import logging
import os
import time
from typing import Any

from huggingface_hub import PyTorchModelHubMixin
from pytorch_lightning import Trainer, LightningModule, LightningDataModule
from pytorch_lightning.utilities.types import OptimizerLRScheduler, STEP_OUTPUT, EVAL_DATALOADERS
from torch.utils.data import DataLoader, Dataset, IterableDataset
from torcheval.metrics import BinaryAccuracy, BinaryAUROC, BinaryF1Score, BinaryPrecision, BinaryRecall
from transformers import BertModel, BatchEncoding, BertTokenizer, TrainingArguments
from transformers.modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions
import torch
from torch import nn
from datasets import load_dataset

timber = logging.getLogger()
# logging.basicConfig(level=logging.DEBUG)
logging.basicConfig(level=logging.INFO)  # change to level=logging.DEBUG to print more logs...

NO_REGULARIZATION = 0
L1_REGULARIZATION_CODE = 1
L2_REGULARIZATION_CODE = 2
L1_AND_L2_REGULARIZATION_CODE = 3

black = "\u001b[30m"
red = "\u001b[31m"
green = "\u001b[32m"
yellow = "\u001b[33m"
blue = "\u001b[34m"
magenta = "\u001b[35m"
cyan = "\u001b[36m"
white = "\u001b[37m"

FORWARD = "FORWARD_INPUT"
BACKWARD = "BACKWARD_INPUT"

DNA_BERT_6 = "zhihan1996/DNA_bert_6"


class CommonAttentionLayer(nn.Module):
  def __init__(self, hidden_size, *args, **kwargs):
    super().__init__(*args, **kwargs)
    self.attention_linear = nn.Linear(hidden_size, 1)
    pass

  def forward(self, hidden_states):
    # Apply linear layer
    attn_weights = self.attention_linear(hidden_states)
    # Apply softmax to get attention scores
    attn_weights = torch.softmax(attn_weights, dim=1)
    # Apply attention weights to hidden states
    context_vector = torch.sum(attn_weights * hidden_states, dim=1)
    return context_vector, attn_weights


class DNABert6MqtlClassifier(nn.Module, PyTorchModelHubMixin):
  def __init__(self,
               bert_model=BertModel.from_pretrained(pretrained_model_name_or_path=DNA_BERT_6),
               hidden_size=768,  # I got mat-mul error, looks like this will be 12 times :/
               num_classes=1,
               *args,
               **kwargs):
    super().__init__(*args, **kwargs)
    self.model_name = "DNABert6MqtlClassifier"
    self.bert_model = bert_model
    self.attention = CommonAttentionLayer(hidden_size)  # Optional if you want to use attention

    classifier_input_size = 8  # cz mat-mul error
    self.classifier = nn.Linear(classifier_input_size, num_classes)

  def forward(self, input_ids, attention_mask, token_type_ids):
    # Run BERT on each sub-sequence and collect the embeddings
    embeddings = []
    for i in range(input_ids.size(0)):  # Iterate over sub-sequences
      outputs = self.bert_model(
        input_ids=input_ids[i],
        attention_mask=attention_mask[i],
        token_type_ids=token_type_ids[i] if token_type_ids is not None else None
      )
      last_hidden_state = outputs.last_hidden_state
      embedding = last_hidden_state.mean(dim=1)  # Example: taking the mean of hidden states
      embeddings.append(embedding)

    # Concatenate embeddings from all sub-sequences
    concatenated_embedding = torch.cat(embeddings, dim=1)

    # apply attention here
    context_vector, _ = self.attention(concatenated_embedding)

    # Classify
    y_probability = self.classifier(context_vector)
    return y_probability  # float / double


class TorchMetrics:
  def __init__(self):
    self.binary_accuracy = BinaryAccuracy()  #.to(device)
    self.binary_auc = BinaryAUROC()  # .to(device)
    self.binary_f1_score = BinaryF1Score()  # .to(device)
    self.binary_precision = BinaryPrecision()  # .to(device)
    self.binary_recall = BinaryRecall()  # .to(device)
    pass

  def update_on_each_step(self, batch_predicted_labels, batch_actual_labels):  # todo: Add log if needed
    # it looks like the library maintainers changed preds to input, ie, before: preds, now: input
    self.binary_accuracy.update(input=batch_predicted_labels, target=batch_actual_labels)
    self.binary_auc.update(input=batch_predicted_labels, target=batch_actual_labels)
    self.binary_f1_score.update(input=batch_predicted_labels, target=batch_actual_labels)
    self.binary_precision.update(input=batch_predicted_labels, target=batch_actual_labels)
    self.binary_recall.update(input=batch_predicted_labels, target=batch_actual_labels)
    pass

  def compute_metrics_and_log(self, log, log_prefix: str, log_color: str = green):
    b_accuracy = self.binary_accuracy.compute()
    b_auc = self.binary_auc.compute()
    b_f1_score = self.binary_f1_score.compute()
    b_precision = self.binary_precision.compute()
    b_recall = self.binary_recall.compute()
    timber.info(
      log_color + f"{log_prefix}_acc = {b_accuracy}, {log_prefix}_auc = {b_auc}, {log_prefix}_f1_score = {b_f1_score}, {log_prefix}_precision = {b_precision}, {log_prefix}_recall = {b_recall}")
    log(f"{log_prefix}_accuracy", b_accuracy)
    log(f"{log_prefix}_auc", b_auc)
    log(f"{log_prefix}_f1_score", b_f1_score)
    log(f"{log_prefix}_precision", b_precision)
    log(f"{log_prefix}_recall", b_recall)

    pass

  def reset_on_epoch_end(self):
    self.binary_accuracy.reset()
    self.binary_auc.reset()
    self.binary_f1_score.reset()
    self.binary_precision.reset()
    self.binary_recall.reset()


class MQtlBertClassifierLightningModule(LightningModule):
  def __init__(self,
               classifier: nn.Module,
               criterion=nn.BCEWithLogitsLoss(),
               regularization: int = L2_REGULARIZATION_CODE,
               # 1 == L1, 2 == L2, 3 (== 1 | 2) == both l1 and l2, else ignore / don't care
               l1_lambda=0.0001,
               l2_wright_decay=0.0001,
               *args: Any,
               **kwargs: Any):
    super().__init__(*args, **kwargs)
    self.classifier = classifier
    self.criterion = criterion
    self.train_metrics = TorchMetrics()
    self.validate_metrics = TorchMetrics()
    self.test_metrics = TorchMetrics()

    self.regularization = regularization
    self.l1_lambda = l1_lambda
    self.l2_weight_decay = l2_wright_decay
    pass

  def forward(self, input_ids, attention_mask, token_type_ids, *args: Any, **kwargs: Any) -> Any:
    # print(f"\n{ type(input_ids) = }, {input_ids = }")
    # print(f"{ type(attention_mask) = }, { attention_mask = }")
    # print(f"{ type(token_type_ids) = }, { token_type_ids = }")

    return self.classifier.forward(input_ids, attention_mask, token_type_ids)

  def configure_optimizers(self) -> OptimizerLRScheduler:
    # Here we add weight decay (L2 regularization) to the optimizer
    weight_decay = 0.0
    if self.regularization == L2_REGULARIZATION_CODE or self.regularization == L1_AND_L2_REGULARIZATION_CODE:
      weight_decay = self.l2_weight_decay
    return torch.optim.Adam(self.parameters(), lr=1e-5, weight_decay=weight_decay)  # , weight_decay=0.005)

  def training_step(self, batch, batch_idx, *args: Any, **kwargs: Any) -> STEP_OUTPUT:
    # Accuracy on training batch data
    input_ids, attention_mask, token_type_ids, y = batch
    probability = self.forward(input_ids, attention_mask, token_type_ids)
    # prediction
    predicted_class = (probability >= 0.5).int()  # Convert to binary and cast to int

    loss = self.criterion(probability, y.float())

    if self.regularization == L1_REGULARIZATION_CODE or self.regularization == L1_AND_L2_REGULARIZATION_CODE:  # apply l1 regularization
      l1_norm = sum(p.abs().sum() for p in self.parameters())
      loss += self.l1_lambda * l1_norm

    self.log("train_loss", loss)
    # calculate the scores start
    self.train_metrics.update_on_each_step(batch_predicted_labels=predicted_class, batch_actual_labels=y)
    self.train_metrics.compute_metrics_and_log(log=self.log, log_prefix="train")
    # self.train_metrics.compute_and_log_on_each_step(log=self.log, log_prefix="train")
    # calculate the scores end
    return loss

  def on_train_epoch_end(self) -> None:
    self.train_metrics.compute_metrics_and_log(log=self.log, log_prefix="train")
    self.train_metrics.reset_on_epoch_end()
    pass

  def validation_step(self, batch, batch_idx, *args: Any, **kwargs: Any) -> STEP_OUTPUT:
    # Accuracy on validation batch data
    # print(f"debug { batch = }")
    input_ids, attention_mask, token_type_ids, y = batch
    probability = self.forward(input_ids, attention_mask, token_type_ids)
    # prediction
    predicted_class = (probability >= 0.5).int()  # Convert to binary and cast to int

    # print(blue+f"{x.shape = }")
    # x should have [32, sth...]
    loss = self.criterion(probability, y.float())
    """ loss = 0 #  <------------------------- maybe the loss calculation is problematic """
    self.log("valid_loss", loss)
    # calculate the scores start
    self.validate_metrics.update_on_each_step(batch_predicted_labels=predicted_class, batch_actual_labels=y)
    self.validate_metrics.compute_metrics_and_log(log=self.log, log_prefix="validate", log_color=blue)
    # self.validate_metrics.compute_and_log_on_each_step(log=self.log, log_prefix="validate", log_color=blue)

    # calculate the scores end
    return loss

  def on_validation_epoch_end(self) -> None:
    self.validate_metrics.compute_metrics_and_log(log=self.log, log_prefix="validate", log_color=blue)
    self.validate_metrics.reset_on_epoch_end()
    return None

  def test_step(self, batch, batch_idx, *args: Any, **kwargs: Any) -> STEP_OUTPUT:
    # Accuracy on validation batch data
    input_ids, attention_mask, token_type_ids, y = batch
    probability = self.forward(input_ids, attention_mask, token_type_ids)
    # prediction
    predicted_class = (probability >= 0.5).int()  # Convert to binary and cast to int

    loss = self.criterion(probability, y.float())
    self.log("test_loss", loss)  # do we need this?
    # calculate the scores start
    self.test_metrics.update_on_each_step(batch_predicted_labels=predicted_class, batch_actual_labels=y)
    self.test_metrics.compute_metrics_and_log(log=self.log, log_prefix="test", log_color=magenta)
    # self.test_metrics.compute_and_log_on_each_step(log=self.log, log_prefix="test", log_color=magenta)

    # calculate the scores end
    return loss

  def on_test_epoch_end(self) -> None:
    self.test_metrics.compute_metrics_and_log(log=self.log, log_prefix="test", log_color=magenta)
    self.test_metrics.reset_on_epoch_end()
    return None

  pass


class PagingMQTLDnaBertDataset(IterableDataset):
  def __init__(self, dataset, tokenizer, max_length=512):
    self.dataset = dataset
    self.bert_tokenizer = tokenizer
    self.max_length = max_length

  def __iter__(self):
    for row in self.dataset:
      processed = self.preprocess(row)
      if processed is not None:
        yield processed

  def preprocess(self, row):
    sequence = row['sequence']
    label = row['label']

    # Split the sequence into chunks of size max_length (512)
    chunks = [sequence[i:i + self.max_length] for i in range(0, len(sequence), self.max_length)]

    # Tokenize each chunk and return the tokenized inputs
    tokenized_inputs = {
      'input_ids': [],
      'attention_mask': [],
      'token_type_ids': []  # If needed for DNABERT
    }

    for chunk in chunks:
      encoded_chunk = self.bert_tokenizer(
        chunk,
        truncation=True,
        padding='max_length',
        max_length=self.max_length,
        return_tensors='pt'
      )

      tokenized_inputs['input_ids'].append(encoded_chunk['input_ids'].squeeze(0))
      tokenized_inputs['attention_mask'].append(encoded_chunk['attention_mask'].squeeze(0))
      tokenized_inputs['token_type_ids'].append(
        encoded_chunk['token_type_ids'].squeeze(0) if 'token_type_ids' in encoded_chunk else None)

    # Convert list of tensors to tensors with an extra batch dimension
    tokenized_inputs = {k: torch.stack(v) for k, v in tokenized_inputs.items() if v[0] is not None}

    input_ids = tokenized_inputs['input_ids']
    attention_mask = tokenized_inputs['attention_mask']
    token_type_ids = tokenized_inputs['token_type_ids']

    # print(f"{type(input_ids) }")
    # print(f"{type(attention_mask) }")
    # print(f"{type(token_type_ids) }")

    # Concatenate these tensors along a new dimension
    # Result will be shape [3, num_chunks, 512]
    # stacked_inputs = torch.stack([input_ids, attention_mask, token_type_ids], dim=0)

    # return stacked_inputs, torch.tensor(label)
    return input_ids, attention_mask, token_type_ids, torch.tensor(label).int()


class DNABERTDataModule(LightningDataModule):
  def __init__(self, model_name=DNA_BERT_6, batch_size=8, WINDOW=-1, is_local=False):
    super().__init__()
    self.tokenized_dataset = None
    self.dataset = None
    self.train_dataset: PagingMQTLDnaBertDataset = None
    self.validate_dataset: PagingMQTLDnaBertDataset = None
    self.test_dataset: PagingMQTLDnaBertDataset = None
    self.tokenizer = BertTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)
    self.batch_size = batch_size
    self.is_local = is_local
    self.window = WINDOW

  def prepare_data(self):
    # Download and prepare dataset
    data_files = {
      # small samples
      "train_binned_200": "/home/soumic/Codes/mqtl-classification/src/inputdata/dataset_200_train_binned.csv",
      "validate_binned_200": "/home/soumic/Codes/mqtl-classification/src/inputdata/dataset_200_validate_binned.csv",
      "test_binned_200": "/home/soumic/Codes/mqtl-classification/src/inputdata/dataset_200_test_binned.csv",
      # medium samples
      "train_binned_1000": "/home/soumic/Codes/mqtl-classification/src/inputdata/dataset_1000_train_binned.csv",
      "validate_binned_1000": "/home/soumic/Codes/mqtl-classification/src/inputdata/dataset_1000_validate_binned.csv",
      "test_binned_1000": "/home/soumic/Codes/mqtl-classification/src/inputdata/dataset_1000_test_binned.csv",

      # large samples
      "train_binned_4000": "/home/soumic/Codes/mqtl-classification/src/inputdata/dataset_4000_train_binned.csv",
      "validate_binned_4000": "/home/soumic/Codes/mqtl-classification/src/inputdata/dataset_4000_validate_binned.csv",
      "test_binned_4000": "/home/soumic/Codes/mqtl-classification/src/inputdata/dataset_4000_test_binned.csv",

      # really tiny
      # "tiny_train": "/home/soumic/Codes/mqtl-classification/src/inputdata/tiny_dataset_4000_train_binned.csv",
      # "tiny_validate": "/home/soumic/Codes/mqtl-classification/src/inputdata/tiny_dataset_4000_validate_binned.csv",
      # "tiny_test": "/home/soumic/Codes/mqtl-classification/src/inputdata/tiny_dataset_4000_test_binned.csv",

      "tiny_train": "/home/soumic/Codes/mqtl-classification/src/inputdata/medium_dataset_4000_train_binned.csv",
      "tiny_validate": "/home/soumic/Codes/mqtl-classification/src/inputdata/medium_dataset_4000_validate_binned.csv",
      "tiny_test": "/home/soumic/Codes/mqtl-classification/src/inputdata/medium_dataset_4000_test_binned.csv",
    }
    if self.is_local:
      self.dataset = load_dataset("csv", data_files=data_files, streaming=True)
    else:
      self.dataset = load_dataset("fahimfarhan/mqtl-classification-datasets")

  def setup(self, stage=None):
    self.train_dataset = PagingMQTLDnaBertDataset(self.dataset['tiny_test'], self.tokenizer)
    self.validate_dataset = PagingMQTLDnaBertDataset(self.dataset['tiny_validate'], self.tokenizer)
    self.test_dataset = PagingMQTLDnaBertDataset(self.dataset['tiny_test'], self.tokenizer)

  def train_dataloader(self):
    return DataLoader(self.train_dataset, batch_size=self.batch_size, num_workers=1)

  def val_dataloader(self):
    return DataLoader(self.validate_dataset, batch_size=self.batch_size, num_workers=1)

  def test_dataloader(self) -> EVAL_DATALOADERS:
    return DataLoader(self.test_dataset, batch_size=self.batch_size, num_workers=1)


def start_bert(classifier_model, model_save_path, criterion, WINDOW, batch_size=4,
               is_binned=True, is_debug=False, max_epochs=10, regularization_code=L2_REGULARIZATION_CODE):
  is_my_laptop = os.path.isfile("/home/soumic/Codes/mqtl-classification/src/inputdata/dataset_4000_train_binned.csv")
  model_local_directory = f"my-awesome-model-{WINDOW}"
  model_remote_repository = f"fahimfarhan/dnabert-6-mqtl-classifier-{WINDOW}"
  file_suffix = ""
  if is_binned:
    file_suffix = "_binned"

  data_module = DNABERTDataModule(batch_size=batch_size, WINDOW=WINDOW, is_local=is_my_laptop)

  # classifier_model = classifier_model.to(DEVICE)

  classifier_module = MQtlBertClassifierLightningModule(
    classifier=classifier_model,
    regularization=regularization_code, criterion=criterion)

  # if os.path.exists(model_save_path):
  #   classifier_module.load_state_dict(torch.load(model_save_path))

  classifier_module = classifier_module  # .double()

  # Prepare data using the DataModule
  data_module.prepare_data()
  data_module.setup()

  trainer = Trainer(max_epochs=max_epochs, precision="32")

  # Train the model
  trainer.fit(model=classifier_module, datamodule=data_module)
  trainer.test(model=classifier_module, datamodule=data_module)
  torch.save(classifier_module.state_dict(), model_save_path)

  # classifier_module.push_to_hub("fahimfarhan/mqtl-classifier-model")

  classifier_model.save_pretrained(save_directory=model_local_directory, safe_serialization=False)
  # push to the hub
  commit_message = f":tada: Push model for window size {WINDOW} from huggingface space"
  if is_my_laptop:
    commit_message = f":tada: Push model for window size {WINDOW} from zephyrus"

  classifier_model.push_to_hub(
    repo_id=model_remote_repository,
    # subfolder=f"my-awesome-model-{WINDOW}", subfolder didn't work :/
    commit_message=commit_message,  # f":tada: Push model for window size {WINDOW}"
    # safe_serialization=False
  )
  pass


if __name__ == "__main__":
  start_time = time.time()

  dataset_folder_prefix = "inputdata/"
  pytorch_model = DNABert6MqtlClassifier()
  start_bert(classifier_model=pytorch_model, model_save_path=f"weights_{pytorch_model.model_name}.pth",
             criterion=nn.BCEWithLogitsLoss(), WINDOW=4000, batch_size=1,  # 12,  # max 14 on my laptop...
             max_epochs=1, regularization_code=L2_REGULARIZATION_CODE)

  # Record the end time
  end_time = time.time()
  # Calculate the duration
  duration = end_time - start_time
  # Print the runtime
  print(f"Runtime: {duration:.2f} seconds")

  pass