# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Tokenization classes for Transformer XL model.
    Adapted from https://github.com/kimiyoung/transformer-xl.
"""


import glob
import logging
import os
import pickle
import re
from collections import Counter, OrderedDict
from typing import Optional

import numpy as np

from tokenizers import Tokenizer
from tokenizers.implementations import BaseTokenizer
from tokenizers.models import WordLevel
from tokenizers.normalizers import Lowercase, Sequence, Strip, unicode_normalizer_from_str
from tokenizers.pre_tokenizers import CharDelimiterSplit, WhitespaceSplit
from tokenizers.processors import BertProcessing

from .file_utils import cached_path, is_torch_available
from .tokenization_utils import PreTrainedTokenizer
from .tokenization_utils_fast import PreTrainedTokenizerFast


if is_torch_available():
    import torch


logger = logging.getLogger(__name__)

VOCAB_FILES_NAMES = {"pretrained_vocab_file": "vocab.bin", "vocab_file": "vocab.txt"}
VOCAB_FILES_NAMES_FAST = {"pretrained_vocab_file": "vocab.json", "vocab_file": "vocab.json"}

PRETRAINED_VOCAB_FILES_MAP = {
    "pretrained_vocab_file": {
        "transfo-xl-wt103": "https://s3.amazonaws.com/models.huggingface.co/bert/transfo-xl-wt103-vocab.bin",
    }
}

PRETRAINED_VOCAB_FILES_MAP_FAST = {
    "pretrained_vocab_file": {
        "transfo-xl-wt103": "https://s3.amazonaws.com/models.huggingface.co/bert/transfo-xl-wt103-vocab.json",
    }
}

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "transfo-xl-wt103": None,
}

PRETRAINED_CORPUS_ARCHIVE_MAP = {
    "transfo-xl-wt103": "https://s3.amazonaws.com/models.huggingface.co/bert/transfo-xl-wt103-corpus.bin",
}
CORPUS_NAME = "corpus.bin"


class TransfoXLTokenizer(PreTrainedTokenizer):
    """
    Transformer-XL tokenizer adapted from Vocab class in https://github.com/kimiyoung/transformer-xl

    This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the methods. Users
    should refer to the superclass for more information regarding methods.
    """

    vocab_files_names = VOCAB_FILES_NAMES
    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    model_input_names = []

    def __init__(
        self,
        special=None,
        min_freq=0,
        max_size=None,
        lower_case=False,
        delimiter=None,
        vocab_file=None,
        pretrained_vocab_file=None,
        never_split=None,
        unk_token="<unk>",
        eos_token="<eos>",
        additional_special_tokens=["<formula>"],
        **kwargs
    ):
        super().__init__(
            unk_token=unk_token, eos_token=eos_token, additional_special_tokens=additional_special_tokens, **kwargs
        )

        if never_split is None:
            never_split = self.all_special_tokens
        if special is None:
            special = []
        self.counter = Counter()
        self.special = special
        self.min_freq = min_freq
        self.max_size = max_size
        self.lower_case = lower_case
        self.delimiter = delimiter
        self.vocab_file = vocab_file
        self.never_split = never_split
        self.punctuation_symbols = '!"#$%&()*+,-./\\:;<=>?@[\\]^_`{|}~'
        self.punction_without_space_before_pattern = re.compile(r"[^\s][{}]".format(self.punctuation_symbols))
        self.punctuation_with_space_around_pattern = self._compile_space_around_punctuation_pattern()

        try:
            if pretrained_vocab_file is not None:
                # Hack because, honestly this tokenizer was not made to be used
                # in a library like ours, at all.
                vocab_dict = torch.load(pretrained_vocab_file)
                for key, value in vocab_dict.items():
                    if key not in self.__dict__:
                        self.__dict__[key] = value

            if vocab_file is not None:
                self.build_vocab()
        except Exception:
            raise ValueError(
                "Unable to parse file {}. Unknown format. "
                "If you tried to load a model saved through TransfoXLTokenizerFast,"
                "please note they are not compatible.".format(pretrained_vocab_file)
            )

        if vocab_file is not None:
            self.build_vocab()

    def _compile_space_around_punctuation_pattern(self):
        look_ahead_for_special_token = "(?=[{}])".format(self.punctuation_symbols)
        look_ahead_to_match_all_except_space = r"(?=[^\s])"
        return re.compile(r"" + look_ahead_for_special_token + look_ahead_to_match_all_except_space)

    def count_file(self, path, verbose=False, add_eos=False):
        if verbose:
            logger.info("counting file {} ...".format(path))
        assert os.path.exists(path), f"Input file {path} not found"

        sents = []
        with open(path, "r", encoding="utf-8") as f:
            for idx, line in enumerate(f):
                if verbose and idx > 0 and idx % 500000 == 0:
                    logger.info("    line {}".format(idx))
                symbols = self.tokenize(line, add_eos=add_eos)
                self.counter.update(symbols)
                sents.append(symbols)

        return sents

    def count_sents(self, sents, verbose=False):
        """
            sents : a list of sentences, each a list of tokenized symbols
        """
        if verbose:
            logger.info("counting {} sents ...".format(len(sents)))
        for idx, symbols in enumerate(sents):
            if verbose and idx > 0 and idx % 500000 == 0:
                logger.info("    line {}".format(idx))
            self.counter.update(symbols)

    def _build_from_file(self, vocab_file):
        self.idx2sym = []
        self.sym2idx = OrderedDict()

        with open(vocab_file, "r", encoding="utf-8") as f:
            for line in f:
                symb = line.strip().split()[0]
                self.add_symbol(symb)
        if "<UNK>" in self.sym2idx:
            self.unk_idx = self.sym2idx["<UNK>"]
        elif "<unk>" in self.sym2idx:
            self.unk_idx = self.sym2idx["<unk>"]
        else:
            raise ValueError("No <unkown> token in vocabulary")

    def save_vocabulary(self, vocab_path):
        """
        Save the vocabulary and special tokens file to a directory.

        Args:
            vocab_path (:obj:`str`):
                The directory in which to save the vocabulary.

        Returns:
            :obj:`Tuple(str)`: Paths to the files saved.
        """

        logger.warning(
            "Please note you will not be able to load the save vocabulary in"
            " Rust-based TransfoXLTokenizerFast as they don't share the same structure."
        )

        if os.path.isdir(vocab_path):
            vocab_file = os.path.join(vocab_path, VOCAB_FILES_NAMES["pretrained_vocab_file"])
        else:
            vocab_file = vocab_path
        torch.save(self.__dict__, vocab_file)
        return (vocab_file,)

    def build_vocab(self):
        if self.vocab_file:
            logger.info("building vocab from {}".format(self.vocab_file))
            self._build_from_file(self.vocab_file)
            logger.info("final vocab size {}".format(len(self)))
        else:
            logger.info("building vocab with min_freq={}, max_size={}".format(self.min_freq, self.max_size))
            self.idx2sym = []
            self.sym2idx = OrderedDict()

            for sym in self.special:
                self.add_special(sym)

            for sym, cnt in self.counter.most_common(self.max_size):
                if cnt < self.min_freq:
                    break
                self.add_symbol(sym)

            logger.info("final vocab size {} from {} unique tokens".format(len(self), len(self.counter)))

    def encode_file(self, path, ordered=False, verbose=False, add_eos=True, add_double_eos=False):
        if verbose:
            logger.info("encoding file {} ...".format(path))
        assert os.path.exists(path), f"Output file {path} not found"
        encoded = []
        with open(path, "r", encoding="utf-8") as f:
            for idx, line in enumerate(f):
                if verbose and idx > 0 and idx % 500000 == 0:
                    logger.info("    line {}".format(idx))
                symbols = self.tokenize(line, add_eos=add_eos, add_double_eos=add_double_eos)
                encoded.append(self.convert_to_tensor(symbols))

        if ordered:
            encoded = torch.cat(encoded)

        return encoded

    def encode_sents(self, sents, ordered=False, verbose=False):
        if verbose:
            logger.info("encoding {} sents ...".format(len(sents)))
        encoded = []
        for idx, symbols in enumerate(sents):
            if verbose and idx > 0 and idx % 500000 == 0:
                logger.info("    line {}".format(idx))
            encoded.append(self.convert_to_tensor(symbols))

        if ordered:
            encoded = torch.cat(encoded)

        return encoded

    def add_special(self, sym):
        if sym not in self.sym2idx:
            self.idx2sym.append(sym)
            self.sym2idx[sym] = len(self.idx2sym) - 1
            setattr(self, "{}_idx".format(sym.strip("<>")), self.sym2idx[sym])

    def add_symbol(self, sym):
        if sym not in self.sym2idx:
            self.idx2sym.append(sym)
            self.sym2idx[sym] = len(self.idx2sym) - 1

    def move_added_token(self, token: str, target_idx: int):
        """
        Moves an added token to a specific position in the vocab.
        This method should be used when resizing an embedding layer other than the last one in the `AdaptiveEmbedding`
        in order to move the token in the tokenizer from the default position (at the very end) to the desired one.

        Args:
            token: The token to move to a specific position in the vocab.
            target_idx: The position where the token should be moved to.
        """
        assert token in self.added_tokens_encoder, "Token which should be moved has to be an added token"
        assert token not in self.idx2sym, "Token which should be moved is already in vocab"

        # Insert sym into vocab
        self.idx2sym.insert(target_idx, token)
        self.sym2idx[token] = target_idx

        # Shift following indices in sym2idx
        for idx in range(target_idx + 1, len(self.idx2sym)):
            current_sym = self.idx2sym[idx]
            self.sym2idx[current_sym] = idx

        # Delete token from added_tokens
        old_index = self.added_tokens_encoder[token]
        del self.added_tokens_decoder[old_index]
        del self.added_tokens_encoder[token]

    def _convert_id_to_token(self, idx):
        """Converts an id in a token (BPE) using the vocab."""
        assert 0 <= idx < len(self), "Index {} out of vocabulary range".format(idx)
        return self.idx2sym[idx]

    def _convert_token_to_id(self, sym):
        """ Converts a token (str) in an id using the vocab. """
        if sym in self.sym2idx:
            return self.sym2idx[sym]
        else:
            # logger.info('encounter unk {}'.format(sym))
            # assert '<eos>' not in sym
            if hasattr(self, "unk_idx"):
                return self.sym2idx.get(sym, self.unk_idx)
            # Backward compatibility with pre-trained models
            elif "<unk>" in self.sym2idx:
                return self.sym2idx["<unk>"]
            elif "<UNK>" in self.sym2idx:
                return self.sym2idx["<UNK>"]
            else:
                raise ValueError("Token not in vocabulary and no <unk> token in vocabulary for replacement")

    def convert_tokens_to_string(self, tokens):
        """ Converts a sequence of tokens (string) in a single string. """
        out_string = " ".join(tokens).strip()
        return out_string

    def convert_to_tensor(self, symbols):
        return torch.LongTensor(self.convert_tokens_to_ids(symbols))

    @property
    def vocab_size(self):
        return len(self.idx2sym)

    def get_vocab(self):
        return dict(self.sym2idx, **self.added_tokens_encoder)

    def _tokenize(self, line, add_eos=False, add_double_eos=False):
        line = line.strip()
        # convert to lower case
        if self.lower_case:
            line = line.lower()

        # empty delimiter '' will evaluate False
        if self.delimiter == "":
            symbols = line
        else:
            symbols = line.split(self.delimiter)

        if add_double_eos:  # lm1b
            return ["<S>"] + symbols + ["<S>"]
        elif add_eos:
            return symbols + ["<eos>"]
        else:
            return symbols

    def prepare_for_tokenization(self, text, is_pretokenized=False, **kwargs):
        # add spaces before punctuation symbols as should be done in transfo-xl
        add_space_before_punct_symbol = kwargs.pop("add_space_before_punct_symbol", False)
        if add_space_before_punct_symbol:
            text = self.punctuation_with_space_around_pattern.sub(r" ", text)
        elif self.punction_without_space_before_pattern.search(text):
            # searches until the first occurence of a punctuation symbol without surrounding spaces
            logger.warning(
                "You might want to consider setting `add_space_before_punct_symbol=True` as an argument to the `tokenizer.encode()` to avoid tokenizing words with punctuation symbols to the `<unk>` token"
            )

        return (text, kwargs)


class _TransfoXLDelimiterLookupTokenizer(BaseTokenizer):
    def __init__(
        self,
        vocab_file,
        delimiter,
        lowercase,
        unk_token,
        eos_token,
        add_eos=False,
        add_double_eos=False,
        normalization: Optional[str] = None,
    ):

        try:
            tokenizer = WordLevel(vocab_file, unk_token=unk_token)
            tokenizer = Tokenizer(tokenizer)
        except Exception:
            raise ValueError(
                "Unable to parse file {}. Unknown format. "
                "If you tried to load a model saved through TransfoXLTokenizer,"
                "please note they are not compatible.".format(vocab_file)
            )

        # Create the correct normalization path
        normalizer = []

        # Include unicode normalization
        if normalization:
            normalizer += [unicode_normalizer_from_str(normalization)]

        # Include case normalization
        if lowercase:
            normalizer += [Lowercase()]

        # Strip normalizer at the end
        normalizer += [Strip(left=True, right=True)]

        if len(normalizer) > 0:
            tokenizer.normalizer = Sequence(normalizer) if len(normalizer) > 1 else normalizer[0]

        # Setup the splitter
        tokenizer.pre_tokenizer = CharDelimiterSplit(delimiter) if delimiter else WhitespaceSplit()

        if add_double_eos:
            tokenizer.post_processor = BertProcessing(
                (eos_token, tokenizer.token_to_id(eos_token)), (eos_token, tokenizer.token_to_id(eos_token))
            )

        parameters = {
            "model": "TransfoXLModel",
            "add_eos": add_eos,
            "add_double_eos": add_double_eos,
            "unk_token": unk_token,
            "eos_token": eos_token,
            "delimiter": delimiter,
            "lowercase": lowercase,
        }

        super().__init__(tokenizer, parameters)


class TransfoXLTokenizerFast(PreTrainedTokenizerFast):
    """
    Construct a "Fast" Transformer-XL tokenizer (backed by HuggingFace's `tokenizers` library).

    The Transformer-XL tokenizer is a word-level tokenizer (no sub-word tokenization).

    Adapted from Vocab class in https://github.com/kimiyoung/transformer-xl

    This tokenizer inherits from :class:`~transformers.PreTrainedTokenizerFast` which contains most of the methods. Users
    should refer to the superclass for more information regarding methods.
    """

    vocab_files_names = VOCAB_FILES_NAMES_FAST
    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP_FAST
    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    model_input_names = []

    def __init__(
        self,
        special=None,
        min_freq=0,
        max_size=None,
        lower_case=False,
        delimiter=None,
        vocab_file=None,
        pretrained_vocab_file=None,
        never_split=None,
        unk_token="<unk>",
        eos_token="<eos>",
        additional_special_tokens=["<formula>"],
        add_eos=False,
        add_double_eos=False,
        normalization=None,
        **kwargs
    ):

        super().__init__(
            _TransfoXLDelimiterLookupTokenizer(
                vocab_file=vocab_file or pretrained_vocab_file,
                delimiter=delimiter,
                lowercase=lower_case,
                unk_token=unk_token,
                eos_token=eos_token,
                add_eos=add_eos,
                add_double_eos=add_double_eos,
                normalization=normalization,
            ),
            unk_token=unk_token,
            eos_token=eos_token,
            additional_special_tokens=additional_special_tokens,
            **kwargs,
        )

    def save_pretrained(self, save_directory):
        logger.warning(
            "Please note you will not be able to load the vocabulary in"
            " Python-based TransfoXLTokenizer as they don't share the same structure."
        )

        return super().save_pretrained(save_directory)


class LMOrderedIterator(object):
    def __init__(self, data, bsz, bptt, device="cpu", ext_len=None):
        """
            data -- LongTensor -- the LongTensor is strictly ordered
        """
        self.bsz = bsz
        self.bptt = bptt
        self.ext_len = ext_len if ext_len is not None else 0

        self.device = device

        # Work out how cleanly we can divide the dataset into bsz parts.
        self.n_step = data.size(0) // bsz

        # Trim off any extra elements that wouldn't cleanly fit (remainders).
        data = data.narrow(0, 0, self.n_step * bsz)

        # Evenly divide the data across the bsz batches.
        self.data = data.view(bsz, -1).t().contiguous().to(device)

        # Number of mini-batches
        self.n_batch = (self.n_step + self.bptt - 1) // self.bptt

    def get_batch(self, i, bptt=None):
        if bptt is None:
            bptt = self.bptt
        seq_len = min(bptt, self.data.size(0) - 1 - i)

        end_idx = i + seq_len
        beg_idx = max(0, i - self.ext_len)

        data = self.data[beg_idx:end_idx]
        target = self.data[i + 1 : i + 1 + seq_len]

        data_out = data.transpose(0, 1).contiguous().to(self.device)
        target_out = target.transpose(0, 1).contiguous().to(self.device)

        return data_out, target_out, seq_len

    def get_fixlen_iter(self, start=0):
        for i in range(start, self.data.size(0) - 1, self.bptt):
            yield self.get_batch(i)

    def get_varlen_iter(self, start=0, std=5, min_len=5, max_deviation=3):
        max_len = self.bptt + max_deviation * std
        i = start
        while True:
            bptt = self.bptt if np.random.random() < 0.95 else self.bptt / 2.0
            bptt = min(max_len, max(min_len, int(np.random.normal(bptt, std))))
            data, target, seq_len = self.get_batch(i, bptt)
            i += seq_len
            yield data, target, seq_len
            if i >= self.data.size(0) - 2:
                break

    def __iter__(self):
        return self.get_fixlen_iter()


class LMShuffledIterator(object):
    def __init__(self, data, bsz, bptt, device="cpu", ext_len=None, shuffle=False):
        """
            data -- list[LongTensor] -- there is no order among the LongTensors
        """
        self.data = data

        self.bsz = bsz
        self.bptt = bptt
        self.ext_len = ext_len if ext_len is not None else 0

        self.device = device
        self.shuffle = shuffle

    def get_sent_stream(self):
        # index iterator
        epoch_indices = np.random.permutation(len(self.data)) if self.shuffle else np.array(range(len(self.data)))

        # sentence iterator
        for idx in epoch_indices:
            yield self.data[idx]

    def stream_iterator(self, sent_stream):
        # streams for each data in the batch
        streams = [None] * self.bsz

        data = torch.LongTensor(self.bptt, self.bsz)
        target = torch.LongTensor(self.bptt, self.bsz)

        n_retain = 0

        while True:
            # data   : [n_retain+bptt x bsz]
            # target : [bptt x bsz]
            data[n_retain:].fill_(-1)
            target.fill_(-1)

            valid_batch = True

            for i in range(self.bsz):
                n_filled = 0
                try:
                    while n_filled < self.bptt:
                        if streams[i] is None or len(streams[i]) <= 1:
                            streams[i] = next(sent_stream)
                        # number of new tokens to fill in
                        n_new = min(len(streams[i]) - 1, self.bptt - n_filled)
                        # first n_retain tokens are retained from last batch
                        data[n_retain + n_filled : n_retain + n_filled + n_new, i] = streams[i][:n_new]
                        target[n_filled : n_filled + n_new, i] = streams[i][1 : n_new + 1]
                        streams[i] = streams[i][n_new:]
                        n_filled += n_new
                except StopIteration:
                    valid_batch = False
                    break

            if not valid_batch:
                return

            data_out = data.transpose(0, 1).contiguous().to(self.device)
            target_out = target.transpose(0, 1).contiguous().to(self.device)

            yield data_out, target_out, self.bptt

            n_retain = min(data.size(0), self.ext_len)
            if n_retain > 0:
                data[:n_retain] = data[-n_retain:]
            data.resize_(n_retain + self.bptt, data.size(1))

    def __iter__(self):
        # sent_stream is an iterator
        sent_stream = self.get_sent_stream()

        for batch in self.stream_iterator(sent_stream):
            yield batch


class LMMultiFileIterator(LMShuffledIterator):
    def __init__(self, paths, vocab, bsz, bptt, device="cpu", ext_len=None, shuffle=False):

        self.paths = paths
        self.vocab = vocab

        self.bsz = bsz
        self.bptt = bptt
        self.ext_len = ext_len if ext_len is not None else 0

        self.device = device
        self.shuffle = shuffle

    def get_sent_stream(self, path):
        sents = self.vocab.encode_file(path, add_double_eos=True)
        if self.shuffle:
            np.random.shuffle(sents)
        sent_stream = iter(sents)

        return sent_stream

    def __iter__(self):
        if self.shuffle:
            np.random.shuffle(self.paths)

        for path in self.paths:
            # sent_stream is an iterator
            sent_stream = self.get_sent_stream(path)
            for batch in self.stream_iterator(sent_stream):
                yield batch


class TransfoXLCorpus(object):
    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
        """
        Instantiate a pre-processed corpus.
        """
        vocab = TransfoXLTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
        if pretrained_model_name_or_path in PRETRAINED_CORPUS_ARCHIVE_MAP:
            corpus_file = PRETRAINED_CORPUS_ARCHIVE_MAP[pretrained_model_name_or_path]
        else:
            corpus_file = os.path.join(pretrained_model_name_or_path, CORPUS_NAME)
        # redirect to the cache, if necessary
        try:
            resolved_corpus_file = cached_path(corpus_file, cache_dir=cache_dir)
        except EnvironmentError:
            logger.error(
                "Corpus '{}' was not found in corpus list ({}). "
                "We assumed '{}' was a path or url but couldn't find files {} "
                "at this path or url.".format(
                    pretrained_model_name_or_path,
                    ", ".join(PRETRAINED_CORPUS_ARCHIVE_MAP.keys()),
                    pretrained_model_name_or_path,
                    corpus_file,
                )
            )
            return None
        if resolved_corpus_file == corpus_file:
            logger.info("loading corpus file {}".format(corpus_file))
        else:
            logger.info("loading corpus file {} from cache at {}".format(corpus_file, resolved_corpus_file))

        # Instantiate tokenizer.
        corpus = cls(*inputs, **kwargs)
        corpus_dict = torch.load(resolved_corpus_file)
        for key, value in corpus_dict.items():
            corpus.__dict__[key] = value
        corpus.vocab = vocab
        if corpus.train is not None:
            corpus.train = torch.tensor(corpus.train, dtype=torch.long)
        if corpus.valid is not None:
            corpus.valid = torch.tensor(corpus.valid, dtype=torch.long)
        if corpus.test is not None:
            corpus.test = torch.tensor(corpus.test, dtype=torch.long)
        return corpus

    def __init__(self, *args, **kwargs):
        self.vocab = TransfoXLTokenizer(*args, **kwargs)
        self.dataset = None
        self.train = None
        self.valid = None
        self.test = None

    def build_corpus(self, path, dataset):
        self.dataset = dataset

        if self.dataset in ["ptb", "wt2", "enwik8", "text8"]:
            self.vocab.count_file(os.path.join(path, "train.txt"))
            self.vocab.count_file(os.path.join(path, "valid.txt"))
            self.vocab.count_file(os.path.join(path, "test.txt"))
        elif self.dataset == "wt103":
            self.vocab.count_file(os.path.join(path, "train.txt"))
        elif self.dataset == "lm1b":
            train_path_pattern = os.path.join(
                path,
                "1-billion-word-language-modeling-benchmark-r13output",
                "training-monolingual.tokenized.shuffled",
                "news.en-*",
            )
            train_paths = glob.glob(train_path_pattern)
            # the vocab will load from file when build_vocab() is called

        self.vocab.build_vocab()

        if self.dataset in ["ptb", "wt2", "wt103"]:
            self.train = self.vocab.encode_file(os.path.join(path, "train.txt"), ordered=True)
            self.valid = self.vocab.encode_file(os.path.join(path, "valid.txt"), ordered=True)
            self.test = self.vocab.encode_file(os.path.join(path, "test.txt"), ordered=True)
        elif self.dataset in ["enwik8", "text8"]:
            self.train = self.vocab.encode_file(os.path.join(path, "train.txt"), ordered=True, add_eos=False)
            self.valid = self.vocab.encode_file(os.path.join(path, "valid.txt"), ordered=True, add_eos=False)
            self.test = self.vocab.encode_file(os.path.join(path, "test.txt"), ordered=True, add_eos=False)
        elif self.dataset == "lm1b":
            self.train = train_paths
            self.valid = self.vocab.encode_file(os.path.join(path, "valid.txt"), ordered=False, add_double_eos=True)
            self.test = self.vocab.encode_file(os.path.join(path, "test.txt"), ordered=False, add_double_eos=True)

    def get_iterator(self, split, *args, **kwargs):
        if split == "train":
            if self.dataset in ["ptb", "wt2", "wt103", "enwik8", "text8"]:
                data_iter = LMOrderedIterator(self.train, *args, **kwargs)
            elif self.dataset == "lm1b":
                kwargs["shuffle"] = True
                data_iter = LMMultiFileIterator(self.train, self.vocab, *args, **kwargs)
        elif split in ["valid", "test"]:
            data = self.valid if split == "valid" else self.test
            if self.dataset in ["ptb", "wt2", "wt103", "enwik8", "text8"]:
                data_iter = LMOrderedIterator(data, *args, **kwargs)
            elif self.dataset == "lm1b":
                data_iter = LMShuffledIterator(data, *args, **kwargs)

        return data_iter


def get_lm_corpus(datadir, dataset):
    fn = os.path.join(datadir, "cache.pt")
    fn_pickle = os.path.join(datadir, "cache.pkl")
    if os.path.exists(fn):
        logger.info("Loading cached dataset...")
        corpus = torch.load(fn_pickle)
    elif os.path.exists(fn):
        logger.info("Loading cached dataset from pickle...")
        with open(fn, "rb") as fp:
            corpus = pickle.load(fp)
    else:
        logger.info("Producing dataset {}...".format(dataset))
        kwargs = {}
        if dataset in ["wt103", "wt2"]:
            kwargs["special"] = ["<eos>"]
            kwargs["lower_case"] = False
        elif dataset == "ptb":
            kwargs["special"] = ["<eos>"]
            kwargs["lower_case"] = True
        elif dataset == "lm1b":
            kwargs["special"] = []
            kwargs["lower_case"] = False
            kwargs["vocab_file"] = os.path.join(datadir, "1b_word_vocab.txt")
        elif dataset in ["enwik8", "text8"]:
            pass

        corpus = TransfoXLCorpus(datadir, dataset, **kwargs)
        torch.save(corpus, fn)

    return corpus
