



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()
lowercase__			   =  logging.get_logger(__name__)


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





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





def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: Optional[Any]					):
							__a		:		str									=       list(state_dict.keys()					)
							__a		:		Optional[Any]									=       {}
							for key in keys:
														__a		:		Union[str, Any]									=       state_dict.pop(_SCREAMING_SNAKE_CASE					)
														__a		:		str									=       rename_keys(_SCREAMING_SNAKE_CASE					)
														if "in_proj_weight" in key:
																					# split fused qkv proj
																					__a		:		int									=       val[:hidden_size, :]
																					__a		:		List[str]									=       val[hidden_size : 2 * hidden_size, :]
																					__a		:		int									=       val[-hidden_size:, :]
														elif "enc_to_dec_proj" in key:
																					__a		:		Optional[Any]									=       val
														else:
																					__a		:		str									=       val
							return state_dict, enc_dec_proj_state_dict





def       __magic_name__    (   _lowerCamelCase				: Optional[Any]					):
							if checkpoint == "small":
														# default config values
														__a		:		Union[str, Any]									=       1_0_2_4
														__a		:		Optional[Any]									=       2_4
														__a		:		int									=       1_6
							elif checkpoint == "medium":
														__a		:		List[str]									=       1_5_3_6
														__a		:		Tuple									=       4_8
														__a		:		List[str]									=       2_4
							elif checkpoint == "large":
														__a		:		Union[str, Any]									=       2_0_4_8
														__a		:		Tuple									=       4_8
														__a		:		List[str]									=       3_2
							else:
														raise ValueError(F'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'''					)
							__a		:		Dict									=       MusicgenDecoderConfig(
							    hidden_size=_SCREAMING_SNAKE_CASE  ,       ffn_dim=hidden_size * 4  ,       num_hidden_layers=_SCREAMING_SNAKE_CASE  ,       num_attention_heads=_SCREAMING_SNAKE_CASE  ,       )
							return config





@torch.no_grad()
def       __magic_name__    (   _lowerCamelCase				: List[str]  ,       _lowerCamelCase				: Optional[Any]=None  ,       _lowerCamelCase				: int=None  ,       _lowerCamelCase				: int="cpu"					):
							__a		:		Union[str, Any]									=       MusicGen.get_pretrained(_SCREAMING_SNAKE_CASE  ,       device=_SCREAMING_SNAKE_CASE					)
							__a		:		Optional[Any]									=       decoder_config_from_checkpoint(_SCREAMING_SNAKE_CASE					)

							__a		:		List[str]									=       fairseq_model.lm.state_dict()
							__a							,     __a		:		Optional[Any]									=       rename_state_dict(
							    _SCREAMING_SNAKE_CASE  ,       hidden_size=decoder_config.hidden_size					)

							__a		:		str									=       TaEncoderModel.from_pretrained("""t5-base"""					)
							__a		:		Optional[int]									=       EncodecModel.from_pretrained("""facebook/encodec_32khz"""					)
							__a		:		Union[str, Any]									=       MusicgenForCausalLM(_SCREAMING_SNAKE_CASE					).eval()

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

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

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

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

							# init the composite model
							__a		:		Tuple									=       MusicgenForConditionalGeneration(text_encoder=_SCREAMING_SNAKE_CASE  ,       audio_encoder=_SCREAMING_SNAKE_CASE  ,       decoder=_SCREAMING_SNAKE_CASE					)

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

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

							with torch.no_grad():
														__a		:		Dict									=       model(input_ids=_SCREAMING_SNAKE_CASE  ,       decoder_input_ids=_SCREAMING_SNAKE_CASE					).logits

							if logits.shape != (8, 1, 2_0_4_8):
														raise ValueError("""Incorrect shape for logits"""					)

							# now construct the processor
							__a		:		str									=       AutoTokenizer.from_pretrained("""t5-base"""					)
							__a		:		Optional[int]									=       AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz"""  ,       padding_side="""left"""					)

							__a		:		Dict									=       MusicgenProcessor(feature_extractor=_SCREAMING_SNAKE_CASE  ,       tokenizer=_SCREAMING_SNAKE_CASE					)

							# set the appropriate bos/pad token ids
							__a		:		int									=       2_0_4_8
							__a		:		int									=       2_0_4_8

							# set other default generation config params
							__a		:		Optional[Any]									=       int(3_0 * audio_encoder.config.frame_rate					)
							__a		:		Dict									=       True
							__a		:		List[str]									=       3.0

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

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


if __name__ == "__main__":
	lowercase__			   =  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."
	)

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






"""simple docstring"""

def       __magic_name__    (   _lowerCamelCase				: list[int]					):
							if not nums:  # Makes sure that the list is not empty
														raise ValueError("""List is empty"""					)

							__a		:		Any									=       sum(_lowerCamelCase					) / len(_lowerCamelCase					)  # Calculate the average
							return sum(abs(x - average					) for x in nums					) / len(_lowerCamelCase					)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

