import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
__magic_name__								=logging.get_logger(__name__)


__magic_name__								='''https://openaipublic.azureedge.net/jukebox/models/'''
__magic_name__								={
    '''jukebox-1b-lyrics''': [
        '''5b/vqvae.pth.tar''',
        '''5b/prior_level_0.pth.tar''',
        '''5b/prior_level_1.pth.tar''',
        '''1b_lyrics/prior_level_2.pth.tar''',
    ],
    '''jukebox-5b-lyrics''': [
        '''5b/vqvae.pth.tar''',
        '''5b/prior_level_0.pth.tar''',
        '''5b/prior_level_1.pth.tar''',
        '''5b_lyrics/prior_level_2.pth.tar''',
    ],
}







def  __UpperCamelCase    (						A							):
     if key.endswith('''.model.1.bias'''							) and len(key.split('''.'''							)							) > 10:
          UpperCamelCase__										=      key.replace('''.model.1.bias''' ,							'''.conv1d_1.bias'''							)
     elif key.endswith('''.model.1.weight'''							) and len(key.split('''.'''							)							) > 10:
          UpperCamelCase__										=      key.replace('''.model.1.weight''' ,							'''.conv1d_1.weight'''							)
     elif key.endswith('''.model.3.bias'''							) and len(key.split('''.'''							)							) > 10:
          UpperCamelCase__										=      key.replace('''.model.3.bias''' ,							'''.conv1d_2.bias'''							)
     elif key.endswith('''.model.3.weight'''							) and len(key.split('''.'''							)							) > 10:
          UpperCamelCase__										=      key.replace('''.model.3.weight''' ,							'''.conv1d_2.weight'''							)

     if "conditioner_blocks.0." in key:
          UpperCamelCase__										=      key.replace('''conditioner_blocks.0''' ,							'''conditioner_blocks'''							)

     if "prime_prior" in key:
          UpperCamelCase__										=      key.replace('''prime_prior''' ,							'''encoder'''							)

     if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
          UpperCamelCase__										=      key.replace('''.emb.''' ,							'''.'''							)

     if key.endswith('''k'''							):  # replace vqvae.X.k with vqvae.X.codebook
          return key.replace('''.k''' ,							'''.codebook'''							)
     if "y_emb." in key:
          return key.replace('''y_emb.''' ,							'''metadata_embedding.'''							)

     if "x_emb.emb." in key:
          UpperCamelCase__										=      key.replace('''0.x_emb.emb''' ,							'''embed_tokens'''							)

     if "prime_state_ln" in key:
          return key.replace('''prime_state_ln''' ,							'''encoder.final_layer_norm'''							)
     if ".ln" in key:
          return key.replace('''.ln''' ,							'''.layer_norm'''							)
     if "_ln" in key:
          return key.replace('''_ln''' ,							'''_layer_norm'''							)

     if "prime_state_proj" in key:
          return key.replace('''prime_state_proj''' ,							'''encoder.proj_in'''							)
     if "prime_x_out" in key:
          return key.replace('''prime_x_out''' ,							'''encoder.lm_head'''							)
     if "prior.x_out" in key:
          return key.replace('''x_out''' ,							'''fc_proj_out'''							)
     if "x_emb" in key:
          return key.replace('''x_emb''' ,							'''embed_tokens'''							)

     return key







def  __UpperCamelCase    (						A ,							A ,							A ,							A							):
     UpperCamelCase__										=      {}
     import re

     UpperCamelCase__										=      re.compile(r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''							)
     UpperCamelCase__										=      re.compile(
         r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''							)
     UpperCamelCase__										=      re.compile(r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''							)

     UpperCamelCase__										=      re.compile(r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''							)
     UpperCamelCase__										=      re.compile(
         r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''							)
     UpperCamelCase__										=      re.compile(r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''							)

     UpperCamelCase__										=      re.compile(r'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'''							)
     UpperCamelCase__										=      re.compile(
         r'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''							)
     UpperCamelCase__										=      re.compile(r'''conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)'''							)

     for original_key, value in state_dict.items():
          # rename vqvae.encoder keys
          if re_encoder_block_conv_in.fullmatch(A							):
               UpperCamelCase__										=      re_encoder_block_conv_in.match(A							)
               UpperCamelCase__										=      regex_match.groups()
               UpperCamelCase__										=      int(groups[2]							) * 2 + int(groups[3]							)
               UpperCamelCase__										=      f"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"
               UpperCamelCase__										=      re_encoder_block_conv_in.sub(A ,							A							)

          elif re_encoder_block_resnet.fullmatch(A							):
               UpperCamelCase__										=      re_encoder_block_resnet.match(A							)
               UpperCamelCase__										=      regex_match.groups()
               UpperCamelCase__										=      int(groups[2]							) * 2 + int(groups[3]							)
               UpperCamelCase__										=      {'''1''': 1, '''3''': 2}[groups[-2]]
               UpperCamelCase__										=      f"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."
               UpperCamelCase__										=      f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
               UpperCamelCase__										=      prefix + resnet_block
               UpperCamelCase__										=      re_encoder_block_resnet.sub(A ,							A							)

          elif re_encoder_block_proj_out.fullmatch(A							):
               UpperCamelCase__										=      re_encoder_block_proj_out.match(A							)
               UpperCamelCase__										=      regex_match.groups()
               UpperCamelCase__										=      f"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
               UpperCamelCase__										=      re_encoder_block_proj_out.sub(A ,							A							)

          # rename vqvae.decoder keys
          elif re_decoder_block_conv_out.fullmatch(A							):
               UpperCamelCase__										=      re_decoder_block_conv_out.match(A							)
               UpperCamelCase__										=      regex_match.groups()
               UpperCamelCase__										=      int(groups[2]							) * 2 + int(groups[3]							) - 2
               UpperCamelCase__										=      f"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"
               UpperCamelCase__										=      re_decoder_block_conv_out.sub(A ,							A							)

          elif re_decoder_block_resnet.fullmatch(A							):
               UpperCamelCase__										=      re_decoder_block_resnet.match(A							)
               UpperCamelCase__										=      regex_match.groups()
               UpperCamelCase__										=      int(groups[2]							) * 2 + int(groups[3]							) - 2
               UpperCamelCase__										=      {'''1''': 1, '''3''': 2}[groups[-2]]
               UpperCamelCase__										=      f"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."
               UpperCamelCase__										=      f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
               UpperCamelCase__										=      prefix + resnet_block
               UpperCamelCase__										=      re_decoder_block_resnet.sub(A ,							A							)

          elif re_decoder_block_proj_in.fullmatch(A							):
               UpperCamelCase__										=      re_decoder_block_proj_in.match(A							)
               UpperCamelCase__										=      regex_match.groups()
               UpperCamelCase__										=      f"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
               UpperCamelCase__										=      re_decoder_block_proj_in.sub(A ,							A							)

          # rename prior cond.model to upsampler.upsample_block and resnet
          elif re_prior_cond_conv_out.fullmatch(A							):
               UpperCamelCase__										=      re_prior_cond_conv_out.match(A							)
               UpperCamelCase__										=      regex_match.groups()
               UpperCamelCase__										=      int(groups[1]							) * 2 + int(groups[2]							) - 2
               UpperCamelCase__										=      f"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
               UpperCamelCase__										=      re_prior_cond_conv_out.sub(A ,							A							)

          elif re_prior_cond_resnet.fullmatch(A							):
               UpperCamelCase__										=      re_prior_cond_resnet.match(A							)
               UpperCamelCase__										=      regex_match.groups()
               UpperCamelCase__										=      int(groups[1]							) * 2 + int(groups[2]							) - 2
               UpperCamelCase__										=      {'''1''': 1, '''3''': 2}[groups[-2]]
               UpperCamelCase__										=      f"conditioner_blocks.upsampler.upsample_block.{block_index}."
               UpperCamelCase__										=      f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
               UpperCamelCase__										=      prefix + resnet_block
               UpperCamelCase__										=      re_prior_cond_resnet.sub(A ,							A							)

          elif re_prior_cond_proj_in.fullmatch(A							):
               UpperCamelCase__										=      re_prior_cond_proj_in.match(A							)
               UpperCamelCase__										=      regex_match.groups()
               UpperCamelCase__										=      f"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
               UpperCamelCase__										=      re_prior_cond_proj_in.sub(A ,							A							)

          # keep original key
          else:
               UpperCamelCase__										=      original_key

          UpperCamelCase__										=      replace_key(A							)

          if f"{key_prefix}.{key}" not in model_state_dict or key is None:
               print(f"failed converting {original_key} to {key}, does not match"							)

          # handle missmatched shape
          elif value.shape != model_state_dict[f"{key_prefix}.{key}"].shape:
               UpperCamelCase__										=      model_state_dict[f"{key_prefix}.{key}"]
               print(f"{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"							)
               UpperCamelCase__										=      original_key

          UpperCamelCase__										=      original_key
          UpperCamelCase__										=      value

     return new_dict







@torch.no_grad()
def  __UpperCamelCase    (						A=None ,							A=None							):
     for file in MODEL_MAPPING[model_name]:
          if not os.path.isfile(f"{pytorch_dump_folder_path}/{file.split('/'							)[-1]}"							):
               UpperCamelCase__										=      requests.get(f"{PREFIX}{file}" ,							allow_redirects=A							)
               os.makedirs(f"{pytorch_dump_folder_path}/" ,							exist_ok=A							)
               open(f"{pytorch_dump_folder_path}/{file.split('/'							)[-1]}" ,							'''wb'''							).write(r.content							)

     UpperCamelCase__										=      MODEL_MAPPING[model_name.split('''/'''							)[-1]]

     UpperCamelCase__										=      JukeboxConfig.from_pretrained(A							)
     UpperCamelCase__										=      JukeboxModel(A							)

     UpperCamelCase__										=      []
     UpperCamelCase__										=      {}
     for i, dict_name in enumerate(A							):
          UpperCamelCase__										=      torch.load(f"{pytorch_dump_folder_path}/{dict_name.split('/'							)[-1]}"							)['''model''']

          UpperCamelCase__										=      {}
          for k in old_dic.keys():
               if k.endswith('''.b'''							):
                    UpperCamelCase__										=      old_dic[k]
               elif k.endswith('''.w'''							):
                    UpperCamelCase__										=      old_dic[k]
               elif "level_2" not in dict_name and "cond.model." in k:
                    UpperCamelCase__										=      old_dic[k]
               else:
                    UpperCamelCase__										=      old_dic[k]

          UpperCamelCase__										=      '''vqvae''' if i == 0 else f"priors.{3 - i}"
          UpperCamelCase__										=      fix_jukebox_keys(A ,							model.state_dict() ,							A ,							A							)
          weight_dict.append(A							)

     UpperCamelCase__										=      weight_dict.pop(0							)
     model.vqvae.load_state_dict(A							)
     for i in range(len(A							)							):
          model.priors[i].load_state_dict(weight_dict[2 - i]							)

     Path(A							).mkdir(exist_ok=A							)
     with open(f"{pytorch_dump_folder_path}/mapping.json" ,							'''w'''							) as txtfile:
          json.dump(A ,							A							)

     print(f"Saving model {model_name} to {pytorch_dump_folder_path}"							)
     model.save_pretrained(A							)

     return weight_dict


if __name__ == "__main__":
     __magic_name__								=argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--model_name''',
         default='''jukebox-5b-lyrics''',
         type=str,
         help='''Name of the model you\'d like to convert.''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''',
         default='''jukebox-5b-lyrics-converted''',
         type=str,
         help='''Path to the output PyTorch model directory.''',
     )
     __magic_name__								=parser.parse_args()
     convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)




from datetime import datetime

import matplotlib.pyplot as plt
import torch







def  __UpperCamelCase    (						A							):
     for param in module.parameters():
          UpperCamelCase__										=      False







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      '''cuda''' if torch.cuda.is_available() else '''cpu'''
     if torch.backends.mps.is_available() and torch.backends.mps.is_built():
          UpperCamelCase__										=      '''mps'''
     if device == "mps":
          print(
              '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
              ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
              ''' with generations.'''							)
     return device







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      plt.imshow(A							)
     fig.axes.get_xaxis().set_visible(A							)
     fig.axes.get_yaxis().set_visible(A							)
     plt.show()







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      datetime.now()
     UpperCamelCase__										=      current_time.strftime('''%H:%M:%S'''							)
     return timestamp



