#=======================================================================================
# https://huggingface.co/spaces/asigalov61/Imagen-POP-Music-Medley-Diffusion-Transformer
#=======================================================================================

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

import torch
from imagen_pytorch import Unet, Imagen, ImagenTrainer
from imagen_pytorch.data import Dataset

import spaces
import gradio as gr

import numpy as np

import random
import tqdm

import TMIDIX
import TPLOTS

from midi_to_colab_audio import midi_to_colab_audio

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

@spaces.GPU
def Generate_POP_Medley(input_num_medley_comps, input_melody_patch):
    
    print('=' * 70)
    print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
    start_time = reqtime.time()
    print('=' * 70)
    
    print('Req number of medley compositions:', input_num_medley_comps)
    print('Req melody MIDI patch number:', input_melody_patch)
    print('=' * 70)
    
    #===============================================================================
    # MIDI Images generation
    #===============================================================================

    print('Loading model...')

    DIM = 64
    CHANS = 1
    TSTEPS = 1000
    DEVICE = 'cpu' # 'cuda'

    unet = Unet(
        dim = DIM,
        dim_mults = (1, 2, 4, 8),
        num_resnet_blocks = 1,
        channels=CHANS,
        layer_attns = (False, False, False, True),
        layer_cross_attns = False
    )
    
    imagen = Imagen(
        condition_on_text = False,  # this must be set to False for unconditional Imagen
        unets = unet,
        channels=CHANS,
        image_sizes = 128,
        timesteps = TSTEPS
    )
    
    trainer = ImagenTrainer(
        imagen = imagen,
        split_valid_from_train = True # whether to split the validation dataset from the training
    ).to(DEVICE)

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

    trainer.load('Imagen_POP909_64_dim_12638_steps_0.00983_loss.ckpt')

    print('=' * 70)
    print('Done!')
    print('=' * 70)
    print('Generating...')
    print('=' * 70)
    
    images = trainer.sample(batch_size = input_num_medley_comps, return_pil_images = True)

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

    print('Processing...')

    threshold = 128
    
    imgs_array = []
    
    for i in images:
      arr = np.array(i)
      farr = np.where(arr < threshold, 0, 1)
      imgs_array.append(farr)

    print('Done!')

    #===============================================================================
    
    print('=' * 70)
    print('Converting images to scores...')
    
    medley_compositions_escores = []

    for i in imgs_array:

        bmatrix = TPLOTS.images_to_binary_matrix([i])
    
        score = TMIDIX.binary_matrix_to_original_escore_notes(bmatrix)

        if input_melody_patch > -1:
            score = TMIDIX.add_melody_to_enhanced_score_notes(score, melody_patch=input_melody_patch)

        medley_compositions_escores.append(score)

    print('Done!')
    print('=' * 70)
    print('Creating medley score...')

    medley_labels = ['Imagen POP Medley Composition #' + str(i+1) for i in range(len(medley_compositions_escores))]

    medley_escore = TMIDIX.escore_notes_medley(medley_compositions_escores, medley_labels, pause_time_value=16)  
    
    #===============================================================================
    print('Rendering results...')
    print('=' * 70)
    
    print('Sample INTs', medley_escore[:15])
    print('=' * 70)

    fn1 = "Imagen-POP-Music-Medley-Diffusion-Transformer-Composition"

    output_score, patches, overflow_patches = TMIDIX.patch_enhanced_score_notes(medley_escore)
    
    detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(output_score,
                                                              output_signature = 'Imagen POP Music Medley',
                                                              output_file_name = fn1,
                                                              track_name='Project Los Angeles',
                                                              list_of_MIDI_patches=patches,
                                                              timings_multiplier=256
                                                              )
    
    new_fn = fn1+'.mid'
            
    
    audio = midi_to_colab_audio(new_fn, 
                        soundfont_path=soundfont,
                        sample_rate=16000,
                        volume_scale=10,
                        output_for_gradio=True
                        )
    
    print('Done!')
    print('=' * 70)

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

    output_midi_title = str(fn1)
    output_midi_summary = str(output_score[:3])
    output_midi = str(new_fn)
    output_audio = (16000, audio)
    
    output_plot = TMIDIX.plot_ms_SONG(output_score, plot_title=output_midi, return_plt=True, timings_multiplier=256)

    print('Output MIDI file name:', output_midi)
    print('Output MIDI title:', output_midi_title)
    print('Output MIDI summary:', output_midi_summary)
    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_midi_title, output_midi_summary, output_midi, output_audio, output_plot

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

if __name__ == "__main__":
    
    PDT = timezone('US/Pacific')
    
    print('=' * 70)
    print('App start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
    print('=' * 70)

    soundfont = "SGM-v2.01-YamahaGrand-Guit-Bass-v2.7.sf2"

    app = gr.Blocks()
    
    with app:
        
        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Imagen POP Music Medley Diffusion Transformer</h1>")
        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Generate unique POP music medleys with Imagen diffusion transformer</h1>")
        gr.Markdown("![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.Imagen-POP-Music-Medley-Diffusion-Transformer&style=flat)\n\n"
                    "This is a demo for MIDI Images dataset\n\n"
                    "Please see [MIDI Images](https://huggingface.co/datasets/asigalov61/MIDI-Images) Hugging Face repo for more information\n\n"
                    )        

        gr.Markdown("## Choose medley settings")
        
        input_num_medley_comps = gr.Slider(1, 10, value=5, step=1, label="Number of medley compositions")
        input_melody_patch = gr.Slider(-1, 127, value=40, step=1, label="Medley melody MIDI patch number")

        run_btn = gr.Button("Generate POP Medley", variant="primary")

        gr.Markdown("## Generation results")

        output_midi_title = gr.Textbox(label="Output MIDI title")
        output_midi_summary = gr.Textbox(label="Output MIDI summary")
        output_audio = gr.Audio(label="Output MIDI audio", format="wav", elem_id="midi_audio")
        output_plot = gr.Plot(label="Output MIDI score plot")
        output_midi = gr.File(label="Output MIDI file", file_types=[".mid"])

        run_event = run_btn.click(Generate_POP_Medley, [input_num_medley_comps, input_melody_patch],
                                  [output_midi_title, output_midi_summary, output_midi, output_audio, output_plot])

        app.queue().launch()