# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang)
#
# 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.

from dataclasses import dataclass
from typing import Dict, List, Optional

import k2
import torch
from model import Transducer


def greedy_search(
    model: Transducer, encoder_out: torch.Tensor, max_sym_per_frame: int
) -> List[int]:
    """Greedy search for a single utterance.
    Args:
      model:
        An instance of `Transducer`.
      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%.
    Returns:
      Return the decoded result.
    """
    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
    context_size = model.decoder.context_size

    device = model.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)

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

    # 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

    encoder_out_len = torch.tensor([1])
    decoder_out_len = torch.tensor([1])

    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, :]
        # fmt: on
        logits = model.joiner(
            current_encoder_out, decoder_out, encoder_out_len, decoder_out_len
        )
        # logits is (1, vocab_size)

        y = logits.argmax().item()
        if y != blank_id:
            hyp.append(y)
            decoder_input = torch.tensor(
                [hyp[-context_size:]], device=device
            ).reshape(1, context_size)

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

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

    return hyp


def greedy_search_batch(
    model: Transducer, encoder_out: torch.Tensor
) -> List[List[int]]:
    """Greedy search in batch mode. It hardcodes --max-sym-per-frame=1.
    Args:
      model:
        The transducer model.
      encoder_out:
        Output from the encoder. Its shape is (N, T, C), where N >= 1.
    Returns:
      Return a list-of-list of token IDs containing the decoded results.
      len(ans) equals to encoder_out.size(0).
    """
    assert encoder_out.ndim == 3
    assert encoder_out.size(0) >= 1, encoder_out.size(0)

    device = model.device

    batch_size = encoder_out.size(0)
    T = encoder_out.size(1)

    blank_id = model.decoder.blank_id
    context_size = model.decoder.context_size

    hyps = [[blank_id] * context_size for _ in range(batch_size)]

    decoder_input = torch.tensor(
        hyps,
        device=device,
        dtype=torch.int64,
    )  # (batch_size, context_size)
    decoder_out = model.decoder(decoder_input, need_pad=False)
    # decoder_out: (batch_size, 1, decoder_out_dim)

    encoder_out_len = torch.ones(batch_size, dtype=torch.int32)
    decoder_out_len = torch.ones(batch_size, dtype=torch.int32)

    for t in range(T):
        current_encoder_out = encoder_out[:, t : t + 1, :]  # noqa
        # current_encoder_out's shape: (batch_size, 1, encoder_out_dim)
        logits = model.joiner(
            current_encoder_out, decoder_out, encoder_out_len, decoder_out_len
        )  # (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 != blank_id:
                hyps[i].append(v)
                emitted = True

        if emitted:
            # update decoder output
            decoder_input = [h[-context_size:] for h in hyps]
            decoder_input = torch.tensor(
                decoder_input,
                device=device,
                dtype=torch.int64,
            )  # (batch_size, context_size)
            decoder_out = model.decoder(
                decoder_input,
                need_pad=False,
            )  # (batch_size, 1, decoder_out_dim)

    ans = [h[context_size:] for h in hyps]
    return ans


@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

    @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 run_decoder(
    ys: List[int],
    model: Transducer,
    decoder_cache: Dict[str, torch.Tensor],
) -> torch.Tensor:
    """Run the neural decoder model for a given hypothesis.

    Args:
      ys:
        The current hypothesis.
      model:
        The transducer model.
      decoder_cache:
        Cache to save computations.
    Returns:
      Return a 1-D tensor of shape (decoder_out_dim,) containing
      output of `model.decoder`.
    """
    context_size = model.decoder.context_size
    key = "_".join(map(str, ys[-context_size:]))
    if key in decoder_cache:
        return decoder_cache[key]

    device = model.device

    decoder_input = torch.tensor(
        [ys[-context_size:]],
        device=device,
        dtype=torch.int64,
    ).reshape(1, context_size)

    decoder_out = model.decoder(decoder_input, need_pad=False)
    decoder_cache[key] = decoder_out

    return decoder_out


def run_joiner(
    key: str,
    model: Transducer,
    encoder_out: torch.Tensor,
    decoder_out: torch.Tensor,
    encoder_out_len: torch.Tensor,
    decoder_out_len: torch.Tensor,
    joint_cache: Dict[str, torch.Tensor],
):
    """Run the joint network given outputs from the encoder and decoder.

    Args:
      key:
        A key into the `joint_cache`.
      model:
        The transducer model.
      encoder_out:
        A tensor of shape (1, 1, encoder_out_dim).
      decoder_out:
        A tensor of shape (1, 1, decoder_out_dim).
      encoder_out_len:
        A tensor with value [1].
      decoder_out_len:
        A tensor with value [1].
      joint_cache:
        A dict to save computations.
    Returns:
      Return a tensor from the output of log-softmax.
      Its shape is (vocab_size,).
    """
    if key in joint_cache:
        return joint_cache[key]

    logits = model.joiner(
        encoder_out,
        decoder_out,
        encoder_out_len,
        decoder_out_len,
    )

    # TODO(fangjun): Scale the blank posterior
    log_prob = logits.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[key] = log_prob

    return log_prob


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


def modified_beam_search(
    model: Transducer,
    encoder_out: torch.Tensor,
    beam: int = 4,
) -> List[List[int]]:
    """Beam search in batch mode with --max-sym-per-frame=1 being hardcodded.

    Args:
      model:
        The transducer model.
      encoder_out:
        Output from the encoder. Its shape is (N, T, C).
      beam:
        Number of active paths during the beam search.
    Returns:
      Return a list-of-list of token IDs. ans[i] is the decoding results
      for the i-th utterance.
    """
    assert encoder_out.ndim == 3, encoder_out.shape

    batch_size = encoder_out.size(0)
    T = encoder_out.size(1)

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

    encoder_out_len = torch.tensor([1])
    decoder_out_len = torch.tensor([1])
    for t in range(T):
        current_encoder_out = encoder_out[:, t : t + 1, :]  # noqa
        # current_encoder_out's shape is: (batch_size, 1, encoder_out_dim)

        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)
        # decoder_output is of shape (num_hyps, 1, decoder_output_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, encoder_out_dim)

        logits = model.joiner(
            current_encoder_out,
            decoder_out,
            encoder_out_len.expand(decoder_out.size(0)),
            decoder_out_len.expand(decoder_out.size(0)),
        )
        # logits is of shape (num_hyps, vocab_size)

        log_probs = logits.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)

            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]
                if new_token != blank_id:
                    new_ys.append(new_token)

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

    best_hyps = [b.get_most_probable(length_norm=True) for b in B]
    ans = [h.ys[context_size:] for h in best_hyps]

    return ans


def _deprecated_modified_beam_search(
    model: Transducer,
    encoder_out: torch.Tensor,
    beam: int = 4,
) -> List[int]:
    """It limits the maximum number of symbols per frame to 1.

    It decodes only one utterance at a time. We keep it only for reference.
    The function :func:`modified_beam_search` should be preferred as it
    supports batch decoding.

    Args:
      model:
        An instance of `Transducer`.
      encoder_out:
        A tensor of shape (N, T, C) from the encoder. Support only N==1 for now.
      beam:
        Beam size.
    Returns:
      Return the decoded result.
    """

    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
    context_size = model.decoder.context_size

    device = model.device

    T = encoder_out.size(1)

    B = HypothesisList()
    B.add(
        Hypothesis(
            ys=[blank_id] * context_size,
            log_prob=torch.zeros(1, dtype=torch.float32, device=device),
        )
    )

    encoder_out_len = torch.tensor([1])
    decoder_out_len = torch.tensor([1])

    for t in range(T):
        # fmt: off
        current_encoder_out = encoder_out[:, t:t+1, :]
        # current_encoder_out is of shape (1, 1, encoder_out_dim)
        # fmt: on
        A = list(B)
        B = HypothesisList()

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

        decoder_input = torch.tensor(
            [hyp.ys[-context_size:] for hyp in A],
            device=device,
        )
        # decoder_input is of shape (num_hyps, context_size)

        decoder_out = model.decoder(decoder_input, need_pad=False)
        # decoder_output is of shape (num_hyps, 1, decoder_output_dim)

        current_encoder_out = current_encoder_out.expand(
            decoder_out.size(0), 1, -1
        )

        logits = model.joiner(
            current_encoder_out,
            decoder_out,
            encoder_out_len.expand(decoder_out.size(0)),
            decoder_out_len.expand(decoder_out.size(0)),
        )
        # logits is of shape (num_hyps, vocab_size)
        log_probs = logits.log_softmax(dim=-1)

        log_probs.add_(ys_log_probs)

        log_probs = log_probs.reshape(-1)
        topk_log_probs, topk_indexes = log_probs.topk(beam)

        # topk_hyp_indexes are indexes into `A`
        topk_hyp_indexes = topk_indexes // logits.size(-1)
        topk_token_indexes = topk_indexes % logits.size(-1)

        topk_hyp_indexes = topk_hyp_indexes.tolist()
        topk_token_indexes = topk_token_indexes.tolist()

        for i in range(len(topk_hyp_indexes)):
            hyp = A[topk_hyp_indexes[i]]
            new_ys = hyp.ys[:]
            new_token = topk_token_indexes[i]
            if new_token != blank_id:
                new_ys.append(new_token)
            new_log_prob = topk_log_probs[i]
            new_hyp = Hypothesis(ys=new_ys, log_prob=new_log_prob)
            B.add(new_hyp)

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

    return ys


def beam_search(
    model: Transducer,
    encoder_out: torch.Tensor,
    beam: int = 4,
) -> List[int]:
    """
    It implements Algorithm 1 in https://arxiv.org/pdf/1211.3711.pdf

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

    Args:
      model:
        An instance of `Transducer`.
      encoder_out:
        A tensor of shape (N, T, C) from the encoder. Support only N==1 for now.
      beam:
        Beam size.
    Returns:
      Return the decoded result.
    """
    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
    context_size = model.decoder.context_size

    device = model.device

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

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

    T = encoder_out.size(1)
    t = 0

    B = HypothesisList()
    B.add(
        Hypothesis(
            ys=[blank_id] * context_size,
            log_prob=torch.zeros(1, dtype=torch.float32, device=device),
        )
    )

    max_sym_per_utt = 20000

    sym_per_utt = 0

    encoder_out_len = torch.tensor([1])
    decoder_out_len = torch.tensor([1])

    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, :]
        # fmt: on
        A = B
        B = HypothesisList()

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

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

            decoder_out = run_decoder(
                ys=y_star.ys, model=model, decoder_cache=decoder_cache
            )

            key = "_".join(map(str, y_star.ys[-context_size:]))
            key += f"-t-{t}"
            log_prob = run_joiner(
                key=key,
                model=model,
                encoder_out=current_encoder_out,
                decoder_out=decoder_out,
                encoder_out_len=encoder_out_len,
                decoder_out_len=decoder_out_len,
                joint_cache=joint_cache,
            )

            # 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))

            # Second, process other non-blank labels
            values, indices = log_prob.topk(beam + 1)
            for idx in range(values.size(0)):
                i = indices[idx].item()
                if i == blank_id:
                    continue

                new_ys = y_star.ys + [i]

                new_log_prob = y_star.log_prob + values[idx]
                A.add(Hypothesis(ys=new_ys, log_prob=new_log_prob))

            # 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
    return ys