


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()
_lowercase       		= logging.get_logger(__name__)


_lowercase       		= 'https://openaipublic.azureedge.net/jukebox/models/'
_lowercase       		= {
    '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			(      snake_case__):
						if key.endswith(".model.1.bias") and len(key.split(".")) > 10:
												lowerCAmelCase_	:     List[str]          = key.replace(".model.1.bias"						,  ".conv1d_1.bias")
						elif key.endswith(".model.1.weight") and len(key.split(".")) > 10:
												lowerCAmelCase_	:     List[str]          = key.replace(".model.1.weight"						,  ".conv1d_1.weight")
						elif key.endswith(".model.3.bias") and len(key.split(".")) > 10:
												lowerCAmelCase_	:     int          = key.replace(".model.3.bias"						,  ".conv1d_2.bias")
						elif key.endswith(".model.3.weight") and len(key.split(".")) > 10:
												lowerCAmelCase_	:     Optional[int]          = key.replace(".model.3.weight"						,  ".conv1d_2.weight")

						if "conditioner_blocks.0." in key:
												lowerCAmelCase_	:     List[Any]          = key.replace("conditioner_blocks.0"						,  "conditioner_blocks")

						if "prime_prior" in key:
												lowerCAmelCase_	:     Union[str, Any]          = 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[Any]          = 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			(      snake_case__						,  snake_case__						,  snake_case__						,  snake_case__):
						lowerCAmelCase_	:     str          = {}
						import re

						lowerCAmelCase_	:     int          = re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)")
						lowerCAmelCase_	:     List[Any]          = re.compile(
						    R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)")
						lowerCAmelCase_	:     Optional[Any]          = re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)")

						lowerCAmelCase_	:     List[str]          = re.compile(R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)")
						lowerCAmelCase_	:     Optional[Any]          = 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_	:     Any          = re.compile(R"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)")
						lowerCAmelCase_	:     Any          = re.compile(
						    R"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)")
						lowerCAmelCase_	:     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(A__):
																		lowerCAmelCase_	:     Any          = re_encoder_block_conv_in.match(A__)
																		lowerCAmelCase_	:     Optional[int]          = regex_match.groups()
																		lowerCAmelCase_	:     Any          = 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_	:     Any          = re_encoder_block_conv_in.sub(A__						,  A__)

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

												elif re_encoder_block_proj_out.fullmatch(A__):
																		lowerCAmelCase_	:     List[Any]          = re_encoder_block_proj_out.match(A__)
																		lowerCAmelCase_	:     Any          = regex_match.groups()
																		lowerCAmelCase_	:     Optional[Any]          = F'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
																		lowerCAmelCase_	:     Any          = re_encoder_block_proj_out.sub(A__						,  A__)

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

												elif re_decoder_block_resnet.fullmatch(A__):
																		lowerCAmelCase_	:     Union[str, Any]          = re_decoder_block_resnet.match(A__)
																		lowerCAmelCase_	:     Optional[int]          = regex_match.groups()
																		lowerCAmelCase_	:     List[Any]          = int(groups[2]) * 2 + int(groups[3]) - 2
																		lowerCAmelCase_	:     Optional[Any]          = {"1": 1, "3": 2}[groups[-2]]
																		lowerCAmelCase_	:     Dict          = 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_	:     int          = re_decoder_block_resnet.sub(A__						,  A__)

												elif re_decoder_block_proj_in.fullmatch(A__):
																		lowerCAmelCase_	:     str          = re_decoder_block_proj_in.match(A__)
																		lowerCAmelCase_	:     Union[str, Any]          = 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(A__						,  A__)

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

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

												elif re_prior_cond_proj_in.fullmatch(A__):
																		lowerCAmelCase_	:     Tuple          = re_prior_cond_proj_in.match(A__)
																		lowerCAmelCase_	:     Any          = regex_match.groups()
																		lowerCAmelCase_	:     List[str]          = F'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
																		lowerCAmelCase_	:     List[str]          = re_prior_cond_proj_in.sub(A__						,  A__)

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

												lowerCAmelCase_	:     str          = replace_key(A__)

												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_	:     int          = model_state_dict[F'''{key_prefix}.{key}''']
																		print(F'''{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match''')
																		lowerCAmelCase_	:     List[Any]          = original_key

												lowerCAmelCase_	:     List[Any]          = original_key
												lowerCAmelCase_	:     Optional[Any]          = value

						return new_dict

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

						lowerCAmelCase_	:     Tuple          = MODEL_MAPPING[model_name.split("/")[-1]]

						lowerCAmelCase_	:     str          = JukeboxConfig.from_pretrained(A__)
						lowerCAmelCase_	:     Dict          = JukeboxModel(A__)

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

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

												lowerCAmelCase_	:     Dict          = "vqvae" if i == 0 else F'''priors.{3 - i}'''
												lowerCAmelCase_	:     Tuple          = fix_jukebox_keys(A__						,  model.state_dict()						,  A__						,  A__)
												weight_dict.append(A__)

						lowerCAmelCase_	:     List[Any]          = weight_dict.pop(0)
						model.vqvae.load_state_dict(A__)
						for i in range(len(A__)):
												model.priors[i].load_state_dict(weight_dict[2 - i])

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

						print(F'''Saving model {model_name} to {pytorch_dump_folder_path}''')
						model.save_pretrained(A__)

						return weight_dict


if __name__ == "__main__":
							_lowercase       		= 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.''',
							)
							_lowercase       		= parser.parse_args()
							convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)





def 	__a     (      A__			:  int       ):
    if not isinstance(A__			,  A__       ):
        raise ValueError("Input must be an integer"       )
    if input_num <= 0:
        raise ValueError("Input must be positive"       )
    return sum(
        divisor for divisor in range(1			,  input_num // 2 + 1       ) if input_num % divisor == 0       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()