import json
import math
from collections import namedtuple
from typing import List, Tuple

import sentencepiece as spm
import torch
import torchaudio
from torchaudio.models import Hypothesis


MODEL_TYPE_LIBRISPEECH = "librispeech"
MODEL_TYPE_TEDLIUM3 = "tedlium3"
MODEL_TYPE_MUSTC = "mustc"


DECIBEL = 2 * 20 * math.log10(torch.iinfo(torch.int16).max)
GAIN = pow(10, 0.05 * DECIBEL)
spectrogram_transform = torchaudio.transforms.MelSpectrogram(sample_rate=16000, n_fft=400, n_mels=80, hop_length=160)

Batch = namedtuple("Batch", ["features", "feature_lengths", "targets", "target_lengths"])


def piecewise_linear_log(x):
    x = x * GAIN
    x[x > math.e] = torch.log(x[x > math.e])
    x[x <= math.e] = x[x <= math.e] / math.e
    return x


def batch_by_token_count(idx_target_lengths, token_limit):
    batches = []
    current_batch = []
    current_token_count = 0
    for idx, target_length in idx_target_lengths:
        if current_token_count + target_length > token_limit:
            batches.append(current_batch)
            current_batch = [idx]
            current_token_count = target_length
        else:
            current_batch.append(idx)
            current_token_count += target_length

    if current_batch:
        batches.append(current_batch)

    return batches


def post_process_hypos(
    hypos: List[Hypothesis], sp_model: spm.SentencePieceProcessor
) -> List[Tuple[str, float, List[int], List[int]]]:
    tokens_idx = 0
    score_idx = 3
    post_process_remove_list = [
        sp_model.unk_id(),
        sp_model.eos_id(),
        sp_model.pad_id(),
    ]
    filtered_hypo_tokens = [
        [token_index for token_index in h[tokens_idx][1:] if token_index not in post_process_remove_list] for h in hypos
    ]
    hypos_str = [sp_model.decode(s) for s in filtered_hypo_tokens]
    hypos_ids = [h[tokens_idx][1:] for h in hypos]
    hypos_score = [[math.exp(h[score_idx])] for h in hypos]

    nbest_batch = list(zip(hypos_str, hypos_score, hypos_ids))

    return nbest_batch


class FunctionalModule(torch.nn.Module):
    def __init__(self, functional):
        super().__init__()
        self.functional = functional

    def forward(self, input):
        return self.functional(input)


class GlobalStatsNormalization(torch.nn.Module):
    def __init__(self, global_stats_path):
        super().__init__()

        with open(global_stats_path) as f:
            blob = json.loads(f.read())

        self.mean = torch.tensor(blob["mean"])
        self.invstddev = torch.tensor(blob["invstddev"])

    def forward(self, input):
        return (input - self.mean) * self.invstddev


class WarmupLR(torch.optim.lr_scheduler._LRScheduler):
    def __init__(self, optimizer, warmup_updates, last_epoch=-1, verbose=False):
        self.warmup_updates = warmup_updates
        super().__init__(optimizer, last_epoch=last_epoch)

    def get_lr(self):
        return [(min(1.0, self._step_count / self.warmup_updates)) * base_lr for base_lr in self.base_lrs]

import json
import math
from collections import namedtuple
from typing import List, Tuple

import sentencepiece as spm
import torch
import torchaudio
from torchaudio.models import Hypothesis


MODEL_TYPE_LIBRISPEECH = "librispeech"
MODEL_TYPE_TEDLIUM3 = "tedlium3"
MODEL_TYPE_MUSTC = "mustc"


DECIBEL = 2 * 20 * math.log10(torch.iinfo(torch.int16).max)
GAIN = pow(10, 0.05 * DECIBEL)
spectrogram_transform = torchaudio.transforms.MelSpectrogram(sample_rate=16000, n_fft=400, n_mels=80, hop_length=160)

Batch = namedtuple("Batch", ["features", "feature_lengths", "targets", "target_lengths"])


def piecewise_linear_log(x):
    x = x * GAIN
    x[x > math.e] = torch.log(x[x > math.e])
    x[x <= math.e] = x[x <= math.e] / math.e
    return x


def batch_by_token_count(idx_target_lengths, token_limit):
    batches = []
    current_batch = []
    current_token_count = 0
    for idx, target_length in idx_target_lengths:
        if current_token_count + target_length > token_limit:
            batches.append(current_batch)
            current_batch = [idx]
            current_token_count = target_length
        else:
            current_batch.append(idx)
            current_token_count += target_length

    if current_batch:
        batches.append(current_batch)

    return batches


def post_process_hypos(
    hypos: List[Hypothesis], sp_model: spm.SentencePieceProcessor
) -> List[Tuple[str, float, List[int], List[int]]]:
    tokens_idx = 0
    score_idx = 3
    post_process_remove_list = [
        sp_model.unk_id(),
        sp_model.eos_id(),
        sp_model.pad_id(),
    ]
    filtered_hypo_tokens = [
        [token_index for token_index in h[tokens_idx][1:] if token_index not in post_process_remove_list] for h in hypos
    ]
    hypos_str = [sp_model.decode(s) for s in filtered_hypo_tokens]
    hypos_ids = [h[tokens_idx][1:] for h in hypos]
    hypos_score = [[math.exp(h[score_idx])] for h in hypos]

    nbest_batch = list(zip(hypos_str, hypos_score, hypos_ids))

    return nbest_batch


class FunctionalModule(torch.nn.Module):
    def __init__(self, functional):
        super().__init__()
        self.functional = functional

    def forward(self, input):
        return self.functional(input)


class GlobalStatsNormalization(torch.nn.Module):
    def __init__(self, global_stats_path):
        super().__init__()

        with open(global_stats_path) as f:
            blob = json.loads(f.read())

        self.mean = torch.tensor(blob["mean"])
        self.invstddev = torch.tensor(blob["invstddev"])

    def forward(self, input):
        return (input - self.mean) * self.invstddev


class WarmupLR(torch.optim.lr_scheduler._LRScheduler):
    def __init__(self, optimizer, warmup_updates, last_epoch=-1, verbose=False):
        self.warmup_updates = warmup_updates
        super().__init__(optimizer, last_epoch=last_epoch, verbose=verbose)

    def get_lr(self):
        return [(min(1.0, self._step_count / self.warmup_updates)) * base_lr for base_lr in self.base_lrs]
