



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


lowercase							       =			'''https://openaipublic.azureedge.net/jukebox/models/'''
lowercase							       =			{
    '''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_       (		lowercase__       ):


     '''simple docstring'''







     if key.endswith(".model.1.bias"       ) and len(key.split("."       )       ) > 1_0:
          a_       		=key.replace(".model.1.bias"       ,	".conv1d_1.bias"       )
     elif key.endswith(".model.1.weight"       ) and len(key.split("."       )       ) > 1_0:
          a_       		=key.replace(".model.1.weight"       ,	".conv1d_1.weight"       )
     elif key.endswith(".model.3.bias"       ) and len(key.split("."       )       ) > 1_0:
          a_       		=key.replace(".model.3.bias"       ,	".conv1d_2.bias"       )
     elif key.endswith(".model.3.weight"       ) and len(key.split("."       )       ) > 1_0:
          a_       		=key.replace(".model.3.weight"       ,	".conv1d_2.weight"       )

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

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

     if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
          a_       		=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:
          a_       		=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_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		={}
     import re

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

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

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

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

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

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

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

          elif re_decoder_block_proj_in.fullmatch(lowercase__       ):
               a_       		=re_decoder_block_proj_in.match(lowercase__       )
               a_       		=regex_match.groups()
               a_       		=F"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
               a_       		=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__       ):
               a_       		=re_prior_cond_conv_out.match(lowercase__       )
               a_       		=regex_match.groups()
               a_       		=int(groups[1]       ) * 2 + int(groups[2]       ) - 2
               a_       		=F"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
               a_       		=re_prior_cond_conv_out.sub(lowercase__       ,	lowercase__       )

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

          elif re_prior_cond_proj_in.fullmatch(lowercase__       ):
               a_       		=re_prior_cond_proj_in.match(lowercase__       )
               a_       		=regex_match.groups()
               a_       		=F"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
               a_       		=re_prior_cond_proj_in.sub(lowercase__       ,	lowercase__       )

          # keep original key
          else:
               a_       		=original_key

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

          a_       		=original_key
          a_       		=value

     return new_dict






@torch.no_grad()
def  UpperCAmelCase_       (		lowercase__=None       ,	lowercase__=None       ):


     '''simple docstring'''







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

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

     a_       		=JukeboxConfig.from_pretrained(lowercase__       )
     a_       		=JukeboxModel(lowercase__       )

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

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

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

     a_       		=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__":
  lowercase							       =			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.''',
  )
  lowercase							       =			parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)







'''simple docstring'''


from __future__ import annotations






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     a_       		=str(lowercase__       )
     return len(lowercase__       ) == 9 and set(lowercase__       ) == set("123456789"       )






def  UpperCAmelCase_       (		):


     '''simple docstring'''







     for base_num in range(9_9_9_9       ,	4_9_9_9       ,	-1       ):
          a_       		=1_0_0_0_0_2 * base_num
          if is_9_pandigital(lowercase__       ):
               return candidate

     for base_num in range(3_3_3       ,	9_9       ,	-1       ):
          a_       		=1_0_0_2_0_0_3 * base_num
          if is_9_pandigital(lowercase__       ):
               return candidate

     return None


if __name__ == "__main__":
  print(F"""{solution() = }""")


