


'''simple docstring'''






from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


__A :  Dict    							=				logging.get_logger(__name__)  # pylint: disable=invalid-name

__A :  Union[str, Any]    							=				"\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")\n        >>> pipe_prior.to(\"cuda\")\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")\n        >>> pipe.to(\"cuda\")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save(\"cat.png\")\n        ```\n"







def     UpperCamelCase_      (   A__ :       List[str]							,   A__ :       List[Any]							,   A__ :       Dict=8 ):
		'''simple docstring'''





		lowerCAmelCase_      :				Dict      =       height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		lowerCAmelCase_      :				str      =       width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor






class        __snake_case	(       _snake_case):







				"""simple docstring"""



				def __init__(       self   :  Optional[Any]   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  str   ,						lowerCamelCase   :  Optional[int]   ,						)       ->  List[str]:
						super().__init__()

						self.register_modules(
						    unet=UpperCamelCase__   ,						scheduler=UpperCamelCase__   ,						movq=UpperCamelCase__   ,						)
						lowerCAmelCase_      :				Dict      =       2 ** (len(self.movq.config.block_out_channels		) - 1)

				def __lowercase       (       self   :  Optional[int]   ,						lowerCamelCase   :  Optional[int]   ,						lowerCamelCase   :  Union[str, Any]   ,						lowerCamelCase   :  Union[str, Any]   ,						lowerCamelCase   :  Tuple   ,						lowerCamelCase   :  Optional[int]   ,						lowerCamelCase   :  Dict		)       ->  Union[str, Any]:
						if latents is None:
								lowerCAmelCase_      :				Union[str, Any]      =       randn_tensor(UpperCamelCase__   ,						generator=UpperCamelCase__   ,						device=UpperCamelCase__   ,						dtype=UpperCamelCase__		)
						else:
								if latents.shape != shape:
										raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'		)
								lowerCAmelCase_      :				Tuple      =       latents.to(UpperCamelCase__		)

						lowerCAmelCase_      :				Tuple      =       latents * scheduler.init_noise_sigma
						return latents

				def __lowercase       (       self   :  int   ,						lowerCamelCase   :  Dict=0		)       ->  Tuple:
						if is_accelerate_available():
								from accelerate import cpu_offload
						else:
								raise ImportError("""Please install accelerate via `pip install accelerate`"""		)

						lowerCAmelCase_      :				Union[str, Any]      =       torch.device(F'cuda:{gpu_id}'		)

						lowerCAmelCase_      :				Optional[int]      =       [
						    self.unet,
						    self.movq,
						]
						for cpu_offloaded_model in models:
								if cpu_offloaded_model is not None:
										cpu_offload(UpperCamelCase__   ,						UpperCamelCase__		)

				def __lowercase       (       self   :  Tuple   ,						lowerCamelCase   :  Optional[Any]=0		)       ->  List[Any]:
						if is_accelerate_available() and is_accelerate_version(""">="""   ,						"""0.17.0.dev0"""		):
								from accelerate import cpu_offload_with_hook
						else:
								raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""		)

						lowerCAmelCase_      :				List[str]      =       torch.device(F'cuda:{gpu_id}'		)

						if self.device.type != "cpu":
								self.to("""cpu"""   ,						silence_dtype_warnings=UpperCamelCase__		)
								torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

						lowerCAmelCase_      :				Tuple      =       None
						for cpu_offloaded_model in [self.unet, self.movq]:
								lowerCAmelCase_,							lowerCAmelCase_      :				Dict      =       cpu_offload_with_hook(UpperCamelCase__   ,						UpperCamelCase__   ,						prev_module_hook=UpperCamelCase__		)

						# We'll offload the last model manually.
						lowerCAmelCase_      :				Dict      =       hook

				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def __lowercase       (       self   :  str		)       ->  Union[str, Any]:
						if not hasattr(self.unet   ,						"""_hf_hook"""		):
								return self.device
						for module in self.unet.modules():
								if (
								    hasattr(UpperCamelCase__   ,						"""_hf_hook"""		)
								    and hasattr(module._hf_hook   ,						"""execution_device"""		)
								    and module._hf_hook.execution_device is not None
								):
										return torch.device(module._hf_hook.execution_device		)
						return self.device





				@torch.no_grad()
				@replace_example_docstring(UpperCamelCase__		)
				def __call__(       self   :  Any   ,						lowerCamelCase   :  Optional[int]   ,						lowerCamelCase   :  Optional[Any]   ,						lowerCamelCase   :  Tuple = 5_12   ,						lowerCamelCase   :  List[str] = 5_12   ,						lowerCamelCase   :  int = 1_00   ,						lowerCamelCase   :  List[Any] = 4.0   ,						lowerCamelCase   :  List[str] = 1   ,						lowerCamelCase   :  List[Any] = None   ,						lowerCamelCase   :  str = None   ,						lowerCamelCase   :  Dict = "pil"   ,						lowerCamelCase   :  Any = True   ,						)       ->  Any:
						lowerCAmelCase_      :				Dict      =       self._execution_device

						lowerCAmelCase_      :				Optional[Any]      =       guidance_scale > 1.0

						if isinstance(UpperCamelCase__   ,						UpperCamelCase__		):
								lowerCAmelCase_      :				Optional[Any]      =       torch.cat(UpperCamelCase__   ,						dim=0		)
						lowerCAmelCase_      :				Union[str, Any]      =       image_embeds.shape[0] * num_images_per_prompt
						if isinstance(UpperCamelCase__   ,						UpperCamelCase__		):
								lowerCAmelCase_      :				Any      =       torch.cat(UpperCamelCase__   ,						dim=0		)

						if do_classifier_free_guidance:
								lowerCAmelCase_      :				List[Any]      =       image_embeds.repeat_interleave(UpperCamelCase__   ,						dim=0		)
								lowerCAmelCase_      :				str      =       negative_image_embeds.repeat_interleave(UpperCamelCase__   ,						dim=0		)

						lowerCAmelCase_      :				int      =       torch.cat([negative_image_embeds, image_embeds]   ,						dim=0		).to(dtype=self.unet.dtype   ,						device=UpperCamelCase__		)

						self.scheduler.set_timesteps(UpperCamelCase__   ,						device=UpperCamelCase__		)
						lowerCAmelCase_      :				Optional[int]      =       self.scheduler.timesteps

						lowerCAmelCase_      :				Tuple      =       self.unet.config.in_channels

						lowerCAmelCase_,							lowerCAmelCase_      :				Dict      =       downscale_height_and_width(UpperCamelCase__   ,						UpperCamelCase__   ,						self.movq_scale_factor		)

						# create initial latent
						lowerCAmelCase_      :				Optional[int]      =       self.prepare_latents(
						    (batch_size, num_channels_latents, height, width)   ,						image_embeds.dtype   ,						UpperCamelCase__   ,						UpperCamelCase__   ,						UpperCamelCase__   ,						self.scheduler   ,						)

						for i, t in enumerate(self.progress_bar(UpperCamelCase__		)		):
								# expand the latents if we are doing classifier free guidance
								lowerCAmelCase_      :				List[str]      =       torch.cat([latents] * 2		) if do_classifier_free_guidance else latents

								lowerCAmelCase_      :				List[str]      =       {"""image_embeds""": image_embeds}
								lowerCAmelCase_      :				int      =       self.unet(
								    sample=UpperCamelCase__   ,						timestep=UpperCamelCase__   ,						encoder_hidden_states=UpperCamelCase__   ,						added_cond_kwargs=UpperCamelCase__   ,						return_dict=UpperCamelCase__   ,						)[0]

								if do_classifier_free_guidance:
										lowerCAmelCase_,							lowerCAmelCase_      :				List[str]      =       noise_pred.split(latents.shape[1]   ,						dim=1		)
										lowerCAmelCase_,							lowerCAmelCase_      :				Tuple      =       noise_pred.chunk(2		)
										lowerCAmelCase_,							lowerCAmelCase_      :				List[Any]      =       variance_pred.chunk(2		)
										lowerCAmelCase_      :				Optional[Any]      =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
										lowerCAmelCase_      :				Optional[int]      =       torch.cat([noise_pred, variance_pred_text]   ,						dim=1		)

								if not (
								    hasattr(self.scheduler.config   ,						"""variance_type"""		)
								    and self.scheduler.config.variance_type in ["learned", "learned_range"]
								):
										lowerCAmelCase_,							lowerCAmelCase_      :				Dict      =       noise_pred.split(latents.shape[1]   ,						dim=1		)

								# compute the previous noisy sample x_t -> x_t-1
								lowerCAmelCase_      :				Union[str, Any]      =       self.scheduler.step(
								    UpperCamelCase__   ,						UpperCamelCase__   ,						UpperCamelCase__   ,						generator=UpperCamelCase__   ,						)[0]
						# post-processing
						lowerCAmelCase_      :				Union[str, Any]      =       self.movq.decode(UpperCamelCase__   ,						force_not_quantize=UpperCamelCase__		)["""sample"""]

						if output_type not in ["pt", "np", "pil"]:
								raise ValueError(F'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'		)

						if output_type in ["np", "pil"]:
								lowerCAmelCase_      :				List[str]      =       image * 0.5 + 0.5
								lowerCAmelCase_      :				Dict      =       image.clamp(0   ,						1		)
								lowerCAmelCase_      :				Dict      =       image.cpu().permute(0   ,						2   ,						3   ,						1		).float().numpy()

						if output_type == "pil":
								lowerCAmelCase_      :				Any      =       self.numpy_to_pil(UpperCamelCase__		)

						if not return_dict:
								return (image,)

						return ImagePipelineOutput(images=UpperCamelCase__		)







'''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)



