# Copyright      2021  Piotr Żelasko
#
# 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 argparse
import inspect
import logging
from functools import lru_cache
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Union

import torch
from lhotse import (
    CutSet,
    Fbank,
    FbankConfig,
    load_manifest,
    load_manifest_lazy,
    validate,
)
from lhotse.dataset import (
    CutConcatenate,
    CutMix,
    DynamicBucketingSampler,
    K2SpeechRecognitionDataset,
    SingleCutSampler,
    SpecAugment,
)
from lhotse.dataset.input_strategies import (
    BatchIO,
    OnTheFlyFeatures,
    PrecomputedFeatures,
)
from lhotse.utils import fix_random_seed, ifnone
from text_normalization import replace_full_width_symbol, simple_normalization
from torch.utils.data.dataloader import DataLoader, default_collate

from icefall.utils import str2bool


class LibriHeavyASRDataset(torch.utils.data.Dataset):
    """This is a dataset for LibriHeavy dataset"""

    def __init__(
        self,
        return_cuts: bool = False,
        cut_transforms: List[Callable[[CutSet], CutSet]] = None,
        input_transforms: List[Callable[[torch.Tensor], torch.Tensor]] = None,
        input_strategy: BatchIO = PrecomputedFeatures(),
        text_sampling_func: Optional[Callable[[List[str]], str]] = None,
    ):
        """
        Icefall ASR IterableDataset constructor. See https://github.com/lhotse-speech/lhotse/blob/master/lhotse/dataset/speech_recognition.py
        for more details.

        :param return_cuts: When ``True``, will additionally return a "cut" field in each batch with the Cut
            objects used to create that batch.
        :param cut_transforms: A list of transforms to be applied on each sampled batch,
            before converting cuts to an input representation (audio/features).
            Examples: cut concatenation, noise cuts mixing, etc.
        :param input_transforms: A list of transforms to be applied on each sampled batch,
            after the cuts are converted to audio/features.
            Examples: normalization, SpecAugment, etc.
        :param input_strategy: Converts cuts into a collated batch of audio/features.
            By default, reads pre-computed features from disk.
        :param text_sampling_func: Sampling a text as transcription from a list of texts.
        """
        super().__init__()
        # Initialize the fields
        self.return_cuts = return_cuts
        self.cut_transforms = ifnone(cut_transforms, [])
        self.input_transforms = ifnone(input_transforms, [])
        self.input_strategy = input_strategy

        # a text selection function
        self.text_sampling_func = text_sampling_func

    def __getitem__(self, cuts: CutSet) -> Dict[str, Union[torch.Tensor, List[str]]]:
        """
        Return a new batch, with the batch size automatically determined using the constraints
        of max_frames and max_cuts.
        """
        validate_for_asr(cuts)

        # Sort the cuts by duration so that the first one determines the batch time dimensions.
        cuts = cuts.sort_by_duration(ascending=False)

        # Optional CutSet transforms - e.g. padding, or speed perturbation that adjusts
        # the supervision boundaries.
        for tnfm in self.cut_transforms:
            cuts = tnfm(cuts)

        # Sort the cuts again after transforms
        cuts = cuts.sort_by_duration(ascending=False)

        # Get a tensor with batched feature matrices, shape (B, T, F)
        # Collation performs auto-padding, if necessary.
        input_tpl = self.input_strategy(cuts)
        if len(input_tpl) == 3:
            # An input strategy with fault tolerant audio reading mode.
            # "cuts" may be a subset of the original "cuts" variable,
            # that only has cuts for which we succesfully read the audio.
            inputs, _, cuts = input_tpl
        else:
            inputs, _ = input_tpl

        # Get a dict of tensors that encode the positional information about supervisions
        # in the batch of feature matrices. The tensors are named "sequence_idx",
        # "start_frame/sample" and "num_frames/samples".
        supervision_intervals = self.input_strategy.supervision_intervals(cuts)

        # Apply all available transforms on the inputs, i.e. either audio or features.
        # This could be feature extraction, global MVN, SpecAugment, etc.
        segments = torch.stack(list(supervision_intervals.values()), dim=1)
        for tnfm in self.input_transforms:
            inputs = tnfm(inputs, supervision_segments=segments)

        batch = {
            "inputs": inputs,
            "supervisions": default_collate(
                [
                    simple_normalization(
                        self.text_sampling_func(texts=supervision.texts)
                    )
                    if self.text_sampling_func is not None
                    else {
                        "text": simple_normalization(supervision.texts[0]),
                    }
                    for sequence_idx, cut in enumerate(cuts)
                    for supervision in cut.supervisions
                ]
            ),
        }
        # Update the 'supervisions' field with sequence_idx and start/num frames/samples
        batch["supervisions"].update(supervision_intervals)
        if self.return_cuts:
            batch["supervisions"]["cut"] = [
                cut for cut in cuts for sup in cut.supervisions
            ]

        has_word_alignments = all(
            s.alignment is not None and "word" in s.alignment
            for c in cuts
            for s in c.supervisions
        )

        return batch


class _SeedWorkers:
    def __init__(self, seed: int):
        self.seed = seed

    def __call__(self, worker_id: int):
        fix_random_seed(self.seed + worker_id)


class LibriHeavyAsrDataModule:
    """
    DataModule for k2 ASR experiments.
    It assumes there is always one train and valid dataloader,
    but there can be multiple test dataloaders (e.g. LibriSpeech test-clean
    and test-other).

    It contains all the common data pipeline modules used in ASR
    experiments, e.g.:
    - dynamic batch size,
    - bucketing samplers,
    - cut concatenation,
    - augmentation,
    - on-the-fly feature extraction

    This class should be derived for specific corpora used in ASR tasks.
    """

    def __init__(self, args: argparse.Namespace):
        self.args = args

    @classmethod
    def add_arguments(cls, parser: argparse.ArgumentParser):
        group = parser.add_argument_group(
            title="ASR data related options",
            description="These options are used for the preparation of "
            "PyTorch DataLoaders from Lhotse CutSet's -- they control the "
            "effective batch sizes, sampling strategies, applied data "
            "augmentations, etc.",
        )
        group.add_argument(
            "--manifest-dir",
            type=Path,
            default=Path("data/fbank"),
            help="Path to directory with train/valid/test cuts.",
        )
        group.add_argument(
            "--max-duration",
            type=int,
            default=200.0,
            help="Maximum pooled recordings duration (seconds) in a "
            "single batch. You can reduce it if it causes CUDA OOM.",
        )
        group.add_argument(
            "--bucketing-sampler",
            type=str2bool,
            default=True,
            help="When enabled, the batches will come from buckets of "
            "similar duration (saves padding frames).",
        )
        group.add_argument(
            "--num-buckets",
            type=int,
            default=30,
            help="The number of buckets for the DynamicBucketingSampler"
            "(you might want to increase it for larger datasets).",
        )
        group.add_argument(
            "--concatenate-cuts",
            type=str2bool,
            default=False,
            help="When enabled, utterances (cuts) will be concatenated "
            "to minimize the amount of padding.",
        )
        group.add_argument(
            "--duration-factor",
            type=float,
            default=1.0,
            help="Determines the maximum duration of a concatenated cut "
            "relative to the duration of the longest cut in a batch.",
        )
        group.add_argument(
            "--gap",
            type=float,
            default=1.0,
            help="The amount of padding (in seconds) inserted between "
            "concatenated cuts. This padding is filled with noise when "
            "noise augmentation is used.",
        )
        group.add_argument(
            "--on-the-fly-feats",
            type=str2bool,
            default=False,
            help="When enabled, use on-the-fly cut mixing and feature "
            "extraction. Will drop existing precomputed feature manifests "
            "if available.",
        )
        group.add_argument(
            "--shuffle",
            type=str2bool,
            default=True,
            help="When enabled (=default), the examples will be "
            "shuffled for each epoch.",
        )
        group.add_argument(
            "--return-cuts",
            type=str2bool,
            default=True,
            help="When enabled, each batch will have the "
            "field: batch['supervisions']['cut'] with the cuts that "
            "were used to construct it.",
        )

        group.add_argument(
            "--num-workers",
            type=int,
            default=2,
            help="The number of training dataloader workers that "
            "collect the batches.",
        )

        group.add_argument(
            "--enable-spec-aug",
            type=str2bool,
            default=True,
            help="When enabled, use SpecAugment for training dataset.",
        )

        group.add_argument(
            "--spec-aug-time-warp-factor",
            type=int,
            default=80,
            help="Used only when --enable-spec-aug is True. "
            "It specifies the factor for time warping in SpecAugment. "
            "Larger values mean more warping. "
            "A value less than 1 means to disable time warp.",
        )

        group.add_argument(
            "--enable-musan",
            type=str2bool,
            default=True,
            help="When enabled, select noise from MUSAN and mix it "
            "with training dataset. ",
        )

        # Libriheavy specific arguments
        group.add_argument(
            "--subset",
            type=str,
            default="small",
            help="Select the Libriheavy subset (small|medium|large)",
        )

    def train_dataloaders(
        self,
        cuts_train: CutSet,
        sampler_state_dict: Optional[Dict[str, Any]] = None,
        text_sampling_func: Optional[Callable[[List[str]], str]] = None,
    ) -> DataLoader:
        """
        Args:
          cuts_train:
            CutSet for training.
          sampler_state_dict:
            The state dict for the training sampler.
        """

        transforms = []
        if self.args.enable_musan:
            logging.info("Enable MUSAN")
            logging.info("About to get Musan cuts")
            cuts_musan = load_manifest(self.args.manifest_dir / "musan_cuts.jsonl.gz")
            transforms.append(
                CutMix(cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True)
            )
        else:
            logging.info("Disable MUSAN")

        if self.args.concatenate_cuts:
            logging.info(
                f"Using cut concatenation with duration factor "
                f"{self.args.duration_factor} and gap {self.args.gap}."
            )
            # Cut concatenation should be the first transform in the list,
            # so that if we e.g. mix noise in, it will fill the gaps between
            # different utterances.
            transforms = [
                CutConcatenate(
                    duration_factor=self.args.duration_factor, gap=self.args.gap
                )
            ] + transforms

        input_transforms = []
        if self.args.enable_spec_aug:
            logging.info("Enable SpecAugment")
            logging.info(f"Time warp factor: {self.args.spec_aug_time_warp_factor}")
            # Set the value of num_frame_masks according to Lhotse's version.
            # In different Lhotse's versions, the default of num_frame_masks is
            # different.
            num_frame_masks = 10
            num_frame_masks_parameter = inspect.signature(
                SpecAugment.__init__
            ).parameters["num_frame_masks"]
            if num_frame_masks_parameter.default == 1:
                num_frame_masks = 2
            logging.info(f"Num frame mask: {num_frame_masks}")
            input_transforms.append(
                SpecAugment(
                    time_warp_factor=self.args.spec_aug_time_warp_factor,
                    num_frame_masks=num_frame_masks,
                    features_mask_size=27,
                    num_feature_masks=2,
                    frames_mask_size=100,
                )
            )
        else:
            logging.info("Disable SpecAugment")

        logging.info("About to create train dataset")
        train = LibriHeavyASRDataset(
            cut_transforms=transforms,
            input_transforms=input_transforms,
            return_cuts=self.args.return_cuts,
            text_sampling_func=text_sampling_func,
        )

        if self.args.on_the_fly_feats:
            # NOTE: the PerturbSpeed transform should be added only if we
            # remove it from data prep stage.
            # Add on-the-fly speed perturbation; since originally it would
            # have increased epoch size by 3, we will apply prob 2/3 and use
            # 3x more epochs.
            # Speed perturbation probably should come first before
            # concatenation, but in principle the transforms order doesn't have
            # to be strict (e.g. could be randomized)
            # transforms = [PerturbSpeed(factors=[0.9, 1.1], p=2/3)] + transforms   # noqa
            # Drop feats to be on the safe side.
            train = LibriHeavyASRDataset(
                cut_transforms=transforms,
                input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
                input_transforms=input_transforms,
                return_cuts=self.args.return_cuts,
                text_sampling_func=text_sampling_func,
            )

        if self.args.bucketing_sampler:
            logging.info("Using DynamicBucketingSampler.")
            train_sampler = DynamicBucketingSampler(
                cuts_train,
                max_duration=self.args.max_duration,
                shuffle=self.args.shuffle,
                num_buckets=self.args.num_buckets,
                drop_last=True,
            )
        else:
            logging.info("Using SingleCutSampler.")
            train_sampler = SingleCutSampler(
                cuts_train,
                max_duration=self.args.max_duration,
                shuffle=self.args.shuffle,
            )
        logging.info("About to create train dataloader")

        if sampler_state_dict is not None:
            logging.info("Loading sampler state dict")
            train_sampler.load_state_dict(sampler_state_dict)

        # 'seed' is derived from the current random state, which will have
        # previously been set in the main process.
        seed = torch.randint(0, 100000, ()).item()
        worker_init_fn = _SeedWorkers(seed)

        train_dl = DataLoader(
            train,
            sampler=train_sampler,
            batch_size=None,
            num_workers=self.args.num_workers,
            persistent_workers=False,
            worker_init_fn=worker_init_fn,
        )

        return train_dl

    def valid_dataloaders(
        self,
        cuts_valid: CutSet,
        text_sampling_func: Optional[Callable[[List[str]], str]] = None,
    ) -> DataLoader:
        transforms = []
        if self.args.concatenate_cuts:
            transforms = [
                CutConcatenate(
                    duration_factor=self.args.duration_factor, gap=self.args.gap
                )
            ] + transforms

        logging.info("About to create dev dataset")
        if self.args.on_the_fly_feats:
            validate = LibriHeavyASRDataset(
                cut_transforms=transforms,
                input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
                return_cuts=self.args.return_cuts,
                text_sampling_func=text_sampling_func,
            )
        else:
            validate = LibriHeavyASRDataset(
                cut_transforms=transforms,
                return_cuts=self.args.return_cuts,
                text_sampling_func=text_sampling_func,
            )
        valid_sampler = DynamicBucketingSampler(
            cuts_valid,
            max_duration=self.args.max_duration,
            shuffle=False,
        )
        logging.info("About to create dev dataloader")
        valid_dl = DataLoader(
            validate,
            sampler=valid_sampler,
            batch_size=None,
            num_workers=2,
            persistent_workers=False,
        )

        return valid_dl

    def test_dataloaders(self, cuts: CutSet) -> DataLoader:
        logging.debug("About to create test dataset")
        test = LibriHeavyASRDataset(
            input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
            if self.args.on_the_fly_feats
            else PrecomputedFeatures(),
            return_cuts=self.args.return_cuts,
        )
        sampler = DynamicBucketingSampler(
            cuts,
            max_duration=self.args.max_duration,
            shuffle=False,
        )
        logging.debug("About to create test dataloader")
        test_dl = DataLoader(
            test,
            batch_size=None,
            sampler=sampler,
            num_workers=self.args.num_workers,
        )
        return test_dl

    @lru_cache()
    def train_cuts(self) -> CutSet:
        logging.info(f"About to get {self.args.subset} cuts")

        path = self.args.manifest_dir / "libriheavy_cuts_small.jsonl.gz"
        cuts_train = CutSet.from_jsonl_lazy(path)
        if self.args.subset == "medium":
            logging.info("Getting medium subset")
            path = self.args.manifest_dir / "libriheavy_cuts_medium.jsonl.gz"
            cuts_train += CutSet.from_jsonl_lazy(path)
        elif self.args.subset == "large":
            logging.info("Getting large subset")
            path = self.args.manifest_dir / "libriheavy_cuts_medium.jsonl.gz"
            cuts_train += CutSet.from_jsonl_lazy(path)
            path = self.args.manifest_dir / "libriheavy_cuts_large.jsonl.gz"
            cuts_train += CutSet.from_jsonl_lazy(path)

        return cuts_train

    def dev_cuts(self) -> CutSet:
        logging.info("About to get dev cuts")
        cuts = load_manifest_lazy(
            self.args.manifest_dir / "libriheavy_cuts_dev.jsonl.gz"
        )
        return cuts

    @lru_cache()
    def test_clean_cuts(self) -> CutSet:
        logging.info("About to get test-clean cuts")
        cuts_valid = load_manifest_lazy(
            self.args.manifest_dir / "libriheavy_cuts_test-clean.jsonl.gz"
        )
        return cuts_valid

    @lru_cache()
    def test_other_cuts(self) -> CutSet:
        logging.info("About to get test-other cuts")
        cuts_valid = load_manifest_lazy(
            self.args.manifest_dir / "libriheavy_cuts_test-other.jsonl.gz"
        )
        return cuts_valid

    @lru_cache()
    def librispeech_test_clean_cuts(self) -> CutSet:
        logging.info("About to get test-clean cuts")
        return load_manifest_lazy(
            self.args.manifest_dir / "librispeech_cuts_test-clean.jsonl.gz"
        )

    @lru_cache()
    def librispeech_test_other_cuts(self) -> CutSet:
        logging.info("About to get test-other cuts")
        return load_manifest_lazy(
            self.args.manifest_dir / "librispeech_cuts_test-other.jsonl.gz"
        )


def validate_for_asr(cuts: CutSet) -> None:
    validate(cuts)
    tol = 2e-3  # 1ms
    for cut in cuts:
        for supervision in cut.supervisions:
            assert supervision.start >= -tol, (
                f"Supervisions starting before the cut are not supported for ASR"
                f" (sup id: {supervision.id}, cut id: {cut.id})"
            )

            # Supervision start time is relative to Cut ...
            # https://lhotse.readthedocs.io/en/v0.10_e/cuts.html
            #
            # 'supervision.end' is end of supervision inside the Cut
            assert supervision.end <= cut.duration + tol, (
                f"Supervisions ending after the cut "
                f"are not supported for ASR"
                f" (sup id: {supervision.id}, cut id: {cut.id})"
            )