import sys
import numpy as np
import logging

logger = logging.getLogger(__name__)

class HypothesisBuffer:

    def __init__(self, logfile=sys.stderr):
        self.commited_in_buffer = []
        self.buffer = []
        self.new = []

        self.last_commited_time = 0
        self.last_commited_word = None

        self.logfile = logfile

    def insert(self, new, offset):
        # compare self.commited_in_buffer and new. It inserts only the words in new that extend the commited_in_buffer, it means they are roughly behind last_commited_time and new in content
        # the new tail is added to self.new

        new = [(a + offset, b + offset, t) for a, b, t in new]
        self.new = [(a, b, t) for a, b, t in new if a > self.last_commited_time - 0.1]

        if len(self.new) >= 1:
            a, b, t = self.new[0]
            if abs(a - self.last_commited_time) < 1:
                if self.commited_in_buffer:
                    # it's going to search for 1, 2, ..., 5 consecutive words (n-grams) that are identical in commited and new. If they are, they're dropped.
                    cn = len(self.commited_in_buffer)
                    nn = len(self.new)
                    for i in range(1, min(min(cn, nn), 5) + 1):  # 5 is the maximum
                        c = " ".join(
                            [self.commited_in_buffer[-j][2] for j in range(1, i + 1)][
                                ::-1
                            ]
                        )
                        tail = " ".join(self.new[j - 1][2] for j in range(1, i + 1))
                        if c == tail:
                            words = []
                            for j in range(i):
                                words.append(repr(self.new.pop(0)))
                            words_msg = " ".join(words)
                            logger.debug(f"removing last {i} words: {words_msg}")
                            break

    def flush(self):
        # returns commited chunk = the longest common prefix of 2 last inserts.

        commit = []
        while self.new:
            na, nb, nt = self.new[0]

            if len(self.buffer) == 0:
                break

            if nt == self.buffer[0][2]:
                commit.append((na, nb, nt))
                self.last_commited_word = nt
                self.last_commited_time = nb
                self.buffer.pop(0)
                self.new.pop(0)
            else:
                break
        self.buffer = self.new
        self.new = []
        self.commited_in_buffer.extend(commit)
        return commit

    def pop_commited(self, time):
        while self.commited_in_buffer and self.commited_in_buffer[0][1] <= time:
            self.commited_in_buffer.pop(0)

    def complete(self):
        return self.buffer


class OnlineASRProcessor:

    SAMPLING_RATE = 16000

    def __init__(
        self,
        asr,
        tokenize_method=None,
        buffer_trimming=("segment", 15),
        logfile=sys.stderr,
    ):
        """
        Initialize OnlineASRProcessor.

        Args:
            asr: WhisperASR object
            tokenize_method: Sentence tokenizer function for the target language.
            Must be a function that takes a list of text as input like MosesSentenceSplitter.
            Can be None if using "segment" buffer trimming option.
            buffer_trimming: Tuple of (option, seconds) where:
            - option: Either "sentence" or "segment"
            - seconds: Number of seconds threshold for buffer trimming
            Default is ("segment", 15)
            logfile: File to store logs

        """
        self.asr = asr
        self.tokenize = tokenize_method
        self.logfile = logfile

        self.init()

        self.buffer_trimming_way, self.buffer_trimming_sec = buffer_trimming

        if self.buffer_trimming_way not in ["sentence", "segment"]:
            raise ValueError("buffer_trimming must be either 'sentence' or 'segment'")
        if self.buffer_trimming_sec <= 0:
            raise ValueError("buffer_trimming_sec must be positive")
        elif self.buffer_trimming_sec > 30:
            logger.warning(
                f"buffer_trimming_sec is set to {self.buffer_trimming_sec}, which is very long. It may cause OOM."
            )

    def init(self, offset=None):
        """run this when starting or restarting processing"""
        self.audio_buffer = np.array([], dtype=np.float32)
        self.transcript_buffer = HypothesisBuffer(logfile=self.logfile)
        self.buffer_time_offset = 0
        if offset is not None:
            self.buffer_time_offset = offset
        self.transcript_buffer.last_commited_time = self.buffer_time_offset
        self.commited = []

    def insert_audio_chunk(self, audio):
        self.audio_buffer = np.append(self.audio_buffer, audio)

    def prompt(self):
        """Returns a tuple: (prompt, context), where "prompt" is a 200-character suffix of commited text that is inside of the scrolled away part of audio buffer.
        "context" is the commited text that is inside the audio buffer. It is transcribed again and skipped. It is returned only for debugging and logging reasons.
        """
        k = max(0, len(self.commited) - 1)
        while k > 0 and self.commited[k - 1][1] > self.buffer_time_offset:
            k -= 1

        p = self.commited[:k]
        p = [t for _, _, t in p]
        prompt = []
        l = 0
        while p and l < 200:  # 200 characters prompt size
            x = p.pop(-1)
            l += len(x) + 1
            prompt.append(x)
        non_prompt = self.commited[k:]
        return self.asr.sep.join(prompt[::-1]), self.asr.sep.join(
            t for _, _, t in non_prompt
        )

    def process_iter(self):
        """Runs on the current audio buffer.
        Returns: a tuple (beg_timestamp, end_timestamp, "text"), or (None, None, "").
        The non-emty text is confirmed (committed) partial transcript.
        """

        prompt, non_prompt = self.prompt()
        logger.debug(f"PROMPT(previous): {prompt}")
        logger.debug(f"CONTEXT: {non_prompt}")
        logger.debug(
            f"transcribing {len(self.audio_buffer)/self.SAMPLING_RATE:2.2f} seconds from {self.buffer_time_offset:2.2f}"
        )
        res = self.asr.transcribe(self.audio_buffer, init_prompt=prompt)

        # transform to [(beg,end,"word1"), ...]
        tsw = self.asr.ts_words(res)

        self.transcript_buffer.insert(tsw, self.buffer_time_offset)
        o = self.transcript_buffer.flush()
        self.commited.extend(o)
        completed = self.to_flush(o)
        logger.debug(f">>>>COMPLETE NOW: {completed[2]}")
        the_rest = self.to_flush(self.transcript_buffer.complete())
        logger.debug(f"INCOMPLETE: {the_rest[2]}")

        # there is a newly confirmed text

        if self.buffer_trimming_way == "sentence":

            self.chunk_completed_sentence()
                


            

        if len(self.audio_buffer) / self.SAMPLING_RATE > self.buffer_trimming_sec :
                
            if self.buffer_trimming_way == "sentence":
                logger.warning(f"Chunck segment after {self.buffer_trimming_sec} seconds!"
                                " Even if no sentence was found!"
                             )
            
            
            self.chunk_completed_segment(res)
       

                # alternative: on any word
                # l = self.buffer_time_offset + len(self.audio_buffer)/self.SAMPLING_RATE - 10
                # let's find commited word that is less
                # k = len(self.commited)-1
                # while k>0 and self.commited[k][1] > l:
                #    k -= 1
                # t = self.commited[k][1]
                # self.chunk_at(t)

        






        logger.debug(
            f"len of buffer now: {len(self.audio_buffer)/self.SAMPLING_RATE:2.2f}"
        )
        return self.to_flush(o)

    def chunk_completed_sentence(self):
        if self.commited == []:
            return

        sents = self.words_to_sentences(self.commited)



        if len(sents) < 2:
            logger.debug(f"[Sentence-segmentation] no sentence segmented.")
            return
        


        identified_sentence= "\n    - ".join([f"{s[0]*1000:.0f}-{s[1]*1000:.0f} {s[2]}" for s in sents])
        logger.debug(f"[Sentence-segmentation] identified sentences:\n    - {identified_sentence}")
       

        # we will continue with audio processing at this timestamp
        chunk_at = sents[-2][1]

        logger.debug(f"[Sentence-segmentation]: sentence will be chunked at {chunk_at:2.2f}")
        self.chunk_at(chunk_at)

    def chunk_completed_segment(self, res):
        if self.commited == []:
            return

        ends = self.asr.segments_end_ts(res)

        t = self.commited[-1][1]

        if len(ends) > 1:

            e = ends[-2] + self.buffer_time_offset
            while len(ends) > 2 and e > t:
                ends.pop(-1)
                e = ends[-2] + self.buffer_time_offset
            if e <= t:
                logger.debug(f"--- segment chunked at {e:2.2f}")
                self.chunk_at(e)
            else:
                logger.debug(f"--- last segment not within commited area")
        else:
            logger.debug(f"--- not enough segments to chunk")

    def chunk_at(self, time):
        """trims the hypothesis and audio buffer at "time" """
        self.transcript_buffer.pop_commited(time)
        cut_seconds = time - self.buffer_time_offset
        self.audio_buffer = self.audio_buffer[int(cut_seconds * self.SAMPLING_RATE) :]
        self.buffer_time_offset = time

    def words_to_sentences(self, words):
        """Uses self.tokenize for sentence segmentation of words.
        Returns: [(beg,end,"sentence 1"),...]
        """

        cwords = [w for w in words]
        t = self.asr.sep.join(o[2] for o in cwords)
        logger.debug(f"[Sentence-segmentation] Raw Text: {t}")
        s = self.tokenize([t])
        out = []
        while s:
            beg = None
            end = None
            sent = s.pop(0).strip()
            fsent = sent
            while cwords:
                b, e, w = cwords.pop(0)
                w = w.strip()
                if beg is None and sent.startswith(w):
                    beg = b
                elif end is None and sent == w:
                    end = e
                    out.append((beg, end, fsent))
                    break
                sent = sent[len(w) :].strip()
        return out

    def finish(self):
        """Flush the incomplete text when the whole processing ends.
        Returns: the same format as self.process_iter()
        """
        o = self.transcript_buffer.complete()
        f = self.to_flush(o)
        logger.debug(f"last, noncommited: {f[0]*1000:.0f}-{f[1]*1000:.0f}: {f[2]}")
        self.buffer_time_offset += len(self.audio_buffer) / 16000
        return f

    def to_flush(
        self,
        sents,
        sep=None,
        offset=0,
    ):
        # concatenates the timestamped words or sentences into one sequence that is flushed in one line
        # sents: [(beg1, end1, "sentence1"), ...] or [] if empty
        # return: (beg1,end-of-last-sentence,"concatenation of sentences") or (None, None, "") if empty
        if sep is None:
            sep = self.asr.sep
        t = sep.join(s[2] for s in sents)
        if len(sents) == 0:
            b = None
            e = None
        else:
            b = offset + sents[0][0]
            e = offset + sents[-1][1]
        return (b, e, t)


class VACOnlineASRProcessor(OnlineASRProcessor):
    """Wraps OnlineASRProcessor with VAC (Voice Activity Controller).

    It works the same way as OnlineASRProcessor: it receives chunks of audio (e.g. 0.04 seconds),
    it runs VAD and continuously detects whether there is speech or not.
    When it detects end of speech (non-voice for 500ms), it makes OnlineASRProcessor to end the utterance immediately.
    """

    def __init__(self, online_chunk_size, *a, **kw):
        self.online_chunk_size = online_chunk_size

        self.online = OnlineASRProcessor(*a, **kw)

        # VAC:
        import torch

        model, _ = torch.hub.load(repo_or_dir="snakers4/silero-vad", model="silero_vad")
        from silero_vad_iterator import FixedVADIterator

        self.vac = FixedVADIterator(
            model
        )  # we use the default options there: 500ms silence, 100ms padding, etc.

        self.logfile = self.online.logfile
        self.init()

    def init(self):
        self.online.init()
        self.vac.reset_states()
        self.current_online_chunk_buffer_size = 0

        self.is_currently_final = False

        self.status = None  # or "voice" or "nonvoice"
        self.audio_buffer = np.array([], dtype=np.float32)
        self.buffer_offset = 0  # in frames

    def clear_buffer(self):
        self.buffer_offset += len(self.audio_buffer)
        self.audio_buffer = np.array([], dtype=np.float32)

    def insert_audio_chunk(self, audio):
        res = self.vac(audio)
        self.audio_buffer = np.append(self.audio_buffer, audio)

        if res is not None:
            frame = list(res.values())[0] - self.buffer_offset
            if "start" in res and "end" not in res:
                self.status = "voice"
                send_audio = self.audio_buffer[frame:]
                self.online.init(
                    offset=(frame + self.buffer_offset) / self.SAMPLING_RATE
                )
                self.online.insert_audio_chunk(send_audio)
                self.current_online_chunk_buffer_size += len(send_audio)
                self.clear_buffer()
            elif "end" in res and "start" not in res:
                self.status = "nonvoice"
                send_audio = self.audio_buffer[:frame]
                self.online.insert_audio_chunk(send_audio)
                self.current_online_chunk_buffer_size += len(send_audio)
                self.is_currently_final = True
                self.clear_buffer()
            else:
                beg = res["start"] - self.buffer_offset
                end = res["end"] - self.buffer_offset
                self.status = "nonvoice"
                send_audio = self.audio_buffer[beg:end]
                self.online.init(offset=(beg + self.buffer_offset) / self.SAMPLING_RATE)
                self.online.insert_audio_chunk(send_audio)
                self.current_online_chunk_buffer_size += len(send_audio)
                self.is_currently_final = True
                self.clear_buffer()
        else:
            if self.status == "voice":
                self.online.insert_audio_chunk(self.audio_buffer)
                self.current_online_chunk_buffer_size += len(self.audio_buffer)
                self.clear_buffer()
            else:
                # We keep 1 second because VAD may later find start of voice in it.
                # But we trim it to prevent OOM.
                self.buffer_offset += max(
                    0, len(self.audio_buffer) - self.SAMPLING_RATE
                )
                self.audio_buffer = self.audio_buffer[-self.SAMPLING_RATE :]

    def process_iter(self):
        if self.is_currently_final:
            return self.finish()
        elif (
            self.current_online_chunk_buffer_size
            > self.SAMPLING_RATE * self.online_chunk_size
        ):
            self.current_online_chunk_buffer_size = 0
            ret = self.online.process_iter()
            return ret
        else:
            print("no online update, only VAD", self.status, file=self.logfile)
            return (None, None, "")

    def finish(self):
        ret = self.online.finish()
        self.current_online_chunk_buffer_size = 0
        self.is_currently_final = False
        return ret