





'''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()
_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE							     =			"https://openaipublic.azureedge.net/jukebox/models/"
_SCREAMING_SNAKE_CASE							     =			{
    "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 	__lowerCamelCase			(      __lowerCAmelCase       :							Any		)		->  Dict:
   if key.endswith(""".model.1.bias"""		) and len(key.split("""."""		)		) > 10:
      snake_case 						=   key.replace(""".model.1.bias"""      ,    """.conv1d_1.bias"""		)
   elif key.endswith(""".model.1.weight"""		) and len(key.split("""."""		)		) > 10:
      snake_case 						=   key.replace(""".model.1.weight"""      ,    """.conv1d_1.weight"""		)
   elif key.endswith(""".model.3.bias"""		) and len(key.split("""."""		)		) > 10:
      snake_case 						=   key.replace(""".model.3.bias"""      ,    """.conv1d_2.bias"""		)
   elif key.endswith(""".model.3.weight"""		) and len(key.split("""."""		)		) > 10:
      snake_case 						=   key.replace(""".model.3.weight"""      ,    """.conv1d_2.weight"""		)

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

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

   if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
      snake_case 						=   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:
      snake_case 						=   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 	__lowerCamelCase			(      __lowerCAmelCase       :							Tuple      ,    __lowerCAmelCase       :							Optional[Any]      ,    __lowerCAmelCase       :							List[Any]      ,    __lowerCAmelCase       :							Dict		)		->  Optional[Any]:
   snake_case 						=   {}
   import re

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

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

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

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

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

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

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

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

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

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

      elif re_prior_cond_proj_in.fullmatch(__lowerCAmelCase		):
         snake_case 						=   re_prior_cond_proj_in.match(__lowerCAmelCase		)
         snake_case 						=   regex_match.groups()
         snake_case 						=   F'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
         snake_case 						=   re_prior_cond_proj_in.sub(__lowerCAmelCase      ,    __lowerCAmelCase		)

      # keep original key
      else:
         snake_case 						=   original_key

      snake_case 						=   replace_key(__lowerCAmelCase		)

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

      snake_case 						=   original_key
      snake_case 						=   value

   return new_dict





@torch.no_grad()
def 	__lowerCamelCase			(      __lowerCAmelCase       :							Union[str, Any]=None      ,    __lowerCAmelCase       :							Union[str, Any]=None		)		->  Optional[Any]:
   for file in MODEL_MAPPING[model_name]:
      if not os.path.isfile(F'''{pytorch_dump_folder_path}/{file.split("/"		)[-1]}'''		):
         snake_case 						=   requests.get(F'''{PREFIX}{file}'''      ,    allow_redirects=__lowerCAmelCase		)
         os.makedirs(F'''{pytorch_dump_folder_path}/'''      ,    exist_ok=__lowerCAmelCase		)
         open(F'''{pytorch_dump_folder_path}/{file.split("/"		)[-1]}'''      ,    """wb"""		).write(r.content		)

   snake_case 						=   MODEL_MAPPING[model_name.split("""/"""		)[-1]]

   snake_case 						=   JukeboxConfig.from_pretrained(__lowerCAmelCase		)
   snake_case 						=   JukeboxModel(__lowerCAmelCase		)

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

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

      snake_case 						=   """vqvae""" if i == 0 else F'''priors.{3 - i}'''
      snake_case 						=   fix_jukebox_keys(__lowerCAmelCase      ,    model.state_dict()      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)
      weight_dict.append(__lowerCAmelCase		)

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

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

   print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''		)
   model.save_pretrained(__lowerCAmelCase		)

   return weight_dict


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








'''simple docstring'''




import re





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str		)		->  list:
   return [char.split() for char in re.split(r"""[^ a-z A-Z 0-9 \s]"""      ,    str_		)]





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str		)		->  str:
   snake_case 						=   split_input(str_		)
   return "".join(
       ["""""".join([char.capitalize() for char in sub_str]		) for sub_str in string_split]		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							bool      ,    __lowerCAmelCase       :							str		)		->  str:
   try:
      snake_case 						=   split_input(__lowerCAmelCase		)
      if upper:
         snake_case 						=   """""".join(
             [
                 separator.join([char.upper() for char in sub_str]		)
                 for sub_str in string_split
             ]		)
      else:
         snake_case 						=   """""".join(
             [
                 separator.join([char.lower() for char in sub_str]		)
                 for sub_str in string_split
             ]		)
      return res_str
   except IndexError:
      return "not valid string"





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str		)		->  str:
   return to_simple_case(__lowerCAmelCase		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str		)		->  str:
   try:
      snake_case 						=   to_simple_case(__lowerCAmelCase		)
      return res_str[0].lower() + res_str[1:]
   except IndexError:
      return "not valid string"





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							bool		)		->  str:
   return to_complex_case(__lowerCAmelCase      ,    __lowerCAmelCase      ,    """_"""		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							bool		)		->  str:
   return to_complex_case(__lowerCAmelCase      ,    __lowerCAmelCase      ,    """-"""		)


if __name__ == "__main__":
 __import__("doctest").testmod()

