


'''simple docstring'''






import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel







def     UpperCamelCase_      (   A__ :       List[str]							,   A__ :       Union[str, Any]=1 ):
  '''simple docstring'''





  if n_shave_prefix_segments >= 0:
    return ".".join(path.split(""".""" )[n_shave_prefix_segments:] )
  else:
    return ".".join(path.split(""".""" )[:n_shave_prefix_segments] )







def     UpperCamelCase_      (   A__ :       str							,   A__ :       Optional[Any]=0 ):
  '''simple docstring'''





  lowerCAmelCase_      :				Union[str, Any]      =       []
  for old_item in old_list:
    lowerCAmelCase_      :				Optional[int]      =       old_item.replace("""in_layers.0"""							,   """norm1""" )
    lowerCAmelCase_      :				List[str]      =       new_item.replace("""in_layers.2"""							,   """conv1""" )

    lowerCAmelCase_      :				Optional[int]      =       new_item.replace("""out_layers.0"""							,   """norm2""" )
    lowerCAmelCase_      :				List[Any]      =       new_item.replace("""out_layers.3"""							,   """conv2""" )

    lowerCAmelCase_      :				Optional[Any]      =       new_item.replace("""emb_layers.1"""							,   """time_emb_proj""" )
    lowerCAmelCase_      :				Optional[Any]      =       new_item.replace("""skip_connection"""							,   """conv_shortcut""" )

    lowerCAmelCase_      :				Optional[Any]      =       shave_segments(A__							,   n_shave_prefix_segments=A__ )

    mapping.append({"""old""": old_item, """new""": new_item} )

  return mapping







def     UpperCamelCase_      (   A__ :       Dict							,   A__ :       int=0 ):
  '''simple docstring'''





  lowerCAmelCase_      :				Any      =       []
  for old_item in old_list:
    lowerCAmelCase_      :				Optional[int]      =       old_item

    lowerCAmelCase_      :				int      =       new_item.replace("""norm.weight"""							,   """group_norm.weight""" )
    lowerCAmelCase_      :				List[str]      =       new_item.replace("""norm.bias"""							,   """group_norm.bias""" )

    lowerCAmelCase_      :				Optional[int]      =       new_item.replace("""proj_out.weight"""							,   """proj_attn.weight""" )
    lowerCAmelCase_      :				Union[str, Any]      =       new_item.replace("""proj_out.bias"""							,   """proj_attn.bias""" )

    lowerCAmelCase_      :				Optional[Any]      =       shave_segments(A__							,   n_shave_prefix_segments=A__ )

    mapping.append({"""old""": old_item, """new""": new_item} )

  return mapping







def     UpperCamelCase_      (   A__ :       int							,   A__ :       Union[str, Any]							,   A__ :       Dict							,   A__ :       List[str]=None							,   A__ :       Dict=None							,   A__ :       Optional[int]=None ):
  '''simple docstring'''





  assert isinstance(A__							,   A__ ), "Paths should be a list of dicts containing 'old' and 'new' keys."

  # Splits the attention layers into three variables.
  if attention_paths_to_split is not None:
    for path, path_map in attention_paths_to_split.items():
      lowerCAmelCase_      :				List[Any]      =       old_checkpoint[path]
      lowerCAmelCase_      :				Optional[int]      =       old_tensor.shape[0] // 3

      lowerCAmelCase_      :				Optional[int]      =       (-1, channels) if len(old_tensor.shape ) == 3 else (-1)

      lowerCAmelCase_      :				Dict      =       old_tensor.shape[0] // config["""num_head_channels"""] // 3

      lowerCAmelCase_      :				Optional[int]      =       old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:] )
      lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_      :				int      =       old_tensor.split(channels // num_heads							,   dim=1 )

      lowerCAmelCase_      :				Tuple      =       query.reshape(A__ )
      lowerCAmelCase_      :				Optional[int]      =       key.reshape(A__ )
      lowerCAmelCase_      :				List[Any]      =       value.reshape(A__ )

  for path in paths:
    lowerCAmelCase_      :				int      =       path["""new"""]

    # These have already been assigned
    if attention_paths_to_split is not None and new_path in attention_paths_to_split:
      continue

    # Global renaming happens here
    lowerCAmelCase_      :				int      =       new_path.replace("""middle_block.0"""							,   """mid_block.resnets.0""" )
    lowerCAmelCase_      :				Optional[int]      =       new_path.replace("""middle_block.1"""							,   """mid_block.attentions.0""" )
    lowerCAmelCase_      :				List[Any]      =       new_path.replace("""middle_block.2"""							,   """mid_block.resnets.1""" )

    if additional_replacements is not None:
      for replacement in additional_replacements:
        lowerCAmelCase_      :				Optional[Any]      =       new_path.replace(replacement["""old"""]							,   replacement["""new"""] )

        # proj_attn.weight has to be converted from conv 1D to linear
    if "proj_attn.weight" in new_path:
      lowerCAmelCase_      :				str      =       old_checkpoint[path["""old"""]][:, :, 0]
    else:
      lowerCAmelCase_      :				Dict      =       old_checkpoint[path["""old"""]]







def     UpperCamelCase_      (   A__ :       Any							,   A__ :       Union[str, Any] ):
  '''simple docstring'''





  lowerCAmelCase_      :				Tuple      =       {}

  lowerCAmelCase_      :				List[Any]      =       checkpoint["""time_embed.0.weight"""]
  lowerCAmelCase_      :				int      =       checkpoint["""time_embed.0.bias"""]
  lowerCAmelCase_      :				Optional[int]      =       checkpoint["""time_embed.2.weight"""]
  lowerCAmelCase_      :				List[str]      =       checkpoint["""time_embed.2.bias"""]

  lowerCAmelCase_      :				List[str]      =       checkpoint["""input_blocks.0.0.weight"""]
  lowerCAmelCase_      :				List[Any]      =       checkpoint["""input_blocks.0.0.bias"""]

  lowerCAmelCase_      :				str      =       checkpoint["""out.0.weight"""]
  lowerCAmelCase_      :				str      =       checkpoint["""out.0.bias"""]
  lowerCAmelCase_      :				Any      =       checkpoint["""out.2.weight"""]
  lowerCAmelCase_      :				int      =       checkpoint["""out.2.bias"""]

  # Retrieves the keys for the input blocks only
  lowerCAmelCase_      :				Any      =       len({""".""".join(layer.split(""".""" )[:2] ) for layer in checkpoint if """input_blocks""" in layer} )
  lowerCAmelCase_      :				List[Any]      =       {
      layer_id: [key for key in checkpoint if f'input_blocks.{layer_id}' in key]
      for layer_id in range(A__ )
  }

  # Retrieves the keys for the middle blocks only
  lowerCAmelCase_      :				str      =       len({""".""".join(layer.split(""".""" )[:2] ) for layer in checkpoint if """middle_block""" in layer} )
  lowerCAmelCase_      :				Dict      =       {
      layer_id: [key for key in checkpoint if f'middle_block.{layer_id}' in key]
      for layer_id in range(A__ )
  }

  # Retrieves the keys for the output blocks only
  lowerCAmelCase_      :				List[str]      =       len({""".""".join(layer.split(""".""" )[:2] ) for layer in checkpoint if """output_blocks""" in layer} )
  lowerCAmelCase_      :				Optional[Any]      =       {
      layer_id: [key for key in checkpoint if f'output_blocks.{layer_id}' in key]
      for layer_id in range(A__ )
  }

  for i in range(1							,   A__ ):
    lowerCAmelCase_      :				Tuple      =       (i - 1) // (config["""num_res_blocks"""] + 1)
    lowerCAmelCase_      :				Optional[int]      =       (i - 1) % (config["""num_res_blocks"""] + 1)

    lowerCAmelCase_      :				Union[str, Any]      =       [key for key in input_blocks[i] if f'input_blocks.{i}.0' in key]
    lowerCAmelCase_      :				List[str]      =       [key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]

    if f'input_blocks.{i}.0.op.weight' in checkpoint:
      lowerCAmelCase_      :				int      =       checkpoint[
          f'input_blocks.{i}.0.op.weight'
      ]
      lowerCAmelCase_      :				List[Any]      =       checkpoint[
          f'input_blocks.{i}.0.op.bias'
      ]
      continue

    lowerCAmelCase_      :				Tuple      =       renew_resnet_paths(A__ )
    lowerCAmelCase_      :				List[str]      =       {"""old""": f'input_blocks.{i}.0', """new""": f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
    lowerCAmelCase_      :				Tuple      =       {"""old""": """resnets.2.op""", """new""": """downsamplers.0.op"""}
    assign_to_checkpoint(
        A__							,   A__							,   A__							,   additional_replacements=[meta_path, resnet_op]							,   config=A__ )

    if len(A__ ):
      lowerCAmelCase_      :				Tuple      =       renew_attention_paths(A__ )
      lowerCAmelCase_      :				int      =       {
          """old""": f'input_blocks.{i}.1',
          """new""": f'down_blocks.{block_id}.attentions.{layer_in_block_id}',
      }
      lowerCAmelCase_      :				List[Any]      =       {
          f'input_blocks.{i}.1.qkv.bias': {
              """key""": f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
              """query""": f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
              """value""": f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
          },
          f'input_blocks.{i}.1.qkv.weight': {
              """key""": f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
              """query""": f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
              """value""": f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
          },
      }
      assign_to_checkpoint(
          A__							,   A__							,   A__							,   additional_replacements=[meta_path]							,   attention_paths_to_split=A__							,   config=A__							,   )

  lowerCAmelCase_      :				Any      =       middle_blocks[0]
  lowerCAmelCase_      :				List[str]      =       middle_blocks[1]
  lowerCAmelCase_      :				Dict      =       middle_blocks[2]

  lowerCAmelCase_      :				int      =       renew_resnet_paths(A__ )
  assign_to_checkpoint(A__							,   A__							,   A__							,   config=A__ )

  lowerCAmelCase_      :				Tuple      =       renew_resnet_paths(A__ )
  assign_to_checkpoint(A__							,   A__							,   A__							,   config=A__ )

  lowerCAmelCase_      :				Optional[int]      =       renew_attention_paths(A__ )
  lowerCAmelCase_      :				Dict      =       {
      """middle_block.1.qkv.bias""": {
          """key""": """mid_block.attentions.0.key.bias""",
          """query""": """mid_block.attentions.0.query.bias""",
          """value""": """mid_block.attentions.0.value.bias""",
      },
      """middle_block.1.qkv.weight""": {
          """key""": """mid_block.attentions.0.key.weight""",
          """query""": """mid_block.attentions.0.query.weight""",
          """value""": """mid_block.attentions.0.value.weight""",
      },
  }
  assign_to_checkpoint(
      A__							,   A__							,   A__							,   attention_paths_to_split=A__							,   config=A__ )

  for i in range(A__ ):
    lowerCAmelCase_      :				Tuple      =       i // (config["""num_res_blocks"""] + 1)
    lowerCAmelCase_      :				List[Any]      =       i % (config["""num_res_blocks"""] + 1)
    lowerCAmelCase_      :				Tuple      =       [shave_segments(A__							,   2 ) for name in output_blocks[i]]
    lowerCAmelCase_      :				Union[str, Any]      =       {}

    for layer in output_block_layers:
      lowerCAmelCase_,							lowerCAmelCase_      :				Tuple      =       layer.split(""".""" )[0], shave_segments(A__							,   1 )
      if layer_id in output_block_list:
        output_block_list[layer_id].append(A__ )
      else:
        lowerCAmelCase_      :				Optional[Any]      =       [layer_name]

    if len(A__ ) > 1:
      lowerCAmelCase_      :				List[str]      =       [key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
      lowerCAmelCase_      :				List[str]      =       [key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]

      lowerCAmelCase_      :				List[Any]      =       renew_resnet_paths(A__ )
      lowerCAmelCase_      :				int      =       renew_resnet_paths(A__ )

      lowerCAmelCase_      :				str      =       {"""old""": f'output_blocks.{i}.0', """new""": f'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
      assign_to_checkpoint(A__							,   A__							,   A__							,   additional_replacements=[meta_path]							,   config=A__ )

      if ["conv.weight", "conv.bias"] in output_block_list.values():
        lowerCAmelCase_      :				Any      =       list(output_block_list.values() ).index(["""conv.weight""", """conv.bias"""] )
        lowerCAmelCase_      :				str      =       checkpoint[
            f'output_blocks.{i}.{index}.conv.weight'
        ]
        lowerCAmelCase_      :				Dict      =       checkpoint[
            f'output_blocks.{i}.{index}.conv.bias'
        ]

        # Clear attentions as they have been attributed above.
        if len(A__ ) == 2:
          lowerCAmelCase_      :				List[Any]      =       []

      if len(A__ ):
        lowerCAmelCase_      :				str      =       renew_attention_paths(A__ )
        lowerCAmelCase_      :				Any      =       {
            """old""": f'output_blocks.{i}.1',
            """new""": f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
        }
        lowerCAmelCase_      :				int      =       {
            f'output_blocks.{i}.1.qkv.bias': {
                """key""": f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
                """query""": f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
                """value""": f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
            },
            f'output_blocks.{i}.1.qkv.weight': {
                """key""": f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
                """query""": f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
                """value""": f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
            },
        }
        assign_to_checkpoint(
            A__							,   A__							,   A__							,   additional_replacements=[meta_path]							,   attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions ) else None							,   config=A__							,   )
    else:
      lowerCAmelCase_      :				List[Any]      =       renew_resnet_paths(A__							,   n_shave_prefix_segments=1 )
      for path in resnet_0_paths:
        lowerCAmelCase_      :				List[str]      =       """.""".join(["""output_blocks""", str(A__ ), path["""old"""]] )
        lowerCAmelCase_      :				Dict      =       """.""".join(["""up_blocks""", str(A__ ), """resnets""", str(A__ ), path["""new"""]] )

        lowerCAmelCase_      :				Optional[int]      =       checkpoint[old_path]

  return new_checkpoint


if __name__ == "__main__":
  __A :  List[Any]    							=				argparse.ArgumentParser()

  parser.add_argument(
      "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
  )

  parser.add_argument(
      "--config_file",
      default=None,
      type=str,
      required=True,
      help="The config json file corresponding to the architecture.",
  )

  parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

  __A :  int    							=				parser.parse_args()

  __A :  Optional[Any]    							=				torch.load(args.checkpoint_path)

  with open(args.config_file) as f:
    __A :  int    							=				json.loads(f.read())

  __A :  List[str]    							=				convert_ldm_checkpoint(checkpoint, config)

  if "ldm" in config:
    del config["ldm"]

  __A :  Optional[int]    							=				UNetaDModel(**config)
  model.load_state_dict(converted_checkpoint)

  try:
    __A :  List[str]    							=				DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
    __A :  List[str]    							=				VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1]))

    __A :  Optional[int]    							=				LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
    pipe.save_pretrained(args.dump_path)
  except:  # noqa: E722
    model.save_pretrained(args.dump_path)







'''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)



