

'''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  :		List[Any]  =		logging.get_logger(__name__)


_UpperCAmelCase  :		List[str]  =		'''https://openaipublic.azureedge.net/jukebox/models/'''
_UpperCAmelCase  :		Union[str, 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 	UpperCamelCase   (       lowercase_						:    int	)		->	Dict:




  '''simple docstring'''



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

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

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

  if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
    lowercase									=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:
    lowercase									=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_						:    List[Any]     ,					lowercase_						:    Optional[Any]     ,					lowercase_						:    Optional[int]     ,					lowercase_						:    int	)		->	str:




  '''simple docstring'''



  lowercase									={}
  import re

  lowercase									=re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''	)
  lowercase									=re.compile(
      R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''	)
  lowercase									=re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''	)

  lowercase									=re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''	)
  lowercase									=re.compile(
      R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''	)
  lowercase									=re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''	)

  lowercase									=re.compile(R'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'''	)
  lowercase									=re.compile(
      R'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''	)
  lowercase									=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_	):
      lowercase									=re_encoder_block_conv_in.match(lowercase_	)
      lowercase									=regex_match.groups()
      lowercase									=int(groups[2]	) * 2 + int(groups[3]	)
      lowercase									=f'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'
      lowercase									=re_encoder_block_conv_in.sub(lowercase_     ,					lowercase_	)

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

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

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

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

    elif re_decoder_block_proj_in.fullmatch(lowercase_	):
      lowercase									=re_decoder_block_proj_in.match(lowercase_	)
      lowercase									=regex_match.groups()
      lowercase									=f'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
      lowercase									=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_	):
      lowercase									=re_prior_cond_conv_out.match(lowercase_	)
      lowercase									=regex_match.groups()
      lowercase									=int(groups[1]	) * 2 + int(groups[2]	) - 2
      lowercase									=f'conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'
      lowercase									=re_prior_cond_conv_out.sub(lowercase_     ,					lowercase_	)

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

    elif re_prior_cond_proj_in.fullmatch(lowercase_	):
      lowercase									=re_prior_cond_proj_in.match(lowercase_	)
      lowercase									=regex_match.groups()
      lowercase									=f'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
      lowercase									=re_prior_cond_proj_in.sub(lowercase_     ,					lowercase_	)

    # keep original key
    else:
      lowercase									=original_key

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

    lowercase									=original_key
    lowercase									=value

  return new_dict




@torch.no_grad()
def 	UpperCamelCase   (       lowercase_						:    Any=None     ,					lowercase_						:    List[Any]=None	)		->	str:




  '''simple docstring'''



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

  lowercase									=MODEL_MAPPING[model_name.split('''/'''	)[-1]]

  lowercase									=JukeboxConfig.from_pretrained(lowercase_	)
  lowercase									=JukeboxModel(lowercase_	)

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

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

    lowercase									='''vqvae''' if i == 0 else f'priors.{3 - i}'
    lowercase									=fix_jukebox_keys(lowercase_     ,					model.state_dict()     ,					lowercase_     ,					lowercase_	)
    weight_dict.append(lowercase_	)

  lowercase									=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[Any]  =		parser.parse_args()
     convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)



import re

from filelock import FileLock


try:
      import nltk

      a_     :    Optional[Any]     		=   True
except (ImportError, ModuleNotFoundError):
      a_     :    Union[str, Any]     		=   False

if NLTK_AVAILABLE:
      with FileLock(".lock") as lock:
            nltk.download("punkt", quiet=True)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str						)		->				str:
 '''simple docstring'''


 re.sub('<n>'		,				''		,				_UpperCamelCase						)  # remove pegasus newline char
 assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
 return "\n".join(nltk.sent_tokenize(_UpperCamelCase						)						)

