





import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     List[Any]:
   lowercase__			    =						{}
   lowercase__			    =						tokenizer(example['content']	,		truncation=_SCREAMING_SNAKE_CASE	)['input_ids']
   lowercase__			    =						len(example['content']	) / len(output['input_ids']	)
   return output


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

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

lowercase_					     =	time.time()
lowercase_					     =	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''')

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


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









from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_					     =	logging.get_logger(__name__)

lowercase_					     =	{
    """facebook/deit-base-distilled-patch16-224""": (
        """https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"""
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	Any				      = 'deit'


   def __init__(					self    :       Any    ,  a    :       Union[str, Any]=768    ,  a    :       Optional[Any]=12    ,  a    :       Union[str, Any]=12    ,  a    :       Optional[int]=3_072    ,  a    :       Optional[int]="gelu"    ,  a    :       Optional[Any]=0.0    ,  a    :       List[Any]=0.0    ,  a    :       int=0.02    ,  a    :       List[str]=1E-1_2    ,  a    :       Optional[int]=224    ,  a    :       Tuple=16    ,  a    :       List[Any]=3    ,  a    :       List[str]=True    ,  a    :       Any=16    ,  **a    :       Union[str, Any]    ,  )->     int:





     """simple docstring"""







     super().__init__(**a )

     lowercase__			    =						hidden_size
     lowercase__			    =						num_hidden_layers
     lowercase__			    =						num_attention_heads
     lowercase__			    =						intermediate_size
     lowercase__			    =						hidden_act
     lowercase__			    =						hidden_dropout_prob
     lowercase__			    =						attention_probs_dropout_prob
     lowercase__			    =						initializer_range
     lowercase__			    =						layer_norm_eps
     lowercase__			    =						image_size
     lowercase__			    =						patch_size
     lowercase__			    =						num_channels
     lowercase__			    =						qkv_bias
     lowercase__			    =						encoder_stride






class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	List[Any]				      = version.parse('1.11'   )


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       int )->     Mapping[str, Mapping[int, str]]:





     """simple docstring"""







     return OrderedDict(
         [
             ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
         ] )


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Any )->     float:





     """simple docstring"""







     return 1E-4


