





from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

UpperCAmelCase__						:			List[Any]									={
    '''uw-madison/mra-base-512-4''': '''https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json''',
}

class       __A     (						a    ):
				__A      =				"""mra"""


				def __init__(		self   , UpperCAmelCase_=50265   , UpperCAmelCase_=768   , UpperCAmelCase_=12   , UpperCAmelCase_=12   , UpperCAmelCase_=3072   , UpperCAmelCase_="gelu"   , UpperCAmelCase_=0.1   , UpperCAmelCase_=0.1   , UpperCAmelCase_=512   , UpperCAmelCase_=1   , UpperCAmelCase_=0.0_2   , UpperCAmelCase_=1E-5   , UpperCAmelCase_="absolute"   , UpperCAmelCase_=4   , UpperCAmelCase_="full"   , UpperCAmelCase_=0   , UpperCAmelCase_=0   , UpperCAmelCase_=1   , UpperCAmelCase_=0   , UpperCAmelCase_=2   , **UpperCAmelCase_   , ):
								super().__init__(pad_token_id=UpperCAmelCase_   , bos_token_id=UpperCAmelCase_   , eos_token_id=UpperCAmelCase_   , **UpperCAmelCase_							)

								lowerCamelCase	 =vocab_size
								lowerCamelCase	 =max_position_embeddings
								lowerCamelCase	 =hidden_size
								lowerCamelCase	 =num_hidden_layers
								lowerCamelCase	 =num_attention_heads
								lowerCamelCase	 =intermediate_size
								lowerCamelCase	 =hidden_act
								lowerCamelCase	 =hidden_dropout_prob
								lowerCamelCase	 =attention_probs_dropout_prob
								lowerCamelCase	 =initializer_range
								lowerCamelCase	 =type_vocab_size
								lowerCamelCase	 =layer_norm_eps
								lowerCamelCase	 =position_embedding_type
								lowerCamelCase	 =block_per_row
								lowerCamelCase	 =approx_mode
								lowerCamelCase	 =initial_prior_first_n_blocks
								lowerCamelCase	 =initial_prior_diagonal_n_blocks












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__						:			Tuple									=logging.get_logger(__name__)


UpperCAmelCase__						:			str									=['''model.decoder.embed_positions.weights''']





def      _lowercase					(						_UpperCAmelCase				)						->  List[Any]:
				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      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase				)						->  Tuple[Dict, Dict]:
				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      _lowercase					(						_UpperCAmelCase				)						->  MusicgenDecoderConfig:
				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=_UpperCAmelCase			,      ffn_dim=hidden_size * 4			,      num_hidden_layers=_UpperCAmelCase			,      num_attention_heads=_UpperCAmelCase			,      )
				return config





@torch.no_grad()
def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase=None			,      _UpperCAmelCase=None			,      _UpperCAmelCase="cpu"				)						->  Dict:
				lowerCamelCase	 =MusicGen.get_pretrained(_UpperCAmelCase			,      device=_UpperCAmelCase				)
				lowerCamelCase	 =decoder_config_from_checkpoint(_UpperCAmelCase				)

				lowerCamelCase	 =fairseq_model.lm.state_dict()
				lowerCamelCase ,  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 ,  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, 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=_UpperCAmelCase			,      tokenizer=_UpperCAmelCase				)

				# 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(_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__						:			List[Any]									=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__						:			Optional[Any]									=parser.parse_args()
							convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)





