

'''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()
lowerCAmelCase__      		=			logging.get_logger(__name__)


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


def 	_A  (					A__   ):






 """simple docstring"""



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


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



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


def 	_A  (					A__   ):






 """simple docstring"""



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


@torch.no_grad()
def 	_A  (					A__  ,       A__=None  ,       A__=None  ,       A__="cpu"   ):






 """simple docstring"""



 __lowercase													=				MusicGen.get_pretrained(A__  ,       device=A__   )
 __lowercase													=				decoder_config_from_checkpoint(A__   )

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


if __name__ == "__main__":
 lowerCAmelCase__      		=			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.'''
 )

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




'''simple docstring'''






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 	_A  (					A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				TaConfig.from_json_file(A__   )
 print(F"Building PyTorch model from configuration: {config}"   )
 __lowercase													=				TaForConditionalGeneration(A__   )

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(A__  ,       A__  ,       A__   )

 # Save pytorch-model
 print(F"Save PyTorch model to {pytorch_dump_path}"   )
 model.save_pretrained(A__   )


if __name__ == "__main__":
 lowerCAmelCase__      		=			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 lowerCAmelCase__      		=			parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

