import bisect
import functools
import os
import warnings

from typing import List, Optional

import numpy as np

# The code below is adapted from https://github.com/snakers4/silero-vad.

def get_assets_path():
    """Returns the path to the assets directory."""
    return os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets")


def get_speech_timestamps(
    audio: np.ndarray,
    *,
    threshold: float = 0.5,
    # min_speech_duration_ms: int = 250,
    min_speech_duration_ms: int = 800,
    max_speech_duration_s: float = float("inf"),
    # min_silence_duration_ms: int = 2000,
    min_silence_duration_ms: int = 1000,
    window_size_samples: int = 1024,
    speech_pad_ms: int = 200,
) -> List[dict]:
    """This method is used for splitting long audios into speech chunks using silero VAD.
    Args:
      audio: One dimensional float array.
      threshold: Speech threshold. Silero VAD outputs speech probabilities for each audio chunk,
        probabilities ABOVE this value are considered as SPEECH. It is better to tune this
        parameter for each dataset separately, but "lazy" 0.5 is pretty good for most datasets.
      min_speech_duration_ms: Final speech chunks shorter min_speech_duration_ms are thrown out.
      max_speech_duration_s: Maximum duration of speech chunks in seconds. Chunks longer
        than max_speech_duration_s will be split at the timestamp of the last silence that
        lasts more than 100s (if any), to prevent agressive cutting. Otherwise, they will be
        split aggressively just before max_speech_duration_s.
      min_silence_duration_ms: In the end of each speech chunk wait for min_silence_duration_ms
        before separating it
      window_size_samples: Audio chunks of window_size_samples size are fed to the silero VAD model.
        WARNING! Silero VAD models were trained using 512, 1024, 1536 samples for 16000 sample rate.
        Values other than these may affect model perfomance!!
      speech_pad_ms: Final speech chunks are padded by speech_pad_ms each side
    Returns:
      List of dicts containing begin and end samples of each speech chunk.
    """
    if window_size_samples not in [512, 1024, 1536]:
        warnings.warn(
            "Unusual window_size_samples! Supported window_size_samples:\n"
            " - [512, 1024, 1536] for 16000 sampling_rate"
        )

    sampling_rate = 16000
    min_speech_samples = sampling_rate * min_speech_duration_ms / 1000
    speech_pad_samples = sampling_rate * speech_pad_ms / 1000
    max_speech_samples = (
        sampling_rate * max_speech_duration_s
        - window_size_samples
        - 2 * speech_pad_samples
    )
    min_silence_samples = sampling_rate * min_silence_duration_ms / 1000
    min_silence_samples_at_max_speech = sampling_rate * 98 / 1000

    audio_length_samples = len(audio)

    model = get_vad_model()
    state = model.get_initial_state(batch_size=1)

    speech_probs = []
    for current_start_sample in range(0, audio_length_samples, window_size_samples):
        chunk = audio[current_start_sample : current_start_sample + window_size_samples]
        if len(chunk) < window_size_samples:
            chunk = np.pad(chunk, (0, int(window_size_samples - len(chunk))))
        speech_prob, state = model(chunk, state, sampling_rate)
        speech_probs.append(speech_prob)

    triggered = False
    speeches = []
    current_speech = {}
    neg_threshold = threshold - 0.15

    # to save potential segment end (and tolerate some silence)
    temp_end = 0
    # to save potential segment limits in case of maximum segment size reached
    prev_end = next_start = 0

    for i, speech_prob in enumerate(speech_probs):
        if (speech_prob >= threshold) and temp_end:
            temp_end = 0
            if next_start < prev_end:
                next_start = window_size_samples * i

        if (speech_prob >= threshold) and not triggered:
            triggered = True
            current_speech["start"] = window_size_samples * i
            continue

        if (
            triggered
            and (window_size_samples * i) - current_speech["start"] > max_speech_samples
        ):
            if prev_end:
                current_speech["end"] = prev_end
                speeches.append(current_speech)
                current_speech = {}
                # previously reached silence (< neg_thres) and is still not speech (< thres)
                if next_start < prev_end:
                    triggered = False
                else:
                    current_speech["start"] = next_start
                prev_end = next_start = temp_end = 0
            else:
                current_speech["end"] = window_size_samples * i
                speeches.append(current_speech)
                current_speech = {}
                prev_end = next_start = temp_end = 0
                triggered = False
                continue

        if (speech_prob < neg_threshold) and triggered:
            if not temp_end:
                temp_end = window_size_samples * i
            # condition to avoid cutting in very short silence
            if (window_size_samples * i) - temp_end > min_silence_samples_at_max_speech:
                prev_end = temp_end
            if (window_size_samples * i) - temp_end < min_silence_samples:
                continue
            else:
                current_speech["end"] = temp_end
                if (
                    current_speech["end"] - current_speech["start"]
                ) > min_speech_samples:
                    speeches.append(current_speech)
                current_speech = {}
                prev_end = next_start = temp_end = 0
                triggered = False
                continue

    if (
        current_speech
        and (audio_length_samples - current_speech["start"]) > min_speech_samples
    ):
        current_speech["end"] = audio_length_samples
        speeches.append(current_speech)

    for i, speech in enumerate(speeches):
        if i == 0:
            speech["start"] = int(max(0, speech["start"] - speech_pad_samples))
        if i != len(speeches) - 1:
            silence_duration = speeches[i + 1]["start"] - speech["end"]
            if silence_duration < 2 * speech_pad_samples:
                speech["end"] += int(silence_duration // 2)
                speeches[i + 1]["start"] = int(
                    max(0, speeches[i + 1]["start"] - silence_duration // 2)
                )
            else:
                speech["end"] = int(
                    min(audio_length_samples, speech["end"] + speech_pad_samples)
                )
                speeches[i + 1]["start"] = int(
                    max(0, speeches[i + 1]["start"] - speech_pad_samples)
                )
        else:
            speech["end"] = int(
                min(audio_length_samples, speech["end"] + speech_pad_samples)
            )

    return speeches


def collect_chunks(audio: np.ndarray, chunks: List[dict]) -> np.ndarray:
    """Collects and concatenates audio chunks."""
    if not chunks:
        return np.array([], dtype=np.float32)

    return np.concatenate([audio[chunk["start"] : chunk["end"]] for chunk in chunks])


class SpeechTimestampsMap:
    """Helper class to restore original speech timestamps."""

    def __init__(self, chunks: List[dict], sampling_rate: int, time_precision: int = 2):
        self.sampling_rate = sampling_rate
        self.time_precision = time_precision
        self.chunk_end_sample = []
        self.total_silence_before = []

        previous_end = 0
        silent_samples = 0

        for chunk in chunks:
            silent_samples += chunk["start"] - previous_end
            previous_end = chunk["end"]

            self.chunk_end_sample.append(chunk["end"] - silent_samples)
            self.total_silence_before.append(silent_samples / sampling_rate)

    def get_original_time(
        self,
        time: float,
        chunk_index: Optional[int] = None,
    ) -> float:
        if chunk_index is None:
            chunk_index = self.get_chunk_index(time)

        total_silence_before = self.total_silence_before[chunk_index]
        return round(total_silence_before + time, self.time_precision)

    def get_chunk_index(self, time: float) -> int:
        sample = int(time * self.sampling_rate)
        return min(
            bisect.bisect(self.chunk_end_sample, sample),
            len(self.chunk_end_sample) - 1,
        )


@functools.lru_cache
def get_vad_model():
    """Returns the VAD model instance."""
    path = os.path.join(get_assets_path(), "silero_vad.onnx")
    return SileroVADModel(path)


class SileroVADModel:
    def __init__(self, path):
        try:
            import onnxruntime
        except ImportError as e:
            raise RuntimeError(
                "Applying the VAD filter requires the onnxruntime package"
            ) from e

        opts = onnxruntime.SessionOptions()
        opts.inter_op_num_threads = 1
        opts.intra_op_num_threads = 1
        opts.log_severity_level = 4

        self.session = onnxruntime.InferenceSession(
            path,
            providers=["CPUExecutionProvider"],
            sess_options=opts,
        )

    def get_initial_state(self, batch_size: int):
        h = np.zeros((2, batch_size, 64), dtype=np.float32)
        c = np.zeros((2, batch_size, 64), dtype=np.float32)
        return h, c

    def __call__(self, x, state, sr: int):
        if len(x.shape) == 1:
            x = np.expand_dims(x, 0)
        if len(x.shape) > 2:
            raise ValueError(
                f"Too many dimensions for input audio chunk {len(x.shape)}"
            )
        if sr / x.shape[1] > 31.25:
            raise ValueError("Input audio chunk is too short")

        h, c = state

        ort_inputs = {
            "input": x,
            "h": h,
            "c": c,
            "sr": np.array(sr, dtype="int64"),
        }

        out, h, c = self.session.run(None, ort_inputs)
        state = (h, c)

        return out, state