import os, sys
import gradio as gr
import regex as re
import shutil
import datetime
import random

from core import (
    run_infer_script,
    run_batch_infer_script,
)

from assets.i18n.i18n import I18nAuto

from rvc.lib.utils import format_title

i18n = I18nAuto()

now_dir = os.getcwd()
sys.path.append(now_dir)

model_root = os.path.join(now_dir, "logs")
audio_root = os.path.join(now_dir, "assets", "audios")
custom_embedder_root = os.path.join(now_dir, "rvc", "embedders", "embedders_custom")

os.makedirs(custom_embedder_root, exist_ok=True)

custom_embedder_root_relative = os.path.relpath(custom_embedder_root, now_dir)
model_root_relative = os.path.relpath(model_root, now_dir)
audio_root_relative = os.path.relpath(audio_root, now_dir)

sup_audioext = {
    "wav",
    "mp3",
    "flac",
    "ogg",
    "opus",
    "m4a",
    "mp4",
    "aac",
    "alac",
    "wma",
    "aiff",
    "webm",
    "ac3",
}

names = [
    os.path.join(root, file)
    for root, _, files in os.walk(model_root_relative, topdown=False)
    for file in files
    if (
        file.endswith((".pth", ".onnx"))
        and not (file.startswith("G_") or file.startswith("D_"))
    )
]

indexes_list = [
    os.path.join(root, name)
    for root, _, files in os.walk(model_root_relative, topdown=False)
    for name in files
    if name.endswith(".index") and "trained" not in name
]

audio_paths = [
    os.path.join(root, name)
    for root, _, files in os.walk(audio_root_relative, topdown=False)
    for name in files
    if name.endswith(tuple(sup_audioext))
    and root == audio_root_relative
    and "_output" not in name
]

custom_embedders = [
    os.path.join(dirpath, filename)
    for dirpath, _, filenames in os.walk(custom_embedder_root_relative)
    for filename in filenames
    if filename.endswith(".pt")
]


def output_path_fn(input_audio_path):
    original_name_without_extension = os.path.basename(input_audio_path).rsplit(".", 1)[
        0
    ]
    new_name = original_name_without_extension + "_output.wav"
    output_path = os.path.join(os.path.dirname(input_audio_path), new_name)
    return output_path


def change_choices():
    names = [
        os.path.join(root, file)
        for root, _, files in os.walk(model_root_relative, topdown=False)
        for file in files
        if (
            file.endswith((".pth", ".onnx"))
            and not (file.startswith("G_") or file.startswith("D_"))
        )
    ]

    indexes_list = [
        os.path.join(root, name)
        for root, _, files in os.walk(model_root_relative, topdown=False)
        for name in files
        if name.endswith(".index") and "trained" not in name
    ]

    audio_paths = [
        os.path.join(root, name)
        for root, _, files in os.walk(audio_root_relative, topdown=False)
        for name in files
        if name.endswith(tuple(sup_audioext))
        and root == audio_root_relative
        and "_output" not in name
    ]

    custom_embedder = [
        os.path.join(dirpath, filename)
        for dirpath, _, filenames in os.walk(custom_embedder_root_relative)
        for filename in filenames
        if filename.endswith(".pt")
    ]

    return (
        {"choices": sorted(names), "__type__": "update"},
        {"choices": sorted(indexes_list), "__type__": "update"},
        {"choices": sorted(audio_paths), "__type__": "update"},
        {"choices": sorted(custom_embedder), "__type__": "update"},
        {"choices": sorted(custom_embedder), "__type__": "update"},
    )


def get_indexes():
    indexes_list = [
        os.path.join(dirpath, filename)
        for dirpath, _, filenames in os.walk(model_root_relative)
        for filename in filenames
        if filename.endswith(".index") and "trained" not in filename
    ]

    return indexes_list if indexes_list else ""


def save_to_wav(record_button):
    if record_button is None:
        pass
    else:
        path_to_file = record_button
        new_name = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + ".wav"
        target_path = os.path.join(audio_root_relative, os.path.basename(new_name))

        shutil.move(path_to_file, target_path)
        return target_path, output_path_fn(target_path)


def save_to_wav2(upload_audio):
    file_path = upload_audio
    formated_name = format_title(os.path.basename(file_path))
    target_path = os.path.join(audio_root_relative, formated_name)

    if os.path.exists(target_path):
        os.remove(target_path)

    shutil.copy(file_path, target_path)
    return target_path, output_path_fn(target_path)


def delete_outputs():
    gr.Info(f"Outputs cleared!")
    for root, _, files in os.walk(audio_root_relative, topdown=False):
        for name in files:
            if name.endswith(tuple(sup_audioext)) and name.__contains__("_output"):
                os.remove(os.path.join(root, name))


def match_index(model_file_value):
    if model_file_value:
        model_folder = os.path.dirname(model_file_value)
        index_files = get_indexes()
        for index_file in index_files:
            if os.path.dirname(index_file) == model_folder:
                return index_file
    return ""


def save_drop_custom_embedder(dropbox):
    if ".pt" not in dropbox:
        gr.Info(
            i18n("The file you dropped is not a valid embedder file. Please try again.")
        )
    else:
        file_name = os.path.basename(dropbox)
        custom_embedder_path = os.path.join(custom_embedder_root, file_name)
        if os.path.exists(custom_embedder_path):
            os.remove(custom_embedder_path)
        os.rename(dropbox, custom_embedder_path)
        gr.Info(
            i18n(
                "Click the refresh button to see the embedder file in the dropdown menu."
            )
        )
    return None


# Inference tab
def inference_tab():
    default_weight = random.choice(names) if names else None
    with gr.Row():
        with gr.Row():
            model_file = gr.Dropdown(
                label=i18n("Voice Model"),
                info=i18n("Select the voice model to use for the conversion."),
                choices=sorted(names, key=lambda path: os.path.getsize(path)),
                interactive=True,
                value=default_weight,
                allow_custom_value=True,
            )

            index_file = gr.Dropdown(
                label=i18n("Index File"),
                info=i18n("Select the index file to use for the conversion."),
                choices=get_indexes(),
                value=match_index(default_weight) if default_weight else "",
                interactive=True,
                allow_custom_value=True,
            )
        with gr.Column():
            refresh_button = gr.Button(i18n("Refresh"))
            unload_button = gr.Button(i18n("Unload Voice"))

            unload_button.click(
                fn=lambda: (
                    {"value": "", "__type__": "update"},
                    {"value": "", "__type__": "update"},
                ),
                inputs=[],
                outputs=[model_file, index_file],
            )

            model_file.select(
                fn=lambda model_file_value: match_index(model_file_value),
                inputs=[model_file],
                outputs=[index_file],
            )

    # Single inference tab
    with gr.Tab(i18n("Single")):
        with gr.Column():
            upload_audio = gr.Audio(
                label=i18n("Upload Audio"), type="filepath", editable=False
            )
            with gr.Row():
                audio = gr.Dropdown(
                    label=i18n("Select Audio"),
                    info=i18n("Select the audio to convert."),
                    choices=sorted(audio_paths),
                    value=audio_paths[0] if audio_paths else "",
                    interactive=True,
                    allow_custom_value=True,
                )

        with gr.Accordion(i18n("Advanced Settings"), open=False):
            with gr.Column():
                clear_outputs_infer = gr.Button(
                    i18n("Clear Outputs (Deletes all audios in assets/audios)")
                )
                output_path = gr.Textbox(
                    label=i18n("Output Path"),
                    placeholder=i18n("Enter output path"),
                    info=i18n(
                        "The path where the output audio will be saved, by default in assets/audios/output.wav"
                    ),
                    value=(
                        output_path_fn(audio_paths[0])
                        if audio_paths
                        else os.path.join(now_dir, "assets", "audios", "output.wav")
                    ),
                    interactive=True,
                )
                export_format = gr.Radio(
                    label=i18n("Export Format"),
                    info=i18n("Select the format to export the audio."),
                    choices=["WAV", "MP3", "FLAC", "OGG", "M4A"],
                    value="WAV",
                    interactive=True,
                )
                split_audio = gr.Checkbox(
                    label=i18n("Split Audio"),
                    info=i18n(
                        "Split the audio into chunks for inference to obtain better results in some cases."
                    ),
                    visible=True,
                    value=False,
                    interactive=True,
                )
                autotune = gr.Checkbox(
                    label=i18n("Autotune"),
                    info=i18n(
                        "Apply a soft autotune to your inferences, recommended for singing conversions."
                    ),
                    visible=True,
                    value=False,
                    interactive=True,
                )
                clean_audio = gr.Checkbox(
                    label=i18n("Clean Audio"),
                    info=i18n(
                        "Clean your audio output using noise detection algorithms, recommended for speaking audios."
                    ),
                    visible=True,
                    value=False,
                    interactive=True,
                )
                clean_strength = gr.Slider(
                    minimum=0,
                    maximum=1,
                    label=i18n("Clean Strength"),
                    info=i18n(
                        "Set the clean-up level to the audio you want, the more you increase it the more it will clean up, but it is possible that the audio will be more compressed."
                    ),
                    visible=False,
                    value=0.5,
                    interactive=True,
                )
                upscale_audio = gr.Checkbox(
                    label=i18n("Upscale Audio"),
                    info=i18n(
                        "Upscale the audio to a higher quality, recommended for low-quality audios. (It could take longer to process the audio)"
                    ),
                    visible=True,
                    value=False,
                    interactive=True,
                )
                pitch = gr.Slider(
                    minimum=-24,
                    maximum=24,
                    step=1,
                    label=i18n("Pitch"),
                    info=i18n(
                        "Set the pitch of the audio, the higher the value, the higher the pitch."
                    ),
                    value=0,
                    interactive=True,
                )
                filter_radius = gr.Slider(
                    minimum=0,
                    maximum=7,
                    label=i18n("Filter Radius"),
                    info=i18n(
                        "If the number is greater than or equal to three, employing median filtering on the collected tone results has the potential to decrease respiration."
                    ),
                    value=3,
                    step=1,
                    interactive=True,
                )
                index_rate = gr.Slider(
                    minimum=0,
                    maximum=1,
                    label=i18n("Search Feature Ratio"),
                    info=i18n(
                        "Influence exerted by the index file; a higher value corresponds to greater influence. However, opting for lower values can help mitigate artifacts present in the audio."
                    ),
                    value=0.75,
                    interactive=True,
                )
                rms_mix_rate = gr.Slider(
                    minimum=0,
                    maximum=1,
                    label=i18n("Volume Envelope"),
                    info=i18n(
                        "Substitute or blend with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is employed."
                    ),
                    value=1,
                    interactive=True,
                )
                protect = gr.Slider(
                    minimum=0,
                    maximum=0.5,
                    label=i18n("Protect Voiceless Consonants"),
                    info=i18n(
                        "Safeguard distinct consonants and breathing sounds to prevent electro-acoustic tearing and other artifacts. Pulling the parameter to its maximum value of 0.5 offers comprehensive protection. However, reducing this value might decrease the extent of protection while potentially mitigating the indexing effect."
                    ),
                    value=0.5,
                    interactive=True,
                )
                hop_length = gr.Slider(
                    minimum=1,
                    maximum=512,
                    step=1,
                    label=i18n("Hop Length"),
                    info=i18n(
                        "Denotes the duration it takes for the system to transition to a significant pitch change. Smaller hop lengths require more time for inference but tend to yield higher pitch accuracy."
                    ),
                    visible=False,
                    value=128,
                    interactive=True,
                )
                f0method = gr.Radio(
                    label=i18n("Pitch extraction algorithm"),
                    info=i18n(
                        "Pitch extraction algorithm to use for the audio conversion. The default algorithm is rmvpe, which is recommended for most cases."
                    ),
                    choices=[
                        "pm",
                        "harvest",
                        "dio",
                        "crepe",
                        "crepe-tiny",
                        "rmvpe",
                        "fcpe",
                        "hybrid[rmvpe+fcpe]",
                    ],
                    value="rmvpe",
                    interactive=True,
                )
                embedder_model = gr.Radio(
                    label=i18n("Embedder Model"),
                    info=i18n("Model used for learning speaker embedding."),
                    choices=["hubert", "contentvec", "custom"],
                    value="hubert",
                    interactive=True,
                )
                with gr.Column(visible=False) as embedder_custom:
                    with gr.Accordion(i18n("Custom Embedder"), open=True):
                        embedder_upload_custom = gr.File(
                            label=i18n("Upload Custom Embedder"),
                            type="filepath",
                            interactive=True,
                        )
                        embedder_custom_refresh = gr.Button(i18n("Refresh"))
                        embedder_model_custom = gr.Dropdown(
                            label=i18n("Custom Embedder"),
                            info=i18n(
                                "Select the custom embedder to use for the conversion."
                            ),
                            choices=sorted(custom_embedders),
                            interactive=True,
                            allow_custom_value=True,
                        )

        convert_button1 = gr.Button(i18n("Convert"))

        with gr.Row():
            vc_output1 = gr.Textbox(
                label=i18n("Output Information"),
                info=i18n("The output information will be displayed here."),
            )
            vc_output2 = gr.Audio(label=i18n("Export Audio"))

    # Batch inference tab
    with gr.Tab(i18n("Batch")):
        with gr.Row():
            with gr.Column():
                input_folder_batch = gr.Textbox(
                    label=i18n("Input Folder"),
                    info=i18n("Select the folder containing the audios to convert."),
                    placeholder=i18n("Enter input path"),
                    value=os.path.join(now_dir, "assets", "audios"),
                    interactive=True,
                )
                output_folder_batch = gr.Textbox(
                    label=i18n("Output Folder"),
                    info=i18n(
                        "Select the folder where the output audios will be saved."
                    ),
                    placeholder=i18n("Enter output path"),
                    value=os.path.join(now_dir, "assets", "audios"),
                    interactive=True,
                )
        with gr.Accordion(i18n("Advanced Settings"), open=False):
            with gr.Column():
                clear_outputs_batch = gr.Button(
                    i18n("Clear Outputs (Deletes all audios in assets/audios)")
                )
                export_format_batch = gr.Radio(
                    label=i18n("Export Format"),
                    info=i18n("Select the format to export the audio."),
                    choices=["WAV", "MP3", "FLAC", "OGG", "M4A"],
                    value="WAV",
                    interactive=True,
                )
                split_audio_batch = gr.Checkbox(
                    label=i18n("Split Audio"),
                    info=i18n(
                        "Split the audio into chunks for inference to obtain better results in some cases."
                    ),
                    visible=True,
                    value=False,
                    interactive=True,
                )
                autotune_batch = gr.Checkbox(
                    label=i18n("Autotune"),
                    info=i18n(
                        "Apply a soft autotune to your inferences, recommended for singing conversions."
                    ),
                    visible=True,
                    value=False,
                    interactive=True,
                )
                clean_audio_batch = gr.Checkbox(
                    label=i18n("Clean Audio"),
                    info=i18n(
                        "Clean your audio output using noise detection algorithms, recommended for speaking audios."
                    ),
                    visible=True,
                    value=False,
                    interactive=True,
                )
                clean_strength_batch = gr.Slider(
                    minimum=0,
                    maximum=1,
                    label=i18n("Clean Strength"),
                    info=i18n(
                        "Set the clean-up level to the audio you want, the more you increase it the more it will clean up, but it is possible that the audio will be more compressed."
                    ),
                    visible=False,
                    value=0.5,
                    interactive=True,
                )
                upscale_audio_batch = gr.Checkbox(
                    label=i18n("Upscale Audio"),
                    info=i18n(
                        "Upscale the audio to a higher quality, recommended for low-quality audios. (It could take longer to process the audio)"
                    ),
                    visible=True,
                    value=False,
                    interactive=True,
                )
                pitch_batch = gr.Slider(
                    minimum=-24,
                    maximum=24,
                    step=1,
                    label=i18n("Pitch"),
                    info=i18n(
                        "Set the pitch of the audio, the higher the value, the higher the pitch."
                    ),
                    value=0,
                    interactive=True,
                )
                filter_radius_batch = gr.Slider(
                    minimum=0,
                    maximum=7,
                    label=i18n("Filter Radius"),
                    info=i18n(
                        "If the number is greater than or equal to three, employing median filtering on the collected tone results has the potential to decrease respiration."
                    ),
                    value=3,
                    step=1,
                    interactive=True,
                )
                index_rate_batch = gr.Slider(
                    minimum=0,
                    maximum=1,
                    label=i18n("Search Feature Ratio"),
                    info=i18n(
                        "Influence exerted by the index file; a higher value corresponds to greater influence. However, opting for lower values can help mitigate artifacts present in the audio."
                    ),
                    value=0.75,
                    interactive=True,
                )
                rms_mix_rate_batch = gr.Slider(
                    minimum=0,
                    maximum=1,
                    label=i18n("Volume Envelope"),
                    info=i18n(
                        "Substitute or blend with the volume envelope of the output. The closer the ratio is to 1, the more the output envelope is employed."
                    ),
                    value=1,
                    interactive=True,
                )
                protect_batch = gr.Slider(
                    minimum=0,
                    maximum=0.5,
                    label=i18n("Protect Voiceless Consonants"),
                    info=i18n(
                        "Safeguard distinct consonants and breathing sounds to prevent electro-acoustic tearing and other artifacts. Pulling the parameter to its maximum value of 0.5 offers comprehensive protection. However, reducing this value might decrease the extent of protection while potentially mitigating the indexing effect."
                    ),
                    value=0.5,
                    interactive=True,
                )
                hop_length_batch = gr.Slider(
                    minimum=1,
                    maximum=512,
                    step=1,
                    label=i18n("Hop Length"),
                    info=i18n(
                        "Denotes the duration it takes for the system to transition to a significant pitch change. Smaller hop lengths require more time for inference but tend to yield higher pitch accuracy."
                    ),
                    visible=False,
                    value=128,
                    interactive=True,
                )
                f0method_batch = gr.Radio(
                    label=i18n("Pitch extraction algorithm"),
                    info=i18n(
                        "Pitch extraction algorithm to use for the audio conversion. The default algorithm is rmvpe, which is recommended for most cases."
                    ),
                    choices=[
                        "pm",
                        "harvest",
                        "dio",
                        "crepe",
                        "crepe-tiny",
                        "rmvpe",
                        "fcpe",
                        "hybrid[rmvpe+fcpe]",
                    ],
                    value="rmvpe",
                    interactive=True,
                )
                embedder_model_bacth = gr.Radio(
                    label=i18n("Embedder Model"),
                    info=i18n("Model used for learning speaker embedding."),
                    choices=["hubert", "contentvec", "custom"],
                    value="hubert",
                    interactive=True,
                )
                with gr.Column(visible=False) as embedder_custom_bacth:
                    with gr.Accordion(i18n("Custom Embedder"), open=True):
                        embedder_upload_custom_bacth = gr.File(
                            label=i18n("Upload Custom Embedder"),
                            type="filepath",
                            interactive=True,
                        )
                        embedder_custom_refresh_bacth = gr.Button(i18n("Refresh"))
                        embedder_model_custom_bacth = gr.Dropdown(
                            label=i18n("Custom Embedder"),
                            info=i18n(
                                "Select the custom embedder to use for the conversion."
                            ),
                            choices=sorted(custom_embedders),
                            interactive=True,
                            allow_custom_value=True,
                        )

        convert_button2 = gr.Button(i18n("Convert"))

        with gr.Row():
            vc_output3 = gr.Textbox(
                label=i18n("Output Information"),
                info=i18n("The output information will be displayed here."),
            )

    def toggle_visible(checkbox):
        return {"visible": checkbox, "__type__": "update"}

    def toggle_visible_hop_length(f0method):
        if f0method == "crepe" or f0method == "crepe-tiny":
            return {"visible": True, "__type__": "update"}
        return {"visible": False, "__type__": "update"}

    def toggle_visible_embedder_custom(embedder_model):
        if embedder_model == "custom":
            return {"visible": True, "__type__": "update"}
        return {"visible": False, "__type__": "update"}

    clean_audio.change(
        fn=toggle_visible,
        inputs=[clean_audio],
        outputs=[clean_strength],
    )
    clean_audio_batch.change(
        fn=toggle_visible,
        inputs=[clean_audio_batch],
        outputs=[clean_strength_batch],
    )
    f0method.change(
        fn=toggle_visible_hop_length,
        inputs=[f0method],
        outputs=[hop_length],
    )
    f0method_batch.change(
        fn=toggle_visible_hop_length,
        inputs=[f0method_batch],
        outputs=[hop_length_batch],
    )
    refresh_button.click(
        fn=change_choices,
        inputs=[],
        outputs=[
            model_file,
            index_file,
            audio,
            embedder_model_custom,
            embedder_model_custom_bacth,
        ],
    )
    audio.change(
        fn=output_path_fn,
        inputs=[audio],
        outputs=[output_path],
    )
    upload_audio.upload(
        fn=save_to_wav2,
        inputs=[upload_audio],
        outputs=[audio, output_path],
    )
    upload_audio.stop_recording(
        fn=save_to_wav,
        inputs=[upload_audio],
        outputs=[audio, output_path],
    )
    clear_outputs_infer.click(
        fn=delete_outputs,
        inputs=[],
        outputs=[],
    )
    clear_outputs_batch.click(
        fn=delete_outputs,
        inputs=[],
        outputs=[],
    )
    embedder_model.change(
        fn=toggle_visible_embedder_custom,
        inputs=[embedder_model],
        outputs=[embedder_custom],
    )
    embedder_upload_custom.upload(
        fn=save_drop_custom_embedder,
        inputs=[embedder_upload_custom],
        outputs=[embedder_upload_custom],
    )
    embedder_custom_refresh.click(
        fn=change_choices,
        inputs=[],
        outputs=[
            model_file,
            index_file,
            audio,
            embedder_model_custom,
            embedder_model_custom_bacth,
        ],
    )
    embedder_model_bacth.change(
        fn=toggle_visible_embedder_custom,
        inputs=[embedder_model_bacth],
        outputs=[embedder_custom_bacth],
    )
    embedder_upload_custom_bacth.upload(
        fn=save_drop_custom_embedder,
        inputs=[embedder_upload_custom_bacth],
        outputs=[embedder_upload_custom_bacth],
    )
    embedder_custom_refresh_bacth.click(
        fn=change_choices,
        inputs=[],
        outputs=[
            model_file,
            index_file,
            audio,
            embedder_model_custom,
            embedder_model_custom_bacth,
        ],
    )
    convert_button1.click(
        fn=run_infer_script,
        inputs=[
            pitch,
            filter_radius,
            index_rate,
            rms_mix_rate,
            protect,
            hop_length,
            f0method,
            audio,
            output_path,
            model_file,
            index_file,
            split_audio,
            autotune,
            clean_audio,
            clean_strength,
            export_format,
            embedder_model,
            embedder_model_custom,
            upscale_audio,
        ],
        outputs=[vc_output1, vc_output2],
    )
    convert_button2.click(
        fn=run_batch_infer_script,
        inputs=[
            pitch_batch,
            filter_radius_batch,
            index_rate_batch,
            rms_mix_rate_batch,
            protect_batch,
            hop_length_batch,
            f0method_batch,
            input_folder_batch,
            output_folder_batch,
            model_file,
            index_file,
            split_audio_batch,
            autotune_batch,
            clean_audio_batch,
            clean_strength_batch,
            export_format_batch,
            embedder_model_bacth,
            embedder_model_custom_bacth,
            upscale_audio_batch,
        ],
        outputs=[vc_output3],
    )