

"""simple docstring"""



from typing import TYPE_CHECKING

from ..utils import _LazyModule


SCREAMING_SNAKE_CASE_					:	Dict							  =						{
    'config': [
        'EXTERNAL_DATA_FORMAT_SIZE_LIMIT',
        'OnnxConfig',
        'OnnxConfigWithPast',
        'OnnxSeq2SeqConfigWithPast',
        'PatchingSpec',
    ],
    'convert': ['export', 'validate_model_outputs'],
    'features': ['FeaturesManager'],
    'utils': ['ParameterFormat', 'compute_serialized_parameters_size'],
}


if TYPE_CHECKING:
	from .config import (
	    EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
	    OnnxConfig,
	    OnnxConfigWithPast,
	    OnnxSeqaSeqConfigWithPast,
	    PatchingSpec,
	)
	from .convert import export, validate_model_outputs
	from .features import FeaturesManager
	from .utils import ParameterFormat, compute_serialized_parameters_size

else:
	import sys

	SCREAMING_SNAKE_CASE_					:	List[Any]							  =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









"""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()
SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_					:	Any							  =						['model.decoder.embed_positions.weights']





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





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





def  _snake_case     (    UpperCAmelCase_  :					str			):
     if checkpoint == "small":
          # default config values
          A__										=  1024
          A__										=  24
          A__										=  16
     elif checkpoint == "medium":
          A__										=  1536
          A__										=  48
          A__										=  24
     elif checkpoint == "large":
          A__										=  2048
          A__										=  48
          A__										=  32
     else:
          raise ValueError(F"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."""			)
     A__										=  MusicgenDecoderConfig(
         hidden_size=UpperCAmelCase_					,   ffn_dim=hidden_size * 4					,   num_hidden_layers=UpperCAmelCase_					,   num_attention_heads=UpperCAmelCase_					,   )
     return config





@torch.no_grad()
def  _snake_case     (    UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					List[Any]=None					,   UpperCAmelCase_  :					Tuple=None					,   UpperCAmelCase_  :					Any="cpu"			):
     A__										=  MusicGen.get_pretrained(UpperCAmelCase_					,   device=UpperCAmelCase_			)
     A__										=  decoder_config_from_checkpoint(UpperCAmelCase_			)

     A__										=  fairseq_model.lm.state_dict()
     A__			,				A__										=  rename_state_dict(
         UpperCAmelCase_					,   hidden_size=decoder_config.hidden_size			)

     A__										=  TaEncoderModel.from_pretrained("""t5-base"""			)
     A__										=  EncodecModel.from_pretrained("""facebook/encodec_32khz"""			)
     A__										=  MusicgenForCausalLM(UpperCAmelCase_			).eval()

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

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

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

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

     # init the composite model
     A__										=  MusicgenForConditionalGeneration(text_encoder=UpperCAmelCase_					,   audio_encoder=UpperCAmelCase_					,   decoder=UpperCAmelCase_			)

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

     # check we can do a forward pass
     A__										=  torch.arange(0					,   8					,   dtype=torch.long			).reshape(2					,   -1			)
     A__										=  input_ids.reshape(2 * 4					,   -1			)

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

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

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

     A__										=  MusicgenProcessor(feature_extractor=UpperCAmelCase_					,   tokenizer=UpperCAmelCase_			)

     # set the appropriate bos/pad token ids
     A__										=  2048
     A__										=  2048

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

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

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


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_					:	int							  =						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.'
 )

 SCREAMING_SNAKE_CASE_					:	Tuple							  =						parser.parse_args()
 convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)






