



'''simple docstring'''





import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


_snake_case	=						"""<<<<<<< This should probably be modified because it mentions: """

_snake_case	=						"""=======
>>>>>>>
"""

_snake_case	=						[
    """TextEncoderConfig""",
    """ByteTextEncoder""",
    """SubwordTextEncoder""",
    """encoder_config""",
    """maybe_build_from_corpus""",
    """manual_dir""",
]

_snake_case	=						[
    # (pattern, replacement)
    # Order is important here for some replacements
    (r"""tfds\.core""", r"""datasets"""),
    (r"""tf\.io\.gfile\.GFile""", r"""open"""),
    (r"""tf\.([\w\d]+)""", r"""datasets.Value('\1')"""),
    (r"""tfds\.features\.Text\(\)""", r"""datasets.Value('string')"""),
    (r"""tfds\.features\.Text\(""", r"""datasets.Value('string'),"""),
    (r"""features\s*=\s*tfds.features.FeaturesDict\(""", r"""features=datasets.Features("""),
    (r"""tfds\.features\.FeaturesDict\(""", r"""dict("""),
    (r"""The TensorFlow Datasets Authors""", r"""The TensorFlow Datasets Authors and the HuggingFace Datasets Authors"""),
    (r"""tfds\.""", r"""datasets."""),
    (r"""dl_manager\.manual_dir""", r"""self.config.data_dir"""),
    (r"""self\.builder_config""", r"""self.config"""),
]






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							return ConvertCommand(args.tfds_path       ,				args.datasets_directory						)



class 						UpperCAmelCase_					( a):







							@staticmethod
							def      snake_case__   (     __a):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					parser.add_parser(
														    "convert",    help="Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.",    )
														train_parser.add_argument(
														    "--tfds_path",    type=__a,    required=__a,    help="Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.",    )
														train_parser.add_argument(
														    "--datasets_directory",    type=__a,    required=__a,    help="Path to the HuggingFace Datasets folder.")
														train_parser.set_defaults(func=__a)







							def __init__(     self,    __a,    __a,    *__a):

														'''simple docstring'''

														_lowerCAmelCase  :							List[str]				     =					get_logger("datasets-cli/converting")

														_lowerCAmelCase  :							Union[str, Any]				     =					tfds_path
														_lowerCAmelCase  :							Any				     =					datasets_directory







							def      snake_case__   (     self):

														'''simple docstring'''

														if os.path.isdir(self._tfds_path):
																					_lowerCAmelCase  :							str				     =					os.path.abspath(self._tfds_path)
														elif os.path.isfile(self._tfds_path):
																					_lowerCAmelCase  :							str				     =					os.path.dirname(self._tfds_path)
														else:
																					raise ValueError("--tfds_path is neither a directory nor a file. Please check path.")

														_lowerCAmelCase  :							str				     =					os.path.abspath(self._datasets_directory)

														self._logger.info(f"Converting datasets from {abs_tfds_path} to {abs_datasets_path}")

														_lowerCAmelCase  :							Optional[Any]				     =					[]
														_lowerCAmelCase  :							Optional[Any]				     =					[]
														_lowerCAmelCase  :							Any				     =					{}

														if os.path.isdir(self._tfds_path):
																					_lowerCAmelCase  :							Tuple				     =					os.listdir(__a)
														else:
																					_lowerCAmelCase  :							Optional[Any]				     =					[os.path.basename(self._tfds_path)]

														for f_name in file_names:
																					self._logger.info(f"Looking at file {f_name}")
																					_lowerCAmelCase  :							Tuple				     =					os.path.join(__a,    __a)
																					_lowerCAmelCase  :							Dict				     =					os.path.join(__a,    __a)

																					if not os.path.isfile(__a) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
																												self._logger.info("Skipping file")
																												continue

																					with open(__a,    encoding="utf-8") as f:
																												_lowerCAmelCase  :							Dict				     =					f.readlines()

																					_lowerCAmelCase  :							Dict				     =					[]
																					_lowerCAmelCase  :							Tuple				     =					False
																					_lowerCAmelCase  :							str				     =					False
																					_lowerCAmelCase  :							Optional[int]				     =					[]
																					for line in lines:
																												_lowerCAmelCase  :							Tuple				     =					line

																												# Convert imports
																												if "import tensorflow.compat.v2 as tf" in out_line:
																																			continue
																												elif "@tfds.core" in out_line:
																																			continue
																												elif "builder=self" in out_line:
																																			continue
																												elif "import tensorflow_datasets.public_api as tfds" in out_line:
																																			_lowerCAmelCase  :							Union[str, Any]				     =					"import datasets\n"
																												elif "import tensorflow" in out_line:
																																			# order is important here
																																			_lowerCAmelCase  :							Optional[Any]				     =					""
																																			continue
																												elif "from absl import logging" in out_line:
																																			_lowerCAmelCase  :							Any				     =					"from datasets import logging\n"
																												elif "getLogger" in out_line:
																																			_lowerCAmelCase  :							Tuple				     =					out_line.replace("getLogger",    "get_logger")
																												elif any(expression in out_line for expression in TO_HIGHLIGHT):
																																			_lowerCAmelCase  :							Optional[Any]				     =					True
																																			_lowerCAmelCase  :							Optional[Any]				     =					list(filter(lambda __a: e in out_line,    __a))
																																			out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(__a) + "\n")
																																			out_lines.append(__a)
																																			out_lines.append(__a)
																																			continue
																												else:
																																			for pattern, replacement in TO_CONVERT:
																																										_lowerCAmelCase  :							str				     =					re.sub(__a,    __a,    __a)

                # Take care of saving utilities (to later move them together with main script)
																												if "tensorflow_datasets" in out_line:
																																			_lowerCAmelCase  :							Any				     =					re.match(R"from\stensorflow_datasets.*import\s([^\.\r\n]+)",    __a)
																																			tfds_imports.extend(imp.strip() for imp in match.group(1).split(","))
																																			_lowerCAmelCase  :							int				     =					"from . import " + match.group(1)

																												# Check we have not forget anything
																												if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
																																			raise ValueError(f"Error converting {out_line.strip()}")

																												if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
																																			_lowerCAmelCase  :							Tuple				     =					True
																												out_lines.append(__a)

																					if is_builder or "wmt" in f_name:
																												# We create a new directory for each dataset
																												_lowerCAmelCase  :							Union[str, Any]				     =					f_name.replace(".py",    "")
																												_lowerCAmelCase  :							Any				     =					os.path.join(__a,    __a)
																												_lowerCAmelCase  :							Optional[int]				     =					os.path.join(__a,    __a)
																												os.makedirs(__a,    exist_ok=__a)
																												self._logger.info(f"Adding directory {output_dir}")
																												imports_to_builder_map.update({imp: output_dir for imp in tfds_imports})
																					else:
																												# Utilities will be moved at the end
																												utils_files.append(__a)

																					if needs_manual_update:
																												with_manual_update.append(__a)

																					with open(__a,    "w",    encoding="utf-8") as f:
																												f.writelines(__a)
																					self._logger.info(f"Converted in {output_file}")

														for utils_file in utils_files:
																					try:
																												_lowerCAmelCase  :							Tuple				     =					os.path.basename(__a)
																												_lowerCAmelCase  :							List[str]				     =					imports_to_builder_map[f_name.replace(".py",    "")]
																												self._logger.info(f"Moving {dest_folder} to {utils_file}")
																												shutil.copy(__a,    __a)
																					except KeyError:
																												self._logger.error(f"Cannot find destination folder for {utils_file}. Please copy manually.")

														if with_manual_update:
																					for file_path in with_manual_update:
																												self._logger.warning(
																												    f"You need to manually update file {file_path} to remove configurations using 'TextEncoderConfig'.")





import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case	=						logging.get_logger(__name__)


_snake_case	=						"https://openaipublic.azureedge.net/jukebox/models/"
_snake_case	=						{
    "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       (      _lowerCamelCase						):





							'''simple docstring'''




							if key.endswith(".model.1.bias"						) and len(key.split("."						)						) > 10:
														_lowerCAmelCase  :							int				     =					key.replace(".model.1.bias"       ,				".conv1d_1.bias"						)
							elif key.endswith(".model.1.weight"						) and len(key.split("."						)						) > 10:
														_lowerCAmelCase  :							Optional[int]				     =					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  :							List[str]				     =					key.replace("conditioner_blocks.0"       ,				"conditioner_blocks"						)

							if "prime_prior" in key:
														_lowerCAmelCase  :							int				     =					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  :							Tuple				     =					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       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Any				     =					{}
							import re

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

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

														elif re_encoder_block_resnet.fullmatch(_lowerCamelCase						):
																					_lowerCAmelCase  :							List[Any]				     =					re_encoder_block_resnet.match(_lowerCamelCase						)
																					_lowerCAmelCase  :							str				     =					regex_match.groups()
																					_lowerCAmelCase  :							Optional[int]				     =					int(groups[2]						) * 2 + int(groups[3]						)
																					_lowerCAmelCase  :							str				     =					{"1": 1, "3": 2}[groups[-2]]
																					_lowerCAmelCase  :							Union[str, Any]				     =					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  :							int				     =					prefix + resnet_block
																					_lowerCAmelCase  :							int				     =					re_encoder_block_resnet.sub(_lowerCamelCase       ,				_lowerCamelCase						)

														elif re_encoder_block_proj_out.fullmatch(_lowerCamelCase						):
																					_lowerCAmelCase  :							Union[str, Any]				     =					re_encoder_block_proj_out.match(_lowerCamelCase						)
																					_lowerCAmelCase  :							List[Any]				     =					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(_lowerCamelCase       ,				_lowerCamelCase						)

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

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

														elif re_decoder_block_proj_in.fullmatch(_lowerCamelCase						):
																					_lowerCAmelCase  :							Optional[int]				     =					re_decoder_block_proj_in.match(_lowerCamelCase						)
																					_lowerCAmelCase  :							Union[str, Any]				     =					regex_match.groups()
																					_lowerCAmelCase  :							Optional[Any]				     =					F"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
																					_lowerCAmelCase  :							Any				     =					re_decoder_block_proj_in.sub(_lowerCamelCase       ,				_lowerCamelCase						)

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

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

														elif re_prior_cond_proj_in.fullmatch(_lowerCamelCase						):
																					_lowerCAmelCase  :							int				     =					re_prior_cond_proj_in.match(_lowerCamelCase						)
																					_lowerCAmelCase  :							Optional[Any]				     =					regex_match.groups()
																					_lowerCAmelCase  :							Optional[int]				     =					F"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
																					_lowerCAmelCase  :							List[str]				     =					re_prior_cond_proj_in.sub(_lowerCamelCase       ,				_lowerCamelCase						)

														# keep original key
														else:
																					_lowerCAmelCase  :							Optional[int]				     =					original_key

														_lowerCAmelCase  :							Tuple				     =					replace_key(_lowerCamelCase						)

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

														_lowerCAmelCase  :							List[Any]				     =					original_key
														_lowerCAmelCase  :							Optional[int]				     =					value

							return new_dict






@torch.no_grad()
def        A       (      _lowerCamelCase=None       ,				_lowerCamelCase=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=_lowerCamelCase						)
																					os.makedirs(F"{pytorch_dump_folder_path}/"       ,				exist_ok=_lowerCamelCase						)
																					open(F"{pytorch_dump_folder_path}/{file.split('/'						)[-1]}"       ,				"wb"						).write(r.content						)

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

							_lowerCAmelCase  :							Tuple				     =					JukeboxConfig.from_pretrained(_lowerCamelCase						)
							_lowerCAmelCase  :							Optional[int]				     =					JukeboxModel(_lowerCamelCase						)

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

														_lowerCAmelCase  :							Union[str, Any]				     =					{}
														for k in old_dic.keys():
																					if k.endswith(".b"						):
																												_lowerCAmelCase  :							Dict				     =					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  :							str				     =					old_dic[k]
																					else:
																												_lowerCAmelCase  :							Union[str, Any]				     =					old_dic[k]

														_lowerCAmelCase  :							Union[str, Any]				     =					"vqvae" if i == 0 else F"priors.{3 - i}"
														_lowerCAmelCase  :							Union[str, Any]				     =					fix_jukebox_keys(_lowerCamelCase       ,				model.state_dict()       ,				_lowerCamelCase       ,				_lowerCamelCase						)
														weight_dict.append(_lowerCamelCase						)

							_lowerCAmelCase  :							Optional[Any]				     =					weight_dict.pop(0						)
							model.vqvae.load_state_dict(_lowerCamelCase						)
							for i in range(len(_lowerCamelCase						)						):
														model.priors[i].load_state_dict(weight_dict[2 - i]						)

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

							print(F"Saving model {model_name} to {pytorch_dump_folder_path}"						)
							model.save_pretrained(_lowerCamelCase						)

							return weight_dict


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