



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()
SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)


SCREAMING_SNAKE_CASE     				=						"https://openaipublic.azureedge.net/jukebox/models/"
SCREAMING_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 snake_case__  (			__SCREAMING_SNAKE_CASE )		->							Tuple:
		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 snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Union[str, Any]:
		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(__SCREAMING_SNAKE_CASE ):
						UpperCAmelCase_						=				re_encoder_block_conv_in.match(__SCREAMING_SNAKE_CASE )
						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(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

				elif re_encoder_block_resnet.fullmatch(__SCREAMING_SNAKE_CASE ):
						UpperCAmelCase_						=				re_encoder_block_resnet.match(__SCREAMING_SNAKE_CASE )
						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(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

				elif re_encoder_block_proj_out.fullmatch(__SCREAMING_SNAKE_CASE ):
						UpperCAmelCase_						=				re_encoder_block_proj_out.match(__SCREAMING_SNAKE_CASE )
						UpperCAmelCase_						=				regex_match.groups()
						UpperCAmelCase_						=				f'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
						UpperCAmelCase_						=				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 ):
						UpperCAmelCase_						=				re_decoder_block_conv_out.match(__SCREAMING_SNAKE_CASE )
						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(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

				elif re_decoder_block_resnet.fullmatch(__SCREAMING_SNAKE_CASE ):
						UpperCAmelCase_						=				re_decoder_block_resnet.match(__SCREAMING_SNAKE_CASE )
						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(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

				elif re_decoder_block_proj_in.fullmatch(__SCREAMING_SNAKE_CASE ):
						UpperCAmelCase_						=				re_decoder_block_proj_in.match(__SCREAMING_SNAKE_CASE )
						UpperCAmelCase_						=				regex_match.groups()
						UpperCAmelCase_						=				f'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
						UpperCAmelCase_						=				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 ):
						UpperCAmelCase_						=				re_prior_cond_conv_out.match(__SCREAMING_SNAKE_CASE )
						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(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

				elif re_prior_cond_resnet.fullmatch(__SCREAMING_SNAKE_CASE ):
						UpperCAmelCase_						=				re_prior_cond_resnet.match(__SCREAMING_SNAKE_CASE )
						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(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

				elif re_prior_cond_proj_in.fullmatch(__SCREAMING_SNAKE_CASE ):
						UpperCAmelCase_						=				re_prior_cond_proj_in.match(__SCREAMING_SNAKE_CASE )
						UpperCAmelCase_						=				regex_match.groups()
						UpperCAmelCase_						=				f'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
						UpperCAmelCase_						=				re_prior_cond_proj_in.sub(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

				# keep original key
				else:
						UpperCAmelCase_						=				original_key

				UpperCAmelCase_						=				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:
						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 snake_case__  (			__SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=None )		->							int:
		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=__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 )

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

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

		UpperCAmelCase_						=				[]
		UpperCAmelCase_						=				{}
		for i, dict_name in enumerate(__SCREAMING_SNAKE_CASE ):
				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(__SCREAMING_SNAKE_CASE			,     model.state_dict()			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
				weight_dict.append(__SCREAMING_SNAKE_CASE )

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






def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Union[str, Any]:
		UpperCAmelCase_						=				0
		while b > 0:
				if b & 1:
						res += a

				a += a
				b >>= 1

		return res







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Tuple:
		UpperCAmelCase_						=				0
		while b > 0:
				if b & 1:
						UpperCAmelCase_						=				((res % c) + (a % c)) % c

				a += a
				b >>= 1

		return res

