






"""simple docstring"""







import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


_lowerCAmelCase            =	'<<<<<<< This should probably be modified because it mentions: '

_lowerCAmelCase            =	'=======\n>>>>>>>\n'

_lowerCAmelCase            =	[
    'TextEncoderConfig',
    'ByteTextEncoder',
    'SubwordTextEncoder',
    'encoder_config',
    'maybe_build_from_corpus',
    'manual_dir',
]

_lowerCAmelCase            =	[
    # (pattern, replacement)
    # Order is important here for some replacements
    (r'tfds\.core', r'datasets'),
    (r'tf\.io\.gfile\.GFile', r'open'),
    (r'tf\.([\w\d]+)', r'datasets.Value(\'\1\')'),
    (r'tfds\.features\.Text\(\)', r'datasets.Value(\'string\')'),
    (r'tfds\.features\.Text\(', r'datasets.Value(\'string\'),'),
    (r'features\s*=\s*tfds.features.FeaturesDict\(', r'features=datasets.Features('),
    (r'tfds\.features\.FeaturesDict\(', r'dict('),
    (r'The TensorFlow Datasets Authors', r'The TensorFlow Datasets Authors and the HuggingFace Datasets Authors'),
    (r'tfds\.', r'datasets.'),
    (r'dl_manager\.manual_dir', r'self.config.data_dir'),
    (r'self\.builder_config', r'self.config'),
]


def      UpperCamelCase       ( _A )     ->       Any:
				return ConvertCommand(args.tfds_path  ,				args.datasets_directory )


class      UpperCamelCase		(__snake_case     ):






						@staticmethod
						def      __snake_case      (		__magic_name__  :ArgumentParser				)      ->List[Any]:
										lowercase    :      List[str]       = parser.add_parser(
										    """convert"""			, help="""Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."""			, )
										train_parser.add_argument(
										    """--tfds_path"""			, type=__magic_name__			, required=__magic_name__			, help="""Path to a TensorFlow Datasets folder to convert or a single tfds file to convert."""			, )
										train_parser.add_argument(
										    """--datasets_directory"""			, type=__magic_name__			, required=__magic_name__			, help="""Path to the HuggingFace Datasets folder."""				)
										train_parser.set_defaults(func=__magic_name__				)






						def __init__(		self  :Dict			, __magic_name__  :str			, __magic_name__  :str			, *__magic_name__  :int				)      ->Tuple:
										lowercase    :      str       = get_logger("""datasets-cli/converting"""				)

										lowercase    :      Union[str, Any]       = tfds_path
										lowercase    :      Union[str, Any]       = datasets_directory






						def      __snake_case      (		self  :Any				)      ->List[str]:
										if os.path.isdir(self._tfds_path				):
														lowercase    :      Tuple       = os.path.abspath(self._tfds_path				)
										elif os.path.isfile(self._tfds_path				):
														lowercase    :      Union[str, Any]       = os.path.dirname(self._tfds_path				)
										else:
														raise ValueError("""--tfds_path is neither a directory nor a file. Please check path."""				)

										lowercase    :      str       = os.path.abspath(self._datasets_directory				)

										self._logger.info(f"""Converting datasets from {abs_tfds_path} to {abs_datasets_path}"""				)

										lowercase    :      str       = []
										lowercase    :      Tuple       = []
										lowercase    :      Union[str, Any]       = {}

										if os.path.isdir(self._tfds_path				):
														lowercase    :      str       = os.listdir(__magic_name__				)
										else:
														lowercase    :      int       = [os.path.basename(self._tfds_path				)]

										for f_name in file_names:
														self._logger.info(f"""Looking at file {f_name}"""				)
														lowercase    :      List[str]       = os.path.join(__magic_name__			, __magic_name__				)
														lowercase    :      str       = os.path.join(__magic_name__			, __magic_name__				)

														if not os.path.isfile(__magic_name__				) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
																		self._logger.info("""Skipping file"""				)
																		continue

														with open(__magic_name__			, encoding="""utf-8"""				) as f:
																		lowercase    :      Any       = f.readlines()

														lowercase    :      Tuple       = []
														lowercase    :      Union[str, Any]       = False
														lowercase    :      Optional[int]       = False
														lowercase    :      int       = []
														for line in lines:
																		lowercase    :      Optional[Any]       = line

																		# Convert imports
																		if "import tensorflow.compat.v2 as tf" in out_line:
																						continue
																		elif "@tfds.core" in out_line:
																						continue
																		elif "builder=self" in out_line:
																						continue
																		elif "import tensorflow_datasets.public_api as tfds" in out_line:
																						lowercase    :      List[str]       = """import datasets\n"""
																		elif "import tensorflow" in out_line:
																						# order is important here
																						lowercase    :      Tuple       = """"""
																						continue
																		elif "from absl import logging" in out_line:
																						lowercase    :      Any       = """from datasets import logging\n"""
																		elif "getLogger" in out_line:
																						lowercase    :      List[str]       = out_line.replace("""getLogger"""			, """get_logger"""				)
																		elif any(expression in out_line for expression in TO_HIGHLIGHT				):
																						lowercase    :      int       = True
																						lowercase    :      Union[str, Any]       = list(filter(lambda __magic_name__				: e in out_line			, __magic_name__				)				)
																						out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(__magic_name__				) + """\n"""				)
																						out_lines.append(__magic_name__				)
																						out_lines.append(__magic_name__				)
																						continue
																		else:
																						for pattern, replacement in TO_CONVERT:
																										lowercase    :      Union[str, Any]       = re.sub(__magic_name__			, __magic_name__			, __magic_name__				)

                # Take care of saving utilities (to later move them together with main script)
																		if "tensorflow_datasets" in out_line:
																						lowercase    :      Any       = re.match(r"""from\stensorflow_datasets.*import\s([^\.\r\n]+)"""			, __magic_name__				)
																						tfds_imports.extend(imp.strip() for imp in match.group(1				).split(""","""				)				)
																						lowercase    :      Any       = """from . import """ + match.group(1				)

																		# Check we have not forget anything
																		if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
																						raise ValueError(f"""Error converting {out_line.strip()}"""				)

																		if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
																						lowercase    :      Optional[int]       = True
																		out_lines.append(__magic_name__				)

														if is_builder or "wmt" in f_name:
																		# We create a new directory for each dataset
																		lowercase    :      Dict       = f_name.replace(""".py"""			, """"""				)
																		lowercase    :      Union[str, Any]       = os.path.join(__magic_name__			, __magic_name__				)
																		lowercase    :      int       = os.path.join(__magic_name__			, __magic_name__				)
																		os.makedirs(__magic_name__			, exist_ok=__magic_name__				)
																		self._logger.info(f"""Adding directory {output_dir}"""				)
																		imports_to_builder_map.update({imp: output_dir for imp in tfds_imports}				)
														else:
																		# Utilities will be moved at the end
																		utils_files.append(__magic_name__				)

														if needs_manual_update:
																		with_manual_update.append(__magic_name__				)

														with open(__magic_name__			, """w"""			, encoding="""utf-8"""				) as f:
																		f.writelines(__magic_name__				)
														self._logger.info(f"""Converted in {output_file}"""				)

										for utils_file in utils_files:
														try:
																		lowercase    :      int       = os.path.basename(__magic_name__				)
																		lowercase    :      Dict       = imports_to_builder_map[f_name.replace(""".py"""			, """"""				)]
																		self._logger.info(f"""Moving {dest_folder} to {utils_file}"""				)
																		shutil.copy(__magic_name__			, __magic_name__				)
														except KeyError:
																		self._logger.error(f"""Cannot find destination folder for {utils_file}. Please copy manually."""				)

										if with_manual_update:
														for file_path in with_manual_update:
																		self._logger.warning(
																		    f"""You need to manually update file {file_path} to remove configurations using 'TextEncoderConfig'."""				)








"""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(7_70)


_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      UpperCamelCase       ( _A  ,				_A=False )     ->       Dict:
				lowercase    :      Union[str, Any]       = model_type
				if use_small:
								key += "_small"
				return os.path.join(_A  ,				REMOTE_MODEL_PATHS[key]["""file_name"""] )


def      UpperCamelCase       ( _A  ,				_A )     ->       List[Any]:
				os.makedirs(_A  ,				exist_ok=_A )
				hf_hub_download(repo_id=_A  ,				filename=_A  ,				local_dir=_A )


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

				# convert Bark model arguments to HF Bark model arguments
				lowercase    :      Optional[int]       = model_args.pop("""n_head""" )
				lowercase    :      Union[str, Any]       = model_args.pop("""n_embd""" )
				lowercase    :      Dict       = model_args.pop("""n_layer""" )

				lowercase    :      List[str]       = ConfigClass(**checkpoint["""model_args"""] )
				lowercase    :      Dict       = ModelClass(config=_A )
				lowercase    :      List[str]       = GenerationConfigClass()

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

												lowercase    :      List[Any]       = state_dict.pop(_A )

				lowercase    :      Dict       = set(state_dict.keys() ) - set(model.state_dict().keys() )
				lowercase    :      Dict       = {k for k in extra_keys if not k.endswith(""".attn.bias""" )}
				lowercase    :      Optional[int]       = set(model.state_dict().keys() ) - set(state_dict.keys() )
				lowercase    :      Dict       = {k for k in missing_keys if not k.endswith(""".attn.bias""" )}
				if len(_A ) != 0:
								raise ValueError(F"""extra keys found: {extra_keys}""" )
				if len(_A ) != 0:
								raise ValueError(F"""missing keys: {missing_keys}""" )
				model.load_state_dict(_A  ,				strict=_A )
				lowercase    :      Union[str, Any]       = model.num_parameters(exclude_embeddings=_A )
				lowercase    :      Union[str, Any]       = checkpoint["""best_val_loss"""].item()
				logger.info(F"""model loaded: {round(n_params/1e6  ,				1 )}M params, {round(_A  ,				3 )} loss""" )
				model.eval()
				model.to(_A )
				del checkpoint, state_dict

				return model


def      UpperCamelCase       ( _A  ,				_A=False  ,				_A="text" )     ->       Dict:
				if model_type not in ("text", "coarse", "fine"):
								raise NotImplementedError()

				lowercase    :      Optional[Any]       = """cpu"""  # do conversion on cpu

				lowercase    :      Dict       = _get_ckpt_path(_A  ,				use_small=_A )
				lowercase    :      Any       = _load_model(_A  ,				_A  ,				model_type=_A  ,				use_small=_A )

				# load bark initial model
				lowercase    :      Tuple       = _bark_load_model(_A  ,				"""cpu"""  ,				model_type=_A  ,				use_small=_A )

				if model_type == "text":
								lowercase    :      str       = bark_model["""model"""]

				if model.num_parameters(exclude_embeddings=_A ) != 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
				lowercase    :      Union[str, Any]       = 5
				lowercase    :      Tuple       = 10

				if model_type in ["text", "coarse"]:
								lowercase    :      Any       = torch.randint(256  ,				(batch_size, sequence_length)  ,				dtype=torch.int )
								lowercase    :      Optional[Any]       = bark_model(_A )[0]

								lowercase    :      Tuple       = model(_A )

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

				else:
								lowercase    :      Dict       = 3
								lowercase    :      Any       = 8
								lowercase    :      str       = torch.randint(256  ,				(batch_size, sequence_length, n_codes_total)  ,				dtype=torch.int )

								lowercase    :      str       = model(_A  ,				_A )
								lowercase    :      List[Any]       = bark_model(_A  ,				_A )

								lowercase    :      Any       = 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(_A ).mkdir(exist_ok=_A )
				model.save_pretrained(_A )


def      UpperCamelCase       ( _A  ,				_A  ,				_A  ,				_A  ,				_A  ,				_A  ,				)     ->       Optional[int]:
				lowercase    :      str       = os.path.join(_A  ,				_A )

				lowercase    :      Dict       = BarkSemanticConfig.from_pretrained(os.path.join(_A  ,				"""config.json""" ) )
				lowercase    :      List[Any]       = BarkCoarseConfig.from_pretrained(os.path.join(_A  ,				"""config.json""" ) )
				lowercase    :      List[Any]       = BarkFineConfig.from_pretrained(os.path.join(_A  ,				"""config.json""" ) )
				lowercase    :      List[Any]       = EncodecConfig.from_pretrained("""facebook/encodec_24khz""" )

				lowercase    :      Any       = BarkSemanticModel.from_pretrained(_A )
				lowercase    :      str       = BarkCoarseModel.from_pretrained(_A )
				lowercase    :      int       = BarkFineModel.from_pretrained(_A )
				lowercase    :      Optional[Any]       = EncodecModel.from_pretrained("""facebook/encodec_24khz""" )

				lowercase    :      int       = BarkConfig.from_sub_model_configs(
				    _A  ,				_A  ,				_A  ,				_A )

				lowercase    :      Dict       = BarkGenerationConfig.from_sub_model_configs(
				    semantic.generation_config  ,				coarseAcoustic.generation_config  ,				fineAcoustic.generation_config )

				lowercase    :      str       = BarkModel(_A )

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

				lowercase    :      int       = bark_generation_config

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


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)
