



'''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_  )      ->				List[Any]:
		_a					:				Dict				=							np.inf

		def set_batch_size(lowerCAmelCase_  ) -> None:
				nonlocal batch_size
				if isinstance(a__						,      a__  ):
						_a					:				str				=							min(a__						,      config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS  )
				elif isinstance(a__						,      a__  ):
						_a					:				str				=							min(a__						,      config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS  )
				elif isinstance(a__						,      a__  ) and feature.dtype == "binary":
						_a					:				List[str]				=							min(a__						,      config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS  )

		_visit(a__						,      a__  )

		return None if batch_size is np.inf else batch_size

class      __magic_name__						(   _a       ):







							def __init__(							self :	List[str]     ,_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	str = None     ,_UpperCAmelCase :	int = None     ,_UpperCAmelCase :	List[str] = None     ,_UpperCAmelCase :	str = False     ,_UpperCAmelCase :	Union[str, Any] = False     ,_UpperCAmelCase :	List[Any] = None     ,**_UpperCAmelCase :	int     ,):
									super().__init__(
									    __lowerCAmelCase     ,split=__lowerCAmelCase     ,features=__lowerCAmelCase     ,cache_dir=__lowerCAmelCase     ,keep_in_memory=__lowerCAmelCase     ,streaming=__lowerCAmelCase     ,num_proc=__lowerCAmelCase     ,**__lowerCAmelCase     ,)
									_a					:				List[str]				=							path_or_paths if isinstance(__lowerCAmelCase     ,__lowerCAmelCase		) else {self.split: path_or_paths}
									_a					:				List[Any]				=							_PACKAGED_DATASETS_MODULES['parquet'][1]
									_a					:				List[Any]				=							Parquet(
									    cache_dir=__lowerCAmelCase     ,data_files=__lowerCAmelCase     ,features=__lowerCAmelCase     ,hash=__lowerCAmelCase     ,**__lowerCAmelCase     ,)







							def 							__lowercase      (							self :	Any		):
									# Build iterable dataset
									if self.streaming:
											_a					:				Any				=							self.builder.as_streaming_dataset(split=self.split		)
									# Build regular (map-style) dataset
									else:
											_a					:				Any				=							None
											_a					:				Union[str, Any]				=							None
											_a					:				str				=							None
											_a					:				Tuple				=							None

											self.builder.download_and_prepare(
											    download_config=__lowerCAmelCase     ,download_mode=__lowerCAmelCase     ,verification_mode=__lowerCAmelCase     ,base_path=__lowerCAmelCase     ,num_proc=self.num_proc     ,)
											_a					:				Optional[int]				=							self.builder.as_dataset(
											    split=self.split     ,verification_mode=__lowerCAmelCase     ,in_memory=self.keep_in_memory		)
									return dataset

class      __magic_name__						:







							def __init__(							self :	Tuple     ,_UpperCAmelCase :	List[str]     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	Union[str, Any] = None     ,**_UpperCAmelCase :	Any     ,):
									_a					:				Tuple				=							dataset
									_a					:				Union[str, Any]				=							path_or_buf
									_a					:				Optional[Any]				=							batch_size or get_writer_batch_size(dataset.features		)
									_a					:				List[str]				=							parquet_writer_kwargs







							def 							__lowercase      (							self :	int		):
									_a					:				Optional[Any]				=							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					:				str				=							self._write(file_obj=__lowerCAmelCase     ,batch_size=__lowerCAmelCase     ,**self.parquet_writer_kwargs		)
									else:
											_a					:				List[str]				=							self._write(file_obj=self.path_or_buf     ,batch_size=__lowerCAmelCase     ,**self.parquet_writer_kwargs		)
									return written







							def 							__lowercase      (							self :	Dict     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	Tuple     ,**_UpperCAmelCase :	Union[str, Any]		):
									_a					:				Dict				=							0
									_a					:				List[Any]				=							parquet_writer_kwargs.pop('path_or_buf'     ,__lowerCAmelCase		)
									_a					:				Tuple				=							self.dataset.features.arrow_schema

									_a					:				List[str]				=							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					:				int				=							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
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase						      =       logging.get_logger(__name__)

set_seed(770)


__lowerCAmelCase						      =       {
    '''c_attn''': '''att_proj''',
    '''c_proj''': '''out_proj''',
    '''c_fc''': '''in_proj''',
    '''transformer.''': '''''',
    '''h.''': '''layers.''',
    '''ln_1''': '''layernorm_1''',
    '''ln_2''': '''layernorm_2''',
    '''ln_f''': '''layernorm_final''',
    '''wpe''': '''position_embeds_layer''',
    '''wte''': '''input_embeds_layer''',
}


__lowerCAmelCase						      =       {
    '''text_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text.pt''',
    },
    '''coarse_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse.pt''',
    },
    '''fine_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine.pt''',
    },
    '''text''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text_2.pt''',
    },
    '''coarse''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse_2.pt''',
    },
    '''fine''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine_2.pt''',
    },
}

__lowerCAmelCase						      =       os.path.dirname(os.path.abspath(__file__))
__lowerCAmelCase						      =       os.path.join(os.path.expanduser('''~'''), '''.cache''')
__lowerCAmelCase						      =       os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_=False  )      ->				Optional[int]:
		_a					:				int				=							model_type
		if use_small:
				key += "_small"
		return os.path.join(lowerCAmelCase_						,      REMOTE_MODEL_PATHS[key]['file_name']  )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				int:
		os.makedirs(lowerCAmelCase_						,      exist_ok=lowerCAmelCase_  )
		hf_hub_download(repo_id=lowerCAmelCase_						,      filename=lowerCAmelCase_						,      local_dir=lowerCAmelCase_  )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=False						,      lowerCAmelCase_="text"  )      ->				List[str]:
		if model_type == "text":
				_a					:				List[str]				=							BarkSemanticModel
				_a					:				Optional[Any]				=							BarkSemanticConfig
				_a					:				Any				=							BarkSemanticGenerationConfig
		elif model_type == "coarse":
				_a					:				Tuple				=							BarkCoarseModel
				_a					:				str				=							BarkCoarseConfig
				_a					:				str				=							BarkCoarseGenerationConfig
		elif model_type == "fine":
				_a					:				List[str]				=							BarkFineModel
				_a					:				Optional[Any]				=							BarkFineConfig
				_a					:				str				=							BarkFineGenerationConfig
		else:
				raise NotImplementedError()
		_a					:				Dict				=							f"""{model_type}_small""" if use_small else model_type
		_a					:				Union[str, Any]				=							REMOTE_MODEL_PATHS[model_key]
		if not os.path.exists(lowerCAmelCase_  ):
				logger.info(f"""{model_type} model not found, downloading into `{CACHE_DIR}`."""  )
				_download(model_info['repo_id']						,      model_info['file_name']  )
		_a					:				int				=							torch.load(lowerCAmelCase_						,      map_location=lowerCAmelCase_  )
		# this is a hack
		_a					:				List[Any]				=							checkpoint['model_args']
		if "input_vocab_size" not in model_args:
				_a					:				Dict				=							model_args['vocab_size']
				_a					:				Dict				=							model_args['vocab_size']
				del model_args["vocab_size"]

		# convert Bark model arguments to HF Bark model arguments
		_a					:				List[Any]				=							model_args.pop('n_head'  )
		_a					:				Any				=							model_args.pop('n_embd'  )
		_a					:				List[Any]				=							model_args.pop('n_layer'  )

		_a					:				Optional[int]				=							ConfigClass(**checkpoint['model_args']  )
		_a					:				List[str]				=							ModelClass(config=lowerCAmelCase_  )
		_a					:				Tuple				=							GenerationConfigClass()

		_a					:				Optional[Any]				=							model_generation_config
		_a					:				Optional[Any]				=							checkpoint['model']
		# fixup checkpoint
		_a					:				int				=							'_orig_mod.'
		for k, v in list(state_dict.items()  ):
				if k.startswith(lowerCAmelCase_  ):
						# replace part of the key with corresponding layer name in HF implementation
						_a					:				str				=							k[len(lowerCAmelCase_  ) :]
						for old_layer_name in new_layer_name_dict:
								_a					:				List[Any]				=							new_k.replace(lowerCAmelCase_						,      new_layer_name_dict[old_layer_name]  )

						_a					:				List[Any]				=							state_dict.pop(lowerCAmelCase_  )

		_a					:				List[Any]				=							set(state_dict.keys()  ) - set(model.state_dict().keys()  )
		_a					:				Tuple				=							{k for k in extra_keys if not k.endswith('.attn.bias'  )}
		_a					:				Tuple				=							set(model.state_dict().keys()  ) - set(state_dict.keys()  )
		_a					:				Optional[Any]				=							{k for k in missing_keys if not k.endswith('.attn.bias'  )}
		if len(lowerCAmelCase_  ) != 0:
				raise ValueError(f"""extra keys found: {extra_keys}"""  )
		if len(lowerCAmelCase_  ) != 0:
				raise ValueError(f"""missing keys: {missing_keys}"""  )
		model.load_state_dict(lowerCAmelCase_						,      strict=lowerCAmelCase_  )
		_a					:				Dict				=							model.num_parameters(exclude_embeddings=lowerCAmelCase_  )
		_a					:				Tuple				=							checkpoint['best_val_loss'].item()
		logger.info(f"""model loaded: {round(n_params/1E6						,      1  )}M params, {round(lowerCAmelCase_						,      3  )} loss"""  )
		model.eval()
		model.to(lowerCAmelCase_  )
		del checkpoint, state_dict

		return model



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_=False						,      lowerCAmelCase_="text"  )      ->				List[Any]:
		if model_type not in ("text", "coarse", "fine"):
				raise NotImplementedError()

		_a					:				Optional[int]				=							'cpu'  # do conversion on cpu

		_a					:				Tuple				=							_get_ckpt_path(lowerCAmelCase_						,      use_small=lowerCAmelCase_  )
		_a					:				List[Any]				=							_load_model(lowerCAmelCase_						,      lowerCAmelCase_						,      model_type=lowerCAmelCase_						,      use_small=lowerCAmelCase_  )

		# load bark initial model
		_a					:				Any				=							_bark_load_model(lowerCAmelCase_						,      'cpu'						,      model_type=lowerCAmelCase_						,      use_small=lowerCAmelCase_  )

		if model_type == "text":
				_a					:				int				=							bark_model['model']

		if model.num_parameters(exclude_embeddings=lowerCAmelCase_  ) != bark_model.get_num_params():
				raise ValueError('initial and new models don\'t have the same number of parameters'  )

		# check if same output as the bark model
		_a					:				Any				=							5
		_a					:				List[str]				=							10

		if model_type in ["text", "coarse"]:
				_a					:				Dict				=							torch.randint(256						,      (batch_size, sequence_length)						,      dtype=torch.int  )
				_a					:				Dict				=							bark_model(lowerCAmelCase_  )[0]

				_a					:				Tuple				=							model(lowerCAmelCase_  )

				# take last logits
				_a					:				Optional[int]				=							output_new_model_total.logits[:, [-1], :]

		else:
				_a					:				List[str]				=							3
				_a					:				List[Any]				=							8
				_a					:				Tuple				=							torch.randint(256						,      (batch_size, sequence_length, n_codes_total)						,      dtype=torch.int  )

				_a					:				Union[str, Any]				=							model(lowerCAmelCase_						,      lowerCAmelCase_  )
				_a					:				int				=							bark_model(lowerCAmelCase_						,      lowerCAmelCase_  )

				_a					:				List[str]				=							output_new_model_total.logits

		# output difference should come from the difference of self-attention implementation design
		if output_new_model.shape != output_old_model.shape:
				raise ValueError('initial and new outputs don\'t have the same shape'  )
		if (output_new_model - output_old_model).abs().max().item() > 1E-3:
				raise ValueError('initial and new outputs are not equal'  )

		Path(lowerCAmelCase_  ).mkdir(exist_ok=lowerCAmelCase_  )
		model.save_pretrained(lowerCAmelCase_  )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      )      ->				Any:
		_a					:				Any				=							os.path.join(lowerCAmelCase_						,      lowerCAmelCase_  )

		_a					:				int				=							BarkSemanticConfig.from_pretrained(os.path.join(lowerCAmelCase_						,      'config.json'  )  )
		_a					:				Any				=							BarkCoarseConfig.from_pretrained(os.path.join(lowerCAmelCase_						,      'config.json'  )  )
		_a					:				List[Any]				=							BarkFineConfig.from_pretrained(os.path.join(lowerCAmelCase_						,      'config.json'  )  )
		_a					:				List[str]				=							EncodecConfig.from_pretrained('facebook/encodec_24khz'  )

		_a					:				str				=							BarkSemanticModel.from_pretrained(lowerCAmelCase_  )
		_a					:				Dict				=							BarkCoarseModel.from_pretrained(lowerCAmelCase_  )
		_a					:				int				=							BarkFineModel.from_pretrained(lowerCAmelCase_  )
		_a					:				List[Any]				=							EncodecModel.from_pretrained('facebook/encodec_24khz'  )

		_a					:				Any				=							BarkConfig.from_sub_model_configs(
		    lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )

		_a					:				List[str]				=							BarkGenerationConfig.from_sub_model_configs(
		    semantic.generation_config						,      coarseAcoustic.generation_config						,      fineAcoustic.generation_config  )

		_a					:				Optional[Any]				=							BarkModel(lowerCAmelCase_  )

		_a					:				List[str]				=							semantic
		_a					:				Union[str, Any]				=							coarseAcoustic
		_a					:				Optional[int]				=							fineAcoustic
		_a					:				Optional[Any]				=							codec

		_a					:				List[Any]				=							bark_generation_config

		Path(lowerCAmelCase_  ).mkdir(exist_ok=lowerCAmelCase_  )
		bark.save_pretrained(lowerCAmelCase_						,      repo_id=lowerCAmelCase_						,      push_to_hub=lowerCAmelCase_  )


if __name__ == "__main__":
							__lowerCAmelCase						      =       argparse.ArgumentParser()
							# Required parameters

							parser.add_argument('''model_type''', type=str, help='''text, coarse or fine.''')
							parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
							parser.add_argument('''--is_small''', action='''store_true''', help='''convert the small version instead of the large.''')

							__lowerCAmelCase						      =       parser.parse_args()

							load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)




