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import sys
import json
from torch.utils.data import DataLoader
from sentence_transformers import SentenceTransformer, LoggingHandler, util, models, evaluation, losses, InputExample
import logging
from datetime import datetime
import gzip
import os
import tarfile
from collections import defaultdict
from torch.utils.data import IterableDataset
import tqdm
from torch.utils.data import Dataset
import random
from shutil import copyfile

import argparse

parser = argparse.ArgumentParser()
parser.add_argument("--train_batch_size", default=64, type=int)
parser.add_argument("--max_seq_length", default=300, type=int)
parser.add_argument("--model_name", required=True)
parser.add_argument("--max_passages", default=0, type=int)
parser.add_argument("--epochs", default=10, type=int)
parser.add_argument("--pooling", default="cls")
parser.add_argument("--negs_to_use", default=None, help="From which systems should negatives be used? Multiple systems seperated by comma. None = all")
parser.add_argument("--warmup_steps", default=1000, type=int)
parser.add_argument("--lr", default=2e-5, type=float)
parser.add_argument("--name", default='')
parser.add_argument("--num_negs_per_system", default=5, type=int) 
parser.add_argument("--use_pre_trained_model", default=False, action="store_true") 
parser.add_argument("--use_all_queries", default=False, action="store_true") 
args = parser.parse_args()

print(args)

#### 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

# The  model we want to fine-tune
train_batch_size = args.train_batch_size          #Increasing the train batch size improves the model performance, but requires more GPU memory
model_name = args.model_name
max_passages = args.max_passages
max_seq_length = args.max_seq_length            #Max length for passages. Increasing it, requires more GPU memory

num_negs_per_system = args.num_negs_per_system  # We used different systems to mine hard negatives. Number of hard negatives to add from each system
num_epochs = args.epochs         # Number of epochs we want to train

# We construct the SentenceTransformer bi-encoder from scratch
if args.use_pre_trained_model:
    print("use pretrained SBERT model")
    model = SentenceTransformer(model_name)
    model.max_seq_length = max_seq_length
else:
    print("Create new SBERT model")
    word_embedding_model = models.Transformer(model_name, max_seq_length=max_seq_length)
    pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(), args.pooling)
    model = SentenceTransformer(modules=[word_embedding_model, pooling_model])

model_save_path = f'output/train_bi-encoder-margin_mse_en-{args.name}-{model_name.replace("/", "-")}-batch_size_{train_batch_size}-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}'


# Write self to path
os.makedirs(model_save_path, exist_ok=True)

train_script_path = os.path.join(model_save_path, 'train_script.py')
copyfile(__file__, train_script_path)
with open(train_script_path, 'a') as fOut:
    fOut.write("\n\n# Script was called via:\n#python " + " ".join(sys.argv))


### Now we read the MS Marco dataset
data_folder = 'msmarco-data'

#### Read the corpus files, that contain all the passages. Store them in the corpus dict
corpus = {}         #dict in the format: passage_id -> passage. Stores all existent passages
collection_filepath = os.path.join(data_folder, 'collection.tsv')
if not os.path.exists(collection_filepath):
    tar_filepath = os.path.join(data_folder, 'collection.tar.gz')
    if not os.path.exists(tar_filepath):
        logging.info("Download collection.tar.gz")
        util.http_get('https://msmarco.blob.core.windows.net/msmarcoranking/collection.tar.gz', tar_filepath)

    with tarfile.open(tar_filepath, "r:gz") as tar:
        tar.extractall(path=data_folder)

logging.info("Read corpus: collection.tsv")
with open(collection_filepath, 'r', encoding='utf8') as fIn:
    for line in fIn:
        pid, passage = line.strip().split("\t")
        corpus[pid] = passage


### Read the train queries, store in queries dict
queries = {}        #dict in the format: query_id -> query. Stores all training queries
queries_filepath = os.path.join(data_folder, 'queries.train.tsv')
if not os.path.exists(queries_filepath):
    tar_filepath = os.path.join(data_folder, 'queries.tar.gz')
    if not os.path.exists(tar_filepath):
        logging.info("Download queries.tar.gz")
        util.http_get('https://msmarco.blob.core.windows.net/msmarcoranking/queries.tar.gz', tar_filepath)

    with tarfile.open(tar_filepath, "r:gz") as tar:
        tar.extractall(path=data_folder)


with open(queries_filepath, 'r', encoding='utf8') as fIn:
    for line in fIn:
        qid, query = line.strip().split("\t")
        queries[qid] = query


# Read our training file: msmarco-hard-negatives.jsonl.gz contains all queries and hard-negatives that were mined with different systems
# For each positive and mined-hard negative passage, we have a Cross-Encoder score from the cross-encoder/ms-marco-MiniLM-L-6-v2 model
# This  Cross-Encoder score allows to de-noise our hard-negatives by requiring that their CE-score is below a certain treshold
train_filepath = '/home/msmarco/data/hard-negatives/msmarco-hard-negatives-v6.jsonl.gz'

#### Create our training data
logging.info("Read train dataset")
train_queries = {}
ce_scores = {}
negs_to_use = None
with gzip.open(train_filepath, 'rt') as fIn:
    for line in tqdm.tqdm(fIn):
        if max_passages > 0 and len(train_queries) >= max_passages:
            break
            
        data = json.loads(line)
        
        if data['qid'] not in ce_scores:
            ce_scores[data['qid']] = {}
        
        # Add pos ce_scores
        for item in data['pos'] :
            ce_scores[data['qid']][item['pid']] = item['ce-score']

        #Get the positive passage ids
        pos_pids = [item['pid'] for item in data['pos']]
       
        #Get the hard negatives
        neg_pids = set()
        if negs_to_use is None:
            if args.negs_to_use is not None:    #Use specific system for negatives
                negs_to_use = args.negs_to_use.split(",")
            else:   #Use all systems
                negs_to_use = list(data['neg'].keys())
            print("Using negatives from the following systems:", negs_to_use)
            
        for system_name in negs_to_use:
            if system_name not in data['neg']:
                continue
                
            system_negs = data['neg'][system_name]
            
            negs_added = 0
            for item in system_negs:
                #Add neg ce_scores
                ce_scores[data['qid']][item['pid']] = item['ce-score']
                
                pid = item['pid']
                if pid not in neg_pids:
                    neg_pids.add(pid)
                    negs_added += 1
                    if negs_added >= num_negs_per_system:
                        break

        if args.use_all_queries or (len(pos_pids) > 0 and len(neg_pids) > 0):
            train_queries[data['qid']] = {'qid': data['qid'], 'query': queries[data['qid']], 'pos': pos_pids, 'neg': neg_pids}

logging.info("Train queries: {}".format(len(train_queries)))

# We create a custom MSMARCO dataset that returns triplets (query, positive, negative)
# on-the-fly based on the information from the mined-hard-negatives jsonl file.
class MSMARCODataset(Dataset):
    def __init__(self, queries, corpus, ce_scores):
        self.queries = queries
        self.queries_ids = list(queries.keys())
        self.corpus = corpus
        self.ce_scores = ce_scores

        for qid in self.queries:
            self.queries[qid]['pos'] = list(self.queries[qid]['pos'])
            self.queries[qid]['neg'] = list(self.queries[qid]['neg'])
            random.shuffle(self.queries[qid]['neg'])

    def __getitem__(self, item):
        query = self.queries[self.queries_ids[item]]
        query_text = query['query']
        qid = query['qid']

        if len(query['pos']) > 0:
            pos_id = query['pos'].pop(0)    #Pop positive and add at end
            pos_text = self.corpus[pos_id]
            query['pos'].append(pos_id)
        else:   #We only have negatives, use two negs
            pos_id = query['neg'].pop(0)    #Pop negative and add at end
            pos_text = self.corpus[pos_id]
            query['neg'].append(pos_id)

        #Get a negative passage
        neg_id = query['neg'].pop(0)    #Pop negative and add at end
        neg_text = self.corpus[neg_id]
        query['neg'].append(neg_id)
        
        pos_score = self.ce_scores[qid][pos_id]
        neg_score = self.ce_scores[qid][neg_id]
        
        return InputExample(texts=[query_text, pos_text, neg_text], label=pos_score-neg_score)

    def __len__(self):
        return len(self.queries)

# For training the SentenceTransformer model, we need a dataset, a dataloader, and a loss used for training.
train_dataset = MSMARCODataset(queries=train_queries, corpus=corpus, ce_scores=ce_scores)
train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=train_batch_size, drop_last=True)
train_loss = losses.MarginMSELoss(model=model)

# Train the model
model.fit(train_objectives=[(train_dataloader, train_loss)],
          epochs=num_epochs,
          warmup_steps=args.warmup_steps,
          use_amp=True,
          checkpoint_path=model_save_path,
          checkpoint_save_steps=10000,
          checkpoint_save_total_limit = 0,
          optimizer_params = {'lr': args.lr},
          )

# Train latest model
model.save(model_save_path)


# Script was called via:
#python train_bi-encoder-margin_mse-en.py --model final-models/co-condenser-margin_mse-sym_mnrl-mean-v1 --lr=1e-5 --warmup_steps=10000 --negs_to_use=co-condenser-margin_mse-sym_mnrl-mean-v1 --num_negs_per_system=10 --epochs=30 --name=cnt_with_mined_negs_mean --use_pre_trained_model --train_batch_size 32


	import sys
	import json
	from torch.utils.data import DataLoader
	from sentence_transformers import SentenceTransformer, LoggingHandler, util, models, evaluation, losses, InputExample
	import logging
	from datetime import datetime
	import gzip
	import os
	import tarfile
	from collections import defaultdict
	from torch.utils.data import IterableDataset
	import tqdm
	from torch.utils.data import Dataset
	import random
	from shutil import copyfile

	import argparse

	parser = argparse.ArgumentParser()
	parser.add_argument("--train_batch_size", default=64, type=int)
	parser.add_argument("--max_seq_length", default=300, type=int)
	parser.add_argument("--model_name", required=True)
	parser.add_argument("--max_passages", default=0, type=int)
	parser.add_argument("--epochs", default=10, type=int)
	parser.add_argument("--pooling", default="cls")
	parser.add_argument("--negs_to_use", default=None, help="From which systems should negatives be used? Multiple systems seperated by comma. None = all")
	parser.add_argument("--warmup_steps", default=1000, type=int)
	parser.add_argument("--lr", default=2e-5, type=float)
	parser.add_argument("--name", default='')
	parser.add_argument("--num_negs_per_system", default=5, type=int)
	parser.add_argument("--use_pre_trained_model", default=False, action="store_true")
	parser.add_argument("--use_all_queries", default=False, action="store_true")
	args = parser.parse_args()

	print(args)

	#### 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

	# The model we want to fine-tune
	train_batch_size = args.train_batch_size #Increasing the train batch size improves the model performance, but requires more GPU memory
	model_name = args.model_name
	max_passages = args.max_passages
	max_seq_length = args.max_seq_length #Max length for passages. Increasing it, requires more GPU memory

	num_negs_per_system = args.num_negs_per_system # We used different systems to mine hard negatives. Number of hard negatives to add from each system
	num_epochs = args.epochs # Number of epochs we want to train

	# We construct the SentenceTransformer bi-encoder from scratch
	if args.use_pre_trained_model:
	print("use pretrained SBERT model")
	model = SentenceTransformer(model_name)
	model.max_seq_length = max_seq_length
	else:
	print("Create new SBERT model")
	word_embedding_model = models.Transformer(model_name, max_seq_length=max_seq_length)
	pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(), args.pooling)
	model = SentenceTransformer(modules=[word_embedding_model, pooling_model])

	model_save_path = f'output/train_bi-encoder-margin_mse_en-{args.name}-{model_name.replace("/", "-")}-batch_size_{train_batch_size}-{datetime.now().strftime("%Y-%m-%d_%H-%M-%S")}'


	# Write self to path
	os.makedirs(model_save_path, exist_ok=True)

	train_script_path = os.path.join(model_save_path, 'train_script.py')
	copyfile(__file__, train_script_path)
	with open(train_script_path, 'a') as fOut:
	fOut.write("\n\n# Script was called via:\n#python " + " ".join(sys.argv))


	### Now we read the MS Marco dataset
	data_folder = 'msmarco-data'

	#### Read the corpus files, that contain all the passages. Store them in the corpus dict
	corpus = {} #dict in the format: passage_id -> passage. Stores all existent passages
	collection_filepath = os.path.join(data_folder, 'collection.tsv')
	if not os.path.exists(collection_filepath):
	tar_filepath = os.path.join(data_folder, 'collection.tar.gz')
	if not os.path.exists(tar_filepath):
	logging.info("Download collection.tar.gz")
	util.http_get('https://msmarco.blob.core.windows.net/msmarcoranking/collection.tar.gz', tar_filepath)

	with tarfile.open(tar_filepath, "r:gz") as tar:
	tar.extractall(path=data_folder)

	logging.info("Read corpus: collection.tsv")
	with open(collection_filepath, 'r', encoding='utf8') as fIn:
	for line in fIn:
	pid, passage = line.strip().split("\t")
	corpus[pid] = passage


	### Read the train queries, store in queries dict
	queries = {} #dict in the format: query_id -> query. Stores all training queries
	queries_filepath = os.path.join(data_folder, 'queries.train.tsv')
	if not os.path.exists(queries_filepath):
	tar_filepath = os.path.join(data_folder, 'queries.tar.gz')
	if not os.path.exists(tar_filepath):
	logging.info("Download queries.tar.gz")
	util.http_get('https://msmarco.blob.core.windows.net/msmarcoranking/queries.tar.gz', tar_filepath)

	with tarfile.open(tar_filepath, "r:gz") as tar:
	tar.extractall(path=data_folder)


	with open(queries_filepath, 'r', encoding='utf8') as fIn:
	for line in fIn:
	qid, query = line.strip().split("\t")
	queries[qid] = query


	# Read our training file: msmarco-hard-negatives.jsonl.gz contains all queries and hard-negatives that were mined with different systems
	# For each positive and mined-hard negative passage, we have a Cross-Encoder score from the cross-encoder/ms-marco-MiniLM-L-6-v2 model
	# This Cross-Encoder score allows to de-noise our hard-negatives by requiring that their CE-score is below a certain treshold
	train_filepath = '/home/msmarco/data/hard-negatives/msmarco-hard-negatives-v6.jsonl.gz'

	#### Create our training data
	logging.info("Read train dataset")
	train_queries = {}
	ce_scores = {}
	negs_to_use = None
	with gzip.open(train_filepath, 'rt') as fIn:
	for line in tqdm.tqdm(fIn):
	if max_passages > 0 and len(train_queries) >= max_passages:
	break

	data = json.loads(line)

	if data['qid'] not in ce_scores:
	ce_scores[data['qid']] = {}

	# Add pos ce_scores
	for item in data['pos'] :
	ce_scores[data['qid']][item['pid']] = item['ce-score']

	#Get the positive passage ids
	pos_pids = [item['pid'] for item in data['pos']]

	#Get the hard negatives
	neg_pids = set()
	if negs_to_use is None:
	if args.negs_to_use is not None: #Use specific system for negatives
	negs_to_use = args.negs_to_use.split(",")
	else: #Use all systems
	negs_to_use = list(data['neg'].keys())
	print("Using negatives from the following systems:", negs_to_use)

	for system_name in negs_to_use:
	if system_name not in data['neg']:
	continue

	system_negs = data['neg'][system_name]

	negs_added = 0
	for item in system_negs:
	#Add neg ce_scores
	ce_scores[data['qid']][item['pid']] = item['ce-score']

	pid = item['pid']
	if pid not in neg_pids:
	neg_pids.add(pid)
	negs_added += 1
	if negs_added >= num_negs_per_system:
	break

	if args.use_all_queries or (len(pos_pids) > 0 and len(neg_pids) > 0):
	train_queries[data['qid']] = {'qid': data['qid'], 'query': queries[data['qid']], 'pos': pos_pids, 'neg': neg_pids}

	logging.info("Train queries: {}".format(len(train_queries)))

	# We create a custom MSMARCO dataset that returns triplets (query, positive, negative)
	# on-the-fly based on the information from the mined-hard-negatives jsonl file.
	class MSMARCODataset(Dataset):
	def __init__(self, queries, corpus, ce_scores):
	self.queries = queries
	self.queries_ids = list(queries.keys())
	self.corpus = corpus
	self.ce_scores = ce_scores

	for qid in self.queries:
	self.queries[qid]['pos'] = list(self.queries[qid]['pos'])
	self.queries[qid]['neg'] = list(self.queries[qid]['neg'])
	random.shuffle(self.queries[qid]['neg'])

	def __getitem__(self, item):
	query = self.queries[self.queries_ids[item]]
	query_text = query['query']
	qid = query['qid']

	if len(query['pos']) > 0:
	pos_id = query['pos'].pop(0) #Pop positive and add at end
	pos_text = self.corpus[pos_id]
	query['pos'].append(pos_id)
	else: #We only have negatives, use two negs
	pos_id = query['neg'].pop(0) #Pop negative and add at end
	pos_text = self.corpus[pos_id]
	query['neg'].append(pos_id)

	#Get a negative passage
	neg_id = query['neg'].pop(0) #Pop negative and add at end
	neg_text = self.corpus[neg_id]
	query['neg'].append(neg_id)

	pos_score = self.ce_scores[qid][pos_id]
	neg_score = self.ce_scores[qid][neg_id]

	return InputExample(texts=[query_text, pos_text, neg_text], label=pos_score-neg_score)

	def __len__(self):
	return len(self.queries)

	# For training the SentenceTransformer model, we need a dataset, a dataloader, and a loss used for training.
	train_dataset = MSMARCODataset(queries=train_queries, corpus=corpus, ce_scores=ce_scores)
	train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=train_batch_size, drop_last=True)
	train_loss = losses.MarginMSELoss(model=model)

	# Train the model
	model.fit(train_objectives=[(train_dataloader, train_loss)],
	epochs=num_epochs,
	warmup_steps=args.warmup_steps,
	use_amp=True,
	checkpoint_path=model_save_path,
	checkpoint_save_steps=10000,
	checkpoint_save_total_limit = 0,
	optimizer_params = {'lr': args.lr},
	)

	# Train latest model
	model.save(model_save_path)


	# Script was called via:
	#python train_bi-encoder-margin_mse-en.py --model final-models/co-condenser-margin_mse-sym_mnrl-mean-v1 --lr=1e-5 --warmup_steps=10000 --negs_to_use=co-condenser-margin_mse-sym_mnrl-mean-v1 --num_negs_per_system=10 --epochs=30 --name=cnt_with_mined_negs_mean --use_pre_trained_model --train_batch_size 32