





'''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()
UpperCAmelCase					:		str		=							logging.get_logger(__name__)


UpperCAmelCase					:		str		=							['model.decoder.embed_positions.weights']




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



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




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



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




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



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




@torch.no_grad()
def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_=None			,      lowerCAmelCase_=None			,      lowerCAmelCase_="cpu"	):





      """simple docstring"""



      _snake_case		:	List[Any]					     =     MusicGen.get_pretrained(lowerCAmelCase_			,      device=lowerCAmelCase_	)
      _snake_case		:	Union[str, Any]					     =     decoder_config_from_checkpoint(lowerCAmelCase_	)

      _snake_case		:	Optional[int]					     =     fairseq_model.lm.state_dict()
      _snake_case     ,							_snake_case		:	str					     =     rename_state_dict(
          lowerCAmelCase_			,      hidden_size=decoder_config.hidden_size	)

      _snake_case		:	int					     =     TaEncoderModel.from_pretrained('''t5-base'''	)
      _snake_case		:	Optional[Any]					     =     EncodecModel.from_pretrained('''facebook/encodec_32khz'''	)
      _snake_case		:	Optional[int]					     =     MusicgenForCausalLM(lowerCAmelCase_	).eval()

      # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
      _snake_case     ,							_snake_case		:	Optional[Any]					     =     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		:	List[str]					     =     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		:	Optional[int]					     =     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		:	List[str]					     =     AutoTokenizer.from_pretrained('''t5-base'''	)
      _snake_case		:	Optional[int]					     =     AutoFeatureExtractor.from_pretrained('''facebook/encodec_32khz'''			,      padding_side='''left'''	)

      _snake_case		:	Union[str, Any]					     =     MusicgenProcessor(feature_extractor=lowerCAmelCase_			,      tokenizer=lowerCAmelCase_	)

      # set the appropriate bos/pad token ids
      _snake_case		:	Tuple					     =     2_048
      _snake_case		:	List[str]					     =     2_048

      # set other default generation config params
      _snake_case		:	List[str]					     =     int(30 * audio_encoder.config.frame_rate	)
      _snake_case		:	Optional[Any]					     =     True
      _snake_case		:	int					     =     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__":
   UpperCAmelCase					:		Any		=							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.'
   )

   UpperCAmelCase					:		List[str]		=							parser.parse_args()
   convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)









'''simple docstring'''

from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands




def    _a    (							):





      """simple docstring"""



      _snake_case		:	List[Any]					     =     ArgumentParser('''Transformers CLI tool'''			,      usage='''transformers-cli <command> [<args>]'''	)
      _snake_case		:	List[str]					     =     parser.add_subparsers(help='''transformers-cli command helpers'''	)

      # Register commands
      ConvertCommand.register_subcommand(lowerCAmelCase_	)
      DownloadCommand.register_subcommand(lowerCAmelCase_	)
      EnvironmentCommand.register_subcommand(lowerCAmelCase_	)
      RunCommand.register_subcommand(lowerCAmelCase_	)
      ServeCommand.register_subcommand(lowerCAmelCase_	)
      UserCommands.register_subcommand(lowerCAmelCase_	)
      AddNewModelCommand.register_subcommand(lowerCAmelCase_	)
      AddNewModelLikeCommand.register_subcommand(lowerCAmelCase_	)
      LfsCommands.register_subcommand(lowerCAmelCase_	)
      PTtoTFCommand.register_subcommand(lowerCAmelCase_	)

      # Let's go
      _snake_case		:	str					     =     parser.parse_args()

      if not hasattr(lowerCAmelCase_			,      '''func'''	):
            parser.print_help()
            exit(1	)

      # Run
      _snake_case		:	Union[str, Any]					     =     args.func(lowerCAmelCase_	)
      service.run()


if __name__ == "__main__":
   main()


