"""
The system trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) on the SNLI + MultiNLI (AllNLI) dataset
with softmax loss function. At every 1000 training steps, the model is evaluated on the
STS benchmark dataset

Usage:
python training_nli.py

OR
python training_nli.py pretrained_transformer_model_name
"""
from torch.utils.data import DataLoader
import math
from sentence_transformers import models, losses
from sentence_transformers import LoggingHandler, SentenceTransformer, util, InputExample
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
import logging
from datetime import datetime
import sys
import os
import gzip
import csv
from tqdm import tqdm

#### 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
nli_dataset_path = "data/processed/train_en_es.tsv"
sts_dataset_path = "data/processed/dev_en_es.tsv"

# 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)


# You can specify any huggingface/transformers pre-trained model here, for example, bert-base-uncased, roberta-base, xlm-roberta-base
model_name = sys.argv[1] if len(sys.argv) > 1 else "bert-base-uncased"

# Read the dataset
train_batch_size = 16


model_save_path = (
    "output/training_nli_" + model_name.replace("/", "-") + "-" + datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
)


# Use Huggingface/transformers model (like BERT, RoBERTa, XLNet, XLM-R) 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])


# # Read the AllNLI.tsv.gz file and create the training dataset
# logging.info("Read AllNLI train dataset")

label2int = {"contradiction": 0, "entailment": 1, "neutral": 2}
# train_samples = []

# with open(nli_dataset_path) as f:
#     #test_dict = json.load(f)
#     l = f.readline()
#     #pdb.set_trace()
#     for item in tqdm(f):
#         premise, hypothesis, label, _ = item.strip().split("\t")
#         label_id = label2int[label]
#         train_samples.append(InputExample(texts=[premise, hypothesis], label=label_id))

# train_dataloader = DataLoader(train_samples, shuffle=True, batch_size=train_batch_size)
# train_loss = losses.SoftmaxLoss(
#     model=model, sentence_embedding_dimension=model.get_sentence_embedding_dimension(), num_labels=len(label2int)
# )


# # Read STSbenchmark dataset and use it as development set
# logging.info("Read STSbenchmark dev dataset")
# dev_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:
# #         if row["split"] == "dev":
# #             score = float(row["score"]) / 5.0  # Normalize score to range 0 ... 1
# #             dev_samples.append(InputExample(texts=[row["sentence1"], row["sentence2"]], label=score))


# with open(sts_dataset_path) as f:
#     #test_dict = json.load(f)
#     l = f.readline()
#     #pdb.set_trace()
#     for item in tqdm(f):
#         premise, hypothesis, label, _ = item.strip().split("\t")
#         label_id = label2int[label]
#         dev_samples.append(InputExample(texts=[premise, hypothesis], label=label_id))

# dev_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(
#     dev_samples, batch_size=train_batch_size, name="sts-dev"
# )

# # Configure the training
# num_epochs = 1

# 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=dev_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
#
##############################################################################

test_samples = []
test_dataset_path = "data/processed/test_aym.tsv"
with open(sts_dataset_path) as f:
    #test_dict = json.load(f)
    l = f.readline()
    #pdb.set_trace()
    for item in tqdm(f):
        premise, hypothesis, label, _ = item.strip().split("\t")
        label_id = label2int[label]
        test_samples.append(InputExample(texts=[premise, hypothesis], label=label_id))

model_save_path = "output/training_nli_bert-base-multilingual-cased-2024-01-11_16-04-54/"
model = SentenceTransformer(model_save_path)
test_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(
    test_samples, batch_size=train_batch_size, name="sts-test"
)
test_evaluator(model, output_path=model_save_path)
