





"""simple docstring"""





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()
__A											=						logging.get_logger(__name__)


__A											=						"""https://openaipublic.azureedge.net/jukebox/models/"""
__A											=						{
    """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 					__A  (_SCREAMING_SNAKE_CASE     )   ->List[Any]:





  """simple docstring"""


  if key.endswith('.model.1.bias'     ) and len(key.split('.'     )     ) > 10:
    lowerCAmelCase__     :int											=				key.replace('.model.1.bias'	,  '.conv1d_1.bias'     )
  elif key.endswith('.model.1.weight'     ) and len(key.split('.'     )     ) > 10:
    lowerCAmelCase__     :Dict											=				key.replace('.model.1.weight'	,  '.conv1d_1.weight'     )
  elif key.endswith('.model.3.bias'     ) and len(key.split('.'     )     ) > 10:
    lowerCAmelCase__     :Any											=				key.replace('.model.3.bias'	,  '.conv1d_2.bias'     )
  elif key.endswith('.model.3.weight'     ) and len(key.split('.'     )     ) > 10:
    lowerCAmelCase__     :Optional[int]											=				key.replace('.model.3.weight'	,  '.conv1d_2.weight'     )

  if "conditioner_blocks.0." in key:
    lowerCAmelCase__     :List[Any]											=				key.replace('conditioner_blocks.0'	,  'conditioner_blocks'     )

  if "prime_prior" in key:
    lowerCAmelCase__     :Dict											=				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__     :Any											=				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__     :List[str]											=				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 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->Union[str, Any]:





  """simple docstring"""


  lowerCAmelCase__     :List[Any]											=				{}
  import re

  lowerCAmelCase__     :str											=				re.compile(r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'     )
  lowerCAmelCase__     :List[Any]											=				re.compile(
      r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'     )
  lowerCAmelCase__     :Optional[Any]											=				re.compile(r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'     )

  lowerCAmelCase__     :Tuple											=				re.compile(r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'     )
  lowerCAmelCase__     :Union[str, Any]											=				re.compile(
      r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'     )
  lowerCAmelCase__     :Any											=				re.compile(r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'     )

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

    elif re_encoder_block_resnet.fullmatch(_SCREAMING_SNAKE_CASE     ):
      lowerCAmelCase__     :str											=				re_encoder_block_resnet.match(_SCREAMING_SNAKE_CASE     )
      lowerCAmelCase__     :str											=				regex_match.groups()
      lowerCAmelCase__     :int											=				int(groups[2]     ) * 2 + int(groups[3]     )
      lowerCAmelCase__     :Union[str, Any]											=				{'1': 1, '3': 2}[groups[-2]]
      lowerCAmelCase__     :int											=				F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."
      lowerCAmelCase__     :Tuple											=				F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
      lowerCAmelCase__     :List[Any]											=				prefix + resnet_block
      lowerCAmelCase__     :List[Any]											=				re_encoder_block_resnet.sub(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )

    elif re_encoder_block_proj_out.fullmatch(_SCREAMING_SNAKE_CASE     ):
      lowerCAmelCase__     :str											=				re_encoder_block_proj_out.match(_SCREAMING_SNAKE_CASE     )
      lowerCAmelCase__     :Tuple											=				regex_match.groups()
      lowerCAmelCase__     :Union[str, Any]											=				F"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
      lowerCAmelCase__     :Optional[Any]											=				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__     :str											=				re_decoder_block_conv_out.match(_SCREAMING_SNAKE_CASE     )
      lowerCAmelCase__     :List[str]											=				regex_match.groups()
      lowerCAmelCase__     :Dict											=				int(groups[2]     ) * 2 + int(groups[3]     ) - 2
      lowerCAmelCase__     :Union[str, Any]											=				F"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"
      lowerCAmelCase__     :List[str]											=				re_decoder_block_conv_out.sub(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )

    elif re_decoder_block_resnet.fullmatch(_SCREAMING_SNAKE_CASE     ):
      lowerCAmelCase__     :List[Any]											=				re_decoder_block_resnet.match(_SCREAMING_SNAKE_CASE     )
      lowerCAmelCase__     :Optional[Any]											=				regex_match.groups()
      lowerCAmelCase__     :Tuple											=				int(groups[2]     ) * 2 + int(groups[3]     ) - 2
      lowerCAmelCase__     :Dict											=				{'1': 1, '3': 2}[groups[-2]]
      lowerCAmelCase__     :Any											=				F"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."
      lowerCAmelCase__     :Tuple											=				F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
      lowerCAmelCase__     :Union[str, Any]											=				prefix + resnet_block
      lowerCAmelCase__     :List[Any]											=				re_decoder_block_resnet.sub(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )

    elif re_decoder_block_proj_in.fullmatch(_SCREAMING_SNAKE_CASE     ):
      lowerCAmelCase__     :Optional[Any]											=				re_decoder_block_proj_in.match(_SCREAMING_SNAKE_CASE     )
      lowerCAmelCase__     :Dict											=				regex_match.groups()
      lowerCAmelCase__     :Optional[int]											=				F"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
      lowerCAmelCase__     :Any											=				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__     :Dict											=				re_prior_cond_conv_out.match(_SCREAMING_SNAKE_CASE     )
      lowerCAmelCase__     :int											=				regex_match.groups()
      lowerCAmelCase__     :Optional[int]											=				int(groups[1]     ) * 2 + int(groups[2]     ) - 2
      lowerCAmelCase__     :Optional[int]											=				F"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
      lowerCAmelCase__     :Union[str, Any]											=				re_prior_cond_conv_out.sub(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )

    elif re_prior_cond_resnet.fullmatch(_SCREAMING_SNAKE_CASE     ):
      lowerCAmelCase__     :Tuple											=				re_prior_cond_resnet.match(_SCREAMING_SNAKE_CASE     )
      lowerCAmelCase__     :Optional[Any]											=				regex_match.groups()
      lowerCAmelCase__     :Optional[int]											=				int(groups[1]     ) * 2 + int(groups[2]     ) - 2
      lowerCAmelCase__     :str											=				{'1': 1, '3': 2}[groups[-2]]
      lowerCAmelCase__     :Optional[Any]											=				F"conditioner_blocks.upsampler.upsample_block.{block_index}."
      lowerCAmelCase__     :List[str]											=				F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
      lowerCAmelCase__     :str											=				prefix + resnet_block
      lowerCAmelCase__     :Tuple											=				re_prior_cond_resnet.sub(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )

    elif re_prior_cond_proj_in.fullmatch(_SCREAMING_SNAKE_CASE     ):
      lowerCAmelCase__     :Any											=				re_prior_cond_proj_in.match(_SCREAMING_SNAKE_CASE     )
      lowerCAmelCase__     :List[str]											=				regex_match.groups()
      lowerCAmelCase__     :List[str]											=				F"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
      lowerCAmelCase__     :str											=				re_prior_cond_proj_in.sub(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )

    # keep original key
    else:
      lowerCAmelCase__     :Optional[Any]											=				original_key

    lowerCAmelCase__     :int											=				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__     :Tuple											=				model_state_dict[F"{key_prefix}.{key}"]
      print(F"{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"     )
      lowerCAmelCase__     :List[Any]											=				original_key

    lowerCAmelCase__     :Union[str, Any]											=				original_key
    lowerCAmelCase__     :Tuple											=				value

  return new_dict
@torch.no_grad()
def 					__A  (_SCREAMING_SNAKE_CASE=None	,  _SCREAMING_SNAKE_CASE=None     )   ->Optional[Any]:





  """simple docstring"""


  for file in MODEL_MAPPING[model_name]:
    if not os.path.isfile(F"{pytorch_dump_folder_path}/{file.split('/'     )[-1]}"     ):
      lowerCAmelCase__     :str											=				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__     :List[str]											=				MODEL_MAPPING[model_name.split('/'     )[-1]]

  lowerCAmelCase__     :List[Any]											=				JukeboxConfig.from_pretrained(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :Union[str, Any]											=				JukeboxModel(_SCREAMING_SNAKE_CASE     )

  lowerCAmelCase__     :int											=				[]
  lowerCAmelCase__     :Optional[int]											=				{}
  for i, dict_name in enumerate(_SCREAMING_SNAKE_CASE     ):
    lowerCAmelCase__     :Optional[Any]											=				torch.load(F"{pytorch_dump_folder_path}/{dict_name.split('/'     )[-1]}"     )['model']

    lowerCAmelCase__     :List[Any]											=				{}
    for k in old_dic.keys():
      if k.endswith('.b'     ):
        lowerCAmelCase__     :Dict											=				old_dic[k]
      elif k.endswith('.w'     ):
        lowerCAmelCase__     :Optional[int]											=				old_dic[k]
      elif "level_2" not in dict_name and "cond.model." in k:
        lowerCAmelCase__     :Dict											=				old_dic[k]
      else:
        lowerCAmelCase__     :List[str]											=				old_dic[k]

    lowerCAmelCase__     :Union[str, Any]											=				'vqvae' if i == 0 else F"priors.{3 - i}"
    lowerCAmelCase__     :Optional[Any]											=				fix_jukebox_keys(_SCREAMING_SNAKE_CASE	,  model.state_dict()	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
    weight_dict.append(_SCREAMING_SNAKE_CASE     )

  lowerCAmelCase__     :Union[str, Any]											=				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__":
  __A											=						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.""",
  )
  __A											=						parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)







"""simple docstring"""





def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->list[str]:





  """simple docstring"""


  return [sentence[i : i + ngram_size] for i in range(len(_SCREAMING_SNAKE_CASE     ) - ngram_size + 1     )]


if __name__ == "__main__":
  from doctest import testmod

  testmod()
