



import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase						    =  logging.get_logger(__name__)

_lowerCamelCase						    =  {
    """microsoft/unispeech-sat-base-100h-libri-ft""": (
        """https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json"""
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}

class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):
     lowerCAmelCase					=			"""unispeech-sat"""





     def __init__(       self :							Any     ,  UpperCamelCase :							int=3_2     ,  UpperCamelCase :							List[Any]=7_6_8     ,  UpperCamelCase :							str=1_2     ,  UpperCamelCase :							Dict=1_2     ,  UpperCamelCase :							str=3_0_7_2     ,  UpperCamelCase :							int="gelu"     ,  UpperCamelCase :							Dict=0.1     ,  UpperCamelCase :							Tuple=0.1     ,  UpperCamelCase :							Union[str, Any]=0.1     ,  UpperCamelCase :							List[str]=0.0     ,  UpperCamelCase :							List[str]=0.0     ,  UpperCamelCase :							str=0.1     ,  UpperCamelCase :							str=0.1     ,  UpperCamelCase :							int=0.0_2     ,  UpperCamelCase :							List[Any]=1E-5     ,  UpperCamelCase :							Optional[int]="group"     ,  UpperCamelCase :							int="gelu"     ,  UpperCamelCase :							str=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)     ,  UpperCamelCase :							int=(5, 2, 2, 2, 2, 2, 2)     ,  UpperCamelCase :							Optional[Any]=(1_0, 3, 3, 3, 3, 2, 2)     ,  UpperCamelCase :							Optional[int]=False     ,  UpperCamelCase :							int=1_2_8     ,  UpperCamelCase :							Dict=1_6     ,  UpperCamelCase :							Tuple=False     ,  UpperCamelCase :							Optional[Any]=True     ,  UpperCamelCase :							List[Any]=0.0_5     ,  UpperCamelCase :							int=1_0     ,  UpperCamelCase :							Optional[Any]=2     ,  UpperCamelCase :							List[Any]=0.0     ,  UpperCamelCase :							Optional[int]=1_0     ,  UpperCamelCase :							Union[str, Any]=0     ,  UpperCamelCase :							Union[str, Any]=3_2_0     ,  UpperCamelCase :							str=2     ,  UpperCamelCase :							List[str]=0.1     ,  UpperCamelCase :							List[str]=1_0_0     ,  UpperCamelCase :							List[str]=2_5_6     ,  UpperCamelCase :							Optional[int]=2_5_6     ,  UpperCamelCase :							str=0.1     ,  UpperCamelCase :							Union[str, Any]="mean"     ,  UpperCamelCase :							Dict=False     ,  UpperCamelCase :							Optional[Any]=False     ,  UpperCamelCase :							int=2_5_6     ,  UpperCamelCase :							Tuple=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)     ,  UpperCamelCase :							Dict=(5, 3, 3, 1, 1)     ,  UpperCamelCase :							Union[str, Any]=(1, 2, 3, 1, 1)     ,  UpperCamelCase :							List[Any]=5_1_2     ,  UpperCamelCase :							str=0     ,  UpperCamelCase :							Any=1     ,  UpperCamelCase :							List[Any]=2     ,  UpperCamelCase :							Tuple=5_0_4     ,  **UpperCamelCase :							Dict     ,  )->Dict:
      super().__init__(**UpperCamelCase     ,  pad_token_id=UpperCamelCase     ,  bos_token_id=UpperCamelCase     ,  eos_token_id=UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	hidden_size
      __SCREAMING_SNAKE_CASE       :    Any       =	feat_extract_norm
      __SCREAMING_SNAKE_CASE       :    str       =	feat_extract_activation
      __SCREAMING_SNAKE_CASE       :    Tuple       =	list(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    int       =	list(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	list(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	conv_bias
      __SCREAMING_SNAKE_CASE       :    Dict       =	num_conv_pos_embeddings
      __SCREAMING_SNAKE_CASE       :    Any       =	num_conv_pos_embedding_groups
      __SCREAMING_SNAKE_CASE       :    Tuple       =	len(self.conv_dim				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	num_hidden_layers
      __SCREAMING_SNAKE_CASE       :    Tuple       =	intermediate_size
      __SCREAMING_SNAKE_CASE       :    List[str]       =	hidden_act
      __SCREAMING_SNAKE_CASE       :    Dict       =	num_attention_heads
      __SCREAMING_SNAKE_CASE       :    Any       =	hidden_dropout
      __SCREAMING_SNAKE_CASE       :    Dict       =	attention_dropout
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	activation_dropout
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	feat_proj_dropout
      __SCREAMING_SNAKE_CASE       :    Any       =	final_dropout
      __SCREAMING_SNAKE_CASE       :    Any       =	layerdrop
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	layer_norm_eps
      __SCREAMING_SNAKE_CASE       :    Dict       =	initializer_range
      __SCREAMING_SNAKE_CASE       :    str       =	vocab_size
      __SCREAMING_SNAKE_CASE       :    List[str]       =	num_clusters
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	do_stable_layer_norm
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	use_weighted_layer_sum

      if (
          (len(self.conv_stride				) != self.num_feat_extract_layers)
          or (len(self.conv_kernel				) != self.num_feat_extract_layers)
          or (len(self.conv_dim				) != self.num_feat_extract_layers)
      ):
       raise ValueError(
           "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
           " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
           F""" {len(self.conv_dim				)}`, `len(config.conv_stride) = {len(self.conv_stride				)}`,"""
           F""" `len(config.conv_kernel) = {len(self.conv_kernel				)}`."""				)

      # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
      __SCREAMING_SNAKE_CASE       :    Tuple       =	apply_spec_augment
      __SCREAMING_SNAKE_CASE       :    str       =	mask_time_prob
      __SCREAMING_SNAKE_CASE       :    List[str]       =	mask_time_length
      __SCREAMING_SNAKE_CASE       :    str       =	mask_time_min_masks
      __SCREAMING_SNAKE_CASE       :    Dict       =	mask_feature_prob
      __SCREAMING_SNAKE_CASE       :    str       =	mask_feature_length
      __SCREAMING_SNAKE_CASE       :    int       =	mask_feature_min_masks

      # parameters for pretraining with codevector quantized representations
      __SCREAMING_SNAKE_CASE       :    str       =	num_codevectors_per_group
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	num_codevector_groups
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	contrastive_logits_temperature
      __SCREAMING_SNAKE_CASE       :    Dict       =	feat_quantizer_dropout
      __SCREAMING_SNAKE_CASE       :    str       =	num_negatives
      __SCREAMING_SNAKE_CASE       :    int       =	codevector_dim
      __SCREAMING_SNAKE_CASE       :    List[str]       =	proj_codevector_dim
      __SCREAMING_SNAKE_CASE       :    Any       =	diversity_loss_weight

      # ctc loss
      __SCREAMING_SNAKE_CASE       :    str       =	ctc_loss_reduction
      __SCREAMING_SNAKE_CASE       :    Any       =	ctc_zero_infinity

      # SequenceClassification-specific parameter. Feel free to ignore for other classes.
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	classifier_proj_size

      # XVector-specific parameters. Feel free to ignore for other classes.
      __SCREAMING_SNAKE_CASE       :    str       =	list(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    str       =	list(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	list(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	xvector_output_dim






     @property
     def 	__snake_case    (       self :							Optional[int]				)->Tuple:
      return functools.reduce(operator.mul     ,  self.conv_stride     ,  1				)











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 	_lowerCAmelCase						(				__lowerCamelCase   :     Any     ):



 """simple docstring"""





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

 if "conditioner_blocks.0." in key:
  __SCREAMING_SNAKE_CASE       :    List[Any]       =	key.replace("conditioner_blocks.0"		,	"conditioner_blocks"     )

 if "prime_prior" in key:
  __SCREAMING_SNAKE_CASE       :    Dict       =	key.replace("prime_prior"		,	"encoder"     )

 if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
  __SCREAMING_SNAKE_CASE       :    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:
  __SCREAMING_SNAKE_CASE       :    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 	_lowerCAmelCase						(				__lowerCamelCase   :     Tuple		,	__lowerCamelCase   :     Union[str, Any]		,	__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     Optional[Any]     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    str       =	{}
 import re

 __SCREAMING_SNAKE_CASE       :    str       =	re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"     )
 __SCREAMING_SNAKE_CASE       :    Tuple       =	re.compile(
     r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"     )
 __SCREAMING_SNAKE_CASE       :    Tuple       =	re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"     )

 __SCREAMING_SNAKE_CASE       :    int       =	re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"     )
 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	re.compile(
     r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"     )
 __SCREAMING_SNAKE_CASE       :    List[str]       =	re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"     )

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

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

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

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

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

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

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

  elif re_prior_cond_proj_in.fullmatch(__lowerCamelCase     ):
   __SCREAMING_SNAKE_CASE       :    Tuple       =	re_prior_cond_proj_in.match(__lowerCamelCase     )
   __SCREAMING_SNAKE_CASE       :    Dict       =	regex_match.groups()
   __SCREAMING_SNAKE_CASE       :    Dict       =	F"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
   __SCREAMING_SNAKE_CASE       :    int       =	re_prior_cond_proj_in.sub(__lowerCamelCase		,	__lowerCamelCase     )

  # keep original key
  else:
   __SCREAMING_SNAKE_CASE       :    Tuple       =	original_key

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

  __SCREAMING_SNAKE_CASE       :    List[str]       =	original_key
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	value

 return new_dict






@torch.no_grad()
def 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[int]=None		,	__lowerCamelCase   :     List[Any]=None     ):



 """simple docstring"""





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

 __SCREAMING_SNAKE_CASE       :    int       =	MODEL_MAPPING[model_name.split("/"     )[-1]]

 __SCREAMING_SNAKE_CASE       :    List[str]       =	JukeboxConfig.from_pretrained(__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    str       =	JukeboxModel(__lowerCamelCase     )

 __SCREAMING_SNAKE_CASE       :    int       =	[]
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	{}
 for i, dict_name in enumerate(__lowerCamelCase     ):
  __SCREAMING_SNAKE_CASE       :    str       =	torch.load(F"""{pytorch_dump_folder_path}/{dict_name.split("/"     )[-1]}"""     )["model"]

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

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

 __SCREAMING_SNAKE_CASE       :    Dict       =	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__":
      _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)






