import os.path

import time as reqtime
import datetime
from pytz import timezone

import torch

import spaces
import gradio as gr

from x_transformer_1_23_2 import *
import random
import tqdm

from midi_to_colab_audio import midi_to_colab_audio
import TMIDIX

import matplotlib.pyplot as plt

in_space = os.getenv("SYSTEM") == "spaces"
         
# =================================================================================================
                       
@spaces.GPU
def InpaintPitches(input_midi, input_num_of_notes, input_patch_number):
    print('=' * 70)
    print('Req start time: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now(PDT)))
    start_time = reqtime.time()

    print('Loading model...')

    SEQ_LEN = 8192 # Models seq len
    PAD_IDX = 19463 # Models pad index
    DEVICE = 'cuda' # 'cuda'

    # instantiate the model

    model = TransformerWrapper(
        num_tokens = PAD_IDX+1,
        max_seq_len = SEQ_LEN,
        attn_layers = Decoder(dim = 1024, depth = 32, heads = 32, attn_flash = True)
        )
    
    model = AutoregressiveWrapper(model, ignore_index = PAD_IDX)

    model.to(DEVICE)
    print('=' * 70)

    print('Loading model checkpoint...')

    model.load_state_dict(
        torch.load('Giant_Music_Transformer_Large_Trained_Model_36074_steps_0.3067_loss_0.927_acc.pth',
                   map_location=DEVICE))
    print('=' * 70)

    model.eval()

    if DEVICE == 'cpu':
        dtype = torch.bfloat16
    else:
        dtype = torch.bfloat16

    ctx = torch.amp.autocast(device_type=DEVICE, dtype=dtype)

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

    fn = os.path.basename(input_midi.name)
    fn1 = fn.split('.')[0]

    input_num_of_notes = max(8, min(2048, input_num_of_notes))

    print('-' * 70)
    print('Input file name:', fn)
    print('Req num of notes:', input_num_of_notes)
    print('Req patch number:', input_patch_number)
    print('-' * 70)

    #===============================================================================
    raw_score = TMIDIX.midi2single_track_ms_score(input_midi.name)
    
    #===============================================================================
    # Enhanced score notes
    
    events_matrix1 = TMIDIX.advanced_score_processor(raw_score, return_enhanced_score_notes=True)[0]
    
    #=======================================================
    # PRE-PROCESSING
    
    # checking number of instruments in a composition
    instruments_list_without_drums = list(set([y[3] for y in events_matrix1 if y[3] != 9]))
    instruments_list = list(set([y[3] for y in events_matrix1]))
    
    if len(events_matrix1) > 0 and len(instruments_list_without_drums) > 0:
    
      #======================================
    
      events_matrix2 = []
    
      # Recalculating timings
      for e in events_matrix1:
    
          # Original timings
          e[1] = int(e[1] / 16)
          e[2] = int(e[2] / 16)
    
      #===================================
      # ORIGINAL COMPOSITION
      #===================================
    
      # Sorting by patch, pitch, then by start-time
    
      events_matrix1.sort(key=lambda x: x[6])
      events_matrix1.sort(key=lambda x: x[4], reverse=True)
      events_matrix1.sort(key=lambda x: x[1])
    
      #=======================================================
      # FINAL PROCESSING
    
      melody_chords = []
      melody_chords2 = []
    
      # Break between compositions / Intro seq
    
      if 9 in instruments_list:
          drums_present = 19331 # Yes
      else:
          drums_present = 19330 # No
    
      if events_matrix1[0][3] != 9:
          pat = events_matrix1[0][6]
      else:
          pat = 128
    
      melody_chords.extend([19461, drums_present, 19332+pat]) # Intro seq
    
      #=======================================================
      # MAIN PROCESSING CYCLE
      #=======================================================
    
      abs_time = 0
    
      pbar_time = 0
    
      pe = events_matrix1[0]
    
      chords_counter = 1
    
      comp_chords_len = len(list(set([y[1] for y in events_matrix1])))
    
      for e in events_matrix1:
    
          #=======================================================
          # Timings...
    
          # Cliping all values...
          delta_time = max(0, min(255, e[1]-pe[1]))
    
          # Durations and channels
    
          dur = max(0, min(255, e[2]))
          cha = max(0, min(15, e[3]))
    
          # Patches
          if cha == 9: # Drums patch will be == 128
              pat = 128
    
          else:
              pat = e[6]
    
          # Pitches
    
          ptc = max(1, min(127, e[4]))
    
          # Velocities
    
          # Calculating octo-velocity
          vel = max(8, min(127, e[5]))
          velocity = round(vel / 15)-1
    
          #=======================================================
          # FINAL NOTE SEQ
    
          # Writing final note asynchronously
    
          dur_vel = (8 * dur) + velocity
          pat_ptc = (129 * pat) + ptc
    
          melody_chords.extend([delta_time, dur_vel+256, pat_ptc+2304])
          melody_chords2.append([delta_time, dur_vel+256, pat_ptc+2304])
    
          pe = e
    
        
    #==================================================================

    print('=' * 70)
    print('Number of tokens:', len(melody_chords))
    print('Number of notes:', len(melody_chords2))
    print('Sample output events', melody_chords[:5])
    print('=' * 70)
    print('Generating...')

    output = []
    
    max_chords_limit = 8
    temperature=0.9
    num_memory_tokens=4096

    output = []
    
    idx = 0
    
    for c in chords[:input_num_tokens]:
     
        output.append(c)
    
        if input_conditioning_type == 'Chords-Times' or input_conditioning_type == 'Chords-Times-Durations':
          output.append(times[idx])
    
        if input_conditioning_type == 'Chords-Times-Durations':
          output.append(durs[idx])

        x = torch.tensor([output] * 1, dtype=torch.long, device=DEVICE)
        
        o = 0
        
        ncount = 0
        
        while o < 384 and ncount < max_chords_limit:
          with ctx:
            out = model.generate(x[-num_memory_tokens:],
                                1,
                                temperature=temperature,
                                return_prime=False,
                                verbose=False)
        
          o = out.tolist()[0][0]
        
          if 256 <= o < 384:
            ncount += 1
        
          if o < 384:
            x = torch.cat((x, out), 1)
        
        outy =  x.tolist()[0][len(output):]
          
        output.extend(outy)
    
        idx += 1

        if idx == len(chords[:input_num_tokens])-1:
            break

    print('=' * 70)
    print('Done!')
    print('=' * 70)
    
    #===============================================================================
    print('Rendering results...')
    
    print('=' * 70)
    print('Sample INTs', output[:12])
    print('=' * 70)
    
    out1 = output

    if len(out1) != 0:
    
        song = out1
        song_f = []
    
        time = 0
        dur = 0
        vel = 90
        pitch = 0
        channel = 0
        
        patches = [0] * 16
        
        channel = 0
        
        for ss in song:
        
          if 0 <= ss < 128:
        
              time += ss * 32
        
          if 128 <= ss < 256:
        
              dur = (ss-128) * 32
        
          if 256 <= ss < 384:
        
              pitch = (ss-256)
        
              vel = max(40, pitch)
        
              song_f.append(['note', time, dur, channel, pitch, vel, 0])

    fn1 = "Chords-Progressions-Transformer-Composition"
    
    detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(song_f,
                                                              output_signature = 'Chords Progressions Transformer',
                                                              output_file_name = fn1,
                                                              track_name='Project Los Angeles',
                                                              list_of_MIDI_patches=patches
                                                              )
    
    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(song_f[:3])
    output_midi = str(new_fn)
    output_audio = (16000, audio)
    
    output_plot = TMIDIX.plot_ms_SONG(song_f, plot_title=output_midi, return_plt=True)

    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'>Chords Progressions Transformer</h1>")
        gr.Markdown("<h1 style='text-align: center; margin-bottom: 1rem'>Chords-conditioned music transformer</h1>")
        gr.Markdown(
            "![Visitors](https://api.visitorbadge.io/api/visitors?path=asigalov61.Chords-Progressions-Transformer&style=flat)\n\n"
            "Generate music based on chords progressions\n\n"
            "Check out [Chords Progressions Transformer](https://github.com/asigalov61/Chords-Progressions-Transformer) on GitHub!\n\n"
            "[Open In Colab]"
            "(https://colab.research.google.com/github/asigalov61/Chords-Progressions-Transformer/blob/main/Chords_Progressions_Transformer.ipynb)"
            " for faster execution and endless generation"
        )
        gr.Markdown("## Upload your MIDI or select a sample example MIDI")
        
        input_midi = gr.File(label="Input MIDI", file_types=[".midi", ".mid", ".kar"])
        input_num_of_notes = gr.Slider(8, 2048, value=128, step=8, label="Number of composition notes to inpaint")
        input_patch_number = gr.Slider(0, 127, value=0, step=1, label="Composition MIDI patch to inpaint")
        
        run_btn = gr.Button("generate", 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(InpaintPitches, [input_midi, input_num_of_notes, input_patch_number],
                                  [output_midi_title, output_midi_summary, output_midi, output_audio, output_plot])

        gr.Examples(
            [["Giant-Music-Transformer-Piano-Seed-1.mid", 128, 0], 
             ["Giant-Music-Transformer-Piano-Seed-2.mid", 128, 0], 
             ["Giant-Music-Transformer-Piano-Seed-3.mid", 128, 0],
             ["Giant-Music-Transformer-Piano-Seed-4.mid", 128, 0],
             ["Giant-Music-Transformer-Piano-Seed-5.mid", 128, 0],
             ["Giant-Music-Transformer-Piano-Seed-6.mid", 128, 0],
             ["Giant-Music-Transformer-MI-Seed-1.mid", 128, 71],
             ["Giant-Music-Transformer-MI-Seed-2.mid", 128, 40],
             ["Giant-Music-Transformer-MI-Seed-3.mid", 128, 40],
             ["Giant-Music-Transformer-MI-Seed-4.mid", 128, 40],
             ["Giant-Music-Transformer-MI-Seed-5.mid", 128, 40],
             ["Giant-Music-Transformer-MI-Seed-6.mid", 128, 0]
            ],
            [input_midi, input_num_of_notes, input_patch_number],
            [output_midi_title, output_midi_summary, output_midi, output_audio, output_plot],
            InpaintPitches,
            cache_examples=True,
        )
        
        app.queue().launch()