#!/usr/bin/env python
# coding: utf-8

# In[ ]:


import pandas as pd
from sklearn import preprocessing

model_name = "roberta-large"

import io
import os
import torch
from tqdm.notebook import tqdm
from torch.utils.data import Dataset, DataLoader
from sklearn.metrics import classification_report, accuracy_score
from transformers import (set_seed,
                          TrainingArguments,
                          Trainer,
                          AutoConfig,
                          AutoTokenizer,
                          AdamW,
                          get_linear_schedule_with_warmup,
                          AutoModelForSequenceClassification)

from sklearn.metrics import precision_recall_fscore_support as score


# In[ ]:


use_wandb = False
epochs = 8


# In[3]:


import random
import numpy as np

def set_seed(seed=12):
    """Set seed for reproducibility.
    """
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)

    os.environ['PYTHONHASHSEED'] = str(seed)

set_seed()


# In[4]:


if use_wandb:

    import wandb

    wandb.login(key="69968957548c81fa530d32661ab316213ff08545")
    wandb.init(project="multi-class-text-classifier")


    wandb.run.name = "two-gpts"
    wandb.run.save()


    config = wandb.config
    config.description = "Establishing baseline from GPT2 model"
    config.model_used = "GPT2-Large"


# In[5]:


def get_model_tokenizer():

    tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)


    model = AutoModelForSequenceClassification.from_pretrained(pretrained_model_name_or_path=model_name, num_labels=len(labels_ids))
    model = model.to(device)

    return model, tokenizer


# In[6]:


class Gpt2ClassificationCollator(object):

    def __init__(self, use_tokenizer, labels_encoder, max_sequence_len=None):

        self.use_tokenizer = use_tokenizer
        self.max_sequence_len = use_tokenizer.model_max_length if max_sequence_len is None else max_sequence_len
        self.labels_encoder = labels_encoder
        return

    def __call__(self, sequences):

        texts = [sequence['text'] for sequence in sequences]
        labels = [sequence['label'] for sequence in sequences]
        inputs = self.use_tokenizer(text=texts, return_tensors="pt", padding=True, truncation=True,  max_length=self.max_sequence_len)
        inputs.update({'labels':torch.tensor(labels)})

        return inputs


# In[7]:


class ClassificationDataset(Dataset):


    def __init__(self, df):

        self.texts = df.text.values.tolist()
        self.labels = df.label.values.tolist()

    def __len__(self):

        return len(self.texts)

    def __getitem__(self, item):

        return {'text':self.texts[item],
            'label':self.labels[item]}


# In[8]:




def train_epoch(dataloader, optimizer_, scheduler_, device_, model):

    predictions_labels = []
    true_labels = []
    total_loss = 0

    model.train()

    for batch in tqdm(dataloader, total=len(dataloader)):

        true_labels += batch['labels'].numpy().flatten().tolist()
        batch = {k:v.type(torch.long).to(device_) for k,v in batch.items()}

        model.zero_grad()

        outputs = model(**batch)

        loss, logits = outputs[:2]

        total_loss += loss.item()

        loss.backward()

        torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)

        optimizer.step()

        scheduler.step()

        logits = logits.detach().cpu().numpy()

        predictions_labels += logits.argmax(axis=-1).flatten().tolist()

        avg_epoch_loss = total_loss / len(dataloader)

    return true_labels, predictions_labels, avg_epoch_loss



def validation(dataloader, device_, model):

    predictions_labels = []
    true_labels = []
    total_loss = 0

    model.eval()

    for batch in tqdm(dataloader, total=len(dataloader)):

        true_labels += batch['labels'].numpy().flatten().tolist()
        batch = {k:v.type(torch.long).to(device_) for k,v in batch.items()}

        with torch.no_grad():

            outputs = model(**batch)

            loss, logits = outputs[:2]
            logits = logits.detach().cpu().numpy()
            total_loss += loss.item()

            predict_content = logits.argmax(axis=-1).flatten().tolist()

            predictions_labels += predict_content

    avg_epoch_loss = total_loss / len(dataloader)

    return true_labels, predictions_labels, avg_epoch_loss


# In[9]:

import argparse
def train_model(model, epochs, train_dataloader, valid_dataloader, device, scheduler,  prefix=""):

    best_val_acc = 0
    best_preds = None
    best_labels = None

    if use_wandb:  wandb.watch(model)


    print('Epoch')
    for epoch in tqdm(range(epochs)):

        print()
        print('Training on batches...')

        train_labels, train_predict, train_loss = train_epoch(train_dataloader, optimizer, scheduler, device, model)
        train_acc = accuracy_score(train_labels, train_predict)

        print('Validation on batches...')
        valid_labels, valid_predict, val_loss = validation(valid_dataloader, device, model)
        val_acc = accuracy_score(valid_labels, valid_predict)

        if use_wandb:
            #Log
            wandb.log({"train_loss": train_loss})
            wandb.log({"train_acc": train_acc})

            wandb.log({"test_loss": val_loss})
            wandb.log({"test_acc": val_acc})

            precision, recall, fscore, support = score(valid_labels, valid_predict)

            for k, n in enumerate(labels_ids):
                wandb.log({f"precision-{n}": precision[k]})
                wandb.log({f"recall-{n}": recall[k]})
                wandb.log({f"fscore-{n}": fscore[k]})


        if val_acc > best_val_acc:
            torch.save(model, prefix+"best_model.pt")
            best_val_acc = val_acc

            best_preds = valid_predict
            best_labels = valid_labels


        # Print loss and accuracy values to see how training evolves.
        print("  train_loss: %.5f - val_loss: %.5f - train_acc: %.5f - valid_acc: %.5f"%(train_loss, val_loss, train_acc, val_acc))
        print()

    return best_preds, best_labels, best_val_acc



# In[10]:

parser = argparse.ArgumentParser()
parser.add_argument("--lr", type=float)
parser.add_argument("--batch_size", type=int)

args = parser.parse_args()


lr, batch_size = args.lr, args.batch_size
print("============")
print(str(lr) + " " + str(batch_size))

train = pd.read_csv("../input/upwork02/train.csv").sample(frac=1)
test = pd.read_csv("../input/upwork02/test.csv").sample(frac=1)


train = train[(train.label != 'Expertise - "Teach Me"') & (train.label != "Other") & (train.label != "Business Opportunity ")]

le = preprocessing.LabelEncoder()
le.fit(train.label)


train.label = le.transform(train.label)
test.label = le.transform(test.label)

train["label_names"] = le.inverse_transform(train.label)
test["label_names"] = le.inverse_transform(test.label)

labels_ids = dict(zip(le.classes_, le.transform(le.classes_)))

#######################

train_df = train
test_df = test

model_name = "roberta-large"
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model, tokenizer = get_model_tokenizer()


gpt2_classificaiton_collator = Gpt2ClassificationCollator(use_tokenizer=tokenizer,
                                                        labels_encoder=labels_ids,
                                                        max_sequence_len=96)


train_dataset = ClassificationDataset(train_df)
train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, collate_fn=gpt2_classificaiton_collator)

valid_dataset = ClassificationDataset(test_df)
valid_dataloader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=False, collate_fn=gpt2_classificaiton_collator)


optimizer = AdamW(model.parameters(),
                lr = lr, # default is 5e-5, our notebook had 2e-5
                eps = 1e-8 # default is 1e-8.
                )


total_steps = len(train_dataloader) * epochs

scheduler = get_linear_schedule_with_warmup(optimizer,
                                            num_warmup_steps = 0, # Default value in run_glue.py
                                            num_training_steps = total_steps)

best_preds, best_labels, best_val_acc = train_model(model, epochs, train_dataloader, valid_dataloader, device, scheduler,  prefix="binary_")


print(best_val_acc)