# Copyright (c) 2021 Graphcore Ltd. All rights reserved.
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.


# copied from fairseq/fairseq/data/indexed_dataset.py
# Removed IndexedRawTextDataset since it relied on Fairseq dictionary
# other slight modifications to remove fairseq dependencies
# Added document index to index file and made it accessible.
#    An empty sentence no longer separates documents.

import os
import struct
import logging

import torch
import numpy as np

from functools import lru_cache
from itertools import accumulate
from torch.utils.data import Dataset


def logger(msg):
    logging.info(msg)


dtypes = {1: np.uint8, 2: np.int8, 3: np.int16, 4: np.int32, 5: np.int64, 6: float, 7: np.double, 8: np.uint16}


def _warmup_mmap_file(path):
    with open(path, "rb") as stream:
        while stream.read(100 * 1024 * 1024):
            pass


def index_file_path(prefix_path):
    return prefix_path + ".idx"


def data_file_path(prefix_path):
    return prefix_path + ".bin"


class MMapIndexedDataset(Dataset):
    class Index(object):
        _HDR_MAGIC = b"MMIDIDX\x00\x00"

        def __init__(self, path, skip_warmup=False):
            with open(path, "rb") as stream:
                magic_test = stream.read(9)
                assert self._HDR_MAGIC == magic_test, (
                    "Index file doesn't match expected format. " "Make sure that --dataset-impl is configured properly."
                )
                version = struct.unpack("<Q", stream.read(8))
                assert (1,) == version

                (dtype_code,) = struct.unpack("<B", stream.read(1))
                self._dtype = dtypes[dtype_code]
                self._dtype_size = self._dtype().itemsize

                self._len = struct.unpack("<Q", stream.read(8))[0]
                self._doc_count = struct.unpack("<Q", stream.read(8))[0]
                offset = stream.tell()

            if not skip_warmup:
                logger("    warming up index mmap file...")
                _warmup_mmap_file(path)

            self._bin_buffer_mmap = np.memmap(path, mode="r", order="C")
            self._bin_buffer = memoryview(self._bin_buffer_mmap)
            logger("    reading sizes...")
            self._sizes = np.frombuffer(self._bin_buffer, dtype=np.int32, count=self._len, offset=offset)
            logger("    reading pointers...")
            self._pointers = np.frombuffer(
                self._bin_buffer, dtype=np.int64, count=self._len, offset=offset + self._sizes.nbytes
            )
            logger("    reading document index...")
            self._doc_idx = np.frombuffer(
                self._bin_buffer,
                dtype=np.int64,
                count=self._doc_count,
                offset=offset + self._sizes.nbytes + self._pointers.nbytes,
            )

        def __del__(self):
            self._bin_buffer_mmap._mmap.close()
            del self._bin_buffer_mmap

        @property
        def dtype(self):
            return self._dtype

        @property
        def sizes(self):
            return self._sizes

        @property
        def doc_idx(self):
            return self._doc_idx

        @lru_cache(maxsize=8)
        def __getitem__(self, i):
            return self._pointers[i], self._sizes[i]

        def __len__(self):
            return self._len

    def __init__(self, path, skip_warmup=False):
        super().__init__()

        self._path = None
        self._index = None
        self._bin_buffer = None

        self._do_init(path, skip_warmup)

    def __getstate__(self):
        return self._path

    def __setstate__(self, state):
        self._do_init(state)

    def _do_init(self, path, skip_warmup):
        self._path = path
        self._index = self.Index(index_file_path(self._path), skip_warmup)

        if not skip_warmup:
            logger("    warming up data mmap file...")
            _warmup_mmap_file(data_file_path(self._path))
        logger("    creating numpy buffer of mmap...")
        self._bin_buffer_mmap = np.memmap(data_file_path(self._path), mode="r", order="C")
        logger("    creating memory view of numpy buffer...")
        self._bin_buffer = memoryview(self._bin_buffer_mmap)

    def __del__(self):
        self._bin_buffer_mmap._mmap.close()
        del self._bin_buffer_mmap
        del self._index

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

    # @lru_cache(maxsize=8)
    def __getitem__(self, idx):
        if isinstance(idx, int):
            ptr, size = self._index[idx]
            np_array = np.frombuffer(self._bin_buffer, dtype=self._index.dtype, count=size, offset=ptr)
            return np_array
        elif isinstance(idx, slice):
            start, stop, step = idx.indices(len(self))
            if step != 1:
                raise ValueError("Slices into indexed_dataset must be contiguous")
            ptr = self._index._pointers[start]
            sizes = self._index._sizes[idx]
            offsets = list(accumulate(sizes))
            total_size = sum(sizes)
            np_array = np.frombuffer(self._bin_buffer, dtype=self._index.dtype, count=total_size, offset=ptr)
            sents = np.split(np_array, offsets[:-1])
            return sents

    def get(self, idx, offset=0, length=None):
        """Retrieves a single item from the dataset with the option to only
        return a portion of the item.

        get(idx) is the same as [idx] but get() does not support slicing.
        """
        ptr, size = self._index[idx]
        if length is None:
            length = size - offset
        ptr += offset * np.dtype(self._index.dtype).itemsize
        np_array = np.frombuffer(self._bin_buffer, dtype=self._index.dtype, count=length, offset=ptr)
        return np_array

    @property
    def sizes(self):
        return self._index.sizes

    @property
    def doc_idx(self):
        return self._index.doc_idx

    def get_doc_idx(self):
        return self._index._doc_idx

    def set_doc_idx(self, doc_idx_):
        self._index._doc_idx = doc_idx_

    @property
    def supports_prefetch(self):
        return False

    @staticmethod
    def exists(path):
        return os.path.exists(index_file_path(path)) and os.path.exists(data_file_path(path))


def make_indexed_dataset(path, skip_warmup=False):
    if not MMapIndexedDataset.exists(path):
        print(f"Dataset does not exist: {path}")
        print("Path should be a basename that both .idx and .bin can be appended to get full filenames.")
        return None
    return MMapIndexedDataset(path, skip_warmup)


class GPTDataset(Dataset):
    def __init__(self, args, data_prefix, documents, indexed_dataset, num_epochs=None, num_samples=None):
        self.indexed_dataset = indexed_dataset
        self.seq_length = args.max_len
        self.seed = args.seed
        if num_epochs is None:
            num_epochs = args.epochs
        # Build index mappings.
        self.doc_idx, self.sample_idx, self.shuffle_idx = _build_index_mappings(
            data_prefix, documents, self.indexed_dataset.sizes, self.seq_length, self.seed, num_epochs, num_samples
        )

    def __len__(self):
        # -1 is due to data structure used to retrieve the index:
        #    sample i --> [sample_idx[i], sample_idx[i+1])
        return self.sample_idx.shape[0] - 1

    def __getitem__(self, idx):
        # Get the shuffled index.
        idx = self.shuffle_idx[idx]
        # Start and end documents and offsets.
        doc_index_f = self.sample_idx[idx][0]
        doc_index_l = self.sample_idx[idx + 1][0]
        offset_f = self.sample_idx[idx][1]
        offset_l = self.sample_idx[idx + 1][1]
        # If we are within the same document, just extract the chunk.
        if doc_index_f == doc_index_l:
            sample = self.indexed_dataset.get(
                self.doc_idx[doc_index_f], offset=offset_f, length=offset_l - offset_f + 1
            )
        else:
            # Otherwise, get the rest of the initial document.
            sample_list = [self.indexed_dataset.get(self.doc_idx[doc_index_f], offset=offset_f)]
            # Loop over all in between documents and add the entire document.
            for i in range(doc_index_f + 1, doc_index_l):
                sample_list.append(self.indexed_dataset.get(self.doc_idx[i]))
            # And finally add the relevant portion of last document.
            sample_list.append(self.indexed_dataset.get(self.doc_idx[doc_index_l], length=offset_l + 1))
            sample = np.concatenate(sample_list)
        return np.array(sample, dtype=np.int64)


def _build_index_mappings(data_prefix, documents, sizes, seq_length, seed, num_epochs=None, num_samples=None):
    """Build doc-idx, sample-idx, and shuffle-idx.
    doc-idx: is an array (ordered) of documents to be used in training.
    sample-idx: is the start document index and document offset for each
       training sample.
    shuffle-idx: maps the sample index into a random index into sample-idx.
    """
    # Number of tokens in each epoch and number of required epochs.
    tokens_per_epoch = _num_tokens(documents, sizes)
    if num_epochs is None:
        assert num_samples is not None
        num_epochs = _num_epochs(tokens_per_epoch, seq_length, num_samples)
    # rng state
    np_rng = np.random.RandomState(seed=seed)

    # Filename of the index mappings.
    _filename = "{}_indexmap_{}ns_{}sl_{}s".format(data_prefix, num_epochs, seq_length, seed)
    doc_idx_filename = _filename + "_doc_idx.npy"
    sample_idx_filename = _filename + "_sample_idx.npy"
    shuffle_idx_filename = _filename + "_shuffle_idx.npy"

    # Build the indexed mapping if not exist.
    # Maybe popdist.rank is needed
    if (
        (not os.path.isfile(doc_idx_filename))
        or (not os.path.isfile(sample_idx_filename))
        or (not os.path.isfile(shuffle_idx_filename))
    ):

        # print_rank_0(' > WARNING: could not find index map files, building '
        #              'the indices on rank 0 ...')

        # doc-idx.
        doc_idx = _build_doc_idx(documents, num_epochs, np_rng)
        np.save(doc_idx_filename, doc_idx, allow_pickle=True)
        # sample-idx.
        # Megatron Used C++ implementation for speed here.
        assert doc_idx.dtype == np.int32
        assert sizes.dtype == np.int32
        sample_idx = _build_sample_idx(sizes, doc_idx, seq_length, num_epochs, tokens_per_epoch)
        np.save(sample_idx_filename, sample_idx, allow_pickle=True)
        # shuffle-idx.
        # -1 is due to data structure used to retrieve the index:
        #    sample i --> [sample_idx[i], sample_idx[i+1])
        num_samples_ = sample_idx.shape[0] - 1
        shuffle_idx = _build_shuffle_idx(num_samples_, sample_idx.shape[0] - 1, np_rng)
        np.save(shuffle_idx_filename, shuffle_idx, allow_pickle=True)

    # Load mappings.
    doc_idx = np.load(doc_idx_filename, allow_pickle=True, mmap_mode="r")
    sample_idx = np.load(sample_idx_filename, allow_pickle=True, mmap_mode="r")
    shuffle_idx = np.load(shuffle_idx_filename, allow_pickle=True, mmap_mode="r")
    logger("    total number of samples: {}".format(sample_idx.shape[0]))
    logger("    total number of epochs: {}".format(num_epochs))

    return doc_idx, sample_idx, shuffle_idx


def _num_tokens(documents, sizes):
    """Total number of tokens in the dataset."""
    return np.sum(sizes[documents])


def _num_epochs(tokens_per_epoch, seq_length, num_samples):
    """Based on number of samples and sequence length, calculate how many
    epochs will be needed."""
    num_epochs = 0
    total_tokens = 0
    while True:
        num_epochs += 1
        total_tokens += tokens_per_epoch
        # -1 is because we need to retrieve seq_length + 1 token each time
        # but the last token will overlap with the first token of the next
        # sample except for the last sample.
        if ((total_tokens - 1) // seq_length) >= num_samples:
            return num_epochs


def _build_doc_idx(documents, num_epochs, np_rng, separate_last_epoch=False):
    """Build an array with length = number-of-epochs * number-of-dcuments.
    Each index is mapped to a corresponding document."""
    if not separate_last_epoch or num_epochs == 1:
        doc_idx = np.mgrid[0:num_epochs, 0 : len(documents)][1]
        doc_idx[:] = documents
        doc_idx = doc_idx.reshape(-1)
        doc_idx = doc_idx.astype(np.int32)
        np_rng.shuffle(doc_idx)
        return doc_idx

    doc_idx_first = _build_doc_idx(documents, num_epochs - 1, np_rng, False)
    doc_idx_last = _build_doc_idx(documents, 1, np_rng, False)
    return np.concatenate((doc_idx_first, doc_idx_last))


def _build_sample_idx(sizes, doc_idx, seq_length, num_epochs, tokens_per_epoch):
    """Sample index mapping is a 2D array with sizes
    [number-of-samples + 1, 2] where [..., 0] contains
    the index into `doc_idx` and [..., 1] is the
    starting offset in that document."""

    # Total number of samples. For -1 see comments in `_num_epochs`.
    num_samples = (num_epochs * tokens_per_epoch - 1) // seq_length
    sample_idx = np.zeros([num_samples + 1, 2], dtype=np.int32)

    # Index into sample_idx.
    sample_index = 0
    # Index into doc_idx.
    doc_idx_index = 0
    # Beginning offset for each document.
    doc_offset = 0
    # Start with first document and no offset.
    sample_idx[sample_index][0] = doc_idx_index
    sample_idx[sample_index][1] = doc_offset
    sample_index += 1
    while sample_index <= num_samples:
        # Start with a fresh sequence.
        remaining_seq_length = seq_length + 1
        while remaining_seq_length != 0:
            # Get the document length.
            doc_id = doc_idx[doc_idx_index]
            doc_length = sizes[doc_id] - doc_offset
            # And add it to the current sequence.
            remaining_seq_length -= doc_length
            # If we have more than a full sequence, adjust offset and set
            # remaining length to zero so we return from the while loop.
            # Note that -1 here is for the same reason we have -1 in
            # `_num_epochs` calculations.
            if remaining_seq_length <= 0:
                doc_offset += remaining_seq_length + doc_length - 1
                remaining_seq_length = 0
            else:
                # Otherwise, start from the beginning of the next document.
                doc_idx_index += 1
                doc_offset = 0
        # Record the sequence.
        sample_idx[sample_index][0] = doc_idx_index
        sample_idx[sample_index][1] = doc_offset
        sample_index += 1

    return sample_idx


def _build_shuffle_idx(num_samples, total_size, np_rng):
    """Build the range [0, size) and shuffle."""
    print(
        " > building shuffle index with split [0, {}) and [{}, {}) " "...".format(num_samples, num_samples, total_size),
        flush=True,
    )

    dtype_ = np.uint32
    if total_size >= (np.iinfo(np.uint32).max - 1):
        dtype_ = np.int64

    shuffle_idx_first = np.arange(start=0, stop=num_samples, step=1, dtype=dtype_)
    np_rng.shuffle(shuffle_idx_first)
    if num_samples == total_size:
        return shuffle_idx_first

    shuffle_idx_last = np.arange(start=num_samples, stop=total_size, step=1, dtype=dtype_)
    np_rng.shuffle(shuffle_idx_last)

    return np.concatenate((shuffle_idx_first, shuffle_idx_last))


if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser()
    parser.add_argument("--epochs", default=1, type=int, required=False, help="epochs for training")
    parser.add_argument("--max-len", default=1024, type=int, required=False, help="max length of input sequence")
    parser.add_argument("--seed", type=int, default=42, help="random seed")
    args = parser.parse_args()

    data_prefix = "./data/my-gpt2_text_document"

    from indexed_dataset import make_indexed_dataset

    indexed_dataset = make_indexed_dataset(data_prefix)
    total_num_of_documents = indexed_dataset.sizes.shape[0]
    documents = np.arange(start=0, stop=total_num_of_documents, step=1, dtype=np.int32)
    gpt2_dataset = GPTDataset(args, data_prefix, documents, indexed_dataset)

    gpt2_dataloader = torch.utils.data.DataLoader(gpt2_dataset, batch_size=8, collate_fn=None)

    for batch in gpt2_dataloader:
        input_ids = batch[:, :-1]
        labels = batch[:, 1:]