




from ..utils import DummyObject, requires_backends

class  _lowerCamelCase						(			metaclass=_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: List[Any]    =					["torch", "transformers", "onnx"]







      def __init__(     self					: Any ,       *snake_case					: List[Any] ,       **snake_case					: int				):
          requires_backends(self ,       ['''torch''', '''transformers''', '''onnx''']				)







      @classmethod
      def       snake_case	(     cls					: str ,       *snake_case					: Any ,       **snake_case					: str				):
          requires_backends(cls ,       ['''torch''', '''transformers''', '''onnx''']				)







      @classmethod
      def       snake_case	(     cls					: Union[str, Any] ,       *snake_case					: Any ,       **snake_case					: Tuple				):
          requires_backends(cls ,       ['''torch''', '''transformers''', '''onnx''']				)



class  _lowerCamelCase						(			metaclass=_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: int    =					["torch", "transformers", "onnx"]







      def __init__(     self					: Union[str, Any] ,       *snake_case					: Optional[Any] ,       **snake_case					: str				):
          requires_backends(self ,       ['''torch''', '''transformers''', '''onnx''']				)







      @classmethod
      def       snake_case	(     cls					: Union[str, Any] ,       *snake_case					: Optional[int] ,       **snake_case					: Tuple				):
          requires_backends(cls ,       ['''torch''', '''transformers''', '''onnx''']				)







      @classmethod
      def       snake_case	(     cls					: Optional[int] ,       *snake_case					: Tuple ,       **snake_case					: int				):
          requires_backends(cls ,       ['''torch''', '''transformers''', '''onnx''']				)



class  _lowerCamelCase						(			metaclass=_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Tuple    =					["torch", "transformers", "onnx"]







      def __init__(     self					: Optional[Any] ,       *snake_case					: List[Any] ,       **snake_case					: Union[str, Any]				):
          requires_backends(self ,       ['''torch''', '''transformers''', '''onnx''']				)







      @classmethod
      def       snake_case	(     cls					: Optional[int] ,       *snake_case					: Tuple ,       **snake_case					: Optional[Any]				):
          requires_backends(cls ,       ['''torch''', '''transformers''', '''onnx''']				)







      @classmethod
      def       snake_case	(     cls					: str ,       *snake_case					: List[str] ,       **snake_case					: int				):
          requires_backends(cls ,       ['''torch''', '''transformers''', '''onnx''']				)



class  _lowerCamelCase						(			metaclass=_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Dict    =					["torch", "transformers", "onnx"]







      def __init__(     self					: Tuple ,       *snake_case					: int ,       **snake_case					: int				):
          requires_backends(self ,       ['''torch''', '''transformers''', '''onnx''']				)







      @classmethod
      def       snake_case	(     cls					: Union[str, Any] ,       *snake_case					: Optional[int] ,       **snake_case					: Optional[int]				):
          requires_backends(cls ,       ['''torch''', '''transformers''', '''onnx''']				)







      @classmethod
      def       snake_case	(     cls					: Any ,       *snake_case					: Dict ,       **snake_case					: List[str]				):
          requires_backends(cls ,       ['''torch''', '''transformers''', '''onnx''']				)



class  _lowerCamelCase						(			metaclass=_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Dict    =					["torch", "transformers", "onnx"]







      def __init__(     self					: List[Any] ,       *snake_case					: Union[str, Any] ,       **snake_case					: Optional[int]				):
          requires_backends(self ,       ['''torch''', '''transformers''', '''onnx''']				)







      @classmethod
      def       snake_case	(     cls					: int ,       *snake_case					: str ,       **snake_case					: List[Any]				):
          requires_backends(cls ,       ['''torch''', '''transformers''', '''onnx''']				)







      @classmethod
      def       snake_case	(     cls					: Tuple ,       *snake_case					: Optional[int] ,       **snake_case					: Optional[Any]				):
          requires_backends(cls ,       ['''torch''', '''transformers''', '''onnx''']				)



class  _lowerCamelCase						(			metaclass=_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: int    =					["torch", "transformers", "onnx"]







      def __init__(     self					: List[Any] ,       *snake_case					: List[Any] ,       **snake_case					: List[str]				):
          requires_backends(self ,       ['''torch''', '''transformers''', '''onnx''']				)







      @classmethod
      def       snake_case	(     cls					: str ,       *snake_case					: Optional[int] ,       **snake_case					: Dict				):
          requires_backends(cls ,       ['''torch''', '''transformers''', '''onnx''']				)







      @classmethod
      def       snake_case	(     cls					: Tuple ,       *snake_case					: Optional[Any] ,       **snake_case					: int				):
          requires_backends(cls ,       ['''torch''', '''transformers''', '''onnx''']				)












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 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Optional[Any]:

    """simple docstring"""


    if "emb" in name:
        __UpperCamelCase  		=						name.replace('''emb'''					,     '''model.decoder.embed_tokens'''      )
    if "transformer" in name:
        __UpperCamelCase  		=						name.replace('''transformer'''					,     '''model.decoder'''      )
    if "cross_attention" in name:
        __UpperCamelCase  		=						name.replace('''cross_attention'''					,     '''encoder_attn'''      )
    if "linear1" in name:
        __UpperCamelCase  		=						name.replace('''linear1'''					,     '''fc1'''      )
    if "linear2" in name:
        __UpperCamelCase  		=						name.replace('''linear2'''					,     '''fc2'''      )
    if "norm1" in name:
        __UpperCamelCase  		=						name.replace('''norm1'''					,     '''self_attn_layer_norm'''      )
    if "norm_cross" in name:
        __UpperCamelCase  		=						name.replace('''norm_cross'''					,     '''encoder_attn_layer_norm'''      )
    if "norm2" in name:
        __UpperCamelCase  		=						name.replace('''norm2'''					,     '''final_layer_norm'''      )
    if "out_norm" in name:
        __UpperCamelCase  		=						name.replace('''out_norm'''					,     '''model.decoder.layer_norm'''      )
    if "linears" in name:
        __UpperCamelCase  		=						name.replace('''linears'''					,     '''lm_heads'''      )
    if "condition_provider.conditioners.description.output_proj" in name:
        __UpperCamelCase  		=						name.replace('''condition_provider.conditioners.description.output_proj'''					,     '''enc_to_dec_proj'''      )
    return name




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

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	MusicgenDecoderConfig:

    """simple docstring"""


    if checkpoint == "small":
        # default config values
        __UpperCamelCase  		=						10_24
        __UpperCamelCase  		=						24
        __UpperCamelCase  		=						16
    elif checkpoint == "medium":
        __UpperCamelCase  		=						15_36
        __UpperCamelCase  		=						48
        __UpperCamelCase  		=						24
    elif checkpoint == "large":
        __UpperCamelCase  		=						20_48
        __UpperCamelCase  		=						48
        __UpperCamelCase  		=						32
    else:
        raise ValueError(F"Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."      )
    __UpperCamelCase  		=						MusicgenDecoderConfig(
        hidden_size=lowercase_					,     ffn_dim=hidden_size * 4					,     num_hidden_layers=lowercase_					,     num_attention_heads=lowercase_					,     )
    return config




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

    """simple docstring"""


    __UpperCamelCase  		=						MusicGen.get_pretrained(lowercase_					,     device=lowercase_      )
    __UpperCamelCase  		=						decoder_config_from_checkpoint(lowercase_      )

    __UpperCamelCase  		=						fairseq_model.lm.state_dict()
    __UpperCamelCase		,      __UpperCamelCase  		=						rename_state_dict(
        lowercase_					,     hidden_size=decoder_config.hidden_size      )

    __UpperCamelCase  		=						TaEncoderModel.from_pretrained('''t5-base'''      )
    __UpperCamelCase  		=						EncodecModel.from_pretrained('''facebook/encodec_32khz'''      )
    __UpperCamelCase  		=						MusicgenForCausalLM(lowercase_      ).eval()

    # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
    __UpperCamelCase		,      __UpperCamelCase  		=						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  		=						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  		=						torch.arange(0					,     8					,     dtype=torch.long      ).reshape(2					,     -1      )
    __UpperCamelCase  		=						input_ids.reshape(2 * 4					,     -1      )

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

    if logits.shape != (8, 1, 20_48):
        raise ValueError('''Incorrect shape for logits'''      )

    # now construct the processor
    __UpperCamelCase  		=						AutoTokenizer.from_pretrained('''t5-base'''      )
    __UpperCamelCase  		=						AutoFeatureExtractor.from_pretrained('''facebook/encodec_32khz'''					,     padding_side='''left'''      )

    __UpperCamelCase  		=						MusicgenProcessor(feature_extractor=lowercase_					,     tokenizer=lowercase_      )

    # set the appropriate bos/pad token ids
    __UpperCamelCase  		=						20_48
    __UpperCamelCase  		=						20_48

    # set other default generation config params
    __UpperCamelCase  		=						int(30 * audio_encoder.config.frame_rate      )
    __UpperCamelCase  		=						True
    __UpperCamelCase  		=						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__":
 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)






