




from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode



def    UpperCAmelCase							(  _snake_case=10				):
							for _ in tqdm(range(0    ,			args.n_examples    ,			_snake_case				)				):
														yield [next(_snake_case				)[args.text_column] for _ in range(_snake_case				)]


# Configuration
UpperCAmelCase_    =HfArgumentParser(TokenizerTrainingArguments)
UpperCAmelCase_    =parser.parse_args()

# Base tokenizer
UpperCAmelCase_    =AutoTokenizer.from_pretrained(args.base_tokenizer)
UpperCAmelCase_    =list(bytes_to_unicode().values())

# Load dataset
UpperCAmelCase_    =load_dataset(args.dataset_name, split="""train""", streaming=True)
UpperCAmelCase_    =iter(dataset)


# Training and saving
UpperCAmelCase_    =tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)

new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase_    ={
    """configuration_bark""": [
        """BARK_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BarkCoarseConfig""",
        """BarkConfig""",
        """BarkFineConfig""",
        """BarkSemanticConfig""",
    ],
    """processing_bark""": ["""BarkProcessor"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_    =[
		    """BARK_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """BarkFineModel""",
		    """BarkSemanticModel""",
		    """BarkCoarseModel""",
		    """BarkModel""",
		    """BarkPreTrainedModel""",
		    """BarkCausalModel""",
		]

if TYPE_CHECKING:
		from .configuration_bark import (
		    BARK_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BarkCoarseConfig,
		    BarkConfig,
		    BarkFineConfig,
		    BarkSemanticConfig,
		)
		from .processing_bark import BarkProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bark import (
				    BARK_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BarkCausalModel,
				    BarkCoarseModel,
				    BarkFineModel,
				    BarkModel,
				    BarkPreTrainedModel,
				    BarkSemanticModel,
				)

else:
		import sys

		UpperCAmelCase_    =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



