train_dataloader_sts with train_loss_sts (i.e., CosineSimilarityLoss for STSbenchmark data)
# You can pass as many (dataloader, loss) tuples as you like. They are iterated in a round-robin way.
train_objectives = [(train_dataloader_nli, train_loss_nli), (train_dataloader_sts, train_loss_sts)]

# Train the model
model.fit(
    train_objectives=train_objectives,
    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_sts_samples, name="sts-test")
test_evaluator(model, output_path=model_save_path)

"""
This is an example how to train SentenceTransformers in a multi-task setup.

The system trains BERT on the AllNLI and on the STSbenchmark dataset.
"""
from torch.utils.data import DataLoader
import math
from sentence_transformers import models, losses
from sentence_transformers import LoggingHandler, SentenceTransformer, util
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
from sentence_transformers.readers import InputExample
import logging
from datetime import datetime
import gzip
import csv
import os

#### 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

# Read the dataset
model_name = "bert-base-uncased"
batch_size = 16
model_save_path = "output/training_multi-task_" + model_name + "-" + datetime.now().strftime("%Y-%m-%d_%H-%M-%S")


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

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

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


# Use BERT for mapping tokens to embeddings
word_embedding_model = models.Transformer(model_name)

# Apply mean pooling to get one fixed sized sentence vector
pooling_model = models.Pooling(
    word_embedding_model.get_word_embedding_dimension(),
    pooling_mode_mean_tokens=True,
    pooling_mode_cls_token=False,
    pooling_mode_max_tokens=False,
)

model = SentenceTransformer(modules=[word_embedding_model, pooling_model])


# Convert the dataset to a DataLoader ready for training
logging.info("Read AllNLI train dataset")
label2int = {"contradiction": 0, "entailment": 1, "neutral": 2}
train_nli_samples = []
with gzip.open(nli_dataset_path, "rt", encoding="utf8") as fIn:
    reader = csv.DictReader(fIn, delimiter="\t", quoting=csv.QUOTE_NONE)
    for row in reader:
        if row["split"] == "train":
            label_id = label2int[row["label"]]
            train_nli_samples.append(InputExample(texts=[row["sentence1"], row["sentence2"]], label=label_id))


train_dataloader_nli = DataLoader(train_nli_samples, shuffle=True, batch_size=batch_size)
train_loss_nli = losses.SoftmaxLoss(
    model=model, sentence_embedding_dimension=model.get_sentence_embedding_dimension(), num_labels=len(label2int)
)

logging.info("Read STSbenchmark train dataset")
train_sts_samples = []
dev_sts_samples = []
test_sts_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_sts_samples.append(inp_example)
        elif row["split"] == "test":
            test_sts_samples.append(inp_example)
        else:
            train_sts_samples.append(inp_example)


train_dataloader_sts = DataLoader(train_sts_samples, shuffle=True, batch_size=batch_size)
train_loss_sts = losses.CosineSimilarityLoss(model=model)


logging.info("Read STSbenchmark dev dataset")
evaluator = EmbeddingSimilarityEvaluator.from_input_examples(dev_sts_samples, name="sts-dev")

# Configure the training
num_epochs = 4

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


# Here we define the two train objectives: train_dataloader_nli with train_loss_nli (i.e., SoftmaxLoss for NLI data)
# and train_dataloader_sts with train_loss_sts (i.e., CosineSimilarityLoss for STSbenchmark data)
# You can pass as many (dataloader, loss) tuples as you like. They are iterated in a round-robin way.
train_objectives = [(train_dataloader_nli, train_loss_nli), (train_dataloader_sts, train_loss_sts)]

# Train the model
model.fit(
    train_objectives=train_objectives,
    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_sts_samples, name="sts-test")
test_evaluator(model, output_path=model_save_path)
