




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase	:       List[Any]     =	logging.get_logger(__name__)

__lowerCAmelCase	:       str     =	{'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}




class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							Any											=      """ctrl"""
				SCREAMING_SNAKE_CASE_ :							Optional[int]											=      ["""past_key_values"""]
				SCREAMING_SNAKE_CASE_ :							Tuple											=      {
				    """max_position_embeddings""": """n_positions""",
				    """hidden_size""": """n_embd""",
				    """num_attention_heads""": """n_head""",
				    """num_hidden_layers""": """n_layer""",
				}





				def __init__(   self       :  int      ,   __lowerCamelCase       :  int=24_65_34      ,   __lowerCamelCase       :  Optional[Any]=2_56      ,   __lowerCamelCase       :  List[str]=12_80      ,   __lowerCamelCase       :  List[str]=81_92      ,   __lowerCamelCase       :  Tuple=48      ,   __lowerCamelCase       :  List[Any]=16      ,   __lowerCamelCase       :  Dict=0.1      ,   __lowerCamelCase       :  Optional[Any]=0.1      ,   __lowerCamelCase       :  str=1e-6      ,   __lowerCamelCase       :  Dict=0.02      ,   __lowerCamelCase       :  Union[str, Any]=True      ,   **__lowerCamelCase       :  Optional[int]      ,   )      ->  List[Any]:
							a					=    vocab_size
							a					=    n_positions
							a					=    n_embd
							a					=    n_layer
							a					=    n_head
							a					=    dff
							a					=    resid_pdrop
							a					=    embd_pdrop
							a					=    layer_norm_epsilon
							a					=    initializer_range

							a					=    use_cache

							super().__init__(**__lowerCamelCase							)












import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser




def     __magic_name__       (				A	:      List[str]     ):






			'''simple docstring'''







			a					=    {}
			a					=    tokenizer(example["content"],						truncation=A     )["input_ids"]
			a					=    len(example["content"]     ) / len(output["input_ids"]     )
			return output


__lowerCAmelCase	:       Dict     =	HfArgumentParser(PretokenizationArguments)
__lowerCAmelCase	:       str     =	parser.parse_args()
if args.num_workers is None:
		__lowerCAmelCase	:       List[Any]     =	multiprocessing.cpu_count()
__lowerCAmelCase	:       str     =	AutoTokenizer.from_pretrained(args.tokenizer_dir)

__lowerCAmelCase	:       List[Any]     =	time.time()
__lowerCAmelCase	:       str     =	load_dataset(args.dataset_name, split='train')
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

__lowerCAmelCase	:       int     =	time.time()
__lowerCAmelCase	:       Optional[int]     =	ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        'repo_name',
        'path',
        'copies',
        'size',
        'content',
        'license',
        'hash',
        'line_mean',
        'line_max',
        'alpha_frac',
        'autogenerated',
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

__lowerCAmelCase	:       Tuple     =	time.time()
ds.push_to_hub(args.tokenized_data_repo)






print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')






