


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()
A      :    List[str]		=					logging.get_logger(__name__)


A      :    Tuple		=					"https://openaipublic.azureedge.net/jukebox/models/"
A      :    List[str]		=					{
    "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       lowercase_		(		_A			:    List[str]						):

      """simple docstring"""




      if key.endswith(".model.1.bias"						) and len(key.split("."						)						) > 10:
            lowerCamelCase__		:				Union[str, Any]    =  key.replace(".model.1.bias"    ,					".conv1d_1.bias"						)
      elif key.endswith(".model.1.weight"						) and len(key.split("."						)						) > 10:
            lowerCamelCase__		:				str    =  key.replace(".model.1.weight"    ,					".conv1d_1.weight"						)
      elif key.endswith(".model.3.bias"						) and len(key.split("."						)						) > 10:
            lowerCamelCase__		:				Optional[Any]    =  key.replace(".model.3.bias"    ,					".conv1d_2.bias"						)
      elif key.endswith(".model.3.weight"						) and len(key.split("."						)						) > 10:
            lowerCamelCase__		:				int    =  key.replace(".model.3.weight"    ,					".conv1d_2.weight"						)

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

      if "prime_prior" in key:
            lowerCamelCase__		:				Optional[int]    =  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__		:				Optional[int]    =  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__		:				int    =  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       lowercase_		(		_A			:    Any    ,					_A			:    List[Any]    ,					_A			:    Optional[int]    ,					_A			:    Union[str, Any]						):

      """simple docstring"""




      lowerCamelCase__		:				Optional[Any]    =  {}
      import re

      lowerCamelCase__		:				Optional[Any]    =  re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"						)
      lowerCamelCase__		:				Dict    =  re.compile(
          r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"						)
      lowerCamelCase__		:				int    =  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__		:				List[Any]    =  re.compile(
          r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"						)
      lowerCamelCase__		:				List[str]    =  re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"						)

      lowerCamelCase__		:				str    =  re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"						)
      lowerCamelCase__		:				str    =  re.compile(
          r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"						)
      lowerCamelCase__		:				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(_A						):
                  lowerCamelCase__		:				Optional[int]    =  re_encoder_block_conv_in.match(_A						)
                  lowerCamelCase__		:				Tuple    =  regex_match.groups()
                  lowerCamelCase__		:				Any    =  int(groups[2]						) * 2 + int(groups[3]						)
                  lowerCamelCase__		:				Dict    =  F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"
                  lowerCamelCase__		:				Optional[int]    =  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[Any]    =  regex_match.groups()
                  lowerCamelCase__		:				str    =  int(groups[2]						) * 2 + int(groups[3]						)
                  lowerCamelCase__		:				Any    =  {"1": 1, "3": 2}[groups[-2]]
                  lowerCamelCase__		:				Dict    =  F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."
                  lowerCamelCase__		:				Dict    =  F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
                  lowerCamelCase__		:				List[Any]    =  prefix + resnet_block
                  lowerCamelCase__		:				List[str]    =  re_encoder_block_resnet.sub(_A    ,					_A						)

            elif re_encoder_block_proj_out.fullmatch(_A						):
                  lowerCamelCase__		:				str    =  re_encoder_block_proj_out.match(_A						)
                  lowerCamelCase__		:				Optional[Any]    =  regex_match.groups()
                  lowerCamelCase__		:				Optional[Any]    =  F"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
                  lowerCamelCase__		:				str    =  re_encoder_block_proj_out.sub(_A    ,					_A						)

            # rename vqvae.decoder keys
            elif re_decoder_block_conv_out.fullmatch(_A						):
                  lowerCamelCase__		:				Optional[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__		:				Dict    =  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__		:				Any    =  re_decoder_block_resnet.match(_A						)
                  lowerCamelCase__		:				List[Any]    =  regex_match.groups()
                  lowerCamelCase__		:				int    =  int(groups[2]						) * 2 + int(groups[3]						) - 2
                  lowerCamelCase__		:				Tuple    =  {"1": 1, "3": 2}[groups[-2]]
                  lowerCamelCase__		:				List[Any]    =  F"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."
                  lowerCamelCase__		:				Union[str, Any]    =  F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
                  lowerCamelCase__		:				Dict    =  prefix + resnet_block
                  lowerCamelCase__		:				Optional[int]    =  re_decoder_block_resnet.sub(_A    ,					_A						)

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

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

            elif re_prior_cond_proj_in.fullmatch(_A						):
                  lowerCamelCase__		:				str    =  re_prior_cond_proj_in.match(_A						)
                  lowerCamelCase__		:				List[str]    =  regex_match.groups()
                  lowerCamelCase__		:				Tuple    =  F"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
                  lowerCamelCase__		:				int    =  re_prior_cond_proj_in.sub(_A    ,					_A						)

            # keep original key
            else:
                  lowerCamelCase__		:				Tuple    =  original_key

            lowerCamelCase__		:				Optional[int]    =  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__		:				Optional[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__		:				Tuple    =  original_key
            lowerCamelCase__		:				str    =  value

      return new_dict

@torch.no_grad()
def       lowercase_		(		_A			:    str=None    ,					_A			:    Union[str, Any]=None						):

      """simple docstring"""




      for file in MODEL_MAPPING[model_name]:
            if not os.path.isfile(F"{pytorch_dump_folder_path}/{file.split('/'						)[-1]}"						):
                  lowerCamelCase__		:				Optional[int]    =  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__		:				Any    =  MODEL_MAPPING[model_name.split("/"						)[-1]]

      lowerCamelCase__		:				Optional[int]    =  JukeboxConfig.from_pretrained(_A						)
      lowerCamelCase__		:				List[str]    =  JukeboxModel(_A						)

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

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

            lowerCamelCase__		:				Union[str, Any]    =  "vqvae" if i == 0 else F"priors.{3 - i}"
            lowerCamelCase__		:				Union[str, Any]    =  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__":
    A      :    str		=					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.",
    )
    A      :    List[Any]		=					parser.parse_args()
    convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)






from .constants import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    TORCH_LAUNCH_PARAMS,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
)
from .dataclasses import (
    BnbQuantizationConfig,
    ComputeEnvironment,
    CustomDtype,
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    DynamoBackend,
    FPaRecipeKwargs,
    FullyShardedDataParallelPlugin,
    GradientAccumulationPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    KwargsHandler,
    LoggerType,
    MegatronLMPlugin,
    PrecisionType,
    ProjectConfiguration,
    RNGType,
    SageMakerDistributedType,
    TensorInformation,
    TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
    get_ccl_version,
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_aim_available,
    is_bfaa_available,
    is_bnb_available,
    is_botoa_available,
    is_ccl_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_fpa_available,
    is_ipex_available,
    is_megatron_lm_available,
    is_mlflow_available,
    is_mps_available,
    is_npu_available,
    is_rich_available,
    is_safetensors_available,
    is_sagemaker_available,
    is_tensorboard_available,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
from .modeling import (
    check_device_map,
    check_tied_parameters_in_config,
    check_tied_parameters_on_same_device,
    compute_module_sizes,
    convert_file_size_to_int,
    dtype_byte_size,
    find_tied_parameters,
    get_balanced_memory,
    get_max_layer_size,
    get_max_memory,
    get_mixed_precision_context_manager,
    id_tensor_storage,
    infer_auto_device_map,
    load_checkpoint_in_model,
    load_offloaded_weights,
    load_state_dict,
    named_module_tensors,
    retie_parameters,
    set_module_tensor_to_device,
    shard_checkpoint,
)
from .offload import (
    OffloadedWeightsLoader,
    PrefixedDataset,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
    save_offload_index,
)
from .operations import (
    broadcast,
    broadcast_object_list,
    concatenate,
    convert_outputs_to_fpaa,
    convert_to_fpaa,
    find_batch_size,
    find_device,
    gather,
    gather_object,
    get_data_structure,
    honor_type,
    initialize_tensors,
    is_namedtuple,
    is_tensor_information,
    is_torch_tensor,
    listify,
    pad_across_processes,
    recursively_apply,
    reduce,
    send_to_device,
    slice_tensors,
)
from .versions import compare_versions, is_torch_version


if is_deepspeed_available():
    from .deepspeed import (
        DeepSpeedEngineWrapper,
        DeepSpeedOptimizerWrapper,
        DeepSpeedSchedulerWrapper,
        DummyOptim,
        DummyScheduler,
        HfDeepSpeedConfig,
    )

from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
    PrepareForLaunch,
    _filter_args,
    prepare_deepspeed_cmd_env,
    prepare_multi_gpu_env,
    prepare_sagemager_args_inputs,
    prepare_simple_launcher_cmd_env,
    prepare_tpu,
)
from .megatron_lm import (
    AbstractTrainStep,
    BertTrainStep,
    GPTTrainStep,
    MegatronEngine,
    MegatronLMDummyDataLoader,
    MegatronLMDummyScheduler,
    MegatronLMOptimizerWrapper,
    MegatronLMSchedulerWrapper,
    TaTrainStep,
    avg_losses_across_data_parallel_group,
    gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
    extract_model_from_parallel,
    get_pretty_name,
    is_port_in_use,
    merge_dicts,
    patch_environment,
    save,
    wait_for_everyone,
    write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers


