





'''simple docstring'''


import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







def    _lowerCamelCase  (__lowerCamelCase  :     Features     )   ->    Optional[int]:
			a__     = np.inf

			def set_batch_size(__lowerCamelCase  :     FeatureType     ) -> None:
						nonlocal batch_size
						if isinstance(__lowerCamelCase					,  __lowerCamelCase     ):
									a__     = min(__lowerCamelCase					,  config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS     )
						elif isinstance(__lowerCamelCase					,  __lowerCamelCase     ):
									a__     = min(__lowerCamelCase					,  config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS     )
						elif isinstance(__lowerCamelCase					,  __lowerCamelCase     ) and feature.dtype == "binary":
									a__     = min(__lowerCamelCase					,  config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS     )

			_visit(__lowerCamelCase					,  __lowerCamelCase     )

			return None if batch_size is np.inf else batch_size






class       UpperCamelCase__  (		__lowerCAmelCase     ):







	def __init__(				self			:   Tuple , lowerCamelCase			:   NestedDataStructureLike[PathLike] , lowerCamelCase			:   Optional[NamedSplit] = None , lowerCamelCase			:   Optional[Features] = None , lowerCamelCase			:   str = None , lowerCamelCase			:   bool = False , lowerCamelCase			:   bool = False , lowerCamelCase			:   Optional[int] = None , **lowerCamelCase			:   int , ):




				'''simple docstring'''
				super().__init__(
				    lowerCamelCase , split=lowerCamelCase , features=lowerCamelCase , cache_dir=lowerCamelCase , keep_in_memory=lowerCamelCase , streaming=lowerCamelCase , num_proc=lowerCamelCase , **lowerCamelCase , )
				a__     = path_or_paths if isinstance(lowerCamelCase , lowerCamelCase		) else {self.split: path_or_paths}
				a__     = _PACKAGED_DATASETS_MODULES["parquet"][1]
				a__     = Parquet(
				    cache_dir=lowerCamelCase , data_files=lowerCamelCase , features=lowerCamelCase , hash=lowerCamelCase , **lowerCamelCase , )







	def 						__a   (				self			:   Union[str, Any]		):




				'''simple docstring'''
				# Build iterable dataset
				if self.streaming:
							a__     = self.builder.as_streaming_dataset(split=self.split		)
				# Build regular (map-style) dataset
				else:
							a__     = None
							a__     = None
							a__     = None
							a__     = None

							self.builder.download_and_prepare(
							    download_config=lowerCamelCase , download_mode=lowerCamelCase , verification_mode=lowerCamelCase , base_path=lowerCamelCase , num_proc=self.num_proc , )
							a__     = self.builder.as_dataset(
							    split=self.split , verification_mode=lowerCamelCase , in_memory=self.keep_in_memory		)
				return dataset






class       UpperCamelCase__  :







	def __init__(				self			:   List[str] , lowerCamelCase			:   Dataset , lowerCamelCase			:   Union[PathLike, BinaryIO] , lowerCamelCase			:   Optional[int] = None , **lowerCamelCase			:   Any , ):




				'''simple docstring'''
				a__     = dataset
				a__     = path_or_buf
				a__     = batch_size or get_writer_batch_size(dataset.features		)
				a__     = parquet_writer_kwargs







	def 						__a   (				self			:   Any		):




				'''simple docstring'''
				a__     = self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

				if isinstance(self.path_or_buf , (str, bytes, os.PathLike)		):
							with open(self.path_or_buf , "wb+"		) as buffer:
										a__     = self._write(file_obj=lowerCamelCase , batch_size=lowerCamelCase , **self.parquet_writer_kwargs		)
				else:
							a__     = self._write(file_obj=self.path_or_buf , batch_size=lowerCamelCase , **self.parquet_writer_kwargs		)
				return written







	def 						__a   (				self			:   Optional[int] , lowerCamelCase			:   BinaryIO , lowerCamelCase			:   int , **lowerCamelCase			:   Optional[Any]		):




				'''simple docstring'''
				a__     = 0
				a__     = parquet_writer_kwargs.pop("path_or_buf" , lowerCamelCase		)
				a__     = self.dataset.features.arrow_schema

				a__     = pq.ParquetWriter(lowerCamelCase , schema=lowerCamelCase , **lowerCamelCase		)

				for offset in logging.tqdm(
				    range(0 , len(self.dataset		) , lowerCamelCase		) , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating parquet from Arrow format" , ):
							a__     = query_table(
							    table=self.dataset._data , key=slice(lowerCamelCase , offset + batch_size		) , indices=self.dataset._indices if self.dataset._indices is not None else None , )
							writer.write_table(lowerCamelCase		)
							written += batch.nbytes
				writer.close()
				return written










'''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)



