






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__		:							Optional[Any] 				=				logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__		:							Optional[Any] 				=				["""model.decoder.embed_positions.weights"""]





def  _lowerCamelCase						(						__lowerCamelCase       ) ->			List[Any]:

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





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase       ) ->			Tuple[Dict, Dict]:

     '''simple docstring'''
     UpperCAmelCase__      :  Optional[Any]				      =			list(state_dict.keys()       )
     UpperCAmelCase__      :  str				      =			{}
     for key in keys:
          UpperCAmelCase__      :  Tuple				      =			state_dict.pop(__lowerCamelCase       )
          UpperCAmelCase__      :  Union[str, Any]				      =			rename_keys(__lowerCamelCase       )
          if "in_proj_weight" in key:
               # split fused qkv proj
               UpperCAmelCase__      :  Optional[int]				      =			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__      :  Dict				      =			val
          else:
               UpperCAmelCase__      :  Dict				      =			val
     return state_dict, enc_dec_proj_state_dict





def  _lowerCamelCase						(						__lowerCamelCase       ) ->			MusicgenDecoderConfig:

     '''simple docstring'''
     if checkpoint == "small":
          # default config values
          UpperCAmelCase__      :  Union[str, Any]				      =			1024
          UpperCAmelCase__      :  Union[str, Any]				      =			24
          UpperCAmelCase__      :  Dict				      =			16
     elif checkpoint == "medium":
          UpperCAmelCase__      :  Optional[int]				      =			1536
          UpperCAmelCase__      :  int				      =			48
          UpperCAmelCase__      :  Tuple				      =			24
     elif checkpoint == "large":
          UpperCAmelCase__      :  Any				      =			2048
          UpperCAmelCase__      :  Optional[Any]				      =			48
          UpperCAmelCase__      :  List[Any]				      =			32
     else:
          raise ValueError(F"Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."       )
     UpperCAmelCase__      :  Any				      =			MusicgenDecoderConfig(
         hidden_size=__lowerCamelCase ,							ffn_dim=hidden_size * 4 ,							num_hidden_layers=__lowerCamelCase ,							num_attention_heads=__lowerCamelCase ,							)
     return config





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

     '''simple docstring'''
     UpperCAmelCase__      :  List[str]				      =			MusicGen.get_pretrained(__lowerCamelCase ,							device=__lowerCamelCase       )
     UpperCAmelCase__      :  Optional[int]				      =			decoder_config_from_checkpoint(__lowerCamelCase       )

     UpperCAmelCase__      :  List[Any]				      =			fairseq_model.lm.state_dict()
     UpperCAmelCase__      ,		UpperCAmelCase__      :  Dict				      =			rename_state_dict(
         __lowerCamelCase ,							hidden_size=decoder_config.hidden_size       )

     UpperCAmelCase__      :  str				      =			TaEncoderModel.from_pretrained("""t5-base"""       )
     UpperCAmelCase__      :  Tuple				      =			EncodecModel.from_pretrained("""facebook/encodec_32khz"""       )
     UpperCAmelCase__      :  Union[str, Any]				      =			MusicgenForCausalLM(__lowerCamelCase       ).eval()

     # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
     UpperCAmelCase__      ,		UpperCAmelCase__      :  Union[str, 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
     UpperCAmelCase__      :  Optional[int]				      =			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
     UpperCAmelCase__      :  Any				      =			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=__lowerCamelCase ,							decoder_input_ids=__lowerCamelCase       ).logits

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

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

     UpperCAmelCase__      :  Dict				      =			MusicgenProcessor(feature_extractor=__lowerCamelCase ,							tokenizer=__lowerCamelCase       )

     # set the appropriate bos/pad token ids
     UpperCAmelCase__      :  Dict				      =			2048
     UpperCAmelCase__      :  int				      =			2048

     # set other default generation config params
     UpperCAmelCase__      :  Union[str, Any]				      =			int(30 * audio_encoder.config.frame_rate       )
     UpperCAmelCase__      :  Union[str, Any]				      =			True
     UpperCAmelCase__      :  Dict				      =			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__":
      SCREAMING_SNAKE_CASE__		:							str 				=				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__		:							int 				=				parser.parse_args()
      convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)














from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def 		_a       ( a					:Dict[str, torch.Tensor]					)				->     Dict[str, torch.Tensor]:
 a						    = []
 a						    = []
 a						    = []

 for rt in rc.restypes:
  a						    = rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
  restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]					)
  a						    = {name: i for i, name in enumerate(a					)}
  restype_atomaa_to_atomaa_list.append(
      [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]					)

  restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]					)

 # Add dummy mapping for restype 'UNK'
 restype_atomaa_to_atomaa_list.append([0] * 14					)
 restype_atomaa_to_atomaa_list.append([0] * 37					)
 restype_atomaa_mask_list.append([0.0] * 14					)

 a						    = torch.tensor(
     a      ,				dtype=torch.intaa      ,				device=protein['''aatype'''].device      ,				)
 a						    = torch.tensor(
     a      ,				dtype=torch.intaa      ,				device=protein['''aatype'''].device      ,				)
 a						    = torch.tensor(
     a      ,				dtype=torch.floataa      ,				device=protein['''aatype'''].device      ,				)
 a						    = protein['''aatype'''].to(torch.long					)

 # create the mapping for (residx, atom14) --> atom37, i.e. an array
 # with shape (num_res, 14) containing the atom37 indices for this protein
 a						    = restype_atomaa_to_atomaa[protein_aatype]
 a						    = restype_atomaa_mask[protein_aatype]

 a						    = residx_atomaa_mask
 a						    = residx_atomaa_to_atomaa.long()

 # create the gather indices for mapping back
 a						    = restype_atomaa_to_atomaa[protein_aatype]
 a						    = residx_atomaa_to_atomaa.long()

 # create the corresponding mask
 a						    = torch.zeros([21, 37]      ,				dtype=torch.floataa      ,				device=protein['''aatype'''].device					)
 for restype, restype_letter in enumerate(rc.restypes					):
  a						    = rc.restype_atoa[restype_letter]
  a						    = rc.residue_atoms[restype_name]
  for atom_name in atom_names:
   a						    = rc.atom_order[atom_name]
   a						    = 1

 a						    = restype_atomaa_mask[protein_aatype]
 a						    = residx_atomaa_mask

 return protein

def 		_a       ( a					:Dict[str, torch.Tensor]					)				->     Dict[str, np.ndarray]:
 a						    = tree_map(lambda a					: torch.tensor(a      ,				device=batch['''aatype'''].device					)      ,				a      ,				np.ndarray					)
 a						    = tensor_tree_map(lambda a					: np.array(a					)      ,				make_atomaa_masks(a					)					)
 return out


