import random
import logging
from datasets import load_dataset, Dataset
from sentence_transformers import (
    SentenceTransformer,
    SentenceTransformerTrainer,
    SentenceTransformerTrainingArguments,
    SentenceTransformerModelCardData,
)
from typing import Any, Dict, Iterable
import torch
from torch import nn
from sentence_transformers.losses import MultipleNegativesRankingLoss, MultipleNegativesSymmetricRankingLoss
from sentence_transformers import util
from sentence_transformers.training_args import BatchSamplers
from sentence_transformers.evaluation import InformationRetrievalEvaluator


logging.basicConfig(
    format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO
)

# 1. Load a model to finetune with 2. (Optional) model card data
model = SentenceTransformer(
    "microsoft/mpnet-base",
    model_card_data=SentenceTransformerModelCardData(
        language="en",
        license="apache-2.0",
        model_name="MPNet base trained on Natural Questions pairs",
    ),
)
model_name = "mpnet-base-natural-questions-mnsrl"

# 3. Load a dataset to finetune on
dataset = load_dataset("sentence-transformers/natural-questions", split="train")
dataset = dataset.add_column("id", range(len(dataset)))
train_dataset: Dataset = dataset.select(range(90_000))
eval_dataset: Dataset = dataset.select(range(90_000, len(dataset)))

# 4. Define a loss function
class ImprovedContrastiveLoss(nn.Module):
    def __init__(self, model: SentenceTransformer, temperature: float = 0.01):
        super(ImprovedContrastiveLoss, self).__init__()
        self.model = model
        self.temperature = temperature

    def forward(self, sentence_features: Iterable[Dict[str, torch.Tensor]], labels: torch.Tensor = None) -> torch.Tensor:
        # Get the embeddings for each sentence in the batch
        embeddings = [self.model(sentence_feature)['sentence_embedding'] for sentence_feature in sentence_features]
        query_embeddings = embeddings[0]
        doc_embeddings = embeddings[1]

        # Compute similarity scores
        similarity_q_d = util.cos_sim(query_embeddings, doc_embeddings)
        similarity_q_q = util.cos_sim(query_embeddings, query_embeddings)
        similarity_d_d = util.cos_sim(doc_embeddings, doc_embeddings)

        # Move the similarity range from [-1, 1] to [-2, 0] to avoid overflow
        similarity_q_d = similarity_q_d - 1
        similarity_q_q = similarity_q_q - 1
        similarity_d_d = similarity_d_d - 1

        # Compute the partition function
        exp_sim_q_d = torch.exp(similarity_q_d / self.temperature)
        exp_sim_q_q = torch.exp(similarity_q_q / self.temperature)
        exp_sim_d_d = torch.exp(similarity_d_d / self.temperature)
        
        # Ensure the diagonal is not considered in negative samples
        mask = torch.eye(similarity_q_d.size(0), device=similarity_q_d.device).bool()
        exp_sim_q_q = exp_sim_q_q.masked_fill(mask, 0)
        exp_sim_d_d = exp_sim_d_d.masked_fill(mask, 0)

        partition_function = exp_sim_q_d.sum(dim=1) + exp_sim_q_d.sum(dim=0) + exp_sim_q_q.sum(dim=1) + exp_sim_d_d.sum(dim=0)

        # Compute the loss
        loss = -torch.log(exp_sim_q_d.diag() / partition_function).mean()
        return loss

    def get_config_dict(self) -> Dict[str, Any]:
        return {"temperature": self.temperature}

# loss = ImprovedContrastiveLoss(model)
loss = MultipleNegativesSymmetricRankingLoss(model)


# 5. (Optional) Specify training arguments
args = SentenceTransformerTrainingArguments(
    # Required parameter:
    output_dir=f"models/{model_name}",
    # Optional training parameters:
    num_train_epochs=1,
    per_device_train_batch_size=32,
    per_device_eval_batch_size=32,
    learning_rate=2e-5,
    warmup_ratio=0.1,
    fp16=False,  # Set to False if you get an error that your GPU can't run on FP16
    bf16=True,  # Set to True if you have a GPU that supports BF16
    batch_sampler=BatchSamplers.NO_DUPLICATES,  # MultipleNegativesRankingLoss benefits from no duplicate samples in a batch
    # Optional tracking/debugging parameters:
    eval_strategy="steps",
    eval_steps=100,
    save_strategy="steps",
    save_steps=100,
    save_total_limit=2,
    logging_steps=100,
    logging_first_step=True,
    run_name=model_name,  # Will be used in W&B if `wandb` is installed
)

# 6. (Optional) Create an evaluator & evaluate the base model
# The full corpus, but only the evaluation queries
queries = dict(zip(eval_dataset["id"], eval_dataset["query"]))
corpus = {cid: dataset[cid]["answer"] for cid in range(20_000)} | {cid: dataset[cid]["answer"] for cid in eval_dataset["id"]}
relevant_docs = {qid: {qid} for qid in eval_dataset["id"]}
dev_evaluator = InformationRetrievalEvaluator(
    corpus=corpus,
    queries=queries,
    relevant_docs=relevant_docs,
    show_progress_bar=True,
    name="natural-questions-dev",
)
dev_evaluator(model)

# 7. Create a trainer & train
trainer = SentenceTransformerTrainer(
    model=model,
    args=args,
    train_dataset=train_dataset.remove_columns("id"),
    eval_dataset=eval_dataset.remove_columns("id"),
    loss=loss,
    evaluator=dev_evaluator,
)
trainer.train()

# (Optional) Evaluate the trained model on the evaluator after training
dev_evaluator(model)

# 8. Save the trained model
model.save_pretrained(f"models/{model_name}/final")

# 9. (Optional) Push it to the Hugging Face Hub
model.push_to_hub(f"{model_name}")