# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.

import contextlib
import os
import math
import numpy as np


def mask(sentence, mask_id, pad_id, dictionary_token_range, mask_ratio=.15):
    """mask tokens for masked language model training
    Samples mask_ratio tokens that will be predicted by LM.
    - 80%: Replace the word with mask_id
    - 10%: replate the word with random token within dictionary_token_range
    - 10%: keeps word unchanged
    This function may not be efficient enough since we had multiple conversions
    between np and torch, we can replace them with torch operators later
    Args:
        sentence: 1d tensor to be masked
        mask_id: index to use for masking the sentence
        pad_id: index to use for masking the target for tokens we aren't predicting
        dictionary_token_range: range of indices in dictionary which can be used
            for random word replacement (e.g. without special characters)
        mask_ratio: ratio of tokens to be masked in the sentence
    Return:
        masked_sent: masked sentence
        target: target with words which we are not predicting replaced by pad_id
    """
    masked_sent = np.copy(sentence)
    sent_length = len(sentence)
    mask_num = math.ceil(sent_length * mask_ratio)
    mask = np.random.choice(sent_length, mask_num)
    target = np.copy(sentence)
    for i in range(sent_length):
        if i in mask:
            rand = np.random.random()
            if rand < 0.8:
                masked_sent[i] = mask_id
            elif rand < 0.9:
                # sample random token
                masked_sent[i] = (
                    np.random.randint(dictionary_token_range[0], dictionary_token_range[1])
                )
        else:
            target[i] = pad_id

    return masked_sent, target


def infer_language_pair(path):
    """Infer language pair from filename: <split>.<lang1>-<lang2>.(...).idx"""
    src, dst = None, None
    for filename in os.listdir(path):
        parts = filename.split('.')
        if len(parts) >= 3 and len(parts[1].split('-')) == 2:
            return parts[1].split('-')
    return src, dst


def collate_tokens(values, pad_idx, eos_idx, left_pad, move_eos_to_beginning=False):
    """Convert a list of 1d tensors into a padded 2d tensor."""
    size = max(v.size(0) for v in values)
    res = values[0].new(len(values), size).fill_(pad_idx)

    def copy_tensor(src, dst):
        assert dst.numel() == src.numel()
        if move_eos_to_beginning:
            assert src[-1] == eos_idx
            dst[0] = eos_idx
            dst[1:] = src[:-1]
        else:
            dst.copy_(src)

    for i, v in enumerate(values):
        copy_tensor(v, res[i][size - len(v):] if left_pad else res[i][:len(v)])
    return res


@contextlib.contextmanager
def numpy_seed(seed):
    """Context manager which seeds the NumPy PRNG with the specified seed and
    restores the state afterward"""
    if seed is None:
        yield
        return
    state = np.random.get_state()
    np.random.seed(seed)
    try:
        yield
    finally:
        np.random.set_state(state)


def collect_filtered(function, iterable, filtered):
    """
    Similar to :func:`filter` but collects filtered elements in ``filtered``.

    Args:
        function (callable): function that returns ``False`` for elements that
            should be filtered
        iterable (iterable): iterable to filter
        filtered (list): list to store filtered elements
    """
    for el in iterable:
        if function(el):
            yield el
        else:
            filtered.append(el)


def filter_by_size(indices, size_fn, max_positions, raise_exception=False):
    """
    Filter indices based on their size.

    Args:
        indices (List[int]): ordered list of dataset indices
        size_fn (callable): function that returns the size of a given index
        max_positions (tuple): filter elements larger than this size.
            Comparisons are done component-wise.
        raise_exception (bool, optional): if ``True``, raise an exception if
            any elements are filtered (default: False).
    """
    def check_size(idx):
        if isinstance(max_positions, float) or isinstance(max_positions, int):
            return size_fn(idx) <= max_positions
        elif isinstance(max_positions, dict):
            idx_size = size_fn(idx)
            assert isinstance(idx_size, dict)
            intersect_keys = set(max_positions.keys()) & set(idx_size.keys())
            return all(
                all(a is None or b is None or a <= b
                    for a, b in zip(idx_size[key], max_positions[key]))
                for key in intersect_keys
            )
        else:
            return all(a is None or b is None or a <= b
                       for a, b in zip(size_fn(idx), max_positions))

    ignored = []
    itr = collect_filtered(check_size, indices, ignored)

    for idx in itr:
        if len(ignored) > 0 and raise_exception:
            raise Exception((
                'Size of sample #{} is invalid (={}) since max_positions={}, '
                'skip this example with --skip-invalid-size-inputs-valid-test'
            ).format(ignored[0], size_fn(ignored[0]), max_positions))
        yield idx

    if len(ignored) > 0:
        print((
            '| WARNING: {} samples have invalid sizes and will be skipped, '
            'max_positions={}, first few sample ids={}'
        ).format(len(ignored), max_positions, ignored[:10]))


def batch_by_size(
    indices, num_tokens_fn, max_tokens=None, max_sentences=None,
    required_batch_size_multiple=1,
):
    """
    Yield mini-batches of indices bucketed by size. Batches may contain
    sequences of different lengths.

    Args:
        indices (List[int]): ordered list of dataset indices
        num_tokens_fn (callable): function that returns the number of tokens at
            a given index
        max_tokens (int, optional): max number of tokens in each batch
            (default: None).
        max_sentences (int, optional): max number of sentences in each
            batch (default: None).
        required_batch_size_multiple (int, optional): require batch size to
            be a multiple of N (default: 1).
    """
    max_tokens = max_tokens if max_tokens is not None else float('Inf')
    max_sentences = max_sentences if max_sentences is not None else float('Inf')
    bsz_mult = required_batch_size_multiple

    batch = []

    def is_batch_full(num_tokens):
        if len(batch) == 0:
            return False
        if len(batch) == max_sentences:
            return True
        if num_tokens > max_tokens:
            return True
        return False

    sample_len = 0
    sample_lens = []
    for idx in indices:
        sample_lens.append(num_tokens_fn(idx))
        sample_len = max(sample_len, sample_lens[-1])
        assert sample_len <= max_tokens, f"sentence at index {idx} exceeds max_tokens limit!"
        num_tokens = (len(batch) + 1) * sample_len
        if is_batch_full(num_tokens):
            mod_len = max(
                bsz_mult * (len(batch) // bsz_mult),
                len(batch) % bsz_mult,
            )
            yield batch[:mod_len]
            batch = batch[mod_len:]
            sample_lens = sample_lens[mod_len:]
            sample_len = max(sample_lens) if len(sample_lens) > 0 else 0

        batch.append(idx)

    if len(batch) > 0:
        yield batch
