


"""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()
__magic_name__						: Dict    =				logging.get_logger(__name__)


__magic_name__						: List[str]    =				'https://openaipublic.azureedge.net/jukebox/models/'
__magic_name__						: 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       a_ (	lowercase__ :List[Any]     ):
			if key.endswith(""".model.1.bias"""     ) and len(key.split("""."""     )     ) > 10:
						__lowerCamelCase          =      key.replace(""".model.1.bias""",			""".conv1d_1.bias"""     )
			elif key.endswith(""".model.1.weight"""     ) and len(key.split("""."""     )     ) > 10:
						__lowerCamelCase          =      key.replace(""".model.1.weight""",			""".conv1d_1.weight"""     )
			elif key.endswith(""".model.3.bias"""     ) and len(key.split("""."""     )     ) > 10:
						__lowerCamelCase          =      key.replace(""".model.3.bias""",			""".conv1d_2.bias"""     )
			elif key.endswith(""".model.3.weight"""     ) and len(key.split("""."""     )     ) > 10:
						__lowerCamelCase          =      key.replace(""".model.3.weight""",			""".conv1d_2.weight"""     )

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

			if "prime_prior" in key:
						__lowerCamelCase          =      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          =      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          =      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_ (	lowercase__ :Dict,			lowercase__ :List[Any],			lowercase__ :Union[str, Any],			lowercase__ :str     ):
			__lowerCamelCase          =      {}
			import re

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

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

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

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

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

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

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

						elif re_decoder_block_proj_in.fullmatch(lowercase__     ):
									__lowerCamelCase          =      re_decoder_block_proj_in.match(lowercase__     )
									__lowerCamelCase          =      regex_match.groups()
									__lowerCamelCase          =      f'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
									__lowerCamelCase          =      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__     ):
									__lowerCamelCase          =      re_prior_cond_conv_out.match(lowercase__     )
									__lowerCamelCase          =      regex_match.groups()
									__lowerCamelCase          =      int(groups[1]     ) * 2 + int(groups[2]     ) - 2
									__lowerCamelCase          =      f'conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'
									__lowerCamelCase          =      re_prior_cond_conv_out.sub(lowercase__,			lowercase__     )

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

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

						# keep original key
						else:
									__lowerCamelCase          =      original_key

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

						__lowerCamelCase          =      original_key
						__lowerCamelCase          =      value

			return new_dict





@torch.no_grad()
def       a_ (	lowercase__ :Tuple=None,			lowercase__ :Dict=None     ):
			for file in MODEL_MAPPING[model_name]:
						if not os.path.isfile(f'{pytorch_dump_folder_path}/{file.split("/"     )[-1]}'     ):
									__lowerCamelCase          =      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     )

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

			__lowerCamelCase          =      JukeboxConfig.from_pretrained(lowercase__     )
			__lowerCamelCase          =      JukeboxModel(lowercase__     )

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

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

						__lowerCamelCase          =      """vqvae""" if i == 0 else f'priors.{3 - i}'
						__lowerCamelCase          =      fix_jukebox_keys(lowercase__,			model.state_dict(),			lowercase__,			lowercase__     )
						weight_dict.append(lowercase__     )

			__lowerCamelCase          =      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__":
					__magic_name__						: Optional[Any]    =				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.',
					)
					__magic_name__						: Any    =				parser.parse_args()
					convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)








"""simple docstring"""




def       a_ (	lowercase__ :str,			lowercase__ :int     ):
			return [sentence[i : i + ngram_size] for i in range(len(lowercase__     ) - ngram_size + 1     )]


if __name__ == "__main__":
					from doctest import testmod

					testmod()




