



'''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()
_lowerCAmelCase										=     logging.get_logger(__name__)


_lowerCAmelCase										=     '''https://openaipublic.azureedge.net/jukebox/models/'''
_lowerCAmelCase										=     {
    '''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  (	UpperCamelCase      ):



    """simple docstring"""





    if key.endswith(""".model.1.bias"""      ) and len(key.split("""."""      )      ) > 10:
        lowerCAmelCase__			:  int											= 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__			:  List[str]											= key.replace(""".model.3.bias"""							,   """.conv1d_2.bias"""      )
    elif key.endswith(""".model.3.weight"""      ) and len(key.split("""."""      )      ) > 10:
        lowerCAmelCase__			:  str											= key.replace(""".model.3.weight"""							,   """.conv1d_2.weight"""      )

    if "conditioner_blocks.0." in key:
        lowerCAmelCase__			:  str											= key.replace("""conditioner_blocks.0"""							,   """conditioner_blocks"""      )

    if "prime_prior" in key:
        lowerCAmelCase__			:  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__			:  Optional[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:
        lowerCAmelCase__			:  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        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  int											= {}
    import re

    lowerCAmelCase__			:  Optional[int]											= re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""      )
    lowerCAmelCase__			:  Optional[Any]											= re.compile(
        R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""      )
    lowerCAmelCase__			:  Optional[int]											= re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""      )

    lowerCAmelCase__			:  Tuple											= re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""      )
    lowerCAmelCase__			:  Optional[Any]											= re.compile(
        R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""      )
    lowerCAmelCase__			:  Optional[int]											= re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""      )

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

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

        elif re_encoder_block_proj_out.fullmatch(UpperCamelCase      ):
            lowerCAmelCase__			:  Union[str, Any]											= re_encoder_block_proj_out.match(UpperCamelCase      )
            lowerCAmelCase__			:  Tuple											= regex_match.groups()
            lowerCAmelCase__			:  List[Any]											= f"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
            lowerCAmelCase__			:  Tuple											= re_encoder_block_proj_out.sub(UpperCamelCase							,   UpperCamelCase      )

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

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

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

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

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

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

        # keep original key
        else:
            lowerCAmelCase__			:  Any											= original_key

        lowerCAmelCase__			:  Union[str, Any]											= replace_key(UpperCamelCase      )

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

        lowerCAmelCase__			:  List[str]											= original_key
        lowerCAmelCase__			:  str											= value

    return new_dict
@torch.no_grad()
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase=None							,   UpperCamelCase=None      ):



    """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=UpperCamelCase      )
            os.makedirs(f"""{pytorch_dump_folder_path}/"""							,   exist_ok=UpperCamelCase      )
            open(f"""{pytorch_dump_folder_path}/{file.split('/'      )[-1]}"""							,   """wb"""      ).write(r.content      )

    lowerCAmelCase__			:  Dict											= MODEL_MAPPING[model_name.split("""/"""      )[-1]]

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

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

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

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

    lowerCAmelCase__			:  Union[str, Any]											= weight_dict.pop(0      )
    model.vqvae.load_state_dict(UpperCamelCase      )
    for i in range(len(UpperCamelCase      )      ):
        model.priors[i].load_state_dict(weight_dict[2 - i]      )

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

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

    return weight_dict


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







'''simple docstring'''





class 	lowerCAmelCase_:
  '''simple docstring'''




  def __init__(   self  ,__UpperCAmelCase				)   ->		Tuple:
      lowerCAmelCase__			:  Union[str, Any]											= n
      lowerCAmelCase__			:  int											= [None] * self.n
      lowerCAmelCase__			:  Union[str, Any]											= 0  # index of the first element
      lowerCAmelCase__			:  Union[str, Any]											= 0
      lowerCAmelCase__			:  Union[str, Any]											= 0




  def __len__(   self				)   ->		int:
      return self.size




  def 							UpperCAmelCase_     (   self				)   ->		bool:
      return self.size == 0




  def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
      return False if self.is_empty() else self.array[self.front]




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		Union[str, Any]:
      if self.size >= self.n:
          raise Exception("""QUEUE IS FULL"""				)

      lowerCAmelCase__			:  str											= data
      lowerCAmelCase__			:  List[str]											= (self.rear + 1) % self.n
      self.size += 1
      return self






  def 							UpperCAmelCase_     (   self				)   ->		int:
      if self.size == 0:
          raise Exception("""UNDERFLOW"""				)

      lowerCAmelCase__			:  int											= self.array[self.front]
      lowerCAmelCase__			:  Optional[Any]											= None
      lowerCAmelCase__			:  int											= (self.front + 1) % self.n
      self.size -= 1
      return temp


