



'''simple docstring'''

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()
__lowerCAmelCase									=   logging.get_logger(__name__)


__lowerCAmelCase									=   '''https://openaipublic.azureedge.net/jukebox/models/'''
__lowerCAmelCase									=   {
    '''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      __lowerCamelCase     (      lowerCAmelCase_  )			->      str:
		if key.endswith('.model.1.bias'  ) and len(key.split('.'  )  ) > 10:
				_a     :      Optional[Any]        =							key.replace('.model.1.bias'						,			'.conv1d_1.bias'  )
		elif key.endswith('.model.1.weight'  ) and len(key.split('.'  )  ) > 10:
				_a     :      List[Any]        =							key.replace('.model.1.weight'						,			'.conv1d_1.weight'  )
		elif key.endswith('.model.3.bias'  ) and len(key.split('.'  )  ) > 10:
				_a     :      Dict        =							key.replace('.model.3.bias'						,			'.conv1d_2.bias'  )
		elif key.endswith('.model.3.weight'  ) and len(key.split('.'  )  ) > 10:
				_a     :      Any        =							key.replace('.model.3.weight'						,			'.conv1d_2.weight'  )

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

		if "prime_prior" in key:
				_a     :      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:
				_a     :      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:
				_a     :      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      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      Union[str, Any]:
		_a     :      int        =							{}
		import re

		_a     :      List[str]        =							re.compile(r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'  )
		_a     :      List[Any]        =							re.compile(
		    r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'  )
		_a     :      Optional[Any]        =							re.compile(r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'  )

		_a     :      Union[str, Any]        =							re.compile(r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'  )
		_a     :      int        =							re.compile(
		    r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'  )
		_a     :      Optional[Any]        =							re.compile(r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'  )

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

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

				elif re_encoder_block_proj_out.fullmatch(lowerCAmelCase_  ):
						_a     :      Union[str, Any]        =							re_encoder_block_proj_out.match(lowerCAmelCase_  )
						_a     :      Dict        =							regex_match.groups()
						_a     :      Any        =							f"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
						_a     :      Tuple        =							re_encoder_block_proj_out.sub(lowerCAmelCase_						,			lowerCAmelCase_  )

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

				elif re_decoder_block_resnet.fullmatch(lowerCAmelCase_  ):
						_a     :      int        =							re_decoder_block_resnet.match(lowerCAmelCase_  )
						_a     :      List[str]        =							regex_match.groups()
						_a     :      Union[str, Any]        =							int(groups[2]  ) * 2 + int(groups[3]  ) - 2
						_a     :      Union[str, Any]        =							{'1': 1, '3': 2}[groups[-2]]
						_a     :      Any        =							f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."""
						_a     :      Tuple        =							f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
						_a     :      List[Any]        =							prefix + resnet_block
						_a     :      str        =							re_decoder_block_resnet.sub(lowerCAmelCase_						,			lowerCAmelCase_  )

				elif re_decoder_block_proj_in.fullmatch(lowerCAmelCase_  ):
						_a     :      List[str]        =							re_decoder_block_proj_in.match(lowerCAmelCase_  )
						_a     :      List[Any]        =							regex_match.groups()
						_a     :      int        =							f"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
						_a     :      Dict        =							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_  ):
						_a     :      Optional[Any]        =							re_prior_cond_conv_out.match(lowerCAmelCase_  )
						_a     :      Tuple        =							regex_match.groups()
						_a     :      List[str]        =							int(groups[1]  ) * 2 + int(groups[2]  ) - 2
						_a     :      Tuple        =							f"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
						_a     :      List[str]        =							re_prior_cond_conv_out.sub(lowerCAmelCase_						,			lowerCAmelCase_  )

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

				elif re_prior_cond_proj_in.fullmatch(lowerCAmelCase_  ):
						_a     :      Dict        =							re_prior_cond_proj_in.match(lowerCAmelCase_  )
						_a     :      Tuple        =							regex_match.groups()
						_a     :      Optional[int]        =							f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
						_a     :      List[str]        =							re_prior_cond_proj_in.sub(lowerCAmelCase_						,			lowerCAmelCase_  )

				# keep original key
				else:
						_a     :      Tuple        =							original_key

				_a     :      int        =							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:
						_a     :      Dict        =							model_state_dict[f"""{key_prefix}.{key}"""]
						print(f"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""  )
						_a     :      Union[str, Any]        =							original_key

				_a     :      Any        =							original_key
				_a     :      Any        =							value

		return new_dict



@torch.no_grad()
def      __lowerCamelCase     (      lowerCAmelCase_=None						,			lowerCAmelCase_=None  )			->      Optional[int]:
		for file in MODEL_MAPPING[model_name]:
				if not os.path.isfile(f"""{pytorch_dump_folder_path}/{file.split('/'  )[-1]}"""  ):
						_a     :      List[str]        =							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  )

		_a     :      str        =							MODEL_MAPPING[model_name.split('/'  )[-1]]

		_a     :      List[Any]        =							JukeboxConfig.from_pretrained(lowerCAmelCase_  )
		_a     :      Tuple        =							JukeboxModel(lowerCAmelCase_  )

		_a     :      Optional[int]        =							[]
		_a     :      int        =							{}
		for i, dict_name in enumerate(lowerCAmelCase_  ):
				_a     :      int        =							torch.load(f"""{pytorch_dump_folder_path}/{dict_name.split('/'  )[-1]}"""  )['model']

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

				_a     :      Optional[int]        =							'vqvae' if i == 0 else f"""priors.{3 - i}"""
				_a     :      int        =							fix_jukebox_keys(lowerCAmelCase_						,			model.state_dict()						,			lowerCAmelCase_						,			lowerCAmelCase_  )
				weight_dict.append(lowerCAmelCase_  )

		_a     :      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__":
							__lowerCAmelCase									=   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.''',
							)
							__lowerCAmelCase									=   parser.parse_args()
							convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)









'''simple docstring'''

import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=1024						,			lowerCAmelCase_=1024						,			lowerCAmelCase_=False						,			**lowerCAmelCase_  )			->      List[Any]:
		_a     :      str        =							AutoTokenizer.from_pretrained(lowerCAmelCase_  )
		_a     :      List[Any]        =							SeqaSeqDataset(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			type_path='train'						,			**lowerCAmelCase_  )
		_a     :      List[str]        =							tok.pad_token_id

		def get_lens(lowerCAmelCase_  ):
				_a     :      Dict        =							tqdm(
				    DataLoader(lowerCAmelCase_						,			batch_size=512						,			num_workers=8						,			shuffle=lowerCAmelCase_						,			collate_fn=ds.collate_fn  )						,			desc=str(ds.len_file  )						,			)
				_a     :      Union[str, Any]        =							[]
				for batch in dl:
						_a     :      Optional[Any]        =							batch['input_ids'].ne(lowerCAmelCase_  ).sum(1  ).tolist()
						_a     :      Optional[Any]        =							batch['labels'].ne(lowerCAmelCase_  ).sum(1  ).tolist()
						if consider_target:
								for src, tgt in zip(lowerCAmelCase_						,			lowerCAmelCase_  ):
										max_lens.append(max(lowerCAmelCase_						,			lowerCAmelCase_  )  )
						else:
								max_lens.extend(lowerCAmelCase_  )
				return max_lens

		_a     :      str        =							get_lens(lowerCAmelCase_  )
		_a     :      Optional[int]        =							SeqaSeqDataset(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			type_path='val'						,			**lowerCAmelCase_  )
		_a     :      Dict        =							get_lens(lowerCAmelCase_  )
		pickle_save(lowerCAmelCase_						,			train_ds.len_file  )
		pickle_save(lowerCAmelCase_						,			val_ds.len_file  )


if __name__ == "__main__":
							fire.Fire(save_len_file)




