import os
import time

import numpy as np
import torch

from .megatron import mpu


def print_rank_0(message):
    """If distributed is initialized, print only on rank 0."""
    if torch.distributed.is_initialized():
        if torch.distributed.get_rank() == 0:
            print(message, flush=True)
    else:
        print(message, flush=True)


class GPTDataset(torch.utils.data.Dataset):
    def __init__(
        self,
        name,
        data_prefix,
        documents,
        indexed_dataset,
        num_samples,
        seq_length,
        seed,
    ):
        self.name = name
        self.indexed_dataset = indexed_dataset

        # Checks
        assert np.min(documents) >= 0
        assert np.max(documents) < indexed_dataset.sizes.shape[0]

        # Build index mappings.
        self.doc_idx, self.sample_idx, self.shuffle_idx = _build_index_mappings(
            self.name,
            data_prefix,
            documents,
            self.indexed_dataset.sizes,
            num_samples,
            seq_length,
            seed,
        )

    def __len__(self):
        # -1 is due to data structure used to retieve 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 {"text": np.array(sample, dtype=np.int64)}


def _build_index_mappings(
    name,
    data_prefix,
    documents,
    sizes,
    num_samples,
    seq_length,
    seed,
    cutoff_last_epoch=0.95,
):
    """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))
        ):
            print_rank_0(
                " > WARNING: could not find index map files, building "
                "the indices on rank 0 ..."
            )

            # For the last epoch, decide whether include the entire epoch
            # in the global shuffle or not.

            # If we need only one epoch, then separating last epoch  does
            # not mean anything.
            if num_epochs == 1:
                separate_last_epoch = False
                print(
                    " > only one epoch required, setting separate_last_epoch to False",
                    flush=True,
                )

            else:
                # Get the number of samples for the last epoch
                num_samples_from_epochs_minus_one = (
                    (num_epochs - 1) * tokens_per_epoch - 1
                ) // seq_length
                last_epoch_num_samples = num_samples - num_samples_from_epochs_minus_one
                assert last_epoch_num_samples >= 0, (
                    f"last epoch number of samples {last_epoch_num_samples} should be"
                    " non-negative."
                )
                num_samples_per_epoch = (tokens_per_epoch - 1) // seq_length
                assert last_epoch_num_samples <= num_samples_per_epoch, (
                    f"last epoch number of samples {last_epoch_num_samples} exceeded"
                    f" max value {num_samples_per_epoch}."
                )
                # If we have less than cutoff_last_epoch * samples_per_epoch of the samples for the last epoch,
                # seperate out the epoch and treat it differently.
                separate_last_epoch = last_epoch_num_samples < int(
                    cutoff_last_epoch * num_samples_per_epoch
                )
                if separate_last_epoch:
                    string = (
                        " > last epoch number of samples ({}) is smaller "
                        "than {}% of number of samples per epoch ({}), "
                        "setting separate_last_epoch to True"
                    )
                else:
                    string = (
                        " > last epoch number of samples ({}) is larger "
                        "than {}% of number of samples per epoch ({}), "
                        "setting separate_last_epoch to False"
                    )
                print(
                    string.format(
                        last_epoch_num_samples,
                        cutoff_last_epoch * 100,
                        num_samples_per_epoch,
                    ),
                    flush=True,
                )

            # doc-idx.
            start_time = time.time()
            doc_idx = _build_doc_idx(documents, num_epochs, np_rng, separate_last_epoch)
            np.save(doc_idx_filename, doc_idx, allow_pickle=True)
            print_rank_0(
                " > elasped time to build and save doc-idx mapping "
                "(seconds): {:4f}".format(time.time() - start_time)
            )
            # sample-idx.
            start_time = time.time()
            # Use C++ implementation for speed.
            # First compile and then import.
            from .megatron 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)
            print_rank_0(
                " > elasped time to build and save sample-idx mapping "
                "(seconds): {:4f}".format(time.time() - start_time)
            )
            # shuffle-idx.
            start_time = time.time()
            # -1 is due to data structure used to retieve the index:
            #    sample i --> [sample_idx[i], sample_idx[i+1])
            if separate_last_epoch:
                num_samples_ = num_samples_from_epochs_minus_one
            else:
                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)
            print_rank_0(
                " > elasped time to build and save shuffle-idx mapping"
                " (seconds): {:4f}".format(time.time() - start_time)
            )

    # 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, group=mpu.get_data_parallel_group())
    # torch.distributed.all_reduce(counts, group=mpu.get_pipeline_model_parallel_group())
    # assert counts[0].item() == (
    #     torch.distributed.get_world_size() //
    #     torch.distributed.get_world_size(group=mpu.get_tensor_model_parallel_group()))

    # Load mappings.
    start_time = time.time()
    print_rank_0(" > loading doc-idx mapping from {}".format(doc_idx_filename))
    doc_idx = np.load(doc_idx_filename, allow_pickle=True, mmap_mode="r")
    print_rank_0(" > loading sample-idx mapping from {}".format(sample_idx_filename))
    sample_idx = np.load(sample_idx_filename, allow_pickle=True, mmap_mode="r")
    print_rank_0(" > loading shuffle-idx mapping from {}".format(shuffle_idx_filename))
    shuffle_idx = np.load(shuffle_idx_filename, allow_pickle=True, mmap_mode="r")
    print_rank_0(
        "    loaded indexed file in {:3.3f} seconds".format(time.time() - start_time)
    )
    print_rank_0("    total number of samples: {}".format(sample_idx.shape[0]))
    print_rank_0("    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 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_doc_idx(documents, num_epochs, np_rng, separate_last_epoch):
    """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
    # Begining 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 begining 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))
