


'''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()
_lowercase    :    List[Any]			 =  logging.get_logger(__name__)


_lowercase    :    Optional[int]			 =  """https://openaipublic.azureedge.net/jukebox/models/"""
_lowercase    :    int			 =  {
    """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__   ( A     :    Tuple  ):




						'''simple docstring'''






						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    lowerCamelCase__   ( A     :    int   ,     A     :    int   ,     A     :    List[str]   ,     A     :    List[str]  ):




						'''simple docstring'''






						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(A  ):
																		UpperCAmelCase         =			re_encoder_block_conv_in.match(A  )
																		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(A   ,     A  )

												elif re_encoder_block_resnet.fullmatch(A  ):
																		UpperCAmelCase         =			re_encoder_block_resnet.match(A  )
																		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(A   ,     A  )

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

												# rename vqvae.decoder keys
												elif re_decoder_block_conv_out.fullmatch(A  ):
																		UpperCAmelCase         =			re_decoder_block_conv_out.match(A  )
																		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(A   ,     A  )

												elif re_decoder_block_resnet.fullmatch(A  ):
																		UpperCAmelCase         =			re_decoder_block_resnet.match(A  )
																		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(A   ,     A  )

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

												elif re_prior_cond_resnet.fullmatch(A  ):
																		UpperCAmelCase         =			re_prior_cond_resnet.match(A  )
																		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(A   ,     A  )

												elif re_prior_cond_proj_in.fullmatch(A  ):
																		UpperCAmelCase         =			re_prior_cond_proj_in.match(A  )
																		UpperCAmelCase         =			regex_match.groups()
																		UpperCAmelCase         =			f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
																		UpperCAmelCase         =			re_prior_cond_proj_in.sub(A   ,     A  )

												# keep original key
												else:
																		UpperCAmelCase         =			original_key

												UpperCAmelCase         =			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:
																		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    lowerCamelCase__   ( A     :    int=None   ,     A     :    Tuple=None  ):




						'''simple docstring'''






						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=A  )
																		os.makedirs(f"""{pytorch_dump_folder_path}/"""   ,     exist_ok=A  )
																		open(f"""{pytorch_dump_folder_path}/{file.split("/"  )[-1]}"""   ,     '''wb'''  ).write(r.content  )

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

						UpperCAmelCase         =			JukeboxConfig.from_pretrained(A  )
						UpperCAmelCase         =			JukeboxModel(A  )

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

						UpperCAmelCase         =			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    :    Union[str, 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.""",
	)
	_lowercase    :    Optional[Any]			 =  parser.parse_args()
	convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)









'''simple docstring'''



from argparse import ArgumentParser

from . import BaseTransformersCLICommand


def    lowerCamelCase__   ( A     :    List[Any]  ):




						'''simple docstring'''






						return DownloadCommand(args.model   ,     args.cache_dir   ,     args.force   ,     args.trust_remote_code  )






class  UpperCamelCase__(     lowerCAmelCase ):




				@staticmethod
				def   a__(	lowerCAmelCase    :				ArgumentParser   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			parser.add_parser('''download'''   )
										download_parser.add_argument(
										    '''--cache-dir'''   ,					type=lowerCAmelCase   ,					default=lowerCAmelCase   ,					help='''Path to location to store the models'''   )
										download_parser.add_argument(
										    '''--force'''   ,					action='''store_true'''   ,					help='''Force the model to be download even if already in cache-dir'''   )
										download_parser.add_argument(
										    '''--trust-remote-code'''   ,					action='''store_true'''   ,					help='''Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine'''   ,					)
										download_parser.add_argument('''model'''   ,					type=lowerCAmelCase   ,					help='''Name of the model to download'''   )
										download_parser.set_defaults(func=lowerCAmelCase   )




				def __init__(	self    :				Dict   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				bool   ,					lowerCAmelCase    :				bool   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			model
										UpperCAmelCase         =			cache
										UpperCAmelCase         =			force
										UpperCAmelCase         =			trust_remote_code





				def   a__(	self    :				int   )->  Optional[Any]:

										"""simple docstring"""




										from ..models.auto import AutoModel, AutoTokenizer

										AutoModel.from_pretrained(
										    self._model   ,					cache_dir=self._cache   ,					force_download=self._force   ,					trust_remote_code=self._trust_remote_code   )
										AutoTokenizer.from_pretrained(
										    self._model   ,					cache_dir=self._cache   ,					force_download=self._force   ,					trust_remote_code=self._trust_remote_code   )





