
from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)




'''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()
__snake_case:  List[str]								=			logging.get_logger(__name__)


__snake_case:  Dict								=			"https://openaipublic.azureedge.net/jukebox/models/"
__snake_case:  List[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 	_snake_case						(      A_       :    str   ):
  """simple docstring"""







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

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

  if "prime_prior" in key:
    a_      :	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:
    a_      :	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:
    a_      :	Optional[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 	_snake_case						(      A_       :    str     ,		A_       :    int     ,		A_       :    str     ,		A_       :    List[Any]   ):
  """simple docstring"""







  a_      :	List[str]      =							{}
  import re

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

  a_      :	int      =							re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""   )
  a_      :	List[str]      =							re.compile(
      R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""   )
  a_      :	List[Any]      =							re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""   )

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

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

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

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

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

    elif re_decoder_block_proj_in.fullmatch(A_   ):
      a_      :	Tuple      =							re_decoder_block_proj_in.match(A_   )
      a_      :	Any      =							regex_match.groups()
      a_      :	Dict      =							f'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
      a_      :	Tuple      =							re_decoder_block_proj_in.sub(A_     ,		A_   )

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

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

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

    # keep original key
    else:
      a_      :	str      =							original_key

    a_      :	Any      =							replace_key(A_   )

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

    a_      :	Tuple      =							original_key
    a_      :	Union[str, Any]      =							value

  return new_dict






@torch.no_grad()
def 	_snake_case						(      A_       :    Dict=None     ,		A_       :    Optional[Any]=None   ):
  """simple docstring"""







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

  a_      :	List[Any]      =							MODEL_MAPPING[model_name.split("""/"""   )[-1]]

  a_      :	Optional[Any]      =							JukeboxConfig.from_pretrained(A_   )
  a_      :	List[Any]      =							JukeboxModel(A_   )

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

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

    a_      :	List[Any]      =							"""vqvae""" if i == 0 else f'''priors.{3 - i}'''
    a_      :	Any      =							fix_jukebox_keys(A_     ,		model.state_dict()     ,		A_     ,		A_   )
    weight_dict.append(A_   )

  a_      :	str      =							weight_dict.pop(0   )
  model.vqvae.load_state_dict(A_   )
  for i in range(len(A_   )   ):
    model.priors[i].load_state_dict(weight_dict[2 - i]   )

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

  print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''   )
  model.save_pretrained(A_   )

  return weight_dict


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


