

"""simple docstring"""


from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
  import accelerate




def 					lowercase__    (					lowercase_			)					->		Optional[Any]:




      """simple docstring"""

      if not is_accelerate_available():
            return method
      _UpperCamelCase      :	str						=       version.parse(accelerate.__version__			).base_version
      if version.parse(lowercase_			) < version.parse("0.17.0"			):
            return method

      def wrapper(self		,*lowercase_		,**lowercase_			):
            if hasattr(self		,"_hf_hook"			) and hasattr(self._hf_hook		,"pre_forward"			):
                  self._hf_hook.pre_forward(self			)
            return method(self		,*lowercase_		,**lowercase_			)

      return wrapper







"""simple docstring"""


import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple

import torch
from audiocraft.models import MusicGen

from transformers import (
    AutoFeatureExtractor,
    AutoTokenizer,
    EncodecModel,
    MusicgenDecoderConfig,
    MusicgenForConditionalGeneration,
    MusicgenProcessor,
    TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__				  =   logging.get_logger(__name__)


lowerCamelCase__				  =   ["model.decoder.embed_positions.weights"]




def 					lowercase__    (					lowercase_			)					->		Optional[Any]:




      """simple docstring"""

      if "emb" in name:
            _UpperCamelCase      :	List[str]						=       name.replace("emb"		,"model.decoder.embed_tokens"			)
      if "transformer" in name:
            _UpperCamelCase      :	Optional[int]						=       name.replace("transformer"		,"model.decoder"			)
      if "cross_attention" in name:
            _UpperCamelCase      :	Optional[int]						=       name.replace("cross_attention"		,"encoder_attn"			)
      if "linear1" in name:
            _UpperCamelCase      :	Optional[Any]						=       name.replace("linear1"		,"fc1"			)
      if "linear2" in name:
            _UpperCamelCase      :	Union[str, Any]						=       name.replace("linear2"		,"fc2"			)
      if "norm1" in name:
            _UpperCamelCase      :	Optional[Any]						=       name.replace("norm1"		,"self_attn_layer_norm"			)
      if "norm_cross" in name:
            _UpperCamelCase      :	Dict						=       name.replace("norm_cross"		,"encoder_attn_layer_norm"			)
      if "norm2" in name:
            _UpperCamelCase      :	Union[str, Any]						=       name.replace("norm2"		,"final_layer_norm"			)
      if "out_norm" in name:
            _UpperCamelCase      :	Union[str, Any]						=       name.replace("out_norm"		,"model.decoder.layer_norm"			)
      if "linears" in name:
            _UpperCamelCase      :	List[str]						=       name.replace("linears"		,"lm_heads"			)
      if "condition_provider.conditioners.description.output_proj" in name:
            _UpperCamelCase      :	Any						=       name.replace("condition_provider.conditioners.description.output_proj"		,"enc_to_dec_proj"			)
      return name




def 					lowercase__    (					lowercase_		,lowercase_			)					->		Tuple[Dict, Dict]:




      """simple docstring"""

      _UpperCamelCase      :	str						=       list(state_dict.keys()			)
      _UpperCamelCase      :	Optional[Any]						=       {}
      for key in keys:
            _UpperCamelCase      :	Optional[int]						=       state_dict.pop(lowercase_			)
            _UpperCamelCase      :	List[Any]						=       rename_keys(lowercase_			)
            if "in_proj_weight" in key:
                  # split fused qkv proj
                  _UpperCamelCase      :	Tuple						=       val[:hidden_size, :]
                  _UpperCamelCase      :	Optional[Any]						=       val[hidden_size : 2 * hidden_size, :]
                  _UpperCamelCase      :	Optional[Any]						=       val[-hidden_size:, :]
            elif "enc_to_dec_proj" in key:
                  _UpperCamelCase      :	Optional[Any]						=       val
            else:
                  _UpperCamelCase      :	List[str]						=       val
      return state_dict, enc_dec_proj_state_dict




def 					lowercase__    (					lowercase_			)					->		MusicgenDecoderConfig:




      """simple docstring"""

      if checkpoint == "small":
            # default config values
            _UpperCamelCase      :	List[Any]						=       1_024
            _UpperCamelCase      :	List[str]						=       24
            _UpperCamelCase      :	Any						=       16
      elif checkpoint == "medium":
            _UpperCamelCase      :	Tuple						=       1_536
            _UpperCamelCase      :	Dict						=       48
            _UpperCamelCase      :	Tuple						=       24
      elif checkpoint == "large":
            _UpperCamelCase      :	int						=       2_048
            _UpperCamelCase      :	Optional[int]						=       48
            _UpperCamelCase      :	Dict						=       32
      else:
            raise ValueError(F'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'''			)
      _UpperCamelCase      :	str						=       MusicgenDecoderConfig(
          hidden_size=lowercase_		,ffn_dim=hidden_size * 4		,num_hidden_layers=lowercase_		,num_attention_heads=lowercase_		,)
      return config




@torch.no_grad()
def 					lowercase__    (					lowercase_		,lowercase_=None		,lowercase_=None		,lowercase_="cpu"			)					->		List[str]:




      """simple docstring"""

      _UpperCamelCase      :	str						=       MusicGen.get_pretrained(lowercase_		,device=lowercase_			)
      _UpperCamelCase      :	Union[str, Any]						=       decoder_config_from_checkpoint(lowercase_			)

      _UpperCamelCase      :	Optional[int]						=       fairseq_model.lm.state_dict()
      _UpperCamelCase,  _UpperCamelCase      :	Optional[Any]						=       rename_state_dict(
          lowercase_		,hidden_size=decoder_config.hidden_size			)

      _UpperCamelCase      :	Tuple						=       TaEncoderModel.from_pretrained("t5-base"			)
      _UpperCamelCase      :	Union[str, Any]						=       EncodecModel.from_pretrained("facebook/encodec_32khz"			)
      _UpperCamelCase      :	str						=       MusicgenForCausalLM(lowercase_			).eval()

      # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
      _UpperCamelCase,  _UpperCamelCase      :	str						=       decoder.load_state_dict(lowercase_		,strict=lowercase_			)

      for key in missing_keys.copy():
            if key.startswith(("text_encoder", "audio_encoder")			) or key in EXPECTED_MISSING_KEYS:
                  missing_keys.remove(lowercase_			)

      if len(lowercase_			) > 0:
            raise ValueError(F'''Missing key(s) in state_dict: {missing_keys}'''			)

      if len(lowercase_			) > 0:
            raise ValueError(F'''Unexpected key(s) in state_dict: {unexpected_keys}'''			)

      # init the composite model
      _UpperCamelCase      :	str						=       MusicgenForConditionalGeneration(text_encoder=lowercase_		,audio_encoder=lowercase_		,decoder=lowercase_			)

      # load the pre-trained enc-dec projection (from the decoder state dict)
      model.enc_to_dec_proj.load_state_dict(lowercase_			)

      # check we can do a forward pass
      _UpperCamelCase      :	List[str]						=       torch.arange(0		,8		,dtype=torch.long			).reshape(2		,-1			)
      _UpperCamelCase      :	Dict						=       input_ids.reshape(2 * 4		,-1			)

      with torch.no_grad():
            _UpperCamelCase      :	Tuple						=       model(input_ids=lowercase_		,decoder_input_ids=lowercase_			).logits

      if logits.shape != (8, 1, 2_048):
            raise ValueError("Incorrect shape for logits"			)

      # now construct the processor
      _UpperCamelCase      :	int						=       AutoTokenizer.from_pretrained("t5-base"			)
      _UpperCamelCase      :	str						=       AutoFeatureExtractor.from_pretrained("facebook/encodec_32khz"		,padding_side="left"			)

      _UpperCamelCase      :	Optional[int]						=       MusicgenProcessor(feature_extractor=lowercase_		,tokenizer=lowercase_			)

      # set the appropriate bos/pad token ids
      _UpperCamelCase      :	str						=       2_048
      _UpperCamelCase      :	str						=       2_048

      # set other default generation config params
      _UpperCamelCase      :	Optional[Any]						=       int(30 * audio_encoder.config.frame_rate			)
      _UpperCamelCase      :	List[str]						=       True
      _UpperCamelCase      :	int						=       3.0

      if pytorch_dump_folder is not None:
            Path(lowercase_			).mkdir(exist_ok=lowercase_			)
            logger.info(F'''Saving model {checkpoint} to {pytorch_dump_folder}'''			)
            model.save_pretrained(lowercase_			)
            processor.save_pretrained(lowercase_			)

      if repo_id:
            logger.info(F'''Pushing model {checkpoint} to {repo_id}'''			)
            model.push_to_hub(lowercase_			)
            processor.push_to_hub(lowercase_			)


if __name__ == "__main__":
  lowerCamelCase__				  =   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--checkpoint",
      default="small",
      type=str,
      help="Checkpoint size of the MusicGen model you'd like to convert. Can be one of: `['small', 'medium', 'large']`.",
  )
  parser.add_argument(
      "--pytorch_dump_folder",
      required=True,
      default=None,
      type=str,
      help="Path to the output PyTorch model directory.",
  )
  parser.add_argument(
      "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
  )
  parser.add_argument(
      "--device", default="cpu", type=str, help="Torch device to run the conversion, either cpu or cuda."
  )

  lowerCamelCase__				  =   parser.parse_args()
  convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
