import torch.nn as nn
from modeling import LiLT
import torch
## Defining pytorch lightning model
from sklearn.metrics import accuracy_score, confusion_matrix
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import torchmetrics
import pytorch_lightning as pl


id2label = ['scientific_report',
 'resume',
 'memo',
 'file_folder',
 'specification',
 'news_article',
 'letter',
 'form',
 'budget',
 'handwritten',
 'email',
 'invoice',
 'presentation',
 'scientific_publication',
 'questionnaire',
 'advertisement']

class LiLTForClassification(nn.Module):
      
    def __init__(self, config):
      super(LiLTForClassification, self).__init__()

      self.lilt = LiLT(config)
      self.config = config
      self.dropout = nn.Dropout(config['hidden_dropout_prob'])
      self.linear_layer = nn.Linear(in_features = config['hidden_size'] * 2, out_features = len(id2label))  ## Number of Classes

    def forward(self, batch_dict):
        encodings = self.lilt(batch_dict['input_words'], batch_dict['input_boxes'])
        final_out = torch.cat([encodings['layout_hidden_states'][-1], 
                              encodings['text_hidden_states'][-1]
                              ],
                             axis = -1)[:, 0, :]
        final_out = self.linear_layer(final_out)
        return final_out


class LiLTPL(pl.LightningModule):

  def __init__(self, config , lr = 5e-5):
    super(LiLTPL, self).__init__()
    
    self.save_hyperparameters()
    self.config = config
    self.lilt = LiLTForClassification(config)
    
    self.num_classes = len(id2label)
    self.train_accuracy_metric = torchmetrics.Accuracy()
    self.val_accuracy_metric = torchmetrics.Accuracy()
    self.f1_metric = torchmetrics.F1Score(num_classes=self.num_classes)
    self.precision_macro_metric = torchmetrics.Precision(
            average="macro", num_classes=self.num_classes
        )
    self.recall_macro_metric = torchmetrics.Recall(
            average="macro", num_classes=self.num_classes
        )
    self.precision_micro_metric = torchmetrics.Precision(average="micro")
    self.recall_micro_metric = torchmetrics.Recall(average="micro")

  def forward(self, batch_dict):
    logits = self.lilt(batch_dict)
    return logits

  def training_step(self, batch, batch_idx):
    logits = self.forward(batch)

    loss = nn.CrossEntropyLoss()(logits, batch['label'])
    preds = torch.argmax(logits, 1)

    ## Calculating the accuracy score
    train_acc = self.train_accuracy_metric(preds, batch["label"])

    ## Logging
    self.log('train/loss', loss,prog_bar = True, on_epoch=True, logger=True, on_step=True)
    self.log('train/acc', train_acc, prog_bar = True, on_epoch=True, logger=True, on_step=True)

    return loss
  
  def validation_step(self, batch, batch_idx):
    logits = self.forward(batch)
    loss = nn.CrossEntropyLoss()(logits, batch['label'])
    preds = torch.argmax(logits, 1)
    
    labels = batch['label']
    # Metrics
    valid_acc = self.val_accuracy_metric(preds, labels)
    precision_macro = self.precision_macro_metric(preds, labels)
    recall_macro = self.recall_macro_metric(preds, labels)
    precision_micro = self.precision_micro_metric(preds, labels)
    recall_micro = self.recall_micro_metric(preds, labels)
    f1 = self.f1_metric(preds, labels)

    # Logging metrics
    self.log("valid/loss", loss, prog_bar=True, on_step=True, logger=True)
    self.log("valid/acc", valid_acc, prog_bar=True, on_epoch=True, logger=True, on_step=True)
    self.log("valid/precision_macro", precision_macro, prog_bar=True, on_epoch=True, logger=True, on_step=True)
    self.log("valid/recall_macro", recall_macro, prog_bar=True, on_epoch=True, logger=True, on_step=True)
    self.log("valid/precision_micro", precision_micro, prog_bar=True, on_epoch=True, logger=True, on_step=True)
    self.log("valid/recall_micro", recall_micro, prog_bar=True, on_epoch=True, logger=True, on_step=True)
    self.log("valid/f1", f1, prog_bar=True, on_epoch=True)
    
    return {"label": batch['label'], "logits": logits}

  def validation_epoch_end(self, outputs):
        labels = torch.cat([x["label"] for x in outputs])
        logits = torch.cat([x["logits"] for x in outputs])
        preds = torch.argmax(logits, 1)

        wandb.log({"cm": wandb.sklearn.plot_confusion_matrix(labels.cpu().numpy(), preds.cpu().numpy())})
        self.logger.experiment.log(
            {"roc": wandb.plot.roc_curve(labels.cpu().numpy(), logits.cpu().numpy())}
        )
        
  def configure_optimizers(self):
    return torch.optim.AdamW(self.parameters(), lr = self.hparams['lr'])