import torch
import struct
from functools import lru_cache
import os
import numpy as np

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


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

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

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 lenght, 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_index_mappings(name, data_prefix, documents, sizes,
                          num_samples, seq_length, seed):
    """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)
    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 = data_prefix
    _filename += '_{}_indexmap'.format(name)
    _filename += '_{}ns'.format(num_samples)
    _filename += '_{}sl'.format(seq_length)
    _filename += '_{}s'.format(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.
    if torch.distributed.get_rank() == 0:
        if (not os.path.isfile(doc_idx_filename)) or \
                (not os.path.isfile(sample_idx_filename)) or \
                (not os.path.isfile(shuffle_idx_filename)):
            # doc-idx.
            doc_idx = _build_doc_idx(documents, num_epochs, np_rng)
            np.save(doc_idx_filename, doc_idx, allow_pickle=True)
            # sample-idx.
            # Use C++ implementation for speed.
            import helpers
            assert doc_idx.dtype == np.int32
            assert sizes.dtype == np.int32
            sample_idx = helpers.build_sample_idx(sizes, doc_idx, seq_length,
                                                  num_epochs, tokens_per_epoch)
            # 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 retieve the index:
            #    sample i --> [sample_idx[i], sample_idx[i+1])
            shuffle_idx = _build_shuffle_idx(sample_idx.shape[0] - 1, np_rng)
            np.save(shuffle_idx_filename, shuffle_idx, allow_pickle=True)

    # This should be a barrier but nccl barrier assumes
    # device_index=rank which is not the case for model
    # parallel case
    counts = torch.cuda.LongTensor([1])
    torch.distributed.all_reduce(counts)
    assert counts[0].item() == torch.distributed.get_world_size()

    # 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')
    return doc_idx, sample_idx, shuffle_idx


class MMapIndexedDataset(torch.utils.data.Dataset):
    class Index(object):
        _HDR_MAGIC = b'MMIDIDX\x00\x00'

        @classmethod
        def writer(cls, path, dtype):
            class _Writer(object):
                def __enter__(self):
                    self._file = open(path, 'wb')

                    # Write Magic string so we can check the file format then opening it again.
                    self._file.write(cls._HDR_MAGIC)
                    # Write version number
                    # Little endian unsigned 64 Bit integer
                    self._file.write(struct.pack('<Q', 1))
                    # Little endian unsigned 8 Bit integer
                    self._file.write(struct.pack('<B', code(dtype)))

                    return self

                @staticmethod
                def _get_pointers(sizes):
                    dtype_size = dtype().itemsize
                    address = 0
                    pointers = []

                    for size in sizes:
                        pointers.append(address)
                        address += size * dtype_size

                    return pointers

                def write(self, sizes, doc_idx):
                    pointers = self._get_pointers(sizes)

                    # Little endian unsigned 64 Bit integer
                    self._file.write(struct.pack('<Q', len(sizes)))
                    # Little endian unsigned 64 Bit integer
                    self._file.write(struct.pack('<Q', len(doc_idx)))

                    sizes = np.array(sizes, dtype=np.int32)
                    self._file.write(sizes.tobytes(order='C'))
                    del sizes

                    pointers = np.array(pointers, dtype=np.int64)
                    self._file.write(pointers.tobytes(order='C'))
                    del pointers

                    doc_idx = np.array(doc_idx, dtype=np.int64)
                    self._file.write(doc_idx.tobytes(order='C'))

                def __exit__(self, exc_type, exc_val, exc_tb):
                    self._file.close()

            return _Writer()

        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.'
                )
                # Little endian unsigned 64 Bit integer
                version = struct.unpack('<Q', stream.read(8))
                assert (1,) == version

                # Little endian unsigned 8 Bit integer
                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:
                _warmup_mmap_file(path)

            self._bin_buffer_mmap = np.memmap(path, mode='r', order='C')
            self._bin_buffer = memoryview(self._bin_buffer_mmap)
            self._sizes = np.frombuffer(
                self._bin_buffer,
                dtype=np.int32,
                count=self._len,
                offset=offset)
            self._pointers = np.frombuffer(self._bin_buffer, dtype=np.int64, count=self._len,
                                           offset=offset + self._sizes.nbytes)
            ("    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:
            _warmup_mmap_file(data_file_path(self._path))
        self._bin_buffer_mmap = np.memmap(data_file_path(self._path), mode='r', order='C')
        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 build_train_dataset(data_prefix, skip_warmup):
    """Build train dataset"""

    # Indexed dataset.
    indexed_dataset = MMapIndexedDataset(data_prefix, skip_warmup)

    total_num_of_documents = indexed_dataset.sizes.shape[0]
    print(f'Number of documents are:{total_num_of_documents}')
    return indexed_dataset