



"""simple docstring"""




from math import cos, sin


def   lowercase__     (			_UpperCAmelCase				)     ->			list[list[float]]:






      '''simple docstring'''


      lowercase  :      int					=   float(_UpperCAmelCase				)
      return [[scaling_factor * int(x == y				) for x in range(2				)] for y in range(2				)]


def   lowercase__     (			_UpperCAmelCase				)     ->			list[list[float]]:






      '''simple docstring'''


      lowercase	,    lowercase  :      List[Any]					=   cos(_UpperCAmelCase				), sin(_UpperCAmelCase				)
      return [[c, -s], [s, c]]


def   lowercase__     (			_UpperCAmelCase				)     ->			list[list[float]]:






      '''simple docstring'''


      lowercase	,    lowercase  :      Tuple					=   cos(_UpperCAmelCase				), sin(_UpperCAmelCase				)
      lowercase  :      str					=   c * s
      return [[c * c, cs], [cs, s * s]]


def   lowercase__     (			_UpperCAmelCase				)     ->			list[list[float]]:






      '''simple docstring'''


      lowercase	,    lowercase  :      List[Any]					=   cos(_UpperCAmelCase				), sin(_UpperCAmelCase				)
      lowercase  :      Dict					=   c * s
      return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(f'''    {scaling(5) = }''')
print(f'''  {rotation(4_5) = }''')
print(f'''{projection(4_5) = }''')





print(f'''{reflection(4_5) = }''')





"""simple docstring"""




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   lowercase__     (			_UpperCAmelCase=10				)     ->			Any:






      '''simple docstring'''


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


# Configuration
_UpperCamelCase:				Union[str, Any]    							=  HfArgumentParser(TokenizerTrainingArguments)
_UpperCamelCase:				Dict    							=  parser.parse_args()

# Base tokenizer
_UpperCamelCase:				List[Any]    							=  AutoTokenizer.from_pretrained(args.base_tokenizer)
_UpperCamelCase:				int    							=  list(bytes_to_unicode().values())

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


# Training and saving
_UpperCamelCase:				Union[str, 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)
