
'''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()
_lowercase :  List[Any]    							=       logging.get_logger(__name__)


_lowercase :  Dict    							=       ['model.decoder.embed_positions.weights']
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Optional[int]							):



						"""simple docstring"""





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



						"""simple docstring"""





						lowercase_	:     Tuple													=       list(state_dict.keys()							)
						lowercase_	:     Optional[Any]													=       {}
						for key in keys:
												lowercase_	:     str													=       state_dict.pop(__snake_case							)
												lowercase_	:     int													=       rename_keys(__snake_case							)
												if "in_proj_weight" in key:
																		# split fused qkv proj
																		lowercase_	:     List[str]													=       val[:hidden_size, :]
																		lowercase_	:     Dict													=       val[hidden_size : 2 * hidden_size, :]
																		lowercase_	:     Dict													=       val[-hidden_size:, :]
												elif "enc_to_dec_proj" in key:
																		lowercase_	:     Any													=       val
												else:
																		lowercase_	:     str													=       val
						return state_dict, enc_dec_proj_state_dict
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List[str]							):



						"""simple docstring"""





						if checkpoint == "small":
												# default config values
												lowercase_	:     List[str]													=       1024
												lowercase_	:     Union[str, Any]													=       24
												lowercase_	:     Optional[Any]													=       16
						elif checkpoint == "medium":
												lowercase_	:     Union[str, Any]													=       1536
												lowercase_	:     Any													=       48
												lowercase_	:     Dict													=       24
						elif checkpoint == "large":
												lowercase_	:     Optional[int]													=       2048
												lowercase_	:     Dict													=       48
												lowercase_	:     Any													=       32
						else:
												raise ValueError(F'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'''							)
						lowercase_	:     Any													=       MusicgenDecoderConfig(
						    hidden_size=__snake_case      ,      ffn_dim=hidden_size * 4      ,      num_hidden_layers=__snake_case      ,      num_attention_heads=__snake_case      ,      )
						return config
@torch.no_grad()
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List[str]      ,      __SCREAMING_SNAKE_CASE					: Tuple=None      ,      __SCREAMING_SNAKE_CASE					: Union[str, Any]=None      ,      __SCREAMING_SNAKE_CASE					: List[Any]="cpu"							):



						"""simple docstring"""





						lowercase_	:     int													=       MusicGen.get_pretrained(__snake_case      ,      device=__snake_case							)
						lowercase_	:     Optional[int]													=       decoder_config_from_checkpoint(__snake_case							)

						lowercase_	:     List[str]													=       fairseq_model.lm.state_dict()
						lowercase_	:     List[str]													=       rename_state_dict(
						    __snake_case      ,      hidden_size=decoder_config.hidden_size							)

						lowercase_	:     List[str]													=       TaEncoderModel.from_pretrained('''t5-base'''							)
						lowercase_	:     int													=       EncodecModel.from_pretrained('''facebook/encodec_32khz'''							)
						lowercase_	:     List[str]													=       MusicgenForCausalLM(__snake_case							).eval()

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

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

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

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

						# init the composite model
						lowercase_	:     str													=       MusicgenForConditionalGeneration(text_encoder=__snake_case      ,      audio_encoder=__snake_case      ,      decoder=__snake_case							)

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

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

						with torch.no_grad():
												lowercase_	:     Union[str, Any]													=       model(input_ids=__snake_case      ,      decoder_input_ids=__snake_case							).logits

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

						# now construct the processor
						lowercase_	:     Union[str, Any]													=       AutoTokenizer.from_pretrained('''t5-base'''							)
						lowercase_	:     Dict													=       AutoFeatureExtractor.from_pretrained('''facebook/encodec_32khz'''      ,      padding_side='''left'''							)

						lowercase_	:     Any													=       MusicgenProcessor(feature_extractor=__snake_case      ,      tokenizer=__snake_case							)

						# set the appropriate bos/pad token ids
						lowercase_	:     List[str]													=       2048
						lowercase_	:     int													=       2048

						# set other default generation config params
						lowercase_	:     Optional[Any]													=       int(30 * audio_encoder.config.frame_rate							)
						lowercase_	:     Tuple													=       True
						lowercase_	:     Optional[int]													=       3.0

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

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


if __name__ == "__main__":
							_lowercase :  Union[str, 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."
							)

							_lowercase :  List[Any]    							=       parser.parse_args()
							convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)








"""simple docstring"""






import os
from math import logaa


def 			_lowercase				(  __snake_case = "base_exp.txt"	)						->   int:
			__lowerCAmelCase      :  float												=       0
			__lowerCAmelCase      :  Any												=       0
			for i, line in enumerate(open(os.path.join(os.path.dirname(__snake_case	)					,__snake_case	)	)	):
						__lowerCAmelCase						,	__lowerCAmelCase      :  List[str]												=       list(map(__snake_case					,line.split(","	)	)	)
						if x * logaa(__snake_case	) > largest:
									__lowerCAmelCase      :  Tuple												=       x * logaa(__snake_case	)
									__lowerCAmelCase      :  Optional[Any]												=       i + 1
			return result


if __name__ == "__main__":
	print(solution())