

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      snake_case_				(__A					:						Tuple					)      ->     List[str]:
   if key.endswith(""".model.1.bias"""					) and len(key.split("""."""					)					) > 1_0:
      __lowerCAmelCase  :      List[Any]    =						key.replace(""".model.1.bias""" ,       """.conv1d_1.bias"""					)
   elif key.endswith(""".model.1.weight"""					) and len(key.split("""."""					)					) > 1_0:
      __lowerCAmelCase  :      int    =						key.replace(""".model.1.weight""" ,       """.conv1d_1.weight"""					)
   elif key.endswith(""".model.3.bias"""					) and len(key.split("""."""					)					) > 1_0:
      __lowerCAmelCase  :      Any    =						key.replace(""".model.3.bias""" ,       """.conv1d_2.bias"""					)
   elif key.endswith(""".model.3.weight"""					) and len(key.split("""."""					)					) > 1_0:
      __lowerCAmelCase  :      str    =						key.replace(""".model.3.weight""" ,       """.conv1d_2.weight"""					)

   if "conditioner_blocks.0." in key:
      __lowerCAmelCase  :      Optional[Any]    =						key.replace("""conditioner_blocks.0""" ,       """conditioner_blocks"""					)

   if "prime_prior" in key:
      __lowerCAmelCase  :      Any    =						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[Any]    =						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      snake_case_				(__A					:						Optional[Any] ,       __A					:						Dict ,       __A					:						List[Any] ,       __A					:						List[Any]					)      ->     Any:
   __lowerCAmelCase  :      Dict    =						{}
   import re

   __lowerCAmelCase  :      int    =						re.compile(r"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""					)
   __lowerCAmelCase  :      Union[str, Any]    =						re.compile(
       r"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""					)
   __lowerCAmelCase  :      Tuple    =						re.compile(r"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""					)

   __lowerCAmelCase  :      Union[str, Any]    =						re.compile(r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""					)
   __lowerCAmelCase  :      List[Any]    =						re.compile(
       r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""					)
   __lowerCAmelCase  :      int    =						re.compile(r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""					)

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

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

      elif re_encoder_block_proj_out.fullmatch(__A					):
         __lowerCAmelCase  :      Tuple    =						re_encoder_block_proj_out.match(__A					)
         __lowerCAmelCase  :      Optional[Any]    =						regex_match.groups()
         __lowerCAmelCase  :      int    =						f'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
         __lowerCAmelCase  :      str    =						re_encoder_block_proj_out.sub(__A ,       __A					)

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

      elif re_decoder_block_resnet.fullmatch(__A					):
         __lowerCAmelCase  :      int    =						re_decoder_block_resnet.match(__A					)
         __lowerCAmelCase  :      Union[str, Any]    =						regex_match.groups()
         __lowerCAmelCase  :      List[Any]    =						int(groups[2]					) * 2 + int(groups[3]					) - 2
         __lowerCAmelCase  :      Dict    =						{"""1""": 1, """3""": 2}[groups[-2]]
         __lowerCAmelCase  :      List[str]    =						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  :      Optional[int]    =						prefix + resnet_block
         __lowerCAmelCase  :      Union[str, Any]    =						re_decoder_block_resnet.sub(__A ,       __A					)

      elif re_decoder_block_proj_in.fullmatch(__A					):
         __lowerCAmelCase  :      Tuple    =						re_decoder_block_proj_in.match(__A					)
         __lowerCAmelCase  :      int    =						regex_match.groups()
         __lowerCAmelCase  :      Any    =						f'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
         __lowerCAmelCase  :      List[Any]    =						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					):
         __lowerCAmelCase  :      str    =						re_prior_cond_conv_out.match(__A					)
         __lowerCAmelCase  :      str    =						regex_match.groups()
         __lowerCAmelCase  :      Union[str, Any]    =						int(groups[1]					) * 2 + int(groups[2]					) - 2
         __lowerCAmelCase  :      List[str]    =						f'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
         __lowerCAmelCase  :      int    =						re_prior_cond_conv_out.sub(__A ,       __A					)

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

      elif re_prior_cond_proj_in.fullmatch(__A					):
         __lowerCAmelCase  :      Dict    =						re_prior_cond_proj_in.match(__A					)
         __lowerCAmelCase  :      Tuple    =						regex_match.groups()
         __lowerCAmelCase  :      List[str]    =						f'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
         __lowerCAmelCase  :      Optional[int]    =						re_prior_cond_proj_in.sub(__A ,       __A					)

      # keep original key
      else:
         __lowerCAmelCase  :      List[Any]    =						original_key

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

      __lowerCAmelCase  :      Tuple    =						original_key
      __lowerCAmelCase  :      str    =						value

   return new_dict





@torch.no_grad()
def      snake_case_				(__A					:						str=None ,       __A					:						List[str]=None					)      ->     List[Any]:
   for file in MODEL_MAPPING[model_name]:
      if not os.path.isfile(f'''{pytorch_dump_folder_path}/{file.split('/'					)[-1]}'''					):
         __lowerCAmelCase  :      Union[str, Any]    =						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					)

   __lowerCAmelCase  :      Union[str, Any]    =						MODEL_MAPPING[model_name.split("""/"""					)[-1]]

   __lowerCAmelCase  :      List[Any]    =						JukeboxConfig.from_pretrained(__A					)
   __lowerCAmelCase  :      str    =						JukeboxModel(__A					)

   __lowerCAmelCase  :      Optional[int]    =						[]
   __lowerCAmelCase  :      Dict    =						{}
   for i, dict_name in enumerate(__A					):
      __lowerCAmelCase  :      int    =						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  :      Optional[int]    =						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  :      Tuple    =						old_dic[k]

      __lowerCAmelCase  :      Any    =						"""vqvae""" if i == 0 else f'''priors.{3 - i}'''
      __lowerCAmelCase  :      Any    =						fix_jukebox_keys(__A ,       model.state_dict() ,       __A ,       __A					)
      weight_dict.append(__A					)

   __lowerCAmelCase  :      Optional[Any]    =						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__":
   __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 pathlib import Path

import fire





def      snake_case_				(__A					:						str ,       __A					:						str ,       __A					:						int					)      ->     Any:
   __lowerCAmelCase  :      Tuple    =						Path(__A					)
   __lowerCAmelCase  :      Tuple    =						Path(__A					)
   dest_dir.mkdir(exist_ok=__A					)
   for path in src_dir.iterdir():
      __lowerCAmelCase  :      str    =						[x.rstrip() for x in list(path.open().readlines()					)][:n]
      __lowerCAmelCase  :      Dict    =						dest_dir.joinpath(path.name					)
      print(__A					)
      dest_path.open("""w"""					).write("""\n""".join(__A					)					)


if __name__ == "__main__":
   fire.Fire(minify)


