# -*- coding: utf-8 -*-
import hashlib
import os
import requests
import json
import configparser
import copy
import time
from tqdm import tqdm
import re
import emoji
import neologdn
import unicodedata
from typing import Dict, Iterable, List, Tuple
from allennlp.modules.token_embedders import PretrainedTransformerEmbedder

from allennlp.data import (
    DataLoader,
    DatasetReader,
    Instance,
    Vocabulary,
    TextFieldTensors,
)
from allennlp.data.data_loaders import SimpleDataLoader
from allennlp.models import Model
from allennlp.modules.text_field_embedders import BasicTextFieldEmbedder
from allennlp.training.optimizers import AdamOptimizer
from allennlp.training.trainer import Trainer, GradientDescentTrainer

CODE_REGEX = re.compile('[!"#$%&\'\\\\()*+,-./:;<=>?@[\\]^_`{|}~「」〔〕“”〈〉『』【】＆＊・（）＄＃＠。、？！｀＋￥％\s]')

def build_vocab(instances: Iterable[Instance]) -> Vocabulary:
    print("Building the vocabulary")
    return Vocabulary.from_instances(instances)


def build_data_loaders(config,
    train_data: List[Instance],
    dev_data: List[Instance],
    test_data: List[Instance]) -> Tuple[DataLoader, DataLoader, DataLoader]:

    train_loader = SimpleDataLoader(train_data, config.batch_size_for_train, shuffle=True)
    dev_loader = SimpleDataLoader(dev_data, config.batch_size_for_eval, shuffle=False)
    test_loader = SimpleDataLoader(test_data, config.batch_size_for_eval, shuffle=False)

    return train_loader, dev_loader, test_loader

def build_trainer(
    config,
    model: Model,
    train_loader: DataLoader,
    dev_loader: DataLoader,
) -> Trainer:
    parameters = [(n, p) for n, p in model.named_parameters() if p.requires_grad]
    optimizer = AdamOptimizer(parameters, lr=config.lr)  # type: ignore
    model.cuda()
    trainer = GradientDescentTrainer(
        model=model,
        data_loader=train_loader,
        validation_data_loader=dev_loader,
        num_epochs=config.num_epochs,
        optimizer=optimizer,
        cuda_device=0,
        serialization_dir=config.serialization_dir
    )
    return trainer

def emb_returner(config):
    if config.bert_name == 'japanese-bert':
        huggingface_model = 'cl-tohoku/bert-base-japanese'
    else:
        huggingface_model = 'dummy'
        print(config.bert_name,'are not supported')
        exit()
    bert_embedder = PretrainedTransformerEmbedder(model_name="cl-tohoku/bert-base-japanese")
    return bert_embedder, bert_embedder.get_output_dim(), BasicTextFieldEmbedder({'tokens': bert_embedder})


def normalizer(text):
    text = neologdn.normalize(text)
    text = ''.join(['' if c in emoji.UNICODE_EMOJI else c for c in text])
    text = CODE_REGEX.sub('', text)
    text = re.compile(u"([\U0001F600-\U0001F64F]|[\U0001F300-\U0001F5FF])").sub('', text)
    text = unicodedata.normalize("NFKC", text)

    return text

f1dd1c8da76066c0ff2d281d37642f41cb887e6b
<mask token>


def build_vocab(instances: Iterable[Instance]) ->Vocabulary:
    print('Building the vocabulary')
    return Vocabulary.from_instances(instances)


<mask token>


def emb_returner(config):
    if config.bert_name == 'japanese-bert':
        huggingface_model = 'cl-tohoku/bert-base-japanese'
    else:
        huggingface_model = 'dummy'
        print(config.bert_name, 'are not supported')
        exit()
    bert_embedder = PretrainedTransformerEmbedder(model_name=
        'cl-tohoku/bert-base-japanese')
    return bert_embedder, bert_embedder.get_output_dim(
        ), BasicTextFieldEmbedder({'tokens': bert_embedder})


def normalizer(text):
    text = neologdn.normalize(text)
    text = ''.join([('' if c in emoji.UNICODE_EMOJI else c) for c in text])
    text = CODE_REGEX.sub('', text)
    text = re.compile(u'([😀-🙏]|[🌀-🗿])').sub('', text)
    text = unicodedata.normalize('NFKC', text)
    return text

<mask token>


def build_vocab(instances: Iterable[Instance]) ->Vocabulary:
    print('Building the vocabulary')
    return Vocabulary.from_instances(instances)


def build_data_loaders(config, train_data: List[Instance], dev_data: List[
    Instance], test_data: List[Instance]) ->Tuple[DataLoader, DataLoader,
    DataLoader]:
    train_loader = SimpleDataLoader(train_data, config.batch_size_for_train,
        shuffle=True)
    dev_loader = SimpleDataLoader(dev_data, config.batch_size_for_eval,
        shuffle=False)
    test_loader = SimpleDataLoader(test_data, config.batch_size_for_eval,
        shuffle=False)
    return train_loader, dev_loader, test_loader


def build_trainer(config, model: Model, train_loader: DataLoader,
    dev_loader: DataLoader) ->Trainer:
    parameters = [(n, p) for n, p in model.named_parameters() if p.
        requires_grad]
    optimizer = AdamOptimizer(parameters, lr=config.lr)
    model.cuda()
    trainer = GradientDescentTrainer(model=model, data_loader=train_loader,
        validation_data_loader=dev_loader, num_epochs=config.num_epochs,
        optimizer=optimizer, cuda_device=0, serialization_dir=config.
        serialization_dir)
    return trainer


def emb_returner(config):
    if config.bert_name == 'japanese-bert':
        huggingface_model = 'cl-tohoku/bert-base-japanese'
    else:
        huggingface_model = 'dummy'
        print(config.bert_name, 'are not supported')
        exit()
    bert_embedder = PretrainedTransformerEmbedder(model_name=
        'cl-tohoku/bert-base-japanese')
    return bert_embedder, bert_embedder.get_output_dim(
        ), BasicTextFieldEmbedder({'tokens': bert_embedder})


def normalizer(text):
    text = neologdn.normalize(text)
    text = ''.join([('' if c in emoji.UNICODE_EMOJI else c) for c in text])
    text = CODE_REGEX.sub('', text)
    text = re.compile(u'([😀-🙏]|[🌀-🗿])').sub('', text)
    text = unicodedata.normalize('NFKC', text)
    return text

<mask token>
CODE_REGEX = re.compile(
    '[!"#$%&\'\\\\()*+,-./:;<=>?@[\\]^_`{|}~「」〔〕“”〈〉『』【】＆＊・（）＄＃＠。、？！｀＋￥％\\s]')


def build_vocab(instances: Iterable[Instance]) ->Vocabulary:
    print('Building the vocabulary')
    return Vocabulary.from_instances(instances)


def build_data_loaders(config, train_data: List[Instance], dev_data: List[
    Instance], test_data: List[Instance]) ->Tuple[DataLoader, DataLoader,
    DataLoader]:
    train_loader = SimpleDataLoader(train_data, config.batch_size_for_train,
        shuffle=True)
    dev_loader = SimpleDataLoader(dev_data, config.batch_size_for_eval,
        shuffle=False)
    test_loader = SimpleDataLoader(test_data, config.batch_size_for_eval,
        shuffle=False)
    return train_loader, dev_loader, test_loader


def build_trainer(config, model: Model, train_loader: DataLoader,
    dev_loader: DataLoader) ->Trainer:
    parameters = [(n, p) for n, p in model.named_parameters() if p.
        requires_grad]
    optimizer = AdamOptimizer(parameters, lr=config.lr)
    model.cuda()
    trainer = GradientDescentTrainer(model=model, data_loader=train_loader,
        validation_data_loader=dev_loader, num_epochs=config.num_epochs,
        optimizer=optimizer, cuda_device=0, serialization_dir=config.
        serialization_dir)
    return trainer


def emb_returner(config):
    if config.bert_name == 'japanese-bert':
        huggingface_model = 'cl-tohoku/bert-base-japanese'
    else:
        huggingface_model = 'dummy'
        print(config.bert_name, 'are not supported')
        exit()
    bert_embedder = PretrainedTransformerEmbedder(model_name=
        'cl-tohoku/bert-base-japanese')
    return bert_embedder, bert_embedder.get_output_dim(
        ), BasicTextFieldEmbedder({'tokens': bert_embedder})


def normalizer(text):
    text = neologdn.normalize(text)
    text = ''.join([('' if c in emoji.UNICODE_EMOJI else c) for c in text])
    text = CODE_REGEX.sub('', text)
    text = re.compile(u'([😀-🙏]|[🌀-🗿])').sub('', text)
    text = unicodedata.normalize('NFKC', text)
    return text

import hashlib
import os
import requests
import json
import configparser
import copy
import time
from tqdm import tqdm
import re
import emoji
import neologdn
import unicodedata
from typing import Dict, Iterable, List, Tuple
from allennlp.modules.token_embedders import PretrainedTransformerEmbedder
from allennlp.data import DataLoader, DatasetReader, Instance, Vocabulary, TextFieldTensors
from allennlp.data.data_loaders import SimpleDataLoader
from allennlp.models import Model
from allennlp.modules.text_field_embedders import BasicTextFieldEmbedder
from allennlp.training.optimizers import AdamOptimizer
from allennlp.training.trainer import Trainer, GradientDescentTrainer
CODE_REGEX = re.compile(
    '[!"#$%&\'\\\\()*+,-./:;<=>?@[\\]^_`{|}~「」〔〕“”〈〉『』【】＆＊・（）＄＃＠。、？！｀＋￥％\\s]')


def build_vocab(instances: Iterable[Instance]) ->Vocabulary:
    print('Building the vocabulary')
    return Vocabulary.from_instances(instances)


def build_data_loaders(config, train_data: List[Instance], dev_data: List[
    Instance], test_data: List[Instance]) ->Tuple[DataLoader, DataLoader,
    DataLoader]:
    train_loader = SimpleDataLoader(train_data, config.batch_size_for_train,
        shuffle=True)
    dev_loader = SimpleDataLoader(dev_data, config.batch_size_for_eval,
        shuffle=False)
    test_loader = SimpleDataLoader(test_data, config.batch_size_for_eval,
        shuffle=False)
    return train_loader, dev_loader, test_loader


def build_trainer(config, model: Model, train_loader: DataLoader,
    dev_loader: DataLoader) ->Trainer:
    parameters = [(n, p) for n, p in model.named_parameters() if p.
        requires_grad]
    optimizer = AdamOptimizer(parameters, lr=config.lr)
    model.cuda()
    trainer = GradientDescentTrainer(model=model, data_loader=train_loader,
        validation_data_loader=dev_loader, num_epochs=config.num_epochs,
        optimizer=optimizer, cuda_device=0, serialization_dir=config.
        serialization_dir)
    return trainer


def emb_returner(config):
    if config.bert_name == 'japanese-bert':
        huggingface_model = 'cl-tohoku/bert-base-japanese'
    else:
        huggingface_model = 'dummy'
        print(config.bert_name, 'are not supported')
        exit()
    bert_embedder = PretrainedTransformerEmbedder(model_name=
        'cl-tohoku/bert-base-japanese')
    return bert_embedder, bert_embedder.get_output_dim(
        ), BasicTextFieldEmbedder({'tokens': bert_embedder})


def normalizer(text):
    text = neologdn.normalize(text)
    text = ''.join([('' if c in emoji.UNICODE_EMOJI else c) for c in text])
    text = CODE_REGEX.sub('', text)
    text = re.compile(u'([😀-🙏]|[🌀-🗿])').sub('', text)
    text = unicodedata.normalize('NFKC', text)
    return text

# -*- coding: utf-8 -*-
import hashlib
import os
import requests
import json
import configparser
import copy
import time
from tqdm import tqdm
import re
import emoji
import neologdn
import unicodedata
from typing import Dict, Iterable, List, Tuple
from allennlp.modules.token_embedders import PretrainedTransformerEmbedder

from allennlp.data import (
    DataLoader,
    DatasetReader,
    Instance,
    Vocabulary,
    TextFieldTensors,
)
from allennlp.data.data_loaders import SimpleDataLoader
from allennlp.models import Model
from allennlp.modules.text_field_embedders import BasicTextFieldEmbedder
from allennlp.training.optimizers import AdamOptimizer
from allennlp.training.trainer import Trainer, GradientDescentTrainer

CODE_REGEX = re.compile('[!"#$%&\'\\\\()*+,-./:;<=>?@[\\]^_`{|}~「」〔〕“”〈〉『』【】＆＊・（）＄＃＠。、？！｀＋￥％\s]')

def build_vocab(instances: Iterable[Instance]) -> Vocabulary:
    print("Building the vocabulary")
    return Vocabulary.from_instances(instances)


def build_data_loaders(config,
    train_data: List[Instance],
    dev_data: List[Instance],
    test_data: List[Instance]) -> Tuple[DataLoader, DataLoader, DataLoader]:

    train_loader = SimpleDataLoader(train_data, config.batch_size_for_train, shuffle=True)
    dev_loader = SimpleDataLoader(dev_data, config.batch_size_for_eval, shuffle=False)
    test_loader = SimpleDataLoader(test_data, config.batch_size_for_eval, shuffle=False)

    return train_loader, dev_loader, test_loader

def build_trainer(
    config,
    model: Model,
    train_loader: DataLoader,
    dev_loader: DataLoader,
) -> Trainer:
    parameters = [(n, p) for n, p in model.named_parameters() if p.requires_grad]
    optimizer = AdamOptimizer(parameters, lr=config.lr)  # type: ignore
    model.cuda()
    trainer = GradientDescentTrainer(
        model=model,
        data_loader=train_loader,
        validation_data_loader=dev_loader,
        num_epochs=config.num_epochs,
        optimizer=optimizer,
        cuda_device=0,
        serialization_dir=config.serialization_dir
    )
    return trainer

def emb_returner(config):
    if config.bert_name == 'japanese-bert':
        huggingface_model = 'cl-tohoku/bert-base-japanese'
    else:
        huggingface_model = 'dummy'
        print(config.bert_name,'are not supported')
        exit()
    bert_embedder = PretrainedTransformerEmbedder(model_name="cl-tohoku/bert-base-japanese")
    return bert_embedder, bert_embedder.get_output_dim(), BasicTextFieldEmbedder({'tokens': bert_embedder})


def normalizer(text):
    text = neologdn.normalize(text)
    text = ''.join(['' if c in emoji.UNICODE_EMOJI else c for c in text])
    text = CODE_REGEX.sub('', text)
    text = re.compile(u"([\U0001F600-\U0001F64F]|[\U0001F300-\U0001F5FF])").sub('', text)
    text = unicodedata.normalize("NFKC", text)

    return text
