


import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser
def      snake_case_						( __lowercase       ):
   UpperCAmelCase_							:      str						=   {}
   UpperCAmelCase_							:      Tuple						=   tokenizer(example['''content''']			,       truncation=__lowercase       )['''input_ids''']
   UpperCAmelCase_							:      Dict						=   len(example['''content''']       ) / len(output['''input_ids''']       )
   return output


__UpperCamelCase				:  str      					=							HfArgumentParser(PretokenizationArguments)
__UpperCamelCase				:  Union[str, Any]      					=							parser.parse_args()
if args.num_workers is None:
       __UpperCamelCase				:  Any      					=							multiprocessing.cpu_count()
__UpperCamelCase				:  Optional[int]      					=							AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

__UpperCamelCase				:  Optional[int]      					=							time.time()
__UpperCamelCase				:  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')

__UpperCamelCase				:  Any      					=							time.time()
ds.push_to_hub(args.tokenized_data_repo)


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



import copy

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


__UpperCamelCase				:  Tuple      					=							logging.get_logger(__name__)





class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    A_  :     Union[str, Any]			= 'encoder-decoder'
    A_  :     Optional[int]			= True





    def __init__(	self				:   Dict       ,   **__snake_case				:   Union[str, Any]	):





       '''simple docstring'''
       super().__init__(**__snake_case	)
       assert (
           "encoder" in kwargs and "decoder" in kwargs
       ), "Config has to be initialized with encoder and decoder config"
       UpperCAmelCase_							:      int						=   kwargs.pop('''encoder'''	)
       UpperCAmelCase_							:      List[Any]						=   encoder_config.pop('''model_type'''	)
       UpperCAmelCase_							:      int						=   kwargs.pop('''decoder'''	)
       UpperCAmelCase_							:      Union[str, Any]						=   decoder_config.pop('''model_type'''	)

       from ..auto.configuration_auto import AutoConfig

       UpperCAmelCase_							:      Optional[int]						=   AutoConfig.for_model(__snake_case       ,   **__snake_case	)
       UpperCAmelCase_							:      Any						=   AutoConfig.for_model(__snake_case       ,   **__snake_case	)
       UpperCAmelCase_							:      List[Any]						=   True





    @classmethod
    def 					_lowerCamelCase (	cls				:   Optional[Any]       ,   __snake_case				:   PretrainedConfig       ,   __snake_case				:   PretrainedConfig       ,   **__snake_case				:   Union[str, Any]	):





       '''simple docstring'''
       logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''	)
       UpperCAmelCase_							:      Dict						=   True
       UpperCAmelCase_							:      str						=   True

       return cls(encoder=encoder_config.to_dict()       ,   decoder=decoder_config.to_dict()       ,   **__snake_case	)





    def 					_lowerCamelCase (	self				:   Dict	):





       '''simple docstring'''
       UpperCAmelCase_							:      int						=   copy.deepcopy(self.__dict__	)
       UpperCAmelCase_							:      Tuple						=   self.encoder.to_dict()
       UpperCAmelCase_							:      Tuple						=   self.decoder.to_dict()
       UpperCAmelCase_							:      Tuple						=   self.__class__.model_type
       return output