



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 		a__      (		_UpperCamelCase      :			Optional[int]				):

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





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





def 		a__      (		_UpperCamelCase      :			str				):

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





@torch.no_grad()
def 		a__      (		_UpperCamelCase      :			List[Any]			,_UpperCamelCase      :			int=None			,_UpperCamelCase      :			Tuple=None			,_UpperCamelCase      :			Dict="cpu"				):
   __lowerCamelCase    				=     MusicGen.get_pretrained(SCREAMING_SNAKE_CASE__			,device=SCREAMING_SNAKE_CASE__				)
   __lowerCamelCase    				=     decoder_config_from_checkpoint(SCREAMING_SNAKE_CASE__				)

   __lowerCamelCase    				=     fairseq_model.lm.state_dict()
   __lowerCamelCase    				=     rename_state_dict(
       SCREAMING_SNAKE_CASE__			,hidden_size=decoder_config.hidden_size				)

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

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

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

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

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

   # init the composite model
   __lowerCamelCase    				=     MusicgenForConditionalGeneration(text_encoder=SCREAMING_SNAKE_CASE__			,audio_encoder=SCREAMING_SNAKE_CASE__			,decoder=SCREAMING_SNAKE_CASE__				)

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

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

   with torch.no_grad():
      __lowerCamelCase    				=     model(input_ids=SCREAMING_SNAKE_CASE__			,decoder_input_ids=SCREAMING_SNAKE_CASE__				).logits

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

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

   __lowerCamelCase    				=     MusicgenProcessor(feature_extractor=SCREAMING_SNAKE_CASE__			,tokenizer=SCREAMING_SNAKE_CASE__				)

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

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

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

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


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)











"""simple docstring"""




def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  bool:





     '''simple docstring'''

     _UpperCAmelCase     :      str														=       0
     for ch in input_str:
          _UpperCAmelCase     :      Optional[int]														=       ord(SCREAMING_SNAKE_CASE__							)
          _UpperCAmelCase     :      int														=       pow(2		,    SCREAMING_SNAKE_CASE__							)

          # If we already turned on bit for current character's unicode
          if bitmap >> ch_unicode & 1 == 1:
               return False
          bitmap |= ch_bit_index_on
     return True


if __name__ == "__main__":
 import doctest

 doctest.testmod()
