



"""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()
snake_case   					=				logging.get_logger(__name__)


snake_case   					=				["""model.decoder.embed_positions.weights"""]




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




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




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




@torch.no_grad()
def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_="cpu"	)						->				int:
  _snake_case   	=      MusicGen.get_pretrained(lowerCAmelCase_  ,						device=lowerCAmelCase_	)
  _snake_case   	=      decoder_config_from_checkpoint(lowerCAmelCase_	)

  _snake_case   	=      fairseq_model.lm.state_dict()
  _snake_case   	=      rename_state_dict(
      lowerCAmelCase_  ,						hidden_size=decoder_config.hidden_size	)

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

  # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
  _snake_case   	=      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
  _snake_case   	=      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
  _snake_case   	=      torch.arange(0  ,						8  ,						dtype=torch.long	).reshape(2  ,						-1	)
  _snake_case   	=      input_ids.reshape(2 * 4  ,						-1	)

  with torch.no_grad():
    _snake_case   	=      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
  _snake_case   	=      AutoTokenizer.from_pretrained('''t5-base'''	)
  _snake_case   	=      AutoFeatureExtractor.from_pretrained('''facebook/encodec_32khz'''  ,						padding_side='''left'''	)

  _snake_case   	=      MusicgenProcessor(feature_extractor=lowerCAmelCase_  ,						tokenizer=lowerCAmelCase_	)

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

  # set other default generation config params
  _snake_case   	=      int(30 * audio_encoder.config.frame_rate	)
  _snake_case   	=      True
  _snake_case   	=      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__":
     snake_case   					=				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.'''
     )

     snake_case   					=				parser.parse_args()
     convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)








import fire

from utils import calculate_rouge, save_json



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE=None	,**SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Tuple									= [x.strip() for x in open(SCREAMING_SNAKE_CASE      ).readlines()]
	UpperCAmelCase__:	Dict									= [x.strip() for x in open(SCREAMING_SNAKE_CASE      ).readlines()][: len(SCREAMING_SNAKE_CASE      )]
	UpperCAmelCase__:	Any									= calculate_rouge(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,**SCREAMING_SNAKE_CASE      )
	if save_path is not None:
		save_json(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,indent=SCREAMING_SNAKE_CASE      )
	return metrics  # these print nicely


if __name__ == "__main__":
					fire.Fire(calculate_rouge_path)