
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_   	=	logging.get_logger(__name__)


UpperCamelCase_   	=	['model.decoder.embed_positions.weights']






def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[int]  ):






 """simple docstring"""


 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 		_UpperCAmelCase   (	UpperCamelCase:     int			,							UpperCamelCase:     Optional[int]  ):






 """simple docstring"""


 __lowerCAmelCase         =							list(state_dict.keys()  )
 __lowerCAmelCase         =							{}
 for key in keys:
  __lowerCAmelCase         =							state_dict.pop(UpperCamelCase  )
  __lowerCAmelCase         =							rename_keys(UpperCamelCase  )
  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 		_UpperCAmelCase   (	UpperCamelCase:     Optional[Any]  ):






 """simple docstring"""


 if checkpoint == "small":
  # default config values
  __lowerCAmelCase         =							1_0_2_4
  __lowerCAmelCase         =							2_4
  __lowerCAmelCase         =							1_6
 elif checkpoint == "medium":
  __lowerCAmelCase         =							1_5_3_6
  __lowerCAmelCase         =							4_8
  __lowerCAmelCase         =							2_4
 elif checkpoint == "large":
  __lowerCAmelCase         =							2_0_4_8
  __lowerCAmelCase         =							4_8
  __lowerCAmelCase         =							3_2
 else:
  raise ValueError(F"Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."  )
 __lowerCAmelCase         =							MusicgenDecoderConfig(
     hidden_size=UpperCamelCase			,							ffn_dim=hidden_size * 4			,							num_hidden_layers=UpperCamelCase			,							num_attention_heads=UpperCamelCase			,							)
 return config






@torch.no_grad()
def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[int]			,							UpperCamelCase:     Optional[int]=None			,							UpperCamelCase:     Optional[int]=None			,							UpperCamelCase:     Dict="cpu"  ):






 """simple docstring"""


 __lowerCAmelCase         =							MusicGen.get_pretrained(UpperCamelCase			,							device=UpperCamelCase  )
 __lowerCAmelCase         =							decoder_config_from_checkpoint(UpperCamelCase  )

 __lowerCAmelCase         =							fairseq_model.lm.state_dict()
 __lowerCAmelCase         =							rename_state_dict(
     UpperCamelCase			,							hidden_size=decoder_config.hidden_size  )

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

 # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
 __lowerCAmelCase         =							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
 __lowerCAmelCase         =							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
 __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=UpperCamelCase			,							decoder_input_ids=UpperCamelCase  ).logits

 if logits.shape != (8, 1, 2_0_4_8):
  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=UpperCamelCase			,							tokenizer=UpperCamelCase  )

 # set the appropriate bos/pad token ids
 __lowerCAmelCase         =							2_0_4_8
 __lowerCAmelCase         =							2_0_4_8

 # set other default generation config params
 __lowerCAmelCase         =							int(3_0 * audio_encoder.config.frame_rate  )
 __lowerCAmelCase         =							True
 __lowerCAmelCase         =							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__":
     UpperCamelCase_   	=	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_   	=	parser.parse_args()
     convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)








from abc import ABC, abstractmethod
from argparse import ArgumentParser

class       a		(   __UpperCAmelCase					):







							@staticmethod
							@abstractmethod
							def       UpperCAmelCase__					(						snake_case__  :  ArgumentParser ):
								"""simple docstring"""







								raise NotImplementedError()







							@abstractmethod
							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								raise NotImplementedError()






