


'''simple docstring'''






import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    def __lowercase       (       self   :  Dict		)       ->  List[str]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()

    @property
    def __lowercase       (       self   :  Dict		)       ->  Any:
      torch.manual_seed(0		)
      lowerCAmelCase_      :				Union[str, Any]      =       UNetaDModel(
          sample_size=(32, 64)   ,						in_channels=1   ,						out_channels=1   ,						layers_per_block=2   ,						block_out_channels=(1_28, 1_28)   ,						down_block_types=("""AttnDownBlock2D""", """DownBlock2D""")   ,						up_block_types=("""UpBlock2D""", """AttnUpBlock2D""")   ,						)
      return model

    @property
    def __lowercase       (       self   :  List[Any]		)       ->  Any:
      torch.manual_seed(0		)
      lowerCAmelCase_      :				Optional[Any]      =       UNetaDConditionModel(
          sample_size=(64, 32)   ,						in_channels=1   ,						out_channels=1   ,						layers_per_block=2   ,						block_out_channels=(1_28, 1_28)   ,						down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""")   ,						up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""")   ,						cross_attention_dim=10   ,						)
      return model

    @property
    def __lowercase       (       self   :  Dict		)       ->  List[str]:
      torch.manual_seed(0		)
      lowerCAmelCase_      :				Optional[Any]      =       AutoencoderKL(
          sample_size=(1_28, 64)   ,						in_channels=1   ,						out_channels=1   ,						latent_channels=1   ,						layers_per_block=2   ,						block_out_channels=(1_28, 1_28)   ,						down_block_types=("""DownEncoderBlock2D""", """DownEncoderBlock2D""")   ,						up_block_types=("""UpDecoderBlock2D""", """UpDecoderBlock2D""")   ,						)
      lowerCAmelCase_      :				int      =       UNetaDModel(
          sample_size=(64, 32)   ,						in_channels=1   ,						out_channels=1   ,						layers_per_block=2   ,						block_out_channels=(1_28, 1_28)   ,						down_block_types=("""AttnDownBlock2D""", """DownBlock2D""")   ,						up_block_types=("""UpBlock2D""", """AttnUpBlock2D""")   ,						)
      return vqvae, unet





    @slow
    def __lowercase       (       self   :  List[Any]		)       ->  Any:
      lowerCAmelCase_      :				int      =       """cpu"""  # ensure determinism for the device-dependent torch.Generator
      lowerCAmelCase_      :				Optional[Any]      =       Mel(
          x_res=self.dummy_unet.config.sample_size[1]   ,						y_res=self.dummy_unet.config.sample_size[0]   ,						)

      lowerCAmelCase_      :				List[str]      =       DDPMScheduler()
      lowerCAmelCase_      :				int      =       AudioDiffusionPipeline(vqvae=lowerCamelCase   ,						unet=self.dummy_unet   ,						mel=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       pipe.to(lowerCamelCase		)
      pipe.set_progress_bar_config(disable=lowerCamelCase		)

      lowerCAmelCase_      :				Tuple      =       torch.Generator(device=lowerCamelCase		).manual_seed(42		)
      lowerCAmelCase_      :				List[Any]      =       pipe(generator=lowerCamelCase   ,						steps=4		)
      lowerCAmelCase_      :				List[Any]      =       output.audios[0]
      lowerCAmelCase_      :				Any      =       output.images[0]

      lowerCAmelCase_      :				Optional[Any]      =       torch.Generator(device=lowerCamelCase		).manual_seed(42		)
      lowerCAmelCase_      :				Any      =       pipe(generator=lowerCamelCase   ,						steps=4   ,						return_dict=lowerCamelCase		)
      lowerCAmelCase_      :				Optional[Any]      =       output[0][0]

      assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
      assert (
          image.height == self.dummy_unet.config.sample_size[0]
          and image.width == self.dummy_unet.config.sample_size[1]
      )
      lowerCAmelCase_      :				Tuple      =       np.frombuffer(image.tobytes()   ,						dtype="""uint8"""		)[:10]
      lowerCAmelCase_      :				Union[str, Any]      =       np.frombuffer(image_from_tuple.tobytes()   ,						dtype="""uint8"""		)[:10]
      lowerCAmelCase_      :				int      =       np.array([69, 2_55, 2_55, 2_55, 0, 0, 77, 1_81, 12, 1_27]		)

      assert np.abs(image_slice.flatten() - expected_slice		).max() == 0
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() == 0

      lowerCAmelCase_      :				List[Any]      =       Mel(
          x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]   ,						y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]   ,						)

      lowerCAmelCase_      :				int      =       DDIMScheduler()
      lowerCAmelCase_      :				Optional[Any]      =       self.dummy_vqvae_and_unet
      lowerCAmelCase_      :				List[str]      =       AudioDiffusionPipeline(
          vqvae=self.dummy_vqvae_and_unet[0]   ,						unet=dummy_vqvae_and_unet[1]   ,						mel=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      lowerCAmelCase_      :				Optional[int]      =       pipe.to(lowerCamelCase		)
      pipe.set_progress_bar_config(disable=lowerCamelCase		)

      np.random.seed(0		)
      lowerCAmelCase_      :				List[str]      =       np.random.uniform(-1   ,						1   ,						((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)		)
      lowerCAmelCase_      :				Tuple      =       torch.Generator(device=lowerCamelCase		).manual_seed(42		)
      lowerCAmelCase_      :				List[Any]      =       pipe(raw_audio=lowerCamelCase   ,						generator=lowerCamelCase   ,						start_step=5   ,						steps=10		)
      lowerCAmelCase_      :				List[str]      =       output.images[0]

      assert (
          image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
          and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
      )
      lowerCAmelCase_      :				Any      =       np.frombuffer(image.tobytes()   ,						dtype="""uint8"""		)[:10]
      lowerCAmelCase_      :				Optional[int]      =       np.array([1_20, 1_17, 1_10, 1_09, 1_38, 1_67, 1_38, 1_48, 1_32, 1_21]		)

      assert np.abs(image_slice.flatten() - expected_slice		).max() == 0

      lowerCAmelCase_      :				Dict      =       self.dummy_unet_condition
      lowerCAmelCase_      :				Optional[int]      =       AudioDiffusionPipeline(
          vqvae=self.dummy_vqvae_and_unet[0]   ,						unet=lowerCamelCase   ,						mel=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       pipe.to(lowerCamelCase		)
      pipe.set_progress_bar_config(disable=lowerCamelCase		)

      np.random.seed(0		)
      lowerCAmelCase_      :				int      =       torch.rand((1, 1, 10)		)
      lowerCAmelCase_      :				List[Any]      =       pipe(generator=lowerCamelCase   ,						encoding=lowerCamelCase		)
      lowerCAmelCase_      :				str      =       output.images[0]
      lowerCAmelCase_      :				int      =       np.frombuffer(image.tobytes()   ,						dtype="""uint8"""		)[:10]
      lowerCAmelCase_      :				Dict      =       np.array([1_07, 1_03, 1_20, 1_27, 1_42, 1_22, 1_13, 1_22, 97, 1_11]		)

      assert np.abs(image_slice.flatten() - expected_slice		).max() == 0







@slow
@require_torch_gpu
class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    def __lowercase       (       self   :  Dict		)       ->  Optional[int]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()





    def __lowercase       (       self   :  int		)       ->  Tuple:
      lowerCAmelCase_      :				Optional[Any]      =       torch_device

      lowerCAmelCase_      :				Union[str, Any]      =       DiffusionPipeline.from_pretrained("""teticio/audio-diffusion-ddim-256"""		)
      lowerCAmelCase_      :				List[str]      =       pipe.to(lowerCamelCase		)
      pipe.set_progress_bar_config(disable=lowerCamelCase		)

      lowerCAmelCase_      :				List[Any]      =       torch.Generator(device=lowerCamelCase		).manual_seed(42		)
      lowerCAmelCase_      :				Optional[int]      =       pipe(generator=lowerCamelCase		)
      lowerCAmelCase_      :				List[str]      =       output.audios[0]
      lowerCAmelCase_      :				int      =       output.images[0]

      assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
      assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
      lowerCAmelCase_      :				Union[str, Any]      =       np.frombuffer(image.tobytes()   ,						dtype="""uint8"""		)[:10]
      lowerCAmelCase_      :				str      =       np.array([1_51, 1_67, 1_54, 1_44, 1_22, 1_34, 1_21, 1_05, 70, 26]		)

      assert np.abs(image_slice.flatten() - expected_slice		).max() == 0







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


__A :  str    							=				"https://openaipublic.azureedge.net/jukebox/models/"
__A :  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_      (   A__ :       int ):
  '''simple docstring'''





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

  if "conditioner_blocks.0." in key:
    lowerCAmelCase_      :				Any      =       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_      :				int      =       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_      :				List[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     UpperCamelCase_      (   A__ :       Dict							,   A__ :       Optional[Any]							,   A__ :       Tuple							,   A__ :       Any ):
  '''simple docstring'''





  lowerCAmelCase_      :				Union[str, Any]      =       {}
  import re

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

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

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

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

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

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

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

    elif re_decoder_block_proj_in.fullmatch(A__ ):
      lowerCAmelCase_      :				str      =       re_decoder_block_proj_in.match(A__ )
      lowerCAmelCase_      :				Optional[int]      =       regex_match.groups()
      lowerCAmelCase_      :				str      =       f'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
      lowerCAmelCase_      :				Union[str, Any]      =       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__ ):
      lowerCAmelCase_      :				Optional[Any]      =       re_prior_cond_conv_out.match(A__ )
      lowerCAmelCase_      :				Union[str, Any]      =       regex_match.groups()
      lowerCAmelCase_      :				Union[str, Any]      =       int(groups[1] ) * 2 + int(groups[2] ) - 2
      lowerCAmelCase_      :				Optional[int]      =       f'conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'
      lowerCAmelCase_      :				Dict      =       re_prior_cond_conv_out.sub(A__							,   A__ )

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

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

    # keep original key
    else:
      lowerCAmelCase_      :				Optional[Any]      =       original_key

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

    lowerCAmelCase_      :				Dict      =       original_key
    lowerCAmelCase_      :				Optional[int]      =       value

  return new_dict







@torch.no_grad()
def     UpperCamelCase_      (   A__ :       Optional[Any]=None							,   A__ :       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_      :				List[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 )

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

  lowerCAmelCase_      :				List[str]      =       JukeboxConfig.from_pretrained(A__ )
  lowerCAmelCase_      :				Dict      =       JukeboxModel(A__ )

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

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

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

  lowerCAmelCase_      :				int      =       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__":
  __A :  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.",
  )
  __A :  Optional[Any]    							=				parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)



