



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

   if "conditioner_blocks.0." in key:
      UpperCamelCase   :  int						     =     key.replace('conditioner_blocks.0'	,					'conditioner_blocks'     )

   if "prime_prior" in key:
      UpperCamelCase   :  Tuple						     =     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   :  Union[str, 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:
      UpperCamelCase   :  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 		UpperCamelCase     (       snake_case__	:   Union[str, Any]	,					snake_case__	:   Any	,					snake_case__	:   int	,					snake_case__	:   Optional[Any]     )     ->					int:
   UpperCamelCase   :  Union[str, Any]						     =     {}
   import re

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

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

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

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

      elif re_encoder_block_proj_out.fullmatch(snake_case__     ):
         UpperCamelCase   :  str						     =     re_encoder_block_proj_out.match(snake_case__     )
         UpperCamelCase   :  Tuple						     =     regex_match.groups()
         UpperCamelCase   :  Dict						     =     F"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
         UpperCamelCase   :  str						     =     re_encoder_block_proj_out.sub(snake_case__	,					snake_case__     )

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

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

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

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

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

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

      # keep original key
      else:
         UpperCamelCase   :  Tuple						     =     original_key

      UpperCamelCase   :  Dict						     =     replace_key(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:
         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[int]						     =     original_key

      UpperCamelCase   :  Dict						     =     original_key
      UpperCamelCase   :  Any						     =     value

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

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

   UpperCamelCase   :  int						     =     JukeboxConfig.from_pretrained(snake_case__     )
   UpperCamelCase   :  Optional[Any]						     =     JukeboxModel(snake_case__     )

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

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

      UpperCamelCase   :  Optional[int]						     =     'vqvae' if i == 0 else F"""priors.{3 - i}"""
      UpperCamelCase   :  Optional[int]						     =     fix_jukebox_keys(snake_case__	,					model.state_dict()	,					snake_case__	,					snake_case__     )
      weight_dict.append(snake_case__     )

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

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

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

   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 abc import ABC, abstractmethod
from argparse import ArgumentParser



class     lowerCAmelCase_		(  a__							):
       @staticmethod
       @abstractmethod
       def  snake_case_	(      SCREAMING_SNAKE_CASE_	)					->		int:
          raise NotImplementedError()






       @abstractmethod
       def  snake_case_	(      self	)					->		str:
          raise NotImplementedError()



