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  :     Tuple       =  logging.get_logger(__name__)


__a  :     Optional[Any]       =  """https://openaipublic.azureedge.net/jukebox/models/"""
__a  :     Union[str, 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						(    lowercase							):







				"""simple docstring"""



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

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

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

				if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
								__lowercase 			=							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:
								__lowercase 			=							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						(    lowercase							,			lowercase							,			lowercase							,			lowercase							):







				"""simple docstring"""



				__lowercase 			=							{}
				import re

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

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

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

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

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

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

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

								elif re_decoder_block_proj_in.fullmatch(lowercase							):
												__lowercase 			=							re_decoder_block_proj_in.match(lowercase							)
												__lowercase 			=							regex_match.groups()
												__lowercase 			=							F"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
												__lowercase 			=							re_decoder_block_proj_in.sub(lowercase							,			lowercase							)

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

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

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

								# keep original key
								else:
												__lowercase 			=							original_key

								__lowercase 			=							replace_key(lowercase							)

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

								__lowercase 			=							original_key
								__lowercase 			=							value

				return new_dict





@torch.no_grad()
def 				UpperCAmelCase						(    lowercase=None							,			lowercase=None							):







				"""simple docstring"""



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

				__lowercase 			=							MODEL_MAPPING[model_name.split('''/'''							)[-1]]

				__lowercase 			=							JukeboxConfig.from_pretrained(lowercase							)
				__lowercase 			=							JukeboxModel(lowercase							)

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

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

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

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

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

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

				return weight_dict


if __name__ == "__main__":
		__a  :     Optional[int]       =  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  :     int       =  parser.parse_args()
		convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)
import warnings
from functools import wraps
from typing import Callable





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				@wraps(lowercase							)
				def _inner_fn(*lowercase							,			**lowercase							):
								warnings.warn(
								    (F"'{fn.__name__}' is experimental and might be subject to breaking changes in the future.")							,			lowercase							,			)
								return fn(*lowercase							,			**lowercase							)

				return _inner_fn