


"""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()
lowerCAmelCase__				    =       logging.get_logger(__name__)


lowerCAmelCase__				    =       '''https://openaipublic.azureedge.net/jukebox/models/'''
lowerCAmelCase__				    =       {
    '''jukebox-1b-lyrics''': [
        '''5b/vqvae.pth.tar''',
        '''5b/prior_level_0.pth.tar''',
        '''5b/prior_level_1.pth.tar''',
        '''1b_lyrics/prior_level_2.pth.tar''',
    ],
    '''jukebox-5b-lyrics''': [
        '''5b/vqvae.pth.tar''',
        '''5b/prior_level_0.pth.tar''',
        '''5b/prior_level_1.pth.tar''',
        '''5b_lyrics/prior_level_2.pth.tar''',
    ],
}
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ):


  '''simple docstring'''







  if key.endswith(".model.1.bias"    ) and len(key.split("."    )    ) > 1_0:
    lowerCAmelCase					:					Union[str, Any]								=							key.replace(".model.1.bias"    ,				".conv1d_1.bias"    )
  elif key.endswith(".model.1.weight"    ) and len(key.split("."    )    ) > 1_0:
    lowerCAmelCase					:					Optional[int]								=							key.replace(".model.1.weight"    ,				".conv1d_1.weight"    )
  elif key.endswith(".model.3.bias"    ) and len(key.split("."    )    ) > 1_0:
    lowerCAmelCase					:					Tuple								=							key.replace(".model.3.bias"    ,				".conv1d_2.bias"    )
  elif key.endswith(".model.3.weight"    ) and len(key.split("."    )    ) > 1_0:
    lowerCAmelCase					:					Union[str, Any]								=							key.replace(".model.3.weight"    ,				".conv1d_2.weight"    )

  if "conditioner_blocks.0." in key:
    lowerCAmelCase					:					int								=							key.replace("conditioner_blocks.0"    ,				"conditioner_blocks"    )

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

  if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
    lowerCAmelCase					:					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:
    lowerCAmelCase					:					List[str]								=							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  a__      (    SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   List[str]    ,				SCREAMING_SNAKE_CASE   :   int    ,				SCREAMING_SNAKE_CASE   :   Optional[int]    ):


  '''simple docstring'''







  lowerCAmelCase					:					int								=							{}
  import re

  lowerCAmelCase					:					Any								=							re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"    )
  lowerCAmelCase					:					Optional[Any]								=							re.compile(
      r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"    )
  lowerCAmelCase					:					Union[str, Any]								=							re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"    )

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

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

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

    elif re_encoder_block_proj_out.fullmatch(SCREAMING_SNAKE_CASE    ):
      lowerCAmelCase					:					Optional[Any]								=							re_encoder_block_proj_out.match(SCREAMING_SNAKE_CASE    )
      lowerCAmelCase					:					int								=							regex_match.groups()
      lowerCAmelCase					:					List[Any]								=							f"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
      lowerCAmelCase					:					int								=							re_encoder_block_proj_out.sub(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )

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

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

    elif re_decoder_block_proj_in.fullmatch(SCREAMING_SNAKE_CASE    ):
      lowerCAmelCase					:					int								=							re_decoder_block_proj_in.match(SCREAMING_SNAKE_CASE    )
      lowerCAmelCase					:					List[str]								=							regex_match.groups()
      lowerCAmelCase					:					List[str]								=							f"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
      lowerCAmelCase					:					Optional[int]								=							re_decoder_block_proj_in.sub(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )

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

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

    elif re_prior_cond_proj_in.fullmatch(SCREAMING_SNAKE_CASE    ):
      lowerCAmelCase					:					Optional[Any]								=							re_prior_cond_proj_in.match(SCREAMING_SNAKE_CASE    )
      lowerCAmelCase					:					int								=							regex_match.groups()
      lowerCAmelCase					:					str								=							f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
      lowerCAmelCase					:					int								=							re_prior_cond_proj_in.sub(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )

    # keep original key
    else:
      lowerCAmelCase					:					Union[str, Any]								=							original_key

    lowerCAmelCase					:					Any								=							replace_key(SCREAMING_SNAKE_CASE    )

    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					:					Dict								=							original_key

    lowerCAmelCase					:					Union[str, Any]								=							original_key
    lowerCAmelCase					:					Any								=							value

  return new_dict
@torch.no_grad()
def  a__      (    SCREAMING_SNAKE_CASE   :   str=None    ,				SCREAMING_SNAKE_CASE   :   Union[str, Any]=None    ):


  '''simple docstring'''







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

  lowerCAmelCase					:					Optional[Any]								=							MODEL_MAPPING[model_name.split("/"    )[-1]]

  lowerCAmelCase					:					Optional[Any]								=							JukeboxConfig.from_pretrained(SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					str								=							JukeboxModel(SCREAMING_SNAKE_CASE    )

  lowerCAmelCase					:					List[str]								=							[]
  lowerCAmelCase					:					Tuple								=							{}
  for i, dict_name in enumerate(SCREAMING_SNAKE_CASE    ):
    lowerCAmelCase					:					List[Any]								=							torch.load(f"""{pytorch_dump_folder_path}/{dict_name.split("/"    )[-1]}"""    )["model"]

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

    lowerCAmelCase					:					Tuple								=							"vqvae" if i == 0 else f"""priors.{3 - i}"""
    lowerCAmelCase					:					Any								=							fix_jukebox_keys(SCREAMING_SNAKE_CASE    ,				model.state_dict()    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
    weight_dict.append(SCREAMING_SNAKE_CASE    )

  lowerCAmelCase					:					int								=							weight_dict.pop(0    )
  model.vqvae.load_state_dict(SCREAMING_SNAKE_CASE    )
  for i in range(len(SCREAMING_SNAKE_CASE    )    ):
    model.priors[i].load_state_dict(weight_dict[2 - i]    )

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

  print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""    )
  model.save_pretrained(SCREAMING_SNAKE_CASE    )

  return weight_dict


if __name__ == "__main__":
     lowerCAmelCase__				    =       argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--model_name''',
         default='''jukebox-5b-lyrics''',
         type=str,
         help='''Name of the model you\'d like to convert.''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''',
         default='''jukebox-5b-lyrics-converted''',
         type=str,
         help='''Path to the output PyTorch model directory.''',
     )
     lowerCAmelCase__				    =       parser.parse_args()
     convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)









"""simple docstring"""







import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ):  # picklable for multiprocessing


  '''simple docstring'''







  return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def  a__      (    ):


  '''simple docstring'''







  with parallel_backend("spark"    ):
    assert ParallelBackendConfig.backend_name == "spark"

  lowerCAmelCase					:					List[str]								=							[1, 2, 3]
  with pytest.raises(SCREAMING_SNAKE_CASE    ):
    with parallel_backend("unsupported backend"    ):
      map_nested(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				num_proc=2    )

  with pytest.raises(SCREAMING_SNAKE_CASE    ):
    with parallel_backend("unsupported backend"    ):
      map_nested(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				num_proc=-1    )





@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("num_proc"    ,				[2, -1]    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Tuple    ):


  '''simple docstring'''







  lowerCAmelCase					:					Tuple								=							[1, 2]
  lowerCAmelCase					:					int								=							{"a": 1, "b": 2}
  lowerCAmelCase					:					List[str]								=							{"a": [1, 2], "b": [3, 4]}
  lowerCAmelCase					:					Dict								=							{"a": {"1": 1}, "b": 2}
  lowerCAmelCase					:					Tuple								=							{"a": 1, "b": 2, "c": 3, "d": 4}
  lowerCAmelCase					:					Any								=							[2, 3]
  lowerCAmelCase					:					Any								=							{"a": 2, "b": 3}
  lowerCAmelCase					:					Optional[int]								=							{"a": [2, 3], "b": [4, 5]}
  lowerCAmelCase					:					Optional[int]								=							{"a": {"1": 2}, "b": 3}
  lowerCAmelCase					:					str								=							{"a": 2, "b": 3, "c": 4, "d": 5}

  with parallel_backend("spark"    ):
    assert map_nested(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				num_proc=SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa
    assert map_nested(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				num_proc=SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa
    assert map_nested(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				num_proc=SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa
    assert map_nested(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				num_proc=SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa
    assert map_nested(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				num_proc=SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa





