


'''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()
__UpperCamelCase       :       Dict						       =				logging.get_logger(__name__)


__UpperCamelCase       :       Dict						       =				"https://openaipublic.azureedge.net/jukebox/models/"
__UpperCamelCase       :       List[Any]						       =				{
    "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	:				str     ):







 """simple docstring"""



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

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

 if "prime_prior" in key:
  UpperCamelCase__					:    Optional[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:
  UpperCamelCase__					:    int          =   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__					:    Optional[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 			_a  (				SCREAMING_SNAKE_CASE	:				Any    , SCREAMING_SNAKE_CASE	:				Any    , SCREAMING_SNAKE_CASE	:				Any    , SCREAMING_SNAKE_CASE	:				List[str]     ):







 """simple docstring"""



 UpperCamelCase__					:    int          =   {}
 import re

 UpperCamelCase__					:    Tuple          =   re.compile(r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''     )
 UpperCamelCase__					:    Optional[int]          =   re.compile(
     r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''     )
 UpperCamelCase__					:    Optional[int]          =   re.compile(r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''     )

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

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

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

  elif re_encoder_block_proj_out.fullmatch(lowercase__     ):
   UpperCamelCase__					:    Tuple          =   re_encoder_block_proj_out.match(lowercase__     )
   UpperCamelCase__					:    Any          =   regex_match.groups()
   UpperCamelCase__					:    Tuple          =   F"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
   UpperCamelCase__					:    List[str]          =   re_encoder_block_proj_out.sub(lowercase__    , lowercase__     )

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

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

  elif re_decoder_block_proj_in.fullmatch(lowercase__     ):
   UpperCamelCase__					:    Any          =   re_decoder_block_proj_in.match(lowercase__     )
   UpperCamelCase__					:    Optional[int]          =   regex_match.groups()
   UpperCamelCase__					:    Optional[Any]          =   F"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
   UpperCamelCase__					:    Dict          =   re_decoder_block_proj_in.sub(lowercase__    , lowercase__     )

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

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

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

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

  UpperCamelCase__					:    Optional[Any]          =   replace_key(lowercase__     )

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

  UpperCamelCase__					:    List[str]          =   original_key
  UpperCamelCase__					:    Optional[Any]          =   value

 return new_dict

@torch.no_grad()
def 			_a  (				SCREAMING_SNAKE_CASE	:				List[Any]=None    , SCREAMING_SNAKE_CASE	:				Any=None     ):







 """simple docstring"""



 for file in MODEL_MAPPING[model_name]:
  if not os.path.isfile(F"{pytorch_dump_folder_path}/{file.split('/'     )[-1]}"     ):
   UpperCamelCase__					:    Optional[int]          =   requests.get(F"{PREFIX}{file}"    , allow_redirects=lowercase__     )
   os.makedirs(F"{pytorch_dump_folder_path}/"    , exist_ok=lowercase__     )
   open(F"{pytorch_dump_folder_path}/{file.split('/'     )[-1]}"    , '''wb'''     ).write(r.content     )

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

 UpperCamelCase__					:    int          =   JukeboxConfig.from_pretrained(lowercase__     )
 UpperCamelCase__					:    str          =   JukeboxModel(lowercase__     )

 UpperCamelCase__					:    Dict          =   []
 UpperCamelCase__					:    Optional[int]          =   {}
 for i, dict_name in enumerate(lowercase__     ):
  UpperCamelCase__					:    List[str]          =   torch.load(F"{pytorch_dump_folder_path}/{dict_name.split('/'     )[-1]}"     )['''model''']

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

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

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

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

 print(F"Saving model {model_name} to {pytorch_dump_folder_path}"     )
 model.save_pretrained(lowercase__     )

 return weight_dict


if __name__ == "__main__":
       __UpperCamelCase       :       Dict						       =				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       :       Optional[int]						       =				parser.parse_args()
       convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)






import functools

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    str          =   len(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Optional[Any]          =   len(SCREAMING_SNAKE_CASE     )

 @functools.cache
 def min_distance(SCREAMING_SNAKE_CASE	:				int    , SCREAMING_SNAKE_CASE	:				int     ) -> int:
  # if first word index is overflow - delete all from the second word
  if indexa >= len_worda:
   return len_worda - indexa
  # if second word index is overflow - delete all from the first word
  if indexa >= len_worda:
   return len_worda - indexa
  UpperCamelCase__					:    Tuple          =   int(worda[indexa] != worda[indexa]     )  # current letters not identical
  return min(
      1 + min_distance(indexa + 1    , SCREAMING_SNAKE_CASE     )    , 1 + min_distance(SCREAMING_SNAKE_CASE    , indexa + 1     )    , diff + min_distance(indexa + 1    , indexa + 1     )    , )

 return min_distance(0    , 0     )


if __name__ == "__main__":
       import doctest

       doctest.testmod()


