# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang
#                                                  Xiaoyu Yang)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# 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.

import warnings
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Tuple, Union

import k2
import torch
from model import SURT

from icefall import NgramLmStateCost
from icefall.utils import DecodingResults


def greedy_search(
    model: SURT,
    encoder_out: torch.Tensor,
    max_sym_per_frame: int,
    return_timestamps: bool = False,
) -> Union[List[int], DecodingResults]:
    """Greedy search for a single utterance.
    Args:
      model:
        An instance of `SURT`.
      encoder_out:
        A tensor of shape (N, T, C) from the encoder. Support only N==1 for now.
      max_sym_per_frame:
        Maximum number of symbols per frame. If it is set to 0, the WER
        would be 100%.
      return_timestamps:
        Whether to return timestamps.
    Returns:
      If return_timestamps is False, return the decoded result.
      Else, return a DecodingResults object containing
      decoded result and corresponding timestamps.
    """
    assert encoder_out.ndim == 4

    # support only batch_size == 1 for now
    assert encoder_out.size(0) == 1, encoder_out.size(0)

    blank_id = model.decoder.blank_id
    context_size = model.decoder.context_size
    unk_id = getattr(model, "unk_id", blank_id)

    device = next(model.parameters()).device

    decoder_input = torch.tensor(
        [-1] * (context_size - 1) + [blank_id], device=device, dtype=torch.int64
    ).reshape(1, context_size)

    decoder_out = model.decoder(decoder_input, need_pad=False)
    decoder_out = model.joiner.decoder_proj(decoder_out)

    encoder_out = model.joiner.encoder_proj(encoder_out)

    T = encoder_out.size(1)
    t = 0
    hyp = [blank_id] * context_size

    # timestamp[i] is the frame index after subsampling
    # on which hyp[i] is decoded
    timestamp = []

    # Maximum symbols per utterance.
    max_sym_per_utt = 1000

    # symbols per frame
    sym_per_frame = 0

    # symbols per utterance decoded so far
    sym_per_utt = 0

    while t < T and sym_per_utt < max_sym_per_utt:
        if sym_per_frame >= max_sym_per_frame:
            sym_per_frame = 0
            t += 1
            continue

        # fmt: off
        current_encoder_out = encoder_out[:, t:t+1, :].unsqueeze(2)
        # fmt: on
        logits = model.joiner(
            current_encoder_out, decoder_out.unsqueeze(1), project_input=False
        )
        # logits is (1, 1, 1, vocab_size)

        y = logits.argmax().item()
        if y not in (blank_id, unk_id):
            hyp.append(y)
            timestamp.append(t)
            decoder_input = torch.tensor([hyp[-context_size:]], device=device).reshape(
                1, context_size
            )

            decoder_out = model.decoder(decoder_input, need_pad=False)
            decoder_out = model.joiner.decoder_proj(decoder_out)

            sym_per_utt += 1
            sym_per_frame += 1
        else:
            sym_per_frame = 0
            t += 1
    hyp = hyp[context_size:]  # remove blanks

    if not return_timestamps:
        return hyp
    else:
        return DecodingResults(
            hyps=[hyp],
            timestamps=[timestamp],
        )


def greedy_search_batch(
    model: SURT,
    encoder_out: torch.Tensor,
    encoder_out_lens: torch.Tensor,
    return_timestamps: bool = False,
) -> Union[List[List[int]], DecodingResults]:
    """Greedy search in batch mode. It hardcodes --max-sym-per-frame=1.
    Args:
      model:
        The SURT model.
      encoder_out:
        Output from the encoder. Its shape is (N, T, C), where N >= 1.
      encoder_out_lens:
        A 1-D tensor of shape (N,), containing number of valid frames in
        encoder_out before padding.
      return_timestamps:
        Whether to return timestamps.
    Returns:
      If return_timestamps is False, return the decoded result.
      Else, return a DecodingResults object containing
      decoded result and corresponding timestamps.
    """
    assert encoder_out.ndim == 3
    assert encoder_out.size(0) >= 1, encoder_out.size(0)

    packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence(
        input=encoder_out,
        lengths=encoder_out_lens.cpu(),
        batch_first=True,
        enforce_sorted=False,
    )

    device = next(model.parameters()).device

    blank_id = model.decoder.blank_id
    unk_id = getattr(model, "unk_id", blank_id)
    context_size = model.decoder.context_size

    batch_size_list = packed_encoder_out.batch_sizes.tolist()
    N = encoder_out.size(0)
    assert torch.all(encoder_out_lens > 0), encoder_out_lens
    assert N == batch_size_list[0], (N, batch_size_list)

    hyps = [[-1] * (context_size - 1) + [blank_id] for _ in range(N)]

    # timestamp[n][i] is the frame index after subsampling
    # on which hyp[n][i] is decoded
    timestamps = [[] for _ in range(N)]

    decoder_input = torch.tensor(
        hyps,
        device=device,
        dtype=torch.int64,
    )  # (N, context_size)

    decoder_out = model.decoder(decoder_input, need_pad=False)
    decoder_out = model.joiner.decoder_proj(decoder_out)
    # decoder_out: (N, 1, decoder_out_dim)

    encoder_out = model.joiner.encoder_proj(packed_encoder_out.data)

    offset = 0
    for (t, batch_size) in enumerate(batch_size_list):
        start = offset
        end = offset + batch_size
        current_encoder_out = encoder_out.data[start:end]
        current_encoder_out = current_encoder_out.unsqueeze(1).unsqueeze(1)
        # current_encoder_out's shape: (batch_size, 1, 1, encoder_out_dim)
        offset = end

        decoder_out = decoder_out[:batch_size]

        logits = model.joiner(
            current_encoder_out, decoder_out.unsqueeze(1), project_input=False
        )
        # logits'shape (batch_size, 1, 1, vocab_size)

        logits = logits.squeeze(1).squeeze(1)  # (batch_size, vocab_size)
        assert logits.ndim == 2, logits.shape
        y = logits.argmax(dim=1).tolist()
        emitted = False
        for i, v in enumerate(y):
            if v not in (blank_id, unk_id):
                hyps[i].append(v)
                timestamps[i].append(t)
                emitted = True
        if emitted:
            # update decoder output
            decoder_input = [h[-context_size:] for h in hyps[:batch_size]]
            decoder_input = torch.tensor(
                decoder_input,
                device=device,
                dtype=torch.int64,
            )
            decoder_out = model.decoder(decoder_input, need_pad=False)
            decoder_out = model.joiner.decoder_proj(decoder_out)

    sorted_ans = [h[context_size:] for h in hyps]
    ans = []
    ans_timestamps = []
    unsorted_indices = packed_encoder_out.unsorted_indices.tolist()
    for i in range(N):
        ans.append(sorted_ans[unsorted_indices[i]])
        ans_timestamps.append(timestamps[unsorted_indices[i]])

    if not return_timestamps:
        return ans
    else:
        return DecodingResults(
            hyps=ans,
            timestamps=ans_timestamps,
        )


def modified_beam_search(
    model: SURT,
    encoder_out: torch.Tensor,
    encoder_out_lens: torch.Tensor,
    beam: int = 4,
    temperature: float = 1.0,
    return_timestamps: bool = False,
) -> Union[List[List[int]], DecodingResults]:
    """Beam search in batch mode with --max-sym-per-frame=1 being hardcoded.

    Args:
      model:
        The SURT model.
      encoder_out:
        Output from the encoder. Its shape is (N, T, C).
      encoder_out_lens:
        A 1-D tensor of shape (N,), containing number of valid frames in
        encoder_out before padding.
      beam:
        Number of active paths during the beam search.
      temperature:
        Softmax temperature.
      return_timestamps:
        Whether to return timestamps.
    Returns:
      If return_timestamps is False, return the decoded result.
      Else, return a DecodingResults object containing
      decoded result and corresponding timestamps.
    """
    assert encoder_out.ndim == 3, encoder_out.shape
    assert encoder_out.size(0) >= 1, encoder_out.size(0)

    packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence(
        input=encoder_out,
        lengths=encoder_out_lens.cpu(),
        batch_first=True,
        enforce_sorted=False,
    )

    blank_id = model.decoder.blank_id
    unk_id = getattr(model, "unk_id", blank_id)
    context_size = model.decoder.context_size
    device = next(model.parameters()).device

    batch_size_list = packed_encoder_out.batch_sizes.tolist()
    N = encoder_out.size(0)
    assert torch.all(encoder_out_lens > 0), encoder_out_lens
    assert N == batch_size_list[0], (N, batch_size_list)

    B = [HypothesisList() for _ in range(N)]
    for i in range(N):
        B[i].add(
            Hypothesis(
                ys=[blank_id] * context_size,
                log_prob=torch.zeros(1, dtype=torch.float32, device=device),
                timestamp=[],
            )
        )

    encoder_out = model.joiner.encoder_proj(packed_encoder_out.data)

    offset = 0
    finalized_B = []
    for (t, batch_size) in enumerate(batch_size_list):
        start = offset
        end = offset + batch_size
        current_encoder_out = encoder_out.data[start:end]
        current_encoder_out = current_encoder_out.unsqueeze(1).unsqueeze(1)
        # current_encoder_out's shape is (batch_size, 1, 1, encoder_out_dim)
        offset = end

        finalized_B = B[batch_size:] + finalized_B
        B = B[:batch_size]

        hyps_shape = get_hyps_shape(B).to(device)

        A = [list(b) for b in B]
        B = [HypothesisList() for _ in range(batch_size)]

        ys_log_probs = torch.cat(
            [hyp.log_prob.reshape(1, 1) for hyps in A for hyp in hyps]
        )  # (num_hyps, 1)

        decoder_input = torch.tensor(
            [hyp.ys[-context_size:] for hyps in A for hyp in hyps],
            device=device,
            dtype=torch.int64,
        )  # (num_hyps, context_size)

        decoder_out = model.decoder(decoder_input, need_pad=False).unsqueeze(1)
        decoder_out = model.joiner.decoder_proj(decoder_out)
        # decoder_out is of shape (num_hyps, 1, 1, joiner_dim)

        # Note: For torch 1.7.1 and below, it requires a torch.int64 tensor
        # as index, so we use `to(torch.int64)` below.
        current_encoder_out = torch.index_select(
            current_encoder_out,
            dim=0,
            index=hyps_shape.row_ids(1).to(torch.int64),
        )  # (num_hyps, 1, 1, encoder_out_dim)

        logits = model.joiner(
            current_encoder_out,
            decoder_out,
            project_input=False,
        )  # (num_hyps, 1, 1, vocab_size)

        logits = logits.squeeze(1).squeeze(1)  # (num_hyps, vocab_size)

        log_probs = (logits / temperature).log_softmax(dim=-1)  # (num_hyps, vocab_size)

        log_probs.add_(ys_log_probs)

        vocab_size = log_probs.size(-1)

        log_probs = log_probs.reshape(-1)

        row_splits = hyps_shape.row_splits(1) * vocab_size
        log_probs_shape = k2.ragged.create_ragged_shape2(
            row_splits=row_splits, cached_tot_size=log_probs.numel()
        )
        ragged_log_probs = k2.RaggedTensor(shape=log_probs_shape, value=log_probs)

        for i in range(batch_size):
            topk_log_probs, topk_indexes = ragged_log_probs[i].topk(beam)

            with warnings.catch_warnings():
                warnings.simplefilter("ignore")
                topk_hyp_indexes = (topk_indexes // vocab_size).tolist()
                topk_token_indexes = (topk_indexes % vocab_size).tolist()

            for k in range(len(topk_hyp_indexes)):
                hyp_idx = topk_hyp_indexes[k]
                hyp = A[i][hyp_idx]

                new_ys = hyp.ys[:]
                new_token = topk_token_indexes[k]
                new_timestamp = hyp.timestamp[:]
                if new_token not in (blank_id, unk_id):
                    new_ys.append(new_token)
                    new_timestamp.append(t)

                new_log_prob = topk_log_probs[k]
                new_hyp = Hypothesis(
                    ys=new_ys, log_prob=new_log_prob, timestamp=new_timestamp
                )
                B[i].add(new_hyp)

    B = B + finalized_B
    best_hyps = [b.get_most_probable(length_norm=True) for b in B]

    sorted_ans = [h.ys[context_size:] for h in best_hyps]
    sorted_timestamps = [h.timestamp for h in best_hyps]
    ans = []
    ans_timestamps = []
    unsorted_indices = packed_encoder_out.unsorted_indices.tolist()
    for i in range(N):
        ans.append(sorted_ans[unsorted_indices[i]])
        ans_timestamps.append(sorted_timestamps[unsorted_indices[i]])

    if not return_timestamps:
        return ans
    else:
        return DecodingResults(
            hyps=ans,
            timestamps=ans_timestamps,
        )


def beam_search(
    model: SURT,
    encoder_out: torch.Tensor,
    beam: int = 4,
    temperature: float = 1.0,
    return_timestamps: bool = False,
) -> Union[List[int], DecodingResults]:
    """
    It implements Algorithm 1 in https://arxiv.org/pdf/1211.3711.pdf

    espnet/nets/beam_search_SURT.py#L247 is used as a reference.

    Args:
      model:
        An instance of `SURT`.
      encoder_out:
        A tensor of shape (N, T, C) from the encoder. Support only N==1 for now.
      beam:
        Beam size.
      temperature:
        Softmax temperature.
      return_timestamps:
        Whether to return timestamps.

    Returns:
      If return_timestamps is False, return the decoded result.
      Else, return a DecodingResults object containing
      decoded result and corresponding timestamps.
    """
    assert encoder_out.ndim == 3

    # support only batch_size == 1 for now
    assert encoder_out.size(0) == 1, encoder_out.size(0)
    blank_id = model.decoder.blank_id
    unk_id = getattr(model, "unk_id", blank_id)
    context_size = model.decoder.context_size

    device = next(model.parameters()).device

    decoder_input = torch.tensor(
        [blank_id] * context_size,
        device=device,
        dtype=torch.int64,
    ).reshape(1, context_size)

    decoder_out = model.decoder(decoder_input, need_pad=False)
    decoder_out = model.joiner.decoder_proj(decoder_out)

    encoder_out = model.joiner.encoder_proj(encoder_out)

    T = encoder_out.size(1)
    t = 0

    B = HypothesisList()
    B.add(Hypothesis(ys=[blank_id] * context_size, log_prob=0.0, timestamp=[]))

    max_sym_per_utt = 20000

    sym_per_utt = 0

    decoder_cache: Dict[str, torch.Tensor] = {}

    while t < T and sym_per_utt < max_sym_per_utt:
        # fmt: off
        current_encoder_out = encoder_out[:, t:t+1, :].unsqueeze(2)
        # fmt: on
        A = B
        B = HypothesisList()

        joint_cache: Dict[str, torch.Tensor] = {}

        # TODO(fangjun): Implement prefix search to update the `log_prob`
        # of hypotheses in A

        while True:
            y_star = A.get_most_probable()
            A.remove(y_star)

            cached_key = y_star.key

            if cached_key not in decoder_cache:
                decoder_input = torch.tensor(
                    [y_star.ys[-context_size:]],
                    device=device,
                    dtype=torch.int64,
                ).reshape(1, context_size)

                decoder_out = model.decoder(decoder_input, need_pad=False)
                decoder_out = model.joiner.decoder_proj(decoder_out)
                decoder_cache[cached_key] = decoder_out
            else:
                decoder_out = decoder_cache[cached_key]

            cached_key += f"-t-{t}"
            if cached_key not in joint_cache:
                logits = model.joiner(
                    current_encoder_out,
                    decoder_out.unsqueeze(1),
                    project_input=False,
                )

                # TODO(fangjun): Scale the blank posterior
                log_prob = (logits / temperature).log_softmax(dim=-1)
                # log_prob is (1, 1, 1, vocab_size)
                log_prob = log_prob.squeeze()
                # Now log_prob is (vocab_size,)
                joint_cache[cached_key] = log_prob
            else:
                log_prob = joint_cache[cached_key]

            # First, process the blank symbol
            skip_log_prob = log_prob[blank_id]
            new_y_star_log_prob = y_star.log_prob + skip_log_prob

            # ys[:] returns a copy of ys
            B.add(
                Hypothesis(
                    ys=y_star.ys[:],
                    log_prob=new_y_star_log_prob,
                    timestamp=y_star.timestamp[:],
                )
            )

            # Second, process other non-blank labels
            values, indices = log_prob.topk(beam + 1)
            for i, v in zip(indices.tolist(), values.tolist()):
                if i in (blank_id, unk_id):
                    continue
                new_ys = y_star.ys + [i]
                new_log_prob = y_star.log_prob + v
                new_timestamp = y_star.timestamp + [t]
                A.add(
                    Hypothesis(
                        ys=new_ys,
                        log_prob=new_log_prob,
                        timestamp=new_timestamp,
                    )
                )

            # Check whether B contains more than "beam" elements more probable
            # than the most probable in A
            A_most_probable = A.get_most_probable()

            kept_B = B.filter(A_most_probable.log_prob)

            if len(kept_B) >= beam:
                B = kept_B.topk(beam)
                break

        t += 1

    best_hyp = B.get_most_probable(length_norm=True)
    ys = best_hyp.ys[context_size:]  # [context_size:] to remove blanks

    if not return_timestamps:
        return ys
    else:
        return DecodingResults(hyps=[ys], timestamps=[best_hyp.timestamp])


@dataclass
class Hypothesis:
    # The predicted tokens so far.
    # Newly predicted tokens are appended to `ys`.
    ys: List[int]

    # The log prob of ys.
    # It contains only one entry.
    log_prob: torch.Tensor

    # timestamp[i] is the frame index after subsampling
    # on which ys[i] is decoded
    timestamp: List[int] = field(default_factory=list)

    # the lm score for next token given the current ys
    lm_score: Optional[torch.Tensor] = None

    # the RNNLM states (h and c in LSTM)
    state: Optional[Tuple[torch.Tensor, torch.Tensor]] = None

    # N-gram LM state
    state_cost: Optional[NgramLmStateCost] = None

    @property
    def key(self) -> str:
        """Return a string representation of self.ys"""
        return "_".join(map(str, self.ys))


class HypothesisList(object):
    def __init__(self, data: Optional[Dict[str, Hypothesis]] = None) -> None:
        """
        Args:
          data:
            A dict of Hypotheses. Its key is its `value.key`.
        """
        if data is None:
            self._data = {}
        else:
            self._data = data

    @property
    def data(self) -> Dict[str, Hypothesis]:
        return self._data

    def add(self, hyp: Hypothesis) -> None:
        """Add a Hypothesis to `self`.

        If `hyp` already exists in `self`, its probability is updated using
        `log-sum-exp` with the existed one.

        Args:
          hyp:
            The hypothesis to be added.
        """
        key = hyp.key
        if key in self:
            old_hyp = self._data[key]  # shallow copy
            torch.logaddexp(old_hyp.log_prob, hyp.log_prob, out=old_hyp.log_prob)
        else:
            self._data[key] = hyp

    def get_most_probable(self, length_norm: bool = False) -> Hypothesis:
        """Get the most probable hypothesis, i.e., the one with
        the largest `log_prob`.

        Args:
          length_norm:
            If True, the `log_prob` of a hypothesis is normalized by the
            number of tokens in it.
        Returns:
          Return the hypothesis that has the largest `log_prob`.
        """
        if length_norm:
            return max(self._data.values(), key=lambda hyp: hyp.log_prob / len(hyp.ys))
        else:
            return max(self._data.values(), key=lambda hyp: hyp.log_prob)

    def remove(self, hyp: Hypothesis) -> None:
        """Remove a given hypothesis.

        Caution:
          `self` is modified **in-place**.

        Args:
          hyp:
            The hypothesis to be removed from `self`.
            Note: It must be contained in `self`. Otherwise,
            an exception is raised.
        """
        key = hyp.key
        assert key in self, f"{key} does not exist"
        del self._data[key]

    def filter(self, threshold: torch.Tensor) -> "HypothesisList":
        """Remove all Hypotheses whose log_prob is less than threshold.

        Caution:
          `self` is not modified. Instead, a new HypothesisList is returned.

        Returns:
          Return a new HypothesisList containing all hypotheses from `self`
          with `log_prob` being greater than the given `threshold`.
        """
        ans = HypothesisList()
        for _, hyp in self._data.items():
            if hyp.log_prob > threshold:
                ans.add(hyp)  # shallow copy
        return ans

    def topk(self, k: int) -> "HypothesisList":
        """Return the top-k hypothesis."""
        hyps = list(self._data.items())

        hyps = sorted(hyps, key=lambda h: h[1].log_prob, reverse=True)[:k]

        ans = HypothesisList(dict(hyps))
        return ans

    def __contains__(self, key: str):
        return key in self._data

    def __iter__(self):
        return iter(self._data.values())

    def __len__(self) -> int:
        return len(self._data)

    def __str__(self) -> str:
        s = []
        for key in self:
            s.append(key)
        return ", ".join(s)


def get_hyps_shape(hyps: List[HypothesisList]) -> k2.RaggedShape:
    """Return a ragged shape with axes [utt][num_hyps].

    Args:
      hyps:
        len(hyps) == batch_size. It contains the current hypothesis for
        each utterance in the batch.
    Returns:
      Return a ragged shape with 2 axes [utt][num_hyps]. Note that
      the shape is on CPU.
    """
    num_hyps = [len(h) for h in hyps]

    # torch.cumsum() is inclusive sum, so we put a 0 at the beginning
    # to get exclusive sum later.
    num_hyps.insert(0, 0)

    num_hyps = torch.tensor(num_hyps)
    row_splits = torch.cumsum(num_hyps, dim=0, dtype=torch.int32)
    ans = k2.ragged.create_ragged_shape2(
        row_splits=row_splits, cached_tot_size=row_splits[-1].item()
    )
    return ans