


from PIL import Image




def    a__    (		snake_case__					:							Image						,   snake_case__					:							float     ):

  def brightness(snake_case__					:							int     ) -> float:
    return 128 + level + (c - 128)

  if not -255.0 <= level <= 255.0:
    raise ValueError("""level must be between -255.0 (black) and 255.0 (white)"""     )
  return img.point(snake_case__     )


if __name__ == "__main__":
 # Load image
 with Image.open('image_data/lena.jpg') as img:
  # Change brightness to 100
  SCREAMING_SNAKE_CASE__					:     Tuple       =     change_brightness(img, 100)
  brigt_img.save('image_data/lena_brightness.png', format='png')









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()
SCREAMING_SNAKE_CASE__					:     Any       =     logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__					:     Any       =     'https://openaipublic.azureedge.net/jukebox/models/'
SCREAMING_SNAKE_CASE__					:     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    a__    (		snake_case__					:							Union[str, Any]     ):
  if key.endswith(""".model.1.bias"""     ) and len(key.split("""."""     )     ) > 10:
    _UpperCAmelCase		:							Dict							=     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		:							Union[str, Any]							=     key.replace(""".model.3.bias"""						,   """.conv1d_2.bias"""     )
  elif key.endswith(""".model.3.weight"""     ) and len(key.split("""."""     )     ) > 10:
    _UpperCAmelCase		:							Optional[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		:							Union[str, 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		:							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		:							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    a__    (		snake_case__					:							List[str]						,   snake_case__					:							Optional[Any]						,   snake_case__					:							Optional[Any]						,   snake_case__					:							str     ):
  _UpperCAmelCase		:							int							=     {}
  import re

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

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

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

    elif re_encoder_block_resnet.fullmatch(snake_case__     ):
      _UpperCAmelCase		:							Tuple							=     re_encoder_block_resnet.match(snake_case__     )
      _UpperCAmelCase		:							Optional[Any]							=     regex_match.groups()
      _UpperCAmelCase		:							int							=     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		:							Dict							=     f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
      _UpperCAmelCase		:							Optional[int]							=     prefix + resnet_block
      _UpperCAmelCase		:							Any							=     re_encoder_block_resnet.sub(snake_case__						,   snake_case__     )

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

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

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

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

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

    elif re_prior_cond_proj_in.fullmatch(snake_case__     ):
      _UpperCAmelCase		:							Union[str, Any]							=     re_prior_cond_proj_in.match(snake_case__     )
      _UpperCAmelCase		:							Optional[int]							=     regex_match.groups()
      _UpperCAmelCase		:							Optional[Any]							=     f'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
      _UpperCAmelCase		:							Tuple							=     re_prior_cond_proj_in.sub(snake_case__						,   snake_case__     )

    # keep original key
    else:
      _UpperCAmelCase		:							Tuple							=     original_key

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

    _UpperCAmelCase		:							List[Any]							=     original_key
    _UpperCAmelCase		:							int							=     value

  return new_dict




@torch.no_grad()
def    a__    (		snake_case__					:							Any=None						,   snake_case__					:							Optional[Any]=None     ):
  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		:							Optional[Any]							=     JukeboxConfig.from_pretrained(snake_case__     )
  _UpperCAmelCase		:							int							=     JukeboxModel(snake_case__     )

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

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

    _UpperCAmelCase		:							Tuple							=     """vqvae""" if i == 0 else f'''priors.{3 - i}'''
    _UpperCAmelCase		:							str							=     fix_jukebox_keys(snake_case__						,   model.state_dict()						,   snake_case__						,   snake_case__     )
    weight_dict.append(snake_case__     )

  _UpperCAmelCase		:							Optional[Any]							=     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__":
 SCREAMING_SNAKE_CASE__					:     List[str]       =     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.',
 )
 SCREAMING_SNAKE_CASE__					:     List[Any]       =     parser.parse_args()
 convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)





