
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE     :				Optional[int]             =       {
    'configuration_realm': ['REALM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RealmConfig'],
    'tokenization_realm': ['RealmTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE     :				Tuple             =       ['RealmTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE     :				Optional[Any]             =       [
		    'REALM_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'RealmEmbedder',
		    'RealmForOpenQA',
		    'RealmKnowledgeAugEncoder',
		    'RealmPreTrainedModel',
		    'RealmReader',
		    'RealmScorer',
		    'load_tf_weights_in_realm',
		]
		__SCREAMING_SNAKE_CASE     :				List[str]             =       ['RealmRetriever']


if TYPE_CHECKING:
		from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
		from .tokenization_realm import RealmTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_realm import RealmTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_realm import (
				    REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RealmEmbedder,
				    RealmForOpenQA,
				    RealmKnowledgeAugEncoder,
				    RealmPreTrainedModel,
				    RealmReader,
				    RealmScorer,
				    load_tf_weights_in_realm,
				)
				from .retrieval_realm import RealmRetriever


else:
		import sys

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


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)
