




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 lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


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







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


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







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     if checkpoint == "small":
          # default config values
          UpperCAmelCase_     :				str  =     1024
          UpperCAmelCase_     :				int  =     24
          UpperCAmelCase_     :				Any  =     16
     elif checkpoint == "medium":
          UpperCAmelCase_     :				Optional[int]  =     1536
          UpperCAmelCase_     :				Optional[Any]  =     48
          UpperCAmelCase_     :				List[Any]  =     24
     elif checkpoint == "large":
          UpperCAmelCase_     :				Tuple  =     2048
          UpperCAmelCase_     :				Any  =     48
          UpperCAmelCase_     :				List[Any]  =     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 lowerCamelCase__  (     _lowercase					,		_lowercase=None					,		_lowercase=None					,		_lowercase="cpu" ):

     '''simple docstring'''


     UpperCAmelCase_     :				Tuple  =     MusicGen.get_pretrained(_lowercase					,		device=_lowercase )
     UpperCAmelCase_     :				Union[str, Any]  =     decoder_config_from_checkpoint(_lowercase )

     UpperCAmelCase_     :				Union[str, Any]  =     fairseq_model.lm.state_dict()
     UpperCAmelCase_,       UpperCAmelCase_     :				Tuple  =     rename_state_dict(
         _lowercase					,		hidden_size=decoder_config.hidden_size )

     UpperCAmelCase_     :				Tuple  =     TaEncoderModel.from_pretrained('''t5-base''' )
     UpperCAmelCase_     :				int  =     EncodecModel.from_pretrained('''facebook/encodec_32khz''' )
     UpperCAmelCase_     :				Dict  =     MusicgenForCausalLM(_lowercase ).eval()

     # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
     UpperCAmelCase_,       UpperCAmelCase_     :				Any  =     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_     :				Optional[Any]  =     input_ids.reshape(2 * 4					,		-1 )

     with torch.no_grad():
          UpperCAmelCase_     :				Any  =     model(input_ids=_lowercase					,		decoder_input_ids=_lowercase ).logits

     if logits.shape != (8, 1, 2048):
          raise ValueError('''Incorrect shape for logits''' )

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

     UpperCAmelCase_     :				Dict  =     MusicgenProcessor(feature_extractor=_lowercase					,		tokenizer=_lowercase )

     # set the appropriate bos/pad token ids
     UpperCAmelCase_     :				Dict  =     2048
     UpperCAmelCase_     :				List[Any]  =     2048

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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__a							     =					{
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a							     =					[
         'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
         'EncodecModel',
         'EncodecPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_encodec import (
         ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
         EncodecConfig,
     )
     from .feature_extraction_encodec import EncodecFeatureExtractor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_encodec import (
              ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
              EncodecModel,
              EncodecPreTrainedModel,
          )

else:
     import sys

     __a							     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)