# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
#
# 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 os
from itertools import groupby

import soundfile as sf
import torch
from nemo.collections.asr.parts.utils.manifest_utils import get_ctm_line
from utils.constants import BLANK_TOKEN, SPACE_TOKEN
from utils.data_prep import Segment, Word


def make_ctm_files(
    utt_obj,
    output_dir_root,
    ctm_file_config,
    model,
    log_probs,
    output_timestep_duration,
):
    """
    Function to save CTM files for all the utterances in the incoming batch.
    """

    # don't try to make files if utt_obj.segments_and_tokens is empty, which will happen
    # in the case of the ground truth text being empty or the number of tokens being too large vs audio duration
    if not utt_obj.segments_and_tokens:
        return utt_obj

    # get audio file duration if we will need it later
    if ctm_file_config.minimum_timestamp_duration > 0:
        with sf.SoundFile(utt_obj.audio_filepath) as f:
            audio_file_duration = f.frames / f.samplerate
    else:
        audio_file_duration = None

    utt_obj = make_ctm(
        "tokens",
        utt_obj,
        output_dir_root,
        audio_file_duration,
        ctm_file_config,
    )
    utt_obj = make_ctm(
        "words",
        utt_obj,
        output_dir_root,
        audio_file_duration,
        ctm_file_config,
    )
    utt_obj = make_ctm(
        "segments",
        utt_obj,
        output_dir_root,
        audio_file_duration,
        ctm_file_config,
        model,
        log_probs,
        output_timestep_duration,
    )

    return utt_obj


def get_pred_text(
    start_timestamp, end_timestamp, model, log_probs, output_timestep_duration
):
    start_log_prob = round(start_timestamp / output_timestep_duration)
    end_log_prob = round(end_timestamp / output_timestep_duration)
    cur_log_probs = log_probs[start_log_prob:end_log_prob, :]
    cur_token_ids = torch.argmax(cur_log_probs, axis=1).tolist()
    collapsed_token_ids = [k for k, g in groupby(cur_token_ids)]

    text_chars = []
    for token_id in collapsed_token_ids:
        if token_id != len(model.tokenizer.vocab):
            text_chars.append(model.tokenizer.vocab[token_id])

    text = "".join(text_chars)

    while "▁▁" in text:
        text = text.replace("▁▁", "▁")

    while "__" in text:
        text = text.replace("__", "_")

    text = text.replace("▁", "<space>")

    return text


def make_ctm(
    alignment_level,
    utt_obj,
    output_dir_root,
    audio_file_duration,
    ctm_file_config,
    model=None,
    log_probs=None,
    output_timestep_duration=None,
):
    output_dir = os.path.join(output_dir_root, "ctm", alignment_level)
    os.makedirs(output_dir, exist_ok=True)

    boundary_info_utt = []
    for segment_or_token in utt_obj.segments_and_tokens:
        if type(segment_or_token) is Segment:
            segment = segment_or_token
            if alignment_level == "segments":
                segment.pred_text = get_pred_text(
                    segment.t_start,
                    segment.t_end,
                    model,
                    log_probs,
                    output_timestep_duration,
                )
                boundary_info_utt.append(segment)

            for word_or_token in segment.words_and_tokens:
                if type(word_or_token) is Word:
                    word = word_or_token
                    if alignment_level == "words":
                        boundary_info_utt.append(word)

                    for token in word.tokens:
                        if alignment_level == "tokens":
                            boundary_info_utt.append(token)

                else:
                    token = word_or_token
                    if alignment_level == "tokens":
                        boundary_info_utt.append(token)

        else:
            token = segment_or_token
            if alignment_level == "tokens":
                boundary_info_utt.append(token)

    with open(os.path.join(output_dir, f"{utt_obj.utt_id}.ctm"), "w") as f_ctm:
        for boundary_info_ in boundary_info_utt:  # loop over every token/word/segment
            # skip if t_start = t_end = negative number because we used it as a marker to skip some blank tokens
            if not (boundary_info_.t_start < 0 or boundary_info_.t_end < 0):
                text = boundary_info_.text
                start_time = boundary_info_.t_start
                end_time = boundary_info_.t_end

                if (
                    ctm_file_config.minimum_timestamp_duration > 0
                    and ctm_file_config.minimum_timestamp_duration
                    > end_time - start_time
                ):
                    # make the predicted duration of the token/word/segment longer, growing it outwards equal
                    # amounts from the predicted center of the token/word/segment
                    token_mid_point = (start_time + end_time) / 2
                    start_time = max(
                        token_mid_point
                        - ctm_file_config.minimum_timestamp_duration / 2,
                        0.0,
                    )
                    end_time = min(
                        token_mid_point
                        + ctm_file_config.minimum_timestamp_duration / 2,
                        audio_file_duration,
                    )

                if not (
                    text == BLANK_TOKEN and ctm_file_config.remove_blank_tokens
                ):  # don't save blanks if we don't want to
                    # replace any spaces with <space> so we dont introduce extra space characters to our CTM files
                    text = text.replace(" ", SPACE_TOKEN)

                    ctm_line = get_ctm_line(
                        source=utt_obj.utt_id,
                        channel=1,
                        start_time=start_time,
                        duration=end_time - start_time,
                        token=text,
                        conf=None,
                        type_of_token="lex",
                        speaker=None,
                    ).strip()

                    if alignment_level == "segments":
                        ctm_line += f" {boundary_info_.pred_text}"

                    ctm_line += "\n"

                    f_ctm.write(ctm_line)

    utt_obj.saved_output_files[f"{alignment_level}_level_ctm_filepath"] = os.path.join(
        output_dir, f"{utt_obj.utt_id}.ctm"
    )

    return utt_obj