#==================================================================================
# https://huggingface.co/spaces/asigalov61/Karaoke-Transformer
#==================================================================================

print('=' * 70)
print('Karaoke Transformer Gradio App')

print('=' * 70)
print('Loading core Karaoke Transformer modules...')

import os
import copy
import pickle
import time as reqtime
import datetime
from pytz import timezone

print('=' * 70)
print('Loading main Karaoke Transformer modules...')

os.environ['USE_FLASH_ATTENTION'] = '1'

import torch

torch.set_float32_matmul_precision('medium')
torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul
torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn
torch.backends.cuda.enable_mem_efficient_sdp(True)
torch.backends.cuda.enable_math_sdp(True)
torch.backends.cuda.enable_flash_sdp(True)
torch.backends.cuda.enable_cudnn_sdp(True)

from huggingface_hub import hf_hub_download

import TMIDIX
import SyllablesSearch

from midi_to_colab_audio import midi_to_colab_audio

from x_transformer_1_23_2 import *

import random

import tqdm

print('=' * 70)
print('Loading aux Karaoke Transformer modules...')

import matplotlib.pyplot as plt

import gradio as gr
import spaces

print('=' * 70)
print('PyTorch version:', torch.__version__)
print('=' * 70)
print('Done!')
print('Enjoy! :)')
print('=' * 70)

#==================================================================================

KAR_MODEL_CHECKPOINT = 'Karaoke_Transformer_Lyr2Mel_Trained_Model_3910_steps_0.186_loss_0.9456_acc.pth'
ACC_MODEL_CHECKPOINT = 'Guided_Accompaniment_Transformer_Trained_Model_36457_steps_0.5384_loss_0.8417_acc.pth'

SOUDFONT_PATH = 'SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2'

MAX_NUM_GEN_WORDS = 56

#==================================================================================

print('=' * 70)
print('Instantiating karaoke model...')

device_type = 'cuda'
dtype = 'bfloat16'

ptdtype = {'bfloat16': torch.bfloat16, 'float16': torch.float16}[dtype]
ctx = torch.amp.autocast(device_type=device_type, dtype=ptdtype)

SEQ_LEN = 3072
PAD_IDX = 20387

kar_model = TransformerWrapper(
            num_tokens = PAD_IDX+1,
            max_seq_len = SEQ_LEN,
            attn_layers = Decoder(dim = 1024,
                                  depth = 4,
                                  heads = 32,
                                  rotary_pos_emb = True,
                                  attn_flash = True
                                  )
)

kar_model = AutoregressiveWrapper(kar_model, ignore_index=PAD_IDX, pad_value=PAD_IDX)

print('=' * 70)
print('Loading model checkpoint...')      

kar_model_checkpoint = hf_hub_download(repo_id='asigalov61/Karaoke-Transformer', filename=KAR_MODEL_CHECKPOINT)

kar_model.load_state_dict(torch.load(kar_model_checkpoint, map_location='cpu', weights_only=True))

kar_model = torch.compile(kar_model, mode='max-autotune')

print('=' * 70)
print('Done!')
print('=' * 70)
print('Model will use', dtype, 'precision...')
print('=' * 70)

#==================================================================================

print('=' * 70)
print('Instantiating accompaniment model...')

device_type = 'cuda'
dtype = 'bfloat16'

ptdtype = {'bfloat16': torch.bfloat16, 'float16': torch.float16}[dtype]
ctx = torch.amp.autocast(device_type=device_type, dtype=ptdtype)

SEQ_LEN = 4096
PAD_IDX = 1794

acc_model = TransformerWrapper(
            num_tokens = PAD_IDX+1,
            max_seq_len = SEQ_LEN,
            attn_layers = Decoder(dim = 2048,
                                  depth = 4,
                                  heads = 32,
                                  rotary_pos_emb = True,
                                  attn_flash = True
                                  )
)

acc_model = AutoregressiveWrapper(acc_model, ignore_index=PAD_IDX, pad_value=PAD_IDX)

print('=' * 70)
print('Loading model checkpoint...')      

acc_model_checkpoint = hf_hub_download(repo_id='asigalov61/Guided-Accompaniment-Transformer', filename=ACC_MODEL_CHECKPOINT)

acc_model.load_state_dict(torch.load(acc_model_checkpoint, map_location='cpu', weights_only=True))

acc_model = torch.compile(acc_model, mode='max-autotune')

print('=' * 70)
print('Done!')
print('=' * 70)
print('Model will use', dtype, 'precision...')
print('=' * 70)

#==================================================================================

print('Loading karaoke words list and dict...')

kar_words_list_dict_pickle = hf_hub_download(repo_id='asigalov61/Karaoke-Transformer', filename='all_words_list_dict.pickle')

with open(kar_words_list_dict_pickle, 'rb') as f:
    all_words_list, all_words_dict = pickle.load(f)

print('Done!')
print('=' * 70)

#==================================================================================

@spaces.GPU
def Generate_Karaoke(input_lyrics,
                     model_temperature,
                     model_sampling_top_k
                    ):

    #===============================================================================

    def generate_full_seq(input_seq, 
                          max_toks=3072, 
                          temperature=0.9, 
                          top_k_value=15, 
                          verbose=True
                         ):
    
        seq_abs_run_time = sum([t for t in input_seq if t < 128])
    
        cur_time = 0
    
        full_seq = copy.deepcopy(input_seq)
    
        toks_counter = 0
    
        while cur_time <= seq_abs_run_time+32:
    
            if verbose:
                if toks_counter % 128 == 0:
                    print('Generated', toks_counter, 'tokens')
    
            x = torch.LongTensor(full_seq).cuda()
    
            with ctx:
                out = acc_model.generate(x,
                                         1,
                                         filter_logits_fn=top_k,
                                         filter_kwargs={'k': top_k_value},
                                         temperature=temperature,
                                         return_prime=False,
                                         verbose=False
                                        )
            
            y = out.tolist()[0][0]
    
            if y < 128:
                cur_time += y
    
            full_seq.append(y)
    
            toks_counter += 1

            if toks_counter == max_toks:
                return full_seq
    
        return full_seq

    #===============================================================================
    
    print('=' * 70)
    print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
    start_time = reqtime.time()
    print('=' * 70)

    print('Requested settings:')
    print('=' * 70)
    print('Input lyrics:', input_lyrics)
    print('=' * 70)
    print('Model temperature:', model_temperature)
    print('Model top k:', model_sampling_top_k)
    print('=' * 70)

    #==================================================================
    
    print('=' * 70)
    print('Generating...')

    #==================================================================

    kar_model.to(device_type)
    kar_model.eval()

    acc_model.to(device_type)
    acc_model.eval()

    #==================================================================

    lyric_toks = [20384]

    if input_lyrics != '':

        lyrics_clean = TMIDIX.clean_string(input_lyrics.replace('\n', ' '), regex='[^a-zA-Z ]').lower().strip()
        syl_toks = [s for s in SyllablesSearch.split_words(lyrics_clean.split(' ')) if s != ' ']
    
        for l in syl_toks:
            if l in all_words_list:
                lyric_toks.append(all_words_dict[tuple(l)]+384)
        
        lyric_toks.append(20385)
    
    #==================================================================

    x = torch.LongTensor(lyric_toks).cuda()
    
    with ctx:
        out = kar_model.generate(x,
                                 768,
                                 temperature=model_temperature,
                                 filter_logits_fn=top_k,
                                 filter_kwargs={'k': model_sampling_top_k},
                                 return_prime=False,
                                 eos_token=20386,
                                 verbose=True)
    
    y = out.tolist()

    #==================================================================

    decoded_lyrics = []

    for tok in y[0]:
        if 383 < tok < 20384:
            decoded_lyrics.append(all_words_list[tok-384])

    decoded_lyrics = decoded_lyrics[:MAX_NUM_GEN_WORDS]

    print('=' * 70)
    print('Done!')
    print('=' * 70)

    #==================================================================

    score = [t for t in y[0] if t < 384][:MAX_NUM_GEN_WORDS*3]

    #==================================================================

    start_score_seq = [1792] + score + [1793]

    #==================================================================

    print('Generating accompaniment...')

    input_seq = generate_full_seq(start_score_seq, 
                                  temperature=model_temperature, 
                                  top_k_value=model_sampling_top_k
                                 )

    final_song = input_seq[len(start_score_seq):]
   
    print('=' * 70)
    print('Done!')
    print('=' * 70)
    
    #===============================================================================
    
    print('Rendering results...')
    
    print('=' * 70)
    print('Sample INTs', final_song[:15])
    print('=' * 70)

    song_f = []
    psong_f = []
    
    if len(final_song) != 0:
    
        time = 0
        dur = 0
        vel = 90
        pitch = 0
        channel = 0
        patch = 0
    
        channels_map = [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 9, 12, 13, 14, 15]
        patches_map = [40, 0, 10, 19, 24, 35, 40, 52, 56, 9, 65, 73, 0, 0, 0, 0]
        velocities_map = [125, 80, 100, 80, 90, 100, 100, 80, 110, 110, 110, 110, 80, 80, 80, 80]

        widx = 0
    
        for m in final_song:
    
            if 0 <= m < 128:
                time += m * 32
                
            elif 128 < m < 256:
                dur = (m-128) * 32
    
            elif 256 < m < 1792:
                cha = (m-256) // 128
                pitch = (m-256) % 128
    
                channel = channels_map[cha]
                patch = patches_map[channel]
                vel = velocities_map[channel]
    
                song_f.append(['note', time, dur, channel, pitch, vel, patch])
                psong_f.append(['note', time, dur, channel, pitch, vel, patch])

                if cha == 0:
                    song_f.append(['lyric', time, decoded_lyrics[widx]])
                    widx += 1

                    if widx == len(decoded_lyrics):
                        break
                
    fn1 = "Karaoke-Transformer-Composition"
    
    detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,
                                                              output_signature = 'Karaoke Transformer',
                                                              output_file_name = fn1,
                                                              track_name='Project Los Angeles',
                                                              list_of_MIDI_patches=patches_map
                                                              )
    
    new_fn = fn1+'.mid'
            
    
    audio = midi_to_colab_audio(new_fn, 
                        soundfont_path=SOUDFONT_PATH,
                        sample_rate=16000,
                        volume_scale=10,
                        output_for_gradio=True
                        )
    
    print('Done!')
    print('=' * 70)

    #========================================================

    output_midi = str(new_fn)
    output_audio = (16000, audio)
    output_lyrics = ' '.join(decoded_lyrics)
    
    output_plot = TMIDIX.plot_ms_SONG(psong_f, plot_title=output_midi, return_plt=True)

    print('Output MIDI file name:', output_midi)
    print('=' * 70) 
    
    #========================================================
    
    print('-' * 70)
    print('Req end time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
    print('-' * 70)
    print('Req execution time:', (reqtime.time() - start_time), 'sec')

    return output_audio, output_plot, output_midi, output_lyrics
    
#==================================================================================

PDT = timezone('US/Pacific')

print('=' * 70)
print('App start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
print('=' * 70)

#==================================================================================

with gr.Blocks() as demo:

    #==================================================================================

    gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Karaoke Transformer</h1>")
    gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Generate Karaoke MIDI composition from any lyrics</h1>")
    gr.HTML("""            
            <p> 
                <a href="https://huggingface.co/spaces/asigalov61/Karaoke-Transformer?duplicate=true">
                    <img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-md.svg" alt="Duplicate in Hugging Face">
                </a>
            </p>
            
            for faster execution and endless generation!
            """)
    
    #==================================================================================
    
    gr.Markdown("## Enter desired lyrics below")
    
    input_lyrics = gr.Textbox(label="Input lyrics", value="So close no matter how far\nCould not be much more from the heart\nForever trusting who we are\nAnd nothing else matters")
    
    gr.Markdown("## Generation options")
    
    model_temperature = gr.Slider(0.1, 1, value=0.9, step=0.01, label="Model temperature")
    model_sampling_top_k = gr.Slider(1, 100, value=5, step=1, label="Model sampling top k value")
    
    generate_btn = gr.Button("Generate", variant="primary")

    gr.Markdown("## Generation results")
    
    output_audio = gr.Audio(label="MIDI audio", format="wav", elem_id="midi_audio")
    output_plot = gr.Plot(label="MIDI score plot")
    output_lyrics = gr.Textbox(label="MIDI lyrics")
    output_midi = gr.File(label="MIDI file", file_types=[".mid"])
    
    generate_btn.click(Generate_Karaoke, 
                       [input_lyrics, 
                        model_temperature,
                        model_sampling_top_k
                       ], 
                       [output_audio,
                        output_plot,
                        output_midi,
                        output_lyrics
                       ]
                      )
        
#==================================================================================

demo.launch()

#==================================================================================