



'''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()
__a          =		logging.get_logger(__name__)


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





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





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





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





@torch.no_grad()
def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase=None     ,   _lowerCAmelCase=None     ,   _lowerCAmelCase="cpu"		)		-> Any:
      snake_case__  :    List[str]    						=      MusicGen.get_pretrained(_lowerCAmelCase     ,   device=_lowerCAmelCase		)
      snake_case__  :    Any    						=      decoder_config_from_checkpoint(_lowerCAmelCase		)

      snake_case__  :    int    						=      fairseq_model.lm.state_dict()
      snake_case__						,				snake_case__  :    List[Any]    						=      rename_state_dict(
          _lowerCAmelCase     ,   hidden_size=decoder_config.hidden_size		)

      snake_case__  :    int    						=      TaEncoderModel.from_pretrained("""t5-base"""		)
      snake_case__  :    Dict    						=      EncodecModel.from_pretrained("""facebook/encodec_32khz"""		)
      snake_case__  :    str    						=      MusicgenForCausalLM(_lowerCAmelCase		).eval()

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

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

      if len(_lowerCAmelCase		) > 0:
            raise ValueError(f"Missing key(s) in state_dict: {missing_keys}"		)

      if len(_lowerCAmelCase		) > 0:
            raise ValueError(f"Unexpected key(s) in state_dict: {unexpected_keys}"		)

      # init the composite model
      snake_case__  :    Tuple    						=      MusicgenForConditionalGeneration(text_encoder=_lowerCAmelCase     ,   audio_encoder=_lowerCAmelCase     ,   decoder=_lowerCAmelCase		)

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

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

      with torch.no_grad():
            snake_case__  :    Optional[int]    						=      model(input_ids=_lowerCAmelCase     ,   decoder_input_ids=_lowerCAmelCase		).logits

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

      # now construct the processor
      snake_case__  :    Optional[int]    						=      AutoTokenizer.from_pretrained("""t5-base"""		)
      snake_case__  :    Union[str, Any]    						=      AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz"""     ,   padding_side="""left"""		)

      snake_case__  :    Tuple    						=      MusicgenProcessor(feature_extractor=_lowerCAmelCase     ,   tokenizer=_lowerCAmelCase		)

      # set the appropriate bos/pad token ids
      snake_case__  :    Dict    						=      2_048
      snake_case__  :    Optional[int]    						=      2_048

      # set other default generation config params
      snake_case__  :    Tuple    						=      int(30 * audio_encoder.config.frame_rate		)
      snake_case__  :    Tuple    						=      True
      snake_case__  :    Tuple    						=      3.0

      if pytorch_dump_folder is not None:
            Path(_lowerCAmelCase		).mkdir(exist_ok=_lowerCAmelCase		)
            logger.info(f"Saving model {checkpoint} to {pytorch_dump_folder}"		)
            model.save_pretrained(_lowerCAmelCase		)
            processor.save_pretrained(_lowerCAmelCase		)

      if repo_id:
            logger.info(f"Pushing model {checkpoint} to {repo_id}"		)
            model.push_to_hub(_lowerCAmelCase		)
            processor.push_to_hub(_lowerCAmelCase		)


if __name__ == "__main__":
       __a          =		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."
       )

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




'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

__A					:  Optional[Any]											=      {
    "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					'gpt_neox'

    def __init__(   self    :  Optional[int]    ,						lowerCamelCase    :  Tuple=5_04_32    ,						lowerCamelCase    :  Optional[int]=61_44    ,						lowerCamelCase    :  Tuple=44    ,						lowerCamelCase    :  Any=64    ,						lowerCamelCase    :  List[Any]=2_45_76    ,						lowerCamelCase    :  List[Any]="gelu"    ,						lowerCamelCase    :  Optional[Any]=0.25    ,						lowerCamelCase    :  Any=1_00_00    ,						lowerCamelCase    :  Any=0.0    ,						lowerCamelCase    :  str=0.0    ,						lowerCamelCase    :  Optional[int]=0.1    ,						lowerCamelCase    :  List[Any]=20_48    ,						lowerCamelCase    :  List[Any]=0.02    ,						lowerCamelCase    :  Any=1E-5    ,						lowerCamelCase    :  Dict=True    ,						lowerCamelCase    :  Optional[int]=0    ,						lowerCamelCase    :  List[str]=2    ,						lowerCamelCase    :  Dict=False    ,						lowerCamelCase    :  Tuple=True    ,						lowerCamelCase    :  Optional[int]=None    ,						**lowerCamelCase    :  int    ,						)   ->  Optional[Any]:
      super().__init__(bos_token_id=lowerCamelCase    ,						eos_token_id=lowerCamelCase    ,						**lowerCamelCase		)
      lowerCAmelCase_			:      Tuple			=   vocab_size
      lowerCAmelCase_			:      Union[str, Any]			=   max_position_embeddings
      lowerCAmelCase_			:      Any			=   hidden_size
      lowerCAmelCase_			:      List[Any]			=   num_hidden_layers
      lowerCAmelCase_			:      Optional[int]			=   num_attention_heads
      lowerCAmelCase_			:      str			=   intermediate_size
      lowerCAmelCase_			:      int			=   hidden_act
      lowerCAmelCase_			:      List[Any]			=   rotary_pct
      lowerCAmelCase_			:      Any			=   rotary_emb_base
      lowerCAmelCase_			:      List[str]			=   attention_dropout
      lowerCAmelCase_			:      Union[str, Any]			=   hidden_dropout
      lowerCAmelCase_			:      Tuple			=   classifier_dropout
      lowerCAmelCase_			:      Union[str, Any]			=   initializer_range
      lowerCAmelCase_			:      Any			=   layer_norm_eps
      lowerCAmelCase_			:      str			=   use_cache
      lowerCAmelCase_			:      str			=   tie_word_embeddings
      lowerCAmelCase_			:      str			=   use_parallel_residual
      lowerCAmelCase_			:      Any			=   rope_scaling
      self._rope_scaling_validation()

      if self.hidden_size % self.num_attention_heads != 0:
        raise ValueError(
            """The hidden size is not divisble by the number of attention heads! Make sure to update them!"""		)





    def __lowercase   (   self    :  List[str]		)   ->  List[str]:
      if self.rope_scaling is None:
        return

      if not isinstance(self.rope_scaling    ,						lowerCamelCase		) or len(self.rope_scaling		) != 2:
        raise ValueError(
            """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
            F'got {self.rope_scaling}'		)
      lowerCAmelCase_			:      Optional[Any]			=   self.rope_scaling.get("""type"""    ,						lowerCamelCase		)
      lowerCAmelCase_			:      int			=   self.rope_scaling.get("""factor"""    ,						lowerCamelCase		)
      if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
        raise ValueError(
            F'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'		)
      if rope_scaling_factor is None or not isinstance(lowerCamelCase    ,						lowerCamelCase		) or rope_scaling_factor <= 1.0:
        raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'		)



