



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


lowercase__		:				Any          =	"""https://openaipublic.azureedge.net/jukebox/models/"""
lowercase__		:				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_    (    lowerCAmelCase__      :				List[str]						)						->   Optional[int]:

  """simple docstring"""




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

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

  if "prime_prior" in key:
    lowerCAmelCase_   :				List[str]								= key.replace('prime_prior'    ,   'encoder'						)

  if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
    lowerCAmelCase_   :				List[str]								= 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:
    lowerCAmelCase_   :				int								= 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_    (    lowerCAmelCase__      :				Dict    ,   lowerCAmelCase__      :				Union[str, Any]    ,   lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				Optional[int]						)						->   Optional[int]:

  """simple docstring"""




  lowerCAmelCase_   :				List[Any]								= {}
  import re

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

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

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

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

    elif re_encoder_block_proj_out.fullmatch(lowerCAmelCase__						):
      lowerCAmelCase_   :				Dict								= re_encoder_block_proj_out.match(lowerCAmelCase__						)
      lowerCAmelCase_   :				List[Any]								= regex_match.groups()
      lowerCAmelCase_   :				Optional[int]								= f"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
      lowerCAmelCase_   :				Optional[Any]								= re_encoder_block_proj_out.sub(lowerCAmelCase__    ,   lowerCAmelCase__						)

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

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

    elif re_decoder_block_proj_in.fullmatch(lowerCAmelCase__						):
      lowerCAmelCase_   :				int								= re_decoder_block_proj_in.match(lowerCAmelCase__						)
      lowerCAmelCase_   :				Optional[int]								= regex_match.groups()
      lowerCAmelCase_   :				List[str]								= f"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
      lowerCAmelCase_   :				Any								= re_decoder_block_proj_in.sub(lowerCAmelCase__    ,   lowerCAmelCase__						)

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

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

    elif re_prior_cond_proj_in.fullmatch(lowerCAmelCase__						):
      lowerCAmelCase_   :				str								= re_prior_cond_proj_in.match(lowerCAmelCase__						)
      lowerCAmelCase_   :				List[Any]								= regex_match.groups()
      lowerCAmelCase_   :				Optional[Any]								= f"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
      lowerCAmelCase_   :				List[str]								= re_prior_cond_proj_in.sub(lowerCAmelCase__    ,   lowerCAmelCase__						)

    # keep original key
    else:
      lowerCAmelCase_   :				Dict								= original_key

    lowerCAmelCase_   :				Tuple								= replace_key(lowerCAmelCase__						)

    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:
      lowerCAmelCase_   :				Optional[Any]								= model_state_dict[f"{key_prefix}.{key}"]
      print(f"{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"						)
      lowerCAmelCase_   :				Union[str, Any]								= original_key

    lowerCAmelCase_   :				str								= original_key
    lowerCAmelCase_   :				int								= value

  return new_dict

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

  """simple docstring"""




  for file in MODEL_MAPPING[model_name]:
    if not os.path.isfile(f"{pytorch_dump_folder_path}/{file.split('/'						)[-1]}"						):
      lowerCAmelCase_   :				List[Any]								= requests.get(f"{PREFIX}{file}"    ,   allow_redirects=lowerCAmelCase__						)
      os.makedirs(f"{pytorch_dump_folder_path}/"    ,   exist_ok=lowerCAmelCase__						)
      open(f"{pytorch_dump_folder_path}/{file.split('/'						)[-1]}"    ,   'wb'						).write(r.content						)

  lowerCAmelCase_   :				Optional[int]								= MODEL_MAPPING[model_name.split('/'						)[-1]]

  lowerCAmelCase_   :				List[str]								= JukeboxConfig.from_pretrained(lowerCAmelCase__						)
  lowerCAmelCase_   :				Tuple								= JukeboxModel(lowerCAmelCase__						)

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

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

    lowerCAmelCase_   :				int								= 'vqvae' if i == 0 else f"priors.{3 - i}"
    lowerCAmelCase_   :				List[str]								= fix_jukebox_keys(lowerCAmelCase__    ,   model.state_dict()    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)
    weight_dict.append(lowerCAmelCase__						)

  lowerCAmelCase_   :				int								= weight_dict.pop(0						)
  model.vqvae.load_state_dict(lowerCAmelCase__						)
  for i in range(len(lowerCAmelCase__						)						):
    model.priors[i].load_state_dict(weight_dict[2 - i]						)

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

  print(f"Saving model {model_name} to {pytorch_dump_folder_path}"						)
  model.save_pretrained(lowerCAmelCase__						)

  return weight_dict


if __name__ == "__main__":
     lowercase__		:				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.""",
     )
     lowercase__		:				Any          =	parser.parse_args()
     convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)










"""simple docstring"""







import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    """The `image_to_image.py` script is outdated. Please use directly `from diffusers import"""
    """ StableDiffusionImg2ImgPipeline` instead."""
)





