






"""simple docstring"""



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


A_    =							logging.get_logger(__name__)

A_    =							{'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/config.json'''}






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'openai-gpt'
							snake_case_     =							{
							    'max_position_embeddings': 'n_positions',
							    'hidden_size': 'n_embd',
							    'num_attention_heads': 'n_head',
							    'num_hidden_layers': 'n_layer',
							}


							def __init__(			self      :						str			,    snake_case      :						List[str]=4_0478			,    snake_case      :						List[Any]=512			,    snake_case      :						Optional[int]=768			,    snake_case      :						List[str]=12			,    snake_case      :						Any=12			,    snake_case      :						int="gelu"			,    snake_case      :						List[Any]=0.1			,    snake_case      :						Dict=0.1			,    snake_case      :						List[str]=0.1			,    snake_case      :						Union[str, Any]=1e-5			,    snake_case      :						Dict=0.02			,    snake_case      :						int="cls_index"			,    snake_case      :						Optional[int]=True			,    snake_case      :						List[Any]=None			,    snake_case      :						Union[str, Any]=True			,    snake_case      :						Optional[int]=0.1			,    **snake_case      :						Any			,    ):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  vocab_size
													A__						:  Dict		  =  n_positions
													A__						:  Dict		  =  n_embd
													A__						:  List[Any]		  =  n_layer
													A__						:  Tuple		  =  n_head
													A__						:  Optional[int]		  =  afn
													A__						:  Tuple		  =  resid_pdrop
													A__						:  int		  =  embd_pdrop
													A__						:  Any		  =  attn_pdrop
													A__						:  List[str]		  =  layer_norm_epsilon
													A__						:  Dict		  =  initializer_range
													A__						:  List[str]		  =  summary_type
													A__						:  Optional[Any]		  =  summary_use_proj
													A__						:  Dict		  =  summary_activation
													A__						:  Tuple		  =  summary_first_dropout
													A__						:  Dict		  =  summary_proj_to_labels
													super().__init__(**snake_case							)














"""simple docstring"""



import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser




def      _lowerCAmelCase		(							UpperCAmelCase__       :					List[str]      )		->str:
						A__						:  Tuple		  =  {}
						A__						:  Union[str, Any]		  =  tokenizer(example["""content"""],						truncation=UpperCAmelCase__      )["""input_ids"""]
						A__						:  Any		  =  len(example["""content"""]      ) / len(output["""input_ids"""]      )
						return output


A_    =							HfArgumentParser(PretokenizationArguments)
A_    =							parser.parse_args()
if args.num_workers is None:
						A_    =							multiprocessing.cpu_count()
A_    =							AutoTokenizer.from_pretrained(args.tokenizer_dir)

A_    =							time.time()
A_    =							load_dataset(args.dataset_name, split='''train''')
print(F'Dataset loaded in {time.time()-t_start:.2f}s')

A_    =							time.time()
A_    =							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')

A_    =							time.time()
ds.push_to_hub(args.tokenized_data_repo)

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






