




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()
lowerCamelCase							:						str					       =  logging.get_logger(__name__)


lowerCamelCase							:						Optional[int]					       =  "https://openaipublic.azureedge.net/jukebox/models/"
lowerCamelCase							:						str					       =  {
    "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      _SCREAMING_SNAKE_CASE     (				lowercase      :			Any	):


      '''simple docstring'''



      if key.endswith('.model.1.bias'	) and len(key.split('.'	)	) > 10:
            lowerCamelCase_							=       key.replace('.model.1.bias'      ,				'.conv1d_1.bias'	)
      elif key.endswith('.model.1.weight'	) and len(key.split('.'	)	) > 10:
            lowerCamelCase_							=       key.replace('.model.1.weight'      ,				'.conv1d_1.weight'	)
      elif key.endswith('.model.3.bias'	) and len(key.split('.'	)	) > 10:
            lowerCamelCase_							=       key.replace('.model.3.bias'      ,				'.conv1d_2.bias'	)
      elif key.endswith('.model.3.weight'	) and len(key.split('.'	)	) > 10:
            lowerCamelCase_							=       key.replace('.model.3.weight'      ,				'.conv1d_2.weight'	)

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

      if "prime_prior" in key:
            lowerCamelCase_							=       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_							=       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_							=       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      _SCREAMING_SNAKE_CASE     (				lowercase      :			Union[str, Any]      ,				lowercase      :			Any      ,				lowercase      :			Union[str, Any]      ,				lowercase      :			Tuple	):


      '''simple docstring'''



      lowerCamelCase_							=       {}
      import re

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

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

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

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

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

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

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

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

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

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

            # keep original key
            else:
                  lowerCamelCase_							=       original_key

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

            lowerCamelCase_							=       original_key
            lowerCamelCase_							=       value

      return new_dict

@torch.no_grad()
def      _SCREAMING_SNAKE_CASE     (				lowercase      :			List[Any]=None      ,				lowercase      :			List[str]=None	):


      '''simple docstring'''



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

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

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

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

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

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

      lowerCamelCase_							=       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__":
 lowerCamelCase							:						Tuple					       =  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.",
 )
 lowerCamelCase							:						List[str]					       =  parser.parse_args()
 convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)








"""simple docstring"""







from ..utils import DummyObject, requires_backends

class       _a  ( metaclass=SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     ["""keras_nlp"""]



			def __init__(			self							:				int   ,	*_lowercase							:				Optional[int]   ,	**_lowercase							:				Dict		)							->					List[Any]:
									requires_backends(self   ,	["keras_nlp"]		)



