
from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE     :				List[str]             =       {'configuration_regnet': ['REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RegNetConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE     :				Optional[int]             =       [
		    'REGNET_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'RegNetForImageClassification',
		    'RegNetModel',
		    'RegNetPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE     :				Dict             =       [
		    'TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFRegNetForImageClassification',
		    'TFRegNetModel',
		    'TFRegNetPreTrainedModel',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE     :				Optional[Any]             =       [
		    'FlaxRegNetForImageClassification',
		    'FlaxRegNetModel',
		    'FlaxRegNetPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_regnet import (
				    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RegNetForImageClassification,
				    RegNetModel,
				    RegNetPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_regnet import (
				    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRegNetForImageClassification,
				    TFRegNetModel,
				    TFRegNetPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_regnet import (
				    FlaxRegNetForImageClassification,
				    FlaxRegNetModel,
				    FlaxRegNetPreTrainedModel,
				)


else:
		import sys

		__SCREAMING_SNAKE_CASE     :				Any             =       _LazyModule(__name__, globals()['__file__'], _import_structure)


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__							(    __magic_name__  :       str=10					):


					'''simple docstring'''

					for _ in tqdm(range(0							,					args.n_examples							,					__magic_name__					)					):
										yield [next(__magic_name__					)[args.text_column] for _ in range(__magic_name__					)]


# Configuration
__SCREAMING_SNAKE_CASE     :				Dict             =       HfArgumentParser(TokenizerTrainingArguments)
__SCREAMING_SNAKE_CASE     :				str             =       parser.parse_args()

# Base tokenizer
__SCREAMING_SNAKE_CASE     :				Tuple             =       AutoTokenizer.from_pretrained(args.base_tokenizer)
__SCREAMING_SNAKE_CASE     :				List[str]             =       list(bytes_to_unicode().values())

# Load dataset
__SCREAMING_SNAKE_CASE     :				Union[str, Any]             =       load_dataset(args.dataset_name, split='train', streaming=True)
__SCREAMING_SNAKE_CASE     :				Any             =       iter(dataset)


# Training and saving
__SCREAMING_SNAKE_CASE     :				Optional[int]             =       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)
