from typing import List

import k2
import torch
from torch import nn

from icefall.mmi_graph_compiler import MmiTrainingGraphCompiler


def _compute_mmi_loss_exact_optimized(
    dense_fsa_vec: k2.DenseFsaVec,
    texts: List[str],
    graph_compiler: MmiTrainingGraphCompiler,
    den_scale: float = 1.0,
    beam_size: float = 8.0,
) -> torch.Tensor:
    """
    The function name contains `exact`, which means it uses a version of
    intersection without pruning.

    `optimized` in the function name means this function is optimized
    in that it calls k2.intersect_dense only once

    Note:
      It is faster at the cost of using more memory.

    Args:
      dense_fsa_vec:
        It contains the neural network output.
      texts:
        The transcript. Each element consists of space(s) separated words.
      graph_compiler:
        Used to build num_graphs and den_graphs
      den_scale:
        The scale applied to the denominator tot_scores.
    Returns:
      Return a scalar loss. It is the sum over utterances in a batch,
      without normalization.
    """
    num_graphs, den_graphs = graph_compiler.compile(texts, replicate_den=False)

    device = num_graphs.device

    num_fsas = num_graphs.shape[0]
    assert dense_fsa_vec.dim0() == num_fsas

    assert den_graphs.shape[0] == 1

    # The motivation to concatenate num_graphs and den_graphs
    # is to reduce the number of calls to k2.intersect_dense.
    num_den_graphs = k2.cat([num_graphs, den_graphs])

    # NOTE: The a_to_b_map in k2.intersect_dense must be sorted
    # so the following reorders num_den_graphs.
    #
    # The following code computes a_to_b_map

    # [0, 1, 2, ... ]
    num_graphs_indexes = torch.arange(num_fsas, dtype=torch.int32)

    # [num_fsas, num_fsas, num_fsas, ... ]
    den_graphs_indexes = torch.tensor([num_fsas] * num_fsas, dtype=torch.int32)

    # [0, num_fsas, 1, num_fsas, 2, num_fsas, ... ]
    num_den_graphs_indexes = (
        torch.stack([num_graphs_indexes, den_graphs_indexes])
        .t()
        .reshape(-1)
        .to(device)
    )

    num_den_reordered_graphs = k2.index(num_den_graphs, num_den_graphs_indexes)

    # [[0, 1, 2, ...]]
    a_to_b_map = torch.arange(num_fsas, dtype=torch.int32).reshape(1, -1)

    # [[0, 1, 2, ...]] -> [0, 0, 1, 1, 2, 2, ... ]
    a_to_b_map = a_to_b_map.repeat(2, 1).t().reshape(-1).to(device)

    num_den_lats = k2.intersect_dense(
        num_den_reordered_graphs,
        dense_fsa_vec,
        output_beam=beam_size,
        a_to_b_map=a_to_b_map,
    )

    num_den_tot_scores = num_den_lats.get_tot_scores(
        log_semiring=True, use_double_scores=True
    )

    num_tot_scores = num_den_tot_scores[::2]
    den_tot_scores = num_den_tot_scores[1::2]

    tot_scores = num_tot_scores - den_scale * den_tot_scores
    loss = -1 * tot_scores.sum()
    return loss


def _compute_mmi_loss_exact_non_optimized(
    dense_fsa_vec: k2.DenseFsaVec,
    texts: List[str],
    graph_compiler: MmiTrainingGraphCompiler,
    den_scale: float = 1.0,
    beam_size: float = 8.0,
) -> torch.Tensor:
    """
    See :func:`_compute_mmi_loss_exact_optimized` for the meaning
    of the arguments.

    It's more readable, though it invokes k2.intersect_dense twice.

    Note:
      It uses less memory at the cost of speed. It is slower.
    """
    num_graphs, den_graphs = graph_compiler.compile(texts, replicate_den=True)

    # TODO: pass output_beam as function argument
    num_lats = k2.intersect_dense(
        num_graphs, dense_fsa_vec, output_beam=beam_size
    )
    den_lats = k2.intersect_dense(
        den_graphs, dense_fsa_vec, output_beam=beam_size
    )

    num_tot_scores = num_lats.get_tot_scores(
        log_semiring=True, use_double_scores=True
    )

    den_tot_scores = den_lats.get_tot_scores(
        log_semiring=True, use_double_scores=True
    )

    tot_scores = num_tot_scores - den_scale * den_tot_scores

    loss = -1 * tot_scores.sum()
    return loss


def _compute_mmi_loss_pruned(
    dense_fsa_vec: k2.DenseFsaVec,
    texts: List[str],
    graph_compiler: MmiTrainingGraphCompiler,
    den_scale: float = 1.0,
    beam_size: float = 8.0,
) -> torch.Tensor:
    """
    See :func:`_compute_mmi_loss_exact_optimized` for the meaning
    of the arguments.

    `pruned` means it uses k2.intersect_dense_pruned

    Note:
      It uses the least amount of memory, but the loss is not exact due
      to pruning.
    """
    num_graphs, den_graphs = graph_compiler.compile(texts, replicate_den=False)

    num_lats = k2.intersect_dense(num_graphs, dense_fsa_vec, output_beam=10.0)

    # the values for search_beam/output_beam/min_active_states/max_active_states
    # are not tuned. You may want to tune them.
    den_lats = k2.intersect_dense_pruned(
        den_graphs,
        dense_fsa_vec,
        search_beam=20.0,
        output_beam=beam_size,
        min_active_states=30,
        max_active_states=10000,
    )

    num_tot_scores = num_lats.get_tot_scores(
        log_semiring=True, use_double_scores=True
    )

    den_tot_scores = den_lats.get_tot_scores(
        log_semiring=True, use_double_scores=True
    )

    tot_scores = num_tot_scores - den_scale * den_tot_scores

    loss = -1 * tot_scores.sum()
    return loss


class LFMMILoss(nn.Module):
    """
    Computes Lattice-Free Maximum Mutual Information (LFMMI) loss.

    TODO: more detailed description
    """

    def __init__(
        self,
        graph_compiler: MmiTrainingGraphCompiler,
        use_pruned_intersect: bool = False,
        den_scale: float = 1.0,
        beam_size: float = 8.0,
    ):
        super().__init__()
        self.graph_compiler = graph_compiler
        self.den_scale = den_scale
        self.use_pruned_intersect = use_pruned_intersect
        self.beam_size = beam_size

    def forward(
        self,
        dense_fsa_vec: k2.DenseFsaVec,
        texts: List[str],
    ) -> torch.Tensor:
        """
        Args:
          dense_fsa_vec:
            It contains the neural network output.
          texts:
            A list of strings. Each string contains space(s) separated words.
        Returns:
          Return a scalar loss. It is the sum over utterances in a batch,
          without normalization.
        """
        if self.use_pruned_intersect:
            func = _compute_mmi_loss_pruned
        else:
            func = _compute_mmi_loss_exact_non_optimized
            #  func = _compute_mmi_loss_exact_optimized

        return func(
            dense_fsa_vec=dense_fsa_vec,
            texts=texts,
            graph_compiler=self.graph_compiler,
            den_scale=self.den_scale,
            beam_size=self.beam_size,
        )

89b7d988b544ded0d3e2233dbf24774423849d0b
<mask token>


def _compute_mmi_loss_exact_optimized(dense_fsa_vec: k2.DenseFsaVec, texts:
    List[str], graph_compiler: MmiTrainingGraphCompiler, den_scale: float=
    1.0, beam_size: float=8.0) ->torch.Tensor:
    """
    The function name contains `exact`, which means it uses a version of
    intersection without pruning.

    `optimized` in the function name means this function is optimized
    in that it calls k2.intersect_dense only once

    Note:
      It is faster at the cost of using more memory.

    Args:
      dense_fsa_vec:
        It contains the neural network output.
      texts:
        The transcript. Each element consists of space(s) separated words.
      graph_compiler:
        Used to build num_graphs and den_graphs
      den_scale:
        The scale applied to the denominator tot_scores.
    Returns:
      Return a scalar loss. It is the sum over utterances in a batch,
      without normalization.
    """
    num_graphs, den_graphs = graph_compiler.compile(texts, replicate_den=False)
    device = num_graphs.device
    num_fsas = num_graphs.shape[0]
    assert dense_fsa_vec.dim0() == num_fsas
    assert den_graphs.shape[0] == 1
    num_den_graphs = k2.cat([num_graphs, den_graphs])
    num_graphs_indexes = torch.arange(num_fsas, dtype=torch.int32)
    den_graphs_indexes = torch.tensor([num_fsas] * num_fsas, dtype=torch.int32)
    num_den_graphs_indexes = torch.stack([num_graphs_indexes,
        den_graphs_indexes]).t().reshape(-1).to(device)
    num_den_reordered_graphs = k2.index(num_den_graphs, num_den_graphs_indexes)
    a_to_b_map = torch.arange(num_fsas, dtype=torch.int32).reshape(1, -1)
    a_to_b_map = a_to_b_map.repeat(2, 1).t().reshape(-1).to(device)
    num_den_lats = k2.intersect_dense(num_den_reordered_graphs,
        dense_fsa_vec, output_beam=beam_size, a_to_b_map=a_to_b_map)
    num_den_tot_scores = num_den_lats.get_tot_scores(log_semiring=True,
        use_double_scores=True)
    num_tot_scores = num_den_tot_scores[::2]
    den_tot_scores = num_den_tot_scores[1::2]
    tot_scores = num_tot_scores - den_scale * den_tot_scores
    loss = -1 * tot_scores.sum()
    return loss


<mask token>


class LFMMILoss(nn.Module):
    """
    Computes Lattice-Free Maximum Mutual Information (LFMMI) loss.

    TODO: more detailed description
    """

    def __init__(self, graph_compiler: MmiTrainingGraphCompiler,
        use_pruned_intersect: bool=False, den_scale: float=1.0, beam_size:
        float=8.0):
        super().__init__()
        self.graph_compiler = graph_compiler
        self.den_scale = den_scale
        self.use_pruned_intersect = use_pruned_intersect
        self.beam_size = beam_size

    def forward(self, dense_fsa_vec: k2.DenseFsaVec, texts: List[str]
        ) ->torch.Tensor:
        """
        Args:
          dense_fsa_vec:
            It contains the neural network output.
          texts:
            A list of strings. Each string contains space(s) separated words.
        Returns:
          Return a scalar loss. It is the sum over utterances in a batch,
          without normalization.
        """
        if self.use_pruned_intersect:
            func = _compute_mmi_loss_pruned
        else:
            func = _compute_mmi_loss_exact_non_optimized
        return func(dense_fsa_vec=dense_fsa_vec, texts=texts,
            graph_compiler=self.graph_compiler, den_scale=self.den_scale,
            beam_size=self.beam_size)

<mask token>


def _compute_mmi_loss_exact_optimized(dense_fsa_vec: k2.DenseFsaVec, texts:
    List[str], graph_compiler: MmiTrainingGraphCompiler, den_scale: float=
    1.0, beam_size: float=8.0) ->torch.Tensor:
    """
    The function name contains `exact`, which means it uses a version of
    intersection without pruning.

    `optimized` in the function name means this function is optimized
    in that it calls k2.intersect_dense only once

    Note:
      It is faster at the cost of using more memory.

    Args:
      dense_fsa_vec:
        It contains the neural network output.
      texts:
        The transcript. Each element consists of space(s) separated words.
      graph_compiler:
        Used to build num_graphs and den_graphs
      den_scale:
        The scale applied to the denominator tot_scores.
    Returns:
      Return a scalar loss. It is the sum over utterances in a batch,
      without normalization.
    """
    num_graphs, den_graphs = graph_compiler.compile(texts, replicate_den=False)
    device = num_graphs.device
    num_fsas = num_graphs.shape[0]
    assert dense_fsa_vec.dim0() == num_fsas
    assert den_graphs.shape[0] == 1
    num_den_graphs = k2.cat([num_graphs, den_graphs])
    num_graphs_indexes = torch.arange(num_fsas, dtype=torch.int32)
    den_graphs_indexes = torch.tensor([num_fsas] * num_fsas, dtype=torch.int32)
    num_den_graphs_indexes = torch.stack([num_graphs_indexes,
        den_graphs_indexes]).t().reshape(-1).to(device)
    num_den_reordered_graphs = k2.index(num_den_graphs, num_den_graphs_indexes)
    a_to_b_map = torch.arange(num_fsas, dtype=torch.int32).reshape(1, -1)
    a_to_b_map = a_to_b_map.repeat(2, 1).t().reshape(-1).to(device)
    num_den_lats = k2.intersect_dense(num_den_reordered_graphs,
        dense_fsa_vec, output_beam=beam_size, a_to_b_map=a_to_b_map)
    num_den_tot_scores = num_den_lats.get_tot_scores(log_semiring=True,
        use_double_scores=True)
    num_tot_scores = num_den_tot_scores[::2]
    den_tot_scores = num_den_tot_scores[1::2]
    tot_scores = num_tot_scores - den_scale * den_tot_scores
    loss = -1 * tot_scores.sum()
    return loss


def _compute_mmi_loss_exact_non_optimized(dense_fsa_vec: k2.DenseFsaVec,
    texts: List[str], graph_compiler: MmiTrainingGraphCompiler, den_scale:
    float=1.0, beam_size: float=8.0) ->torch.Tensor:
    """
    See :func:`_compute_mmi_loss_exact_optimized` for the meaning
    of the arguments.

    It's more readable, though it invokes k2.intersect_dense twice.

    Note:
      It uses less memory at the cost of speed. It is slower.
    """
    num_graphs, den_graphs = graph_compiler.compile(texts, replicate_den=True)
    num_lats = k2.intersect_dense(num_graphs, dense_fsa_vec, output_beam=
        beam_size)
    den_lats = k2.intersect_dense(den_graphs, dense_fsa_vec, output_beam=
        beam_size)
    num_tot_scores = num_lats.get_tot_scores(log_semiring=True,
        use_double_scores=True)
    den_tot_scores = den_lats.get_tot_scores(log_semiring=True,
        use_double_scores=True)
    tot_scores = num_tot_scores - den_scale * den_tot_scores
    loss = -1 * tot_scores.sum()
    return loss


<mask token>


class LFMMILoss(nn.Module):
    """
    Computes Lattice-Free Maximum Mutual Information (LFMMI) loss.

    TODO: more detailed description
    """

    def __init__(self, graph_compiler: MmiTrainingGraphCompiler,
        use_pruned_intersect: bool=False, den_scale: float=1.0, beam_size:
        float=8.0):
        super().__init__()
        self.graph_compiler = graph_compiler
        self.den_scale = den_scale
        self.use_pruned_intersect = use_pruned_intersect
        self.beam_size = beam_size

    def forward(self, dense_fsa_vec: k2.DenseFsaVec, texts: List[str]
        ) ->torch.Tensor:
        """
        Args:
          dense_fsa_vec:
            It contains the neural network output.
          texts:
            A list of strings. Each string contains space(s) separated words.
        Returns:
          Return a scalar loss. It is the sum over utterances in a batch,
          without normalization.
        """
        if self.use_pruned_intersect:
            func = _compute_mmi_loss_pruned
        else:
            func = _compute_mmi_loss_exact_non_optimized
        return func(dense_fsa_vec=dense_fsa_vec, texts=texts,
            graph_compiler=self.graph_compiler, den_scale=self.den_scale,
            beam_size=self.beam_size)

<mask token>


def _compute_mmi_loss_exact_optimized(dense_fsa_vec: k2.DenseFsaVec, texts:
    List[str], graph_compiler: MmiTrainingGraphCompiler, den_scale: float=
    1.0, beam_size: float=8.0) ->torch.Tensor:
    """
    The function name contains `exact`, which means it uses a version of
    intersection without pruning.

    `optimized` in the function name means this function is optimized
    in that it calls k2.intersect_dense only once

    Note:
      It is faster at the cost of using more memory.

    Args:
      dense_fsa_vec:
        It contains the neural network output.
      texts:
        The transcript. Each element consists of space(s) separated words.
      graph_compiler:
        Used to build num_graphs and den_graphs
      den_scale:
        The scale applied to the denominator tot_scores.
    Returns:
      Return a scalar loss. It is the sum over utterances in a batch,
      without normalization.
    """
    num_graphs, den_graphs = graph_compiler.compile(texts, replicate_den=False)
    device = num_graphs.device
    num_fsas = num_graphs.shape[0]
    assert dense_fsa_vec.dim0() == num_fsas
    assert den_graphs.shape[0] == 1
    num_den_graphs = k2.cat([num_graphs, den_graphs])
    num_graphs_indexes = torch.arange(num_fsas, dtype=torch.int32)
    den_graphs_indexes = torch.tensor([num_fsas] * num_fsas, dtype=torch.int32)
    num_den_graphs_indexes = torch.stack([num_graphs_indexes,
        den_graphs_indexes]).t().reshape(-1).to(device)
    num_den_reordered_graphs = k2.index(num_den_graphs, num_den_graphs_indexes)
    a_to_b_map = torch.arange(num_fsas, dtype=torch.int32).reshape(1, -1)
    a_to_b_map = a_to_b_map.repeat(2, 1).t().reshape(-1).to(device)
    num_den_lats = k2.intersect_dense(num_den_reordered_graphs,
        dense_fsa_vec, output_beam=beam_size, a_to_b_map=a_to_b_map)
    num_den_tot_scores = num_den_lats.get_tot_scores(log_semiring=True,
        use_double_scores=True)
    num_tot_scores = num_den_tot_scores[::2]
    den_tot_scores = num_den_tot_scores[1::2]
    tot_scores = num_tot_scores - den_scale * den_tot_scores
    loss = -1 * tot_scores.sum()
    return loss


def _compute_mmi_loss_exact_non_optimized(dense_fsa_vec: k2.DenseFsaVec,
    texts: List[str], graph_compiler: MmiTrainingGraphCompiler, den_scale:
    float=1.0, beam_size: float=8.0) ->torch.Tensor:
    """
    See :func:`_compute_mmi_loss_exact_optimized` for the meaning
    of the arguments.

    It's more readable, though it invokes k2.intersect_dense twice.

    Note:
      It uses less memory at the cost of speed. It is slower.
    """
    num_graphs, den_graphs = graph_compiler.compile(texts, replicate_den=True)
    num_lats = k2.intersect_dense(num_graphs, dense_fsa_vec, output_beam=
        beam_size)
    den_lats = k2.intersect_dense(den_graphs, dense_fsa_vec, output_beam=
        beam_size)
    num_tot_scores = num_lats.get_tot_scores(log_semiring=True,
        use_double_scores=True)
    den_tot_scores = den_lats.get_tot_scores(log_semiring=True,
        use_double_scores=True)
    tot_scores = num_tot_scores - den_scale * den_tot_scores
    loss = -1 * tot_scores.sum()
    return loss


def _compute_mmi_loss_pruned(dense_fsa_vec: k2.DenseFsaVec, texts: List[str
    ], graph_compiler: MmiTrainingGraphCompiler, den_scale: float=1.0,
    beam_size: float=8.0) ->torch.Tensor:
    """
    See :func:`_compute_mmi_loss_exact_optimized` for the meaning
    of the arguments.

    `pruned` means it uses k2.intersect_dense_pruned

    Note:
      It uses the least amount of memory, but the loss is not exact due
      to pruning.
    """
    num_graphs, den_graphs = graph_compiler.compile(texts, replicate_den=False)
    num_lats = k2.intersect_dense(num_graphs, dense_fsa_vec, output_beam=10.0)
    den_lats = k2.intersect_dense_pruned(den_graphs, dense_fsa_vec,
        search_beam=20.0, output_beam=beam_size, min_active_states=30,
        max_active_states=10000)
    num_tot_scores = num_lats.get_tot_scores(log_semiring=True,
        use_double_scores=True)
    den_tot_scores = den_lats.get_tot_scores(log_semiring=True,
        use_double_scores=True)
    tot_scores = num_tot_scores - den_scale * den_tot_scores
    loss = -1 * tot_scores.sum()
    return loss


class LFMMILoss(nn.Module):
    """
    Computes Lattice-Free Maximum Mutual Information (LFMMI) loss.

    TODO: more detailed description
    """

    def __init__(self, graph_compiler: MmiTrainingGraphCompiler,
        use_pruned_intersect: bool=False, den_scale: float=1.0, beam_size:
        float=8.0):
        super().__init__()
        self.graph_compiler = graph_compiler
        self.den_scale = den_scale
        self.use_pruned_intersect = use_pruned_intersect
        self.beam_size = beam_size

    def forward(self, dense_fsa_vec: k2.DenseFsaVec, texts: List[str]
        ) ->torch.Tensor:
        """
        Args:
          dense_fsa_vec:
            It contains the neural network output.
          texts:
            A list of strings. Each string contains space(s) separated words.
        Returns:
          Return a scalar loss. It is the sum over utterances in a batch,
          without normalization.
        """
        if self.use_pruned_intersect:
            func = _compute_mmi_loss_pruned
        else:
            func = _compute_mmi_loss_exact_non_optimized
        return func(dense_fsa_vec=dense_fsa_vec, texts=texts,
            graph_compiler=self.graph_compiler, den_scale=self.den_scale,
            beam_size=self.beam_size)

from typing import List
import k2
import torch
from torch import nn
from icefall.mmi_graph_compiler import MmiTrainingGraphCompiler


def _compute_mmi_loss_exact_optimized(dense_fsa_vec: k2.DenseFsaVec, texts:
    List[str], graph_compiler: MmiTrainingGraphCompiler, den_scale: float=
    1.0, beam_size: float=8.0) ->torch.Tensor:
    """
    The function name contains `exact`, which means it uses a version of
    intersection without pruning.

    `optimized` in the function name means this function is optimized
    in that it calls k2.intersect_dense only once

    Note:
      It is faster at the cost of using more memory.

    Args:
      dense_fsa_vec:
        It contains the neural network output.
      texts:
        The transcript. Each element consists of space(s) separated words.
      graph_compiler:
        Used to build num_graphs and den_graphs
      den_scale:
        The scale applied to the denominator tot_scores.
    Returns:
      Return a scalar loss. It is the sum over utterances in a batch,
      without normalization.
    """
    num_graphs, den_graphs = graph_compiler.compile(texts, replicate_den=False)
    device = num_graphs.device
    num_fsas = num_graphs.shape[0]
    assert dense_fsa_vec.dim0() == num_fsas
    assert den_graphs.shape[0] == 1
    num_den_graphs = k2.cat([num_graphs, den_graphs])
    num_graphs_indexes = torch.arange(num_fsas, dtype=torch.int32)
    den_graphs_indexes = torch.tensor([num_fsas] * num_fsas, dtype=torch.int32)
    num_den_graphs_indexes = torch.stack([num_graphs_indexes,
        den_graphs_indexes]).t().reshape(-1).to(device)
    num_den_reordered_graphs = k2.index(num_den_graphs, num_den_graphs_indexes)
    a_to_b_map = torch.arange(num_fsas, dtype=torch.int32).reshape(1, -1)
    a_to_b_map = a_to_b_map.repeat(2, 1).t().reshape(-1).to(device)
    num_den_lats = k2.intersect_dense(num_den_reordered_graphs,
        dense_fsa_vec, output_beam=beam_size, a_to_b_map=a_to_b_map)
    num_den_tot_scores = num_den_lats.get_tot_scores(log_semiring=True,
        use_double_scores=True)
    num_tot_scores = num_den_tot_scores[::2]
    den_tot_scores = num_den_tot_scores[1::2]
    tot_scores = num_tot_scores - den_scale * den_tot_scores
    loss = -1 * tot_scores.sum()
    return loss


def _compute_mmi_loss_exact_non_optimized(dense_fsa_vec: k2.DenseFsaVec,
    texts: List[str], graph_compiler: MmiTrainingGraphCompiler, den_scale:
    float=1.0, beam_size: float=8.0) ->torch.Tensor:
    """
    See :func:`_compute_mmi_loss_exact_optimized` for the meaning
    of the arguments.

    It's more readable, though it invokes k2.intersect_dense twice.

    Note:
      It uses less memory at the cost of speed. It is slower.
    """
    num_graphs, den_graphs = graph_compiler.compile(texts, replicate_den=True)
    num_lats = k2.intersect_dense(num_graphs, dense_fsa_vec, output_beam=
        beam_size)
    den_lats = k2.intersect_dense(den_graphs, dense_fsa_vec, output_beam=
        beam_size)
    num_tot_scores = num_lats.get_tot_scores(log_semiring=True,
        use_double_scores=True)
    den_tot_scores = den_lats.get_tot_scores(log_semiring=True,
        use_double_scores=True)
    tot_scores = num_tot_scores - den_scale * den_tot_scores
    loss = -1 * tot_scores.sum()
    return loss


def _compute_mmi_loss_pruned(dense_fsa_vec: k2.DenseFsaVec, texts: List[str
    ], graph_compiler: MmiTrainingGraphCompiler, den_scale: float=1.0,
    beam_size: float=8.0) ->torch.Tensor:
    """
    See :func:`_compute_mmi_loss_exact_optimized` for the meaning
    of the arguments.

    `pruned` means it uses k2.intersect_dense_pruned

    Note:
      It uses the least amount of memory, but the loss is not exact due
      to pruning.
    """
    num_graphs, den_graphs = graph_compiler.compile(texts, replicate_den=False)
    num_lats = k2.intersect_dense(num_graphs, dense_fsa_vec, output_beam=10.0)
    den_lats = k2.intersect_dense_pruned(den_graphs, dense_fsa_vec,
        search_beam=20.0, output_beam=beam_size, min_active_states=30,
        max_active_states=10000)
    num_tot_scores = num_lats.get_tot_scores(log_semiring=True,
        use_double_scores=True)
    den_tot_scores = den_lats.get_tot_scores(log_semiring=True,
        use_double_scores=True)
    tot_scores = num_tot_scores - den_scale * den_tot_scores
    loss = -1 * tot_scores.sum()
    return loss


class LFMMILoss(nn.Module):
    """
    Computes Lattice-Free Maximum Mutual Information (LFMMI) loss.

    TODO: more detailed description
    """

    def __init__(self, graph_compiler: MmiTrainingGraphCompiler,
        use_pruned_intersect: bool=False, den_scale: float=1.0, beam_size:
        float=8.0):
        super().__init__()
        self.graph_compiler = graph_compiler
        self.den_scale = den_scale
        self.use_pruned_intersect = use_pruned_intersect
        self.beam_size = beam_size

    def forward(self, dense_fsa_vec: k2.DenseFsaVec, texts: List[str]
        ) ->torch.Tensor:
        """
        Args:
          dense_fsa_vec:
            It contains the neural network output.
          texts:
            A list of strings. Each string contains space(s) separated words.
        Returns:
          Return a scalar loss. It is the sum over utterances in a batch,
          without normalization.
        """
        if self.use_pruned_intersect:
            func = _compute_mmi_loss_pruned
        else:
            func = _compute_mmi_loss_exact_non_optimized
        return func(dense_fsa_vec=dense_fsa_vec, texts=texts,
            graph_compiler=self.graph_compiler, den_scale=self.den_scale,
            beam_size=self.beam_size)

from typing import List

import k2
import torch
from torch import nn

from icefall.mmi_graph_compiler import MmiTrainingGraphCompiler


def _compute_mmi_loss_exact_optimized(
    dense_fsa_vec: k2.DenseFsaVec,
    texts: List[str],
    graph_compiler: MmiTrainingGraphCompiler,
    den_scale: float = 1.0,
    beam_size: float = 8.0,
) -> torch.Tensor:
    """
    The function name contains `exact`, which means it uses a version of
    intersection without pruning.

    `optimized` in the function name means this function is optimized
    in that it calls k2.intersect_dense only once

    Note:
      It is faster at the cost of using more memory.

    Args:
      dense_fsa_vec:
        It contains the neural network output.
      texts:
        The transcript. Each element consists of space(s) separated words.
      graph_compiler:
        Used to build num_graphs and den_graphs
      den_scale:
        The scale applied to the denominator tot_scores.
    Returns:
      Return a scalar loss. It is the sum over utterances in a batch,
      without normalization.
    """
    num_graphs, den_graphs = graph_compiler.compile(texts, replicate_den=False)

    device = num_graphs.device

    num_fsas = num_graphs.shape[0]
    assert dense_fsa_vec.dim0() == num_fsas

    assert den_graphs.shape[0] == 1

    # The motivation to concatenate num_graphs and den_graphs
    # is to reduce the number of calls to k2.intersect_dense.
    num_den_graphs = k2.cat([num_graphs, den_graphs])

    # NOTE: The a_to_b_map in k2.intersect_dense must be sorted
    # so the following reorders num_den_graphs.
    #
    # The following code computes a_to_b_map

    # [0, 1, 2, ... ]
    num_graphs_indexes = torch.arange(num_fsas, dtype=torch.int32)

    # [num_fsas, num_fsas, num_fsas, ... ]
    den_graphs_indexes = torch.tensor([num_fsas] * num_fsas, dtype=torch.int32)

    # [0, num_fsas, 1, num_fsas, 2, num_fsas, ... ]
    num_den_graphs_indexes = (
        torch.stack([num_graphs_indexes, den_graphs_indexes])
        .t()
        .reshape(-1)
        .to(device)
    )

    num_den_reordered_graphs = k2.index(num_den_graphs, num_den_graphs_indexes)

    # [[0, 1, 2, ...]]
    a_to_b_map = torch.arange(num_fsas, dtype=torch.int32).reshape(1, -1)

    # [[0, 1, 2, ...]] -> [0, 0, 1, 1, 2, 2, ... ]
    a_to_b_map = a_to_b_map.repeat(2, 1).t().reshape(-1).to(device)

    num_den_lats = k2.intersect_dense(
        num_den_reordered_graphs,
        dense_fsa_vec,
        output_beam=beam_size,
        a_to_b_map=a_to_b_map,
    )

    num_den_tot_scores = num_den_lats.get_tot_scores(
        log_semiring=True, use_double_scores=True
    )

    num_tot_scores = num_den_tot_scores[::2]
    den_tot_scores = num_den_tot_scores[1::2]

    tot_scores = num_tot_scores - den_scale * den_tot_scores
    loss = -1 * tot_scores.sum()
    return loss


def _compute_mmi_loss_exact_non_optimized(
    dense_fsa_vec: k2.DenseFsaVec,
    texts: List[str],
    graph_compiler: MmiTrainingGraphCompiler,
    den_scale: float = 1.0,
    beam_size: float = 8.0,
) -> torch.Tensor:
    """
    See :func:`_compute_mmi_loss_exact_optimized` for the meaning
    of the arguments.

    It's more readable, though it invokes k2.intersect_dense twice.

    Note:
      It uses less memory at the cost of speed. It is slower.
    """
    num_graphs, den_graphs = graph_compiler.compile(texts, replicate_den=True)

    # TODO: pass output_beam as function argument
    num_lats = k2.intersect_dense(
        num_graphs, dense_fsa_vec, output_beam=beam_size
    )
    den_lats = k2.intersect_dense(
        den_graphs, dense_fsa_vec, output_beam=beam_size
    )

    num_tot_scores = num_lats.get_tot_scores(
        log_semiring=True, use_double_scores=True
    )

    den_tot_scores = den_lats.get_tot_scores(
        log_semiring=True, use_double_scores=True
    )

    tot_scores = num_tot_scores - den_scale * den_tot_scores

    loss = -1 * tot_scores.sum()
    return loss


def _compute_mmi_loss_pruned(
    dense_fsa_vec: k2.DenseFsaVec,
    texts: List[str],
    graph_compiler: MmiTrainingGraphCompiler,
    den_scale: float = 1.0,
    beam_size: float = 8.0,
) -> torch.Tensor:
    """
    See :func:`_compute_mmi_loss_exact_optimized` for the meaning
    of the arguments.

    `pruned` means it uses k2.intersect_dense_pruned

    Note:
      It uses the least amount of memory, but the loss is not exact due
      to pruning.
    """
    num_graphs, den_graphs = graph_compiler.compile(texts, replicate_den=False)

    num_lats = k2.intersect_dense(num_graphs, dense_fsa_vec, output_beam=10.0)

    # the values for search_beam/output_beam/min_active_states/max_active_states
    # are not tuned. You may want to tune them.
    den_lats = k2.intersect_dense_pruned(
        den_graphs,
        dense_fsa_vec,
        search_beam=20.0,
        output_beam=beam_size,
        min_active_states=30,
        max_active_states=10000,
    )

    num_tot_scores = num_lats.get_tot_scores(
        log_semiring=True, use_double_scores=True
    )

    den_tot_scores = den_lats.get_tot_scores(
        log_semiring=True, use_double_scores=True
    )

    tot_scores = num_tot_scores - den_scale * den_tot_scores

    loss = -1 * tot_scores.sum()
    return loss


class LFMMILoss(nn.Module):
    """
    Computes Lattice-Free Maximum Mutual Information (LFMMI) loss.

    TODO: more detailed description
    """

    def __init__(
        self,
        graph_compiler: MmiTrainingGraphCompiler,
        use_pruned_intersect: bool = False,
        den_scale: float = 1.0,
        beam_size: float = 8.0,
    ):
        super().__init__()
        self.graph_compiler = graph_compiler
        self.den_scale = den_scale
        self.use_pruned_intersect = use_pruned_intersect
        self.beam_size = beam_size

    def forward(
        self,
        dense_fsa_vec: k2.DenseFsaVec,
        texts: List[str],
    ) -> torch.Tensor:
        """
        Args:
          dense_fsa_vec:
            It contains the neural network output.
          texts:
            A list of strings. Each string contains space(s) separated words.
        Returns:
          Return a scalar loss. It is the sum over utterances in a batch,
          without normalization.
        """
        if self.use_pruned_intersect:
            func = _compute_mmi_loss_pruned
        else:
            func = _compute_mmi_loss_exact_non_optimized
            #  func = _compute_mmi_loss_exact_optimized

        return func(
            dense_fsa_vec=dense_fsa_vec,
            texts=texts,
            graph_compiler=self.graph_compiler,
            den_scale=self.den_scale,
            beam_size=self.beam_size,
        )
