



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()
UpperCAmelCase_									=							logging.get_logger(__name__)


UpperCAmelCase_									=							"https://openaipublic.azureedge.net/jukebox/models/"
UpperCAmelCase_									=							{
    "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__		(				_SCREAMING_SNAKE_CASE   :	Any				)->Any:
      if key.endswith('''.model.1.bias'''				) and len(key.split('''.'''				)				) > 1_0:
            _lowerCAmelCase 			=  key.replace('''.model.1.bias'''     ,				'''.conv1d_1.bias'''				)
      elif key.endswith('''.model.1.weight'''				) and len(key.split('''.'''				)				) > 1_0:
            _lowerCAmelCase 			=  key.replace('''.model.1.weight'''     ,				'''.conv1d_1.weight'''				)
      elif key.endswith('''.model.3.bias'''				) and len(key.split('''.'''				)				) > 1_0:
            _lowerCAmelCase 			=  key.replace('''.model.3.bias'''     ,				'''.conv1d_2.bias'''				)
      elif key.endswith('''.model.3.weight'''				) and len(key.split('''.'''				)				) > 1_0:
            _lowerCAmelCase 			=  key.replace('''.model.3.weight'''     ,				'''.conv1d_2.weight'''				)

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

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

      if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
            _lowerCAmelCase 			=  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:
            _lowerCAmelCase 			=  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__		(				_SCREAMING_SNAKE_CASE   :	Optional[int]     ,				_SCREAMING_SNAKE_CASE   :	Tuple     ,				_SCREAMING_SNAKE_CASE   :	List[str]     ,				_SCREAMING_SNAKE_CASE   :	Dict				)->Optional[int]:
      _lowerCAmelCase 			=  {}
      import re

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

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

      _lowerCAmelCase 			=  re.compile(r'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'''				)
      _lowerCAmelCase 			=  re.compile(
          r'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''				)
      _lowerCAmelCase 			=  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(_SCREAMING_SNAKE_CASE				):
                  _lowerCAmelCase 			=  re_encoder_block_conv_in.match(_SCREAMING_SNAKE_CASE				)
                  _lowerCAmelCase 			=  regex_match.groups()
                  _lowerCAmelCase 			=  int(groups[2]				) * 2 + int(groups[3]				)
                  _lowerCAmelCase 			=  f'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'''
                  _lowerCAmelCase 			=  re_encoder_block_conv_in.sub(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)

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

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

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

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

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

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

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

            elif re_prior_cond_proj_in.fullmatch(_SCREAMING_SNAKE_CASE				):
                  _lowerCAmelCase 			=  re_prior_cond_proj_in.match(_SCREAMING_SNAKE_CASE				)
                  _lowerCAmelCase 			=  regex_match.groups()
                  _lowerCAmelCase 			=  f'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
                  _lowerCAmelCase 			=  re_prior_cond_proj_in.sub(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)

            # keep original key
            else:
                  _lowerCAmelCase 			=  original_key

            _lowerCAmelCase 			=  replace_key(_SCREAMING_SNAKE_CASE				)

            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:
                  _lowerCAmelCase 			=  model_state_dict[f'''{key_prefix}.{key}''']
                  print(f'''{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'''				)
                  _lowerCAmelCase 			=  original_key

            _lowerCAmelCase 			=  original_key
            _lowerCAmelCase 			=  value

      return new_dict
@torch.no_grad()
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Any=None     ,				_SCREAMING_SNAKE_CASE   :	List[str]=None				)->Dict:
      for file in MODEL_MAPPING[model_name]:
            if not os.path.isfile(f'''{pytorch_dump_folder_path}/{file.split("/"				)[-1]}'''				):
                  _lowerCAmelCase 			=  requests.get(f'''{PREFIX}{file}'''     ,				allow_redirects=_SCREAMING_SNAKE_CASE				)
                  os.makedirs(f'''{pytorch_dump_folder_path}/'''     ,				exist_ok=_SCREAMING_SNAKE_CASE				)
                  open(f'''{pytorch_dump_folder_path}/{file.split("/"				)[-1]}'''     ,				'''wb'''				).write(r.content				)

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

      _lowerCAmelCase 			=  JukeboxConfig.from_pretrained(_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  JukeboxModel(_SCREAMING_SNAKE_CASE				)

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

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

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

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

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

      print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''				)
      model.save_pretrained(_SCREAMING_SNAKE_CASE				)

      return weight_dict


if __name__ == "__main__":
    UpperCAmelCase_									=							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.",
    )
    UpperCAmelCase_									=							parser.parse_args()
    convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)










from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class 		UpperCAmelCase						(						snake_case_				):
   SCREAMING_SNAKE_CASE__							=     '''ClapFeatureExtractor'''
   SCREAMING_SNAKE_CASE__							=     ('''RobertaTokenizer''', '''RobertaTokenizerFast''')





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         super().__init__(_lowerCAmelCase       ,     _lowerCAmelCase			)





   def __call__(	self       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=None       ,     **_lowerCAmelCase			):
         _lowerCAmelCase 			=  kwargs.pop('''sampling_rate'''       ,     _lowerCAmelCase			)

         if text is None and audios is None:
               raise ValueError('''You have to specify either text or audios. Both cannot be none.'''			)

         if text is not None:
               _lowerCAmelCase 			=  self.tokenizer(_lowerCAmelCase       ,     return_tensors=_lowerCAmelCase       ,     **_lowerCAmelCase			)

         if audios is not None:
               _lowerCAmelCase 			=  self.feature_extractor(
                   _lowerCAmelCase       ,     sampling_rate=_lowerCAmelCase       ,     return_tensors=_lowerCAmelCase       ,     **_lowerCAmelCase			)

         if text is not None and audios is not None:
               _lowerCAmelCase 			=  audio_features.input_features
               return encoding
         elif text is not None:
               return encoding
         else:
               return BatchEncoding(data=dict(**_lowerCAmelCase			)       ,     tensor_type=_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     *_lowerCAmelCase       ,     **_lowerCAmelCase			):
         return self.tokenizer.batch_decode(*_lowerCAmelCase       ,     **_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     *_lowerCAmelCase       ,     **_lowerCAmelCase			):
         return self.tokenizer.decode(*_lowerCAmelCase       ,     **_lowerCAmelCase			)





   @property
   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.tokenizer.model_input_names
         _lowerCAmelCase 			=  self.feature_extractor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names			)			)