{
    "python.pythonPath": "C:\\Users\\BiGCARE\\anaconda3\\envs\\sv2tts_korean\\python.exe"
}
from encoder.data_objects.speaker_verification_dataset import SpeakerVerificationDataset
from encoder.data_objects.speaker_verification_dataset import SpeakerVerificationDataLoader

import random

class RandomCycler:
    """
    Creates an internal copy of a sequence and allows access to its items in a constrained random 
    order. For a source sequence of n items and one or several consecutive queries of a total 
    of m items, the following guarantees hold (one implies the other):
        - Each item will be returned between m // n and ((m - 1) // n) + 1 times.
        - Between two appearances of the same item, there may be at most 2 * (n - 1) other items.
    """
    
    def __init__(self, source):
        if len(source) == 0:
            raise Exception("Can't create RandomCycler from an empty collection")
        self.all_items = list(source)
        self.next_items = []
    
    def sample(self, count: int):
        shuffle = lambda l: random.sample(l, len(l))
        
        out = []
        while count > 0:
            if count >= len(self.all_items):
                out.extend(shuffle(list(self.all_items)))
                count -= len(self.all_items)
                continue
            n = min(count, len(self.next_items))
            out.extend(self.next_items[:n])
            count -= n
            self.next_items = self.next_items[n:]
            if len(self.next_items) == 0:
                self.next_items = shuffle(list(self.all_items))
        return out
    
    def __next__(self):
        return self.sample(1)[0]

        import numpy as np
from typing import List
from encoder.data_objects.speaker import Speaker

class SpeakerBatch:
    def __init__(self, speakers: List[Speaker], utterances_per_speaker: int, n_frames: int):
        self.speakers = speakers
        self.partials = {s: s.random_partial(utterances_per_speaker, n_frames) for s in speakers}
        
        # Array of shape (n_speakers * n_utterances, n_frames, mel_n), e.g. for 3 speakers with
        # 4 utterances each of 160 frames of 40 mel coefficients: (12, 160, 40)
        self.data = np.array([frames for s in speakers for _, frames, _ in self.partials[s]])

from encoder.data_objects.random_cycler import RandomCycler
from encoder.data_objects.speaker_batch import SpeakerBatch
from encoder.data_objects.speaker import Speaker
from encoder.params_data import partials_n_frames
from torch.utils.data import Dataset, DataLoader
from pathlib import Path

# TODO: improve with a pool of speakers for data efficiency

class SpeakerVerificationDataset(Dataset):
    def __init__(self, datasets_root: Path):
        self.root = datasets_root
        speaker_dirs = [f for f in self.root.glob("*") if f.is_dir()]
        if len(speaker_dirs) == 0:
            raise Exception("No speakers found. Make sure you are pointing to the directory "
                            "containing all preprocessed speaker directories.")
        self.speakers = [Speaker(speaker_dir) for speaker_dir in speaker_dirs]
        self.speaker_cycler = RandomCycler(self.speakers)

    def __len__(self):
        return int(1e10)
        
    def __getitem__(self, index):
        return next(self.speaker_cycler)
    
    def get_logs(self):
        log_string = ""
        for log_fpath in self.root.glob("*.txt"):
            with log_fpath.open("r") as log_file:
                log_string += "".join(log_file.readlines())
        return log_string
    
    
class SpeakerVerificationDataLoader(DataLoader):
    def __init__(self, dataset, speakers_per_batch, utterances_per_speaker, sampler=None, 
                 batch_sampler=None, num_workers=0, pin_memory=False, timeout=0, 
                 worker_init_fn=None):
        self.utterances_per_speaker = utterances_per_speaker

        super().__init__(
            dataset=dataset, 
            batch_size=speakers_per_batch, 
            shuffle=False, 
            sampler=sampler, 
            batch_sampler=batch_sampler, 
            num_workers=num_workers,
            collate_fn=self.collate, 
            pin_memory=pin_memory, 
            drop_last=False, 
            timeout=timeout, 
            worker_init_fn=worker_init_fn
        )

    def collate(self, speakers):
        return SpeakerBatch(speakers, self.utterances_per_speaker, partials_n_frames) 

    from encoder.data_objects.random_cycler import RandomCycler
from encoder.data_objects.utterance import Utterance
from pathlib import Path

# Contains the set of utterances of a single speaker
class Speaker:
    def __init__(self, root: Path):
        self.root = root
        self.name = root.name
        self.utterances = None
        self.utterance_cycler = None
        
    def _load_utterances(self):
        with self.root.joinpath("_sources.txt").open("r") as sources_file:
            sources = [l.split(",") for l in sources_file]
        sources = {frames_fname: wave_fpath for frames_fname, wave_fpath in sources}
        self.utterances = [Utterance(self.root.joinpath(f), w) for f, w in sources.items()]
        self.utterance_cycler = RandomCycler(self.utterances)
               
    def random_partial(self, count, n_frames):
        """
        Samples a batch of <count> unique partial utterances from the disk in a way that all 
        utterances come up at least once every two cycles and in a random order every time.
        
        :param count: The number of partial utterances to sample from the set of utterances from 
        that speaker. Utterances are guaranteed not to be repeated if <count> is not larger than 
        the number of utterances available.
        :param n_frames: The number of frames in the partial utterance.
        :return: A list of tuples (utterance, frames, range) where utterance is an Utterance, 
        frames are the frames of the partial utterances and range is the range of the partial 
        utterance with regard to the complete utterance.
        """
        if self.utterances is None:
            self._load_utterances()

        utterances = self.utterance_cycler.sample(count)

        a = [(u,) + u.random_partial(n_frames) for u in utterances]

        return a