





'''simple docstring'''


from __future__ import annotations







def    _lowerCamelCase  (__lowerCamelCase  :     Dict					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     Optional[Any]     )   ->    Union[str, Any]:






			if (voltage, current, resistance).count(0     ) != 1:
						raise ValueError("One and only one argument must be 0"     )
			if resistance < 0:
						raise ValueError("Resistance cannot be negative"     )
			if voltage == 0:
						return {"voltage": float(current * resistance     )}
			elif current == 0:
						return {"current": voltage / resistance}
			elif resistance == 0:
						return {"resistance": voltage / current}
			else:
						raise ValueError("Exactly one argument must be 0"     )


if __name__ == "__main__":
		import doctest

		doctest.testmod()










'''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_   :   List[str]       =					logging.get_logger(__name__)


lowerCAmelCase_   :   Optional[int]       =					["model.decoder.embed_positions.weights"]







def    _lowerCamelCase  (__lowerCamelCase  :     str     )   ->    Optional[int]:
			if "emb" in name:
						a__     = name.replace("emb"					,  "model.decoder.embed_tokens"     )
			if "transformer" in name:
						a__     = name.replace("transformer"					,  "model.decoder"     )
			if "cross_attention" in name:
						a__     = name.replace("cross_attention"					,  "encoder_attn"     )
			if "linear1" in name:
						a__     = name.replace("linear1"					,  "fc1"     )
			if "linear2" in name:
						a__     = name.replace("linear2"					,  "fc2"     )
			if "norm1" in name:
						a__     = name.replace("norm1"					,  "self_attn_layer_norm"     )
			if "norm_cross" in name:
						a__     = name.replace("norm_cross"					,  "encoder_attn_layer_norm"     )
			if "norm2" in name:
						a__     = name.replace("norm2"					,  "final_layer_norm"     )
			if "out_norm" in name:
						a__     = name.replace("out_norm"					,  "model.decoder.layer_norm"     )
			if "linears" in name:
						a__     = name.replace("linears"					,  "lm_heads"     )
			if "condition_provider.conditioners.description.output_proj" in name:
						a__     = name.replace("condition_provider.conditioners.description.output_proj"					,  "enc_to_dec_proj"     )
			return name







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







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







@torch.no_grad()
def    _lowerCamelCase  (__lowerCamelCase  :     Union[str, Any]					,  __lowerCamelCase  :     List[str]=None					,  __lowerCamelCase  :     Dict=None					,  __lowerCamelCase  :     List[Any]="cpu"     )   ->    str:
			a__     = MusicGen.get_pretrained(__lowerCamelCase					,  device=__lowerCamelCase     )
			a__     = decoder_config_from_checkpoint(__lowerCamelCase     )

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

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

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

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

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

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

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

		lowerCAmelCase_   :   str       =					parser.parse_args()
		convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)



