



"""simple docstring"""




import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


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



def 	__a					(   lowerCAmelCase_       : Dict      ,lowerCAmelCase_       : int      ,lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : Optional[Any]    )				->			Union[str, Any]:






						'''simple docstring'''



						UpperCAmelCase_=       original_name.split("""."""    )[0]
						UpperCAmelCase_=       key.split("""."""    )
						UpperCAmelCase_=       int(key_list[key_list.index(lowerCAmelCase_    ) - 2]    )
						UpperCAmelCase_=       int(key_list[key_list.index(lowerCAmelCase_    ) - 1]    )
						UpperCAmelCase_=       orig_block_num - offset

						UpperCAmelCase_=       key.replace(F"""{orig_block_num}.{layer_num}.{original_name}"""      ,F"""block.{new_block_num}.{layer_num}.{new_name}"""    )
						return key



def 	__a					(   lowerCAmelCase_       : Optional[Any]    )				->			List[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       OrderedDict()
						UpperCAmelCase_,       UpperCAmelCase_=       0, 0
						for key, value in state_dict.items():
												if key.startswith("""network"""    ):
																		UpperCAmelCase_=       key.replace("""network"""      ,"""poolformer.encoder"""    )
												if "proj" in key:
																		# Works for the first embedding as well as the internal embedding layers
																		if key.endswith("""bias"""    ) and "patch_embed" not in key:
																								patch_emb_offset += 1
																		UpperCAmelCase_=       key[: key.find("""proj"""    )]
																		UpperCAmelCase_=       key.replace(lowerCAmelCase_      ,F"""patch_embeddings.{total_embed_found}."""    )
																		UpperCAmelCase_=       key.replace("""proj"""      ,"""projection"""    )
																		if key.endswith("""bias"""    ):
																								total_embed_found += 1
												if "patch_embeddings" in key:
																		UpperCAmelCase_=       """poolformer.encoder.""" + key
												if "mlp.fc1" in key:
																		UpperCAmelCase_=       replace_key_with_offset(lowerCAmelCase_      ,lowerCAmelCase_      ,"""mlp.fc1"""      ,"""output.conv1"""    )
												if "mlp.fc2" in key:
																		UpperCAmelCase_=       replace_key_with_offset(lowerCAmelCase_      ,lowerCAmelCase_      ,"""mlp.fc2"""      ,"""output.conv2"""    )
												if "norm1" in key:
																		UpperCAmelCase_=       replace_key_with_offset(lowerCAmelCase_      ,lowerCAmelCase_      ,"""norm1"""      ,"""before_norm"""    )
												if "norm2" in key:
																		UpperCAmelCase_=       replace_key_with_offset(lowerCAmelCase_      ,lowerCAmelCase_      ,"""norm2"""      ,"""after_norm"""    )
												if "layer_scale_1" in key:
																		UpperCAmelCase_=       replace_key_with_offset(lowerCAmelCase_      ,lowerCAmelCase_      ,"""layer_scale_1"""      ,"""layer_scale_1"""    )
												if "layer_scale_2" in key:
																		UpperCAmelCase_=       replace_key_with_offset(lowerCAmelCase_      ,lowerCAmelCase_      ,"""layer_scale_2"""      ,"""layer_scale_2"""    )
												if "head" in key:
																		UpperCAmelCase_=       key.replace("""head"""      ,"""classifier"""    )
												UpperCAmelCase_=       value
						return new_state_dict



def 	__a					(   )				->			Dict:






						'''simple docstring'''



						UpperCAmelCase_=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
						UpperCAmelCase_=       Image.open(requests.get(lowerCAmelCase_      ,stream=lowerCAmelCase_    ).raw    )

						return image



@torch.no_grad()
def 	__a					(   lowerCAmelCase_       : str      ,lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : List[str]    )				->			Tuple:






						'''simple docstring'''



						UpperCAmelCase_=       PoolFormerConfig()

						# set attributes based on model_name
						UpperCAmelCase_=       """huggingface/label-files"""
						UpperCAmelCase_=       model_name[-3:]
						UpperCAmelCase_=       10_00
						UpperCAmelCase_=       """imagenet-1k-id2label.json"""
						UpperCAmelCase_=       (1, 10_00)

						# set config attributes
						UpperCAmelCase_=       json.load(open(hf_hub_download(lowerCAmelCase_      ,lowerCAmelCase_      ,repo_type="""dataset"""    )      ,"""r"""    )    )
						UpperCAmelCase_=       {int(lowerCAmelCase_    ): v for k, v in idalabel.items()}
						UpperCAmelCase_=       idalabel
						UpperCAmelCase_=       {v: k for k, v in idalabel.items()}
						if size == "s12":
												UpperCAmelCase_=       [2, 2, 6, 2]
												UpperCAmelCase_=       [64, 1_28, 3_20, 5_12]
												UpperCAmelCase_=       4.0
												UpperCAmelCase_=       0.9
						elif size == "s24":
												UpperCAmelCase_=       [4, 4, 12, 4]
												UpperCAmelCase_=       [64, 1_28, 3_20, 5_12]
												UpperCAmelCase_=       4.0
												UpperCAmelCase_=       0.9
						elif size == "s36":
												UpperCAmelCase_=       [6, 6, 18, 6]
												UpperCAmelCase_=       [64, 1_28, 3_20, 5_12]
												UpperCAmelCase_=       4.0
												UpperCAmelCase_=       1E-6
												UpperCAmelCase_=       0.9
						elif size == "m36":
												UpperCAmelCase_=       [6, 6, 18, 6]
												UpperCAmelCase_=       [96, 1_92, 3_84, 7_68]
												UpperCAmelCase_=       4.0
												UpperCAmelCase_=       1E-6
												UpperCAmelCase_=       0.95
						elif size == "m48":
												UpperCAmelCase_=       [8, 8, 24, 8]
												UpperCAmelCase_=       [96, 1_92, 3_84, 7_68]
												UpperCAmelCase_=       4.0
												UpperCAmelCase_=       1E-6
												UpperCAmelCase_=       0.95
						else:
												raise ValueError(F"""Size {size} not supported"""    )

						# load image processor
						UpperCAmelCase_=       PoolFormerImageProcessor(crop_pct=lowerCAmelCase_    )

						# Prepare image
						UpperCAmelCase_=       prepare_img()
						UpperCAmelCase_=       image_processor(images=lowerCAmelCase_      ,return_tensors="""pt"""    ).pixel_values

						logger.info(F"""Converting model {model_name}..."""    )

						# load original state dict
						UpperCAmelCase_=       torch.load(lowerCAmelCase_      ,map_location=torch.device("""cpu"""    )    )

						# rename keys
						UpperCAmelCase_=       rename_keys(lowerCAmelCase_    )

						# create HuggingFace model and load state dict
						UpperCAmelCase_=       PoolFormerForImageClassification(lowerCAmelCase_    )
						model.load_state_dict(lowerCAmelCase_    )
						model.eval()

						# Define image processor
						UpperCAmelCase_=       PoolFormerImageProcessor(crop_pct=lowerCAmelCase_    )
						UpperCAmelCase_=       image_processor(images=prepare_img()      ,return_tensors="""pt"""    ).pixel_values

						# forward pass
						UpperCAmelCase_=       model(lowerCAmelCase_    )
						UpperCAmelCase_=       outputs.logits

						# define expected logit slices for different models
						if size == "s12":
												UpperCAmelCase_=       torch.tensor([-0.3_045, -0.6_758, -0.4_869]    )
						elif size == "s24":
												UpperCAmelCase_=       torch.tensor([0.4_402, -0.1_374, -0.8_045]    )
						elif size == "s36":
												UpperCAmelCase_=       torch.tensor([-0.6_080, -0.5_133, -0.5_898]    )
						elif size == "m36":
												UpperCAmelCase_=       torch.tensor([0.3_952, 0.2_263, -1.2_668]    )
						elif size == "m48":
												UpperCAmelCase_=       torch.tensor([0.1_167, -0.0_656, -0.3_423]    )
						else:
												raise ValueError(F"""Size {size} not supported"""    )

						# verify logits
						assert logits.shape == expected_shape
						assert torch.allclose(logits[0, :3]      ,lowerCAmelCase_      ,atol=1E-2    )

						# finally, save model and image processor
						logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""    )
						Path(lowerCAmelCase_    ).mkdir(exist_ok=lowerCAmelCase_    )
						model.save_pretrained(lowerCAmelCase_    )
						print(F"""Saving image processor to {pytorch_dump_folder_path}"""    )
						image_processor.save_pretrained(lowerCAmelCase_    )


if __name__ == "__main__":
	__A       =						argparse.ArgumentParser()

	parser.add_argument(
	    '''--model_name''',
	    default='''poolformer_s12''',
	    type=str,
	    help='''Name of the model you\'d like to convert.''',
	)
	parser.add_argument(
	    '''--checkpoint_path''', default=None, type=str, help='''Path to the original PyTorch checkpoint (.pth file).'''
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
	)
	__A       =						parser.parse_args()
	convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)









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       =						logging.get_logger(__name__)


__A       =						'''https://openaipublic.azureedge.net/jukebox/models/'''
__A       =						{
    '''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_       : List[Any]    )				->			Tuple:






						'''simple docstring'''



						if key.endswith(""".model.1.bias"""    ) and len(key.split("""."""    )    ) > 10:
												UpperCAmelCase_=       key.replace(""".model.1.bias"""      ,""".conv1d_1.bias"""    )
						elif key.endswith(""".model.1.weight"""    ) and len(key.split("""."""    )    ) > 10:
												UpperCAmelCase_=       key.replace(""".model.1.weight"""      ,""".conv1d_1.weight"""    )
						elif key.endswith(""".model.3.bias"""    ) and len(key.split("""."""    )    ) > 10:
												UpperCAmelCase_=       key.replace(""".model.3.bias"""      ,""".conv1d_2.bias"""    )
						elif key.endswith(""".model.3.weight"""    ) and len(key.split("""."""    )    ) > 10:
												UpperCAmelCase_=       key.replace(""".model.3.weight"""      ,""".conv1d_2.weight"""    )

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

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

						if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
												UpperCAmelCase_=       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:
												UpperCAmelCase_=       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_       : int      ,lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : str      ,lowerCAmelCase_       : Any    )				->			Optional[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       {}
						import re

						UpperCAmelCase_=       re.compile(r"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""    )
						UpperCAmelCase_=       re.compile(
						    r"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""    )
						UpperCAmelCase_=       re.compile(r"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""    )

						UpperCAmelCase_=       re.compile(r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""    )
						UpperCAmelCase_=       re.compile(
						    r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""    )
						UpperCAmelCase_=       re.compile(r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""    )

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

												elif re_encoder_block_resnet.fullmatch(lowerCAmelCase_    ):
																		UpperCAmelCase_=       re_encoder_block_resnet.match(lowerCAmelCase_    )
																		UpperCAmelCase_=       regex_match.groups()
																		UpperCAmelCase_=       int(groups[2]    ) * 2 + int(groups[3]    )
																		UpperCAmelCase_=       {"""1""": 1, """3""": 2}[groups[-2]]
																		UpperCAmelCase_=       F"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."""
																		UpperCAmelCase_=       F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
																		UpperCAmelCase_=       prefix + resnet_block
																		UpperCAmelCase_=       re_encoder_block_resnet.sub(lowerCAmelCase_      ,lowerCAmelCase_    )

												elif re_encoder_block_proj_out.fullmatch(lowerCAmelCase_    ):
																		UpperCAmelCase_=       re_encoder_block_proj_out.match(lowerCAmelCase_    )
																		UpperCAmelCase_=       regex_match.groups()
																		UpperCAmelCase_=       F"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
																		UpperCAmelCase_=       re_encoder_block_proj_out.sub(lowerCAmelCase_      ,lowerCAmelCase_    )

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

												elif re_decoder_block_resnet.fullmatch(lowerCAmelCase_    ):
																		UpperCAmelCase_=       re_decoder_block_resnet.match(lowerCAmelCase_    )
																		UpperCAmelCase_=       regex_match.groups()
																		UpperCAmelCase_=       int(groups[2]    ) * 2 + int(groups[3]    ) - 2
																		UpperCAmelCase_=       {"""1""": 1, """3""": 2}[groups[-2]]
																		UpperCAmelCase_=       F"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."""
																		UpperCAmelCase_=       F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
																		UpperCAmelCase_=       prefix + resnet_block
																		UpperCAmelCase_=       re_decoder_block_resnet.sub(lowerCAmelCase_      ,lowerCAmelCase_    )

												elif re_decoder_block_proj_in.fullmatch(lowerCAmelCase_    ):
																		UpperCAmelCase_=       re_decoder_block_proj_in.match(lowerCAmelCase_    )
																		UpperCAmelCase_=       regex_match.groups()
																		UpperCAmelCase_=       F"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
																		UpperCAmelCase_=       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_    ):
																		UpperCAmelCase_=       re_prior_cond_conv_out.match(lowerCAmelCase_    )
																		UpperCAmelCase_=       regex_match.groups()
																		UpperCAmelCase_=       int(groups[1]    ) * 2 + int(groups[2]    ) - 2
																		UpperCAmelCase_=       F"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
																		UpperCAmelCase_=       re_prior_cond_conv_out.sub(lowerCAmelCase_      ,lowerCAmelCase_    )

												elif re_prior_cond_resnet.fullmatch(lowerCAmelCase_    ):
																		UpperCAmelCase_=       re_prior_cond_resnet.match(lowerCAmelCase_    )
																		UpperCAmelCase_=       regex_match.groups()
																		UpperCAmelCase_=       int(groups[1]    ) * 2 + int(groups[2]    ) - 2
																		UpperCAmelCase_=       {"""1""": 1, """3""": 2}[groups[-2]]
																		UpperCAmelCase_=       F"""conditioner_blocks.upsampler.upsample_block.{block_index}."""
																		UpperCAmelCase_=       F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
																		UpperCAmelCase_=       prefix + resnet_block
																		UpperCAmelCase_=       re_prior_cond_resnet.sub(lowerCAmelCase_      ,lowerCAmelCase_    )

												elif re_prior_cond_proj_in.fullmatch(lowerCAmelCase_    ):
																		UpperCAmelCase_=       re_prior_cond_proj_in.match(lowerCAmelCase_    )
																		UpperCAmelCase_=       regex_match.groups()
																		UpperCAmelCase_=       F"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
																		UpperCAmelCase_=       re_prior_cond_proj_in.sub(lowerCAmelCase_      ,lowerCAmelCase_    )

												# keep original key
												else:
																		UpperCAmelCase_=       original_key

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

												UpperCAmelCase_=       original_key
												UpperCAmelCase_=       value

						return new_dict



@torch.no_grad()
def 	__a					(   lowerCAmelCase_       : List[Any]=None      ,lowerCAmelCase_       : str=None    )				->			List[str]:






						'''simple docstring'''



						for file in MODEL_MAPPING[model_name]:
												if not os.path.isfile(F"""{pytorch_dump_folder_path}/{file.split("/"    )[-1]}"""    ):
																		UpperCAmelCase_=       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    )

						UpperCAmelCase_=       MODEL_MAPPING[model_name.split("""/"""    )[-1]]

						UpperCAmelCase_=       JukeboxConfig.from_pretrained(lowerCAmelCase_    )
						UpperCAmelCase_=       JukeboxModel(lowerCAmelCase_    )

						UpperCAmelCase_=       []
						UpperCAmelCase_=       {}
						for i, dict_name in enumerate(lowerCAmelCase_    ):
												UpperCAmelCase_=       torch.load(F"""{pytorch_dump_folder_path}/{dict_name.split("/"    )[-1]}"""    )["""model"""]

												UpperCAmelCase_=       {}
												for k in old_dic.keys():
																		if k.endswith(""".b"""    ):
																								UpperCAmelCase_=       old_dic[k]
																		elif k.endswith(""".w"""    ):
																								UpperCAmelCase_=       old_dic[k]
																		elif "level_2" not in dict_name and "cond.model." in k:
																								UpperCAmelCase_=       old_dic[k]
																		else:
																								UpperCAmelCase_=       old_dic[k]

												UpperCAmelCase_=       """vqvae""" if i == 0 else F"""priors.{3 - i}"""
												UpperCAmelCase_=       fix_jukebox_keys(lowerCAmelCase_      ,model.state_dict()      ,lowerCAmelCase_      ,lowerCAmelCase_    )
												weight_dict.append(lowerCAmelCase_    )

						UpperCAmelCase_=       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__":
	__A       =						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       =						parser.parse_args()
	convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)




