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      SCREAMING_SNAKE_CASE_  (		snake_case__=1_0							)  ->			List[str]:
							for _ in tqdm(range(0     ,						args.n_examples     ,						snake_case__							)							):
														yield [next(snake_case__							)[args.text_column] for _ in range(snake_case__							)]


# Configuration
lowercase__    :       Any						=			HfArgumentParser(TokenizerTrainingArguments)
lowercase__    :       List[str]						=			parser.parse_args()

# Base tokenizer
lowercase__    :       int						=			AutoTokenizer.from_pretrained(args.base_tokenizer)
lowercase__    :       Union[str, Any]						=			list(bytes_to_unicode().values())

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


# Training and saving
lowercase__    :       List[Any]						=			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)




import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
	lowercase__    :       List[Any]						=			[]

	lowercase__    :       Optional[Any]						=			os.path.join('''tests''', '''models''', '''**''', '''test_modeling_*.py''')
	lowercase__    :       Optional[int]						=			glob.glob(pattern)
	# TODO: deal with TF/Flax too
	lowercase__    :       Dict						=			[
	    x for x in test_files if not (x.startswith('''test_modeling_tf_''') or x.startswith('''test_modeling_flax_'''))
	]

	for test_file in test_files:
		lowercase__    :       Tuple						=			get_tester_classes(test_file)
		for tester_class in tester_classes:
			# A few tester classes don't have `parent` parameter in `__init__`.
			# TODO: deal this better
			try:
				lowercase__    :       Union[str, Any]						=			tester_class(parent=None)
			except Exception:
				continue
			if hasattr(tester, '''get_config'''):
				lowercase__    :       Union[str, Any]						=			tester.get_config()
				for k, v in config.to_dict().items():
					if isinstance(v, int):
						lowercase__    :       int						=			None
						if k in ["vocab_size"]:
							lowercase__    :       Optional[int]						=			1_0_0
						elif k in ["max_position_embeddings"]:
							lowercase__    :       Union[str, Any]						=			1_2_8
						elif k in ["hidden_size", "d_model"]:
							lowercase__    :       Optional[Any]						=			4_0
						elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
							lowercase__    :       Dict						=			5
						if target is not None and v > target:
							failures.append(
							    f'{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`'
							    f' with config["{k}"] = {v} which is too large for testing! Set its value to be smaller'
							    f' than {target}.'
							)

	if len(failures) > 0:
		raise Exception(f'There were {len(failures)} failures:\n' + '''\n'''.join(failures))



