



import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


_lowerCamelCase						    =  logging.getLogger(__name__)

_lowerCamelCase						    =  tf.data.AUTOTUNE






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	argparse.ArgumentParser(description="Train a masked language model on TPU."     )
 parser.add_argument(
     "--pretrained_model_config"		,	type=__lowerCamelCase		,	default="roberta-base"		,	help="The model config to use. Note that we don't copy the model's weights, only the config!"		,	)
 parser.add_argument(
     "--tokenizer"		,	type=__lowerCamelCase		,	default="unigram-tokenizer-wikitext"		,	help="The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model's vocab size."		,	)

 parser.add_argument(
     "--per_replica_batch_size"		,	type=__lowerCamelCase		,	default=8		,	help="Batch size per TPU core."		,	)

 parser.add_argument(
     "--no_tpu"		,	action="store_true"		,	help="If set, run on CPU and don't try to initialize a TPU. Useful for debugging on non-TPU instances."		,	)

 parser.add_argument(
     "--tpu_name"		,	type=__lowerCamelCase		,	help="Name of TPU resource to initialize. Should be blank on Colab, and 'local' on TPU VMs."		,	default="local"		,	)

 parser.add_argument(
     "--tpu_zone"		,	type=__lowerCamelCase		,	help="Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes."		,	)

 parser.add_argument(
     "--gcp_project"		,	type=__lowerCamelCase		,	help="Google cloud project name. Only used for non-Colab TPU nodes."     )

 parser.add_argument(
     "--bfloat16"		,	action="store_true"		,	help="Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU."		,	)

 parser.add_argument(
     "--train_dataset"		,	type=__lowerCamelCase		,	help="Path to training dataset to load. If the path begins with `gs://`"
     " then the dataset will be loaded from a Google Cloud Storage bucket."		,	)

 parser.add_argument(
     "--shuffle_buffer_size"		,	type=__lowerCamelCase		,	default=2**18		,	help="Size of the shuffle buffer (in samples)"		,	)

 parser.add_argument(
     "--eval_dataset"		,	type=__lowerCamelCase		,	help="Path to evaluation dataset to load. If the path begins with `gs://`"
     " then the dataset will be loaded from a Google Cloud Storage bucket."		,	)

 parser.add_argument(
     "--num_epochs"		,	type=__lowerCamelCase		,	default=1		,	help="Number of epochs to train for."		,	)

 parser.add_argument(
     "--learning_rate"		,	type=__lowerCamelCase		,	default=1E-4		,	help="Learning rate to use for training."		,	)

 parser.add_argument(
     "--weight_decay_rate"		,	type=__lowerCamelCase		,	default=1E-3		,	help="Weight decay rate to use for training."		,	)

 parser.add_argument(
     "--max_length"		,	type=__lowerCamelCase		,	default=512		,	help="Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py"		,	)

 parser.add_argument(
     "--mlm_probability"		,	type=__lowerCamelCase		,	default=0.15		,	help="Fraction of tokens to mask during training."		,	)

 parser.add_argument("--output_dir"		,	type=__lowerCamelCase		,	required=__lowerCamelCase		,	help="Path to save model checkpoints to."     )
 parser.add_argument("--hub_model_id"		,	type=__lowerCamelCase		,	help="Model ID to upload to on the Hugging Face Hub."     )

 __SCREAMING_SNAKE_CASE       :    List[str]       =	parser.parse_args()
 return args






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str     ):



 """simple docstring"""





 try:
  if args.tpu_name:
   __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	tf.distribute.cluster_resolver.TPUClusterResolver(
       args.tpu_name		,	zone=args.tpu_zone		,	project=args.gcp_project     )
  else:
   __SCREAMING_SNAKE_CASE       :    Any       =	tf.distribute.cluster_resolver.TPUClusterResolver()
 except ValueError:
  raise RuntimeError(
      "Couldn't connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or "
      "--gcp_project. When running on a TPU VM, use --tpu_name local."     )

 tf.config.experimental_connect_to_cluster(__lowerCamelCase     )
 tf.tpu.experimental.initialize_tpu_system(__lowerCamelCase     )

 return tpu






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	0
 for file in file_list:
  __SCREAMING_SNAKE_CASE       :    int       =	file.split("/"     )[-1]
  __SCREAMING_SNAKE_CASE       :    Dict       =	re.search(r"-\d+-(\d+)\.tfrecord"		,	__lowerCamelCase     ).group(1     )
  __SCREAMING_SNAKE_CASE       :    str       =	int(__lowerCamelCase     )
  num_samples += sample_count

 return num_samples






def 	_lowerCAmelCase						(				__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     Tuple		,	__lowerCamelCase   :     int		,	__lowerCamelCase   :     Tuple		,	__lowerCamelCase   :     Optional[int]		,	__lowerCamelCase   :     int=None     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    str       =	count_samples(__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	tf.data.Dataset.from_tensor_slices(__lowerCamelCase     )
 if shuffle:
  __SCREAMING_SNAKE_CASE       :    List[Any]       =	dataset.shuffle(len(__lowerCamelCase     )     )
 __SCREAMING_SNAKE_CASE       :    Dict       =	tf.data.TFRecordDataset(__lowerCamelCase		,	num_parallel_reads=__lowerCamelCase     )
 # TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
 __SCREAMING_SNAKE_CASE       :    Tuple       =	dataset.apply(tf.data.experimental.assert_cardinality(__lowerCamelCase     )     )
 __SCREAMING_SNAKE_CASE       :    Tuple       =	dataset.map(__lowerCamelCase		,	num_parallel_calls=__lowerCamelCase     )
 if shuffle:
  assert shuffle_buffer_size is not None
  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	dataset.shuffle(args.shuffle_buffer_size     )
 __SCREAMING_SNAKE_CASE       :    Any       =	dataset.batch(__lowerCamelCase		,	drop_remainder=__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	dataset.map(__lowerCamelCase		,	num_parallel_calls=__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	dataset.prefetch(__lowerCamelCase     )
 return dataset






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Tuple     ):



 """simple docstring"""





 if not args.no_tpu:
  __SCREAMING_SNAKE_CASE       :    List[str]       =	initialize_tpu(__lowerCamelCase     )
  __SCREAMING_SNAKE_CASE       :    str       =	tf.distribute.TPUStrategy(__lowerCamelCase     )
 else:
  __SCREAMING_SNAKE_CASE       :    Any       =	tf.distribute.OneDeviceStrategy(device="/gpu:0"     )

 if args.bfloataa:
  tf.keras.mixed_precision.set_global_policy("mixed_bfloat16"     )

 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	AutoTokenizer.from_pretrained(args.tokenizer     )
 __SCREAMING_SNAKE_CASE       :    List[str]       =	AutoConfig.from_pretrained(args.pretrained_model_config     )
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	tokenizer.vocab_size

 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	tf.io.gfile.glob(os.path.join(args.train_dataset		,	"*.tfrecord"     )     )
 if not training_records:
  raise ValueError(F"""No .tfrecord files found in {args.train_dataset}."""     )
 __SCREAMING_SNAKE_CASE       :    str       =	tf.io.gfile.glob(os.path.join(args.eval_dataset		,	"*.tfrecord"     )     )
 if not eval_records:
  raise ValueError(F"""No .tfrecord files found in {args.eval_dataset}."""     )

 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	count_samples(__lowerCamelCase     )

 __SCREAMING_SNAKE_CASE       :    Dict       =	num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	steps_per_epoch * args.num_epochs

 with strategy.scope():
  __SCREAMING_SNAKE_CASE       :    List[str]       =	TFAutoModelForMaskedLM.from_config(__lowerCamelCase     )
  model(model.dummy_inputs     )  # Pass some dummy inputs through the model to ensure all the weights are built
  __SCREAMING_SNAKE_CASE       :    int       =	create_optimizer(
      num_train_steps=__lowerCamelCase		,	num_warmup_steps=total_train_steps // 20		,	init_lr=args.learning_rate		,	weight_decay_rate=args.weight_decay_rate		,	)

  # Transformers models compute the right loss for their task by default when labels are passed, and will
  # use this for training unless you specify your own loss function in compile().
  model.compile(optimizer=__lowerCamelCase		,	metrics=["accuracy"]     )

 def decode_fn(__lowerCamelCase   :     Union[str, Any]     ):
  __SCREAMING_SNAKE_CASE       :    List[Any]       =	{
      "input_ids": tf.io.FixedLenFeature(dtype=tf.intaa		,	shape=(args.max_length,)     ),
      "attention_mask": tf.io.FixedLenFeature(dtype=tf.intaa		,	shape=(args.max_length,)     ),
  }
  return tf.io.parse_single_example(__lowerCamelCase		,	__lowerCamelCase     )

 # Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
 # use their methods in our data pipeline.
 __SCREAMING_SNAKE_CASE       :    Tuple       =	DataCollatorForLanguageModeling(
     tokenizer=__lowerCamelCase		,	mlm_probability=args.mlm_probability		,	mlm=__lowerCamelCase		,	return_tensors="tf"     )

 def mask_with_collator(__lowerCamelCase   :     Optional[Any]     ):
  # TF really needs an isin() function
  __SCREAMING_SNAKE_CASE       :    Dict       =	(
      ~tf.cast(batch["attention_mask"]		,	tf.bool     )
      | (batch["input_ids"] == tokenizer.cls_token_id)
      | (batch["input_ids"] == tokenizer.sep_token_id)
  )
  __SCREAMING_SNAKE_CASE       :    Any       =	data_collator.tf_mask_tokens(
      batch["input_ids"]		,	vocab_size=len(__lowerCamelCase     )		,	mask_token_id=tokenizer.mask_token_id		,	special_tokens_mask=__lowerCamelCase		,	)
  return batch

 __SCREAMING_SNAKE_CASE       :    Any       =	args.per_replica_batch_size * strategy.num_replicas_in_sync

 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	prepare_dataset(
     __lowerCamelCase		,	decode_fn=__lowerCamelCase		,	mask_fn=__lowerCamelCase		,	batch_size=__lowerCamelCase		,	shuffle=__lowerCamelCase		,	shuffle_buffer_size=args.shuffle_buffer_size		,	)

 __SCREAMING_SNAKE_CASE       :    List[str]       =	prepare_dataset(
     __lowerCamelCase		,	decode_fn=__lowerCamelCase		,	mask_fn=__lowerCamelCase		,	batch_size=__lowerCamelCase		,	shuffle=__lowerCamelCase		,	)

 __SCREAMING_SNAKE_CASE       :    str       =	[]
 if args.hub_model_id:
  callbacks.append(
      PushToHubCallback(output_dir=args.output_dir		,	hub_model_id=args.hub_model_id		,	tokenizer=__lowerCamelCase     )     )

 model.fit(
     __lowerCamelCase		,	validation_data=__lowerCamelCase		,	epochs=args.num_epochs		,	callbacks=__lowerCamelCase		,	)

 model.save_pretrained(args.output_dir     )


if __name__ == "__main__":
    _lowerCamelCase						    =  parse_args()
    main(args)











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   :     Any     ):



 """simple docstring"""





 if key.endswith(".model.1.bias"     ) and len(key.split("."     )     ) > 10:
  __SCREAMING_SNAKE_CASE       :    Dict       =	key.replace(".model.1.bias"		,	".conv1d_1.bias"     )
 elif key.endswith(".model.1.weight"     ) and len(key.split("."     )     ) > 10:
  __SCREAMING_SNAKE_CASE       :    Tuple       =	key.replace(".model.1.weight"		,	".conv1d_1.weight"     )
 elif key.endswith(".model.3.bias"     ) and len(key.split("."     )     ) > 10:
  __SCREAMING_SNAKE_CASE       :    Dict       =	key.replace(".model.3.bias"		,	".conv1d_2.bias"     )
 elif key.endswith(".model.3.weight"     ) and len(key.split("."     )     ) > 10:
  __SCREAMING_SNAKE_CASE       :    List[str]       =	key.replace(".model.3.weight"		,	".conv1d_2.weight"     )

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

 if "prime_prior" in key:
  __SCREAMING_SNAKE_CASE       :    Dict       =	key.replace("prime_prior"		,	"encoder"     )

 if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
  __SCREAMING_SNAKE_CASE       :    Optional[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:
  __SCREAMING_SNAKE_CASE       :    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   :     Tuple		,	__lowerCamelCase   :     Union[str, Any]		,	__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     Optional[Any]     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    str       =	{}
 import re

 __SCREAMING_SNAKE_CASE       :    str       =	re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"     )
 __SCREAMING_SNAKE_CASE       :    Tuple       =	re.compile(
     r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"     )
 __SCREAMING_SNAKE_CASE       :    Tuple       =	re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"     )

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

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

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

  elif re_encoder_block_proj_out.fullmatch(__lowerCamelCase     ):
   __SCREAMING_SNAKE_CASE       :    Any       =	re_encoder_block_proj_out.match(__lowerCamelCase     )
   __SCREAMING_SNAKE_CASE       :    int       =	regex_match.groups()
   __SCREAMING_SNAKE_CASE       :    Optional[int]       =	F"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
   __SCREAMING_SNAKE_CASE       :    Dict       =	re_encoder_block_proj_out.sub(__lowerCamelCase		,	__lowerCamelCase     )

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

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

  elif re_decoder_block_proj_in.fullmatch(__lowerCamelCase     ):
   __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	re_decoder_block_proj_in.match(__lowerCamelCase     )
   __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	regex_match.groups()
   __SCREAMING_SNAKE_CASE       :    List[str]       =	F"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
   __SCREAMING_SNAKE_CASE       :    int       =	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     ):
   __SCREAMING_SNAKE_CASE       :    List[Any]       =	re_prior_cond_conv_out.match(__lowerCamelCase     )
   __SCREAMING_SNAKE_CASE       :    Optional[int]       =	regex_match.groups()
   __SCREAMING_SNAKE_CASE       :    str       =	int(groups[1]     ) * 2 + int(groups[2]     ) - 2
   __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	F"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
   __SCREAMING_SNAKE_CASE       :    Any       =	re_prior_cond_conv_out.sub(__lowerCamelCase		,	__lowerCamelCase     )

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

  elif re_prior_cond_proj_in.fullmatch(__lowerCamelCase     ):
   __SCREAMING_SNAKE_CASE       :    Tuple       =	re_prior_cond_proj_in.match(__lowerCamelCase     )
   __SCREAMING_SNAKE_CASE       :    Dict       =	regex_match.groups()
   __SCREAMING_SNAKE_CASE       :    Dict       =	F"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
   __SCREAMING_SNAKE_CASE       :    int       =	re_prior_cond_proj_in.sub(__lowerCamelCase		,	__lowerCamelCase     )

  # keep original key
  else:
   __SCREAMING_SNAKE_CASE       :    Tuple       =	original_key

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

  __SCREAMING_SNAKE_CASE       :    List[str]       =	original_key
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	value

 return new_dict






@torch.no_grad()
def 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[int]=None		,	__lowerCamelCase   :     List[Any]=None     ):



 """simple docstring"""





 for file in MODEL_MAPPING[model_name]:
  if not os.path.isfile(F"""{pytorch_dump_folder_path}/{file.split("/"     )[-1]}"""     ):
   __SCREAMING_SNAKE_CASE       :    Dict       =	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     )

 __SCREAMING_SNAKE_CASE       :    int       =	MODEL_MAPPING[model_name.split("/"     )[-1]]

 __SCREAMING_SNAKE_CASE       :    List[str]       =	JukeboxConfig.from_pretrained(__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    str       =	JukeboxModel(__lowerCamelCase     )

 __SCREAMING_SNAKE_CASE       :    int       =	[]
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	{}
 for i, dict_name in enumerate(__lowerCamelCase     ):
  __SCREAMING_SNAKE_CASE       :    str       =	torch.load(F"""{pytorch_dump_folder_path}/{dict_name.split("/"     )[-1]}"""     )["model"]

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

  __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	"vqvae" if i == 0 else F"""priors.{3 - i}"""
  __SCREAMING_SNAKE_CASE       :    int       =	fix_jukebox_keys(__lowerCamelCase		,	model.state_dict()		,	__lowerCamelCase		,	__lowerCamelCase     )
  weight_dict.append(__lowerCamelCase     )

 __SCREAMING_SNAKE_CASE       :    Dict       =	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)






