


"""simple docstring"""







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


lowerCamelCase_       :       Tuple      						=  logging.get_logger(__name__)

lowerCamelCase_       :       int      						=  {
    """microsoft/cvt-13""": """https://huggingface.co/microsoft/cvt-13/resolve/main/config.json""",
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  "cvt"


 def __init__(		self			,   __A=3			,   __A=[7, 3, 3]			,   __A=[4, 2, 2]			,   __A=[2, 1, 1]			,   __A=[64, 192, 384]			,   __A=[1, 3, 6]			,   __A=[1, 2, 10]			,   __A=[4.0, 4.0, 4.0]			,   __A=[0.0, 0.0, 0.0]			,   __A=[0.0, 0.0, 0.0]			,   __A=[0.0, 0.0, 0.1]			,   __A=[True, True, True]			,   __A=[False, False, True]			,   __A=["dw_bn", "dw_bn", "dw_bn"]			,   __A=[3, 3, 3]			,   __A=[1, 1, 1]			,   __A=[2, 2, 2]			,   __A=[1, 1, 1]			,   __A=[1, 1, 1]			,   __A=0.02			,   __A=1E-1_2			,   **__A			,   ) ->					str:
     super().__init__(**__A						)
     a					=num_channels
     a					=patch_sizes
     a					=patch_stride
     a					=patch_padding
     a					=embed_dim
     a					=num_heads
     a					=depth
     a					=mlp_ratio
     a					=attention_drop_rate
     a					=drop_rate
     a					=drop_path_rate
     a					=qkv_bias
     a					=cls_token
     a					=qkv_projection_method
     a					=kernel_qkv
     a					=padding_kv
     a					=stride_kv
     a					=padding_q
     a					=stride_q
     a					=initializer_range
     a					=layer_norm_eps



"""simple docstring"""







import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser
def _A						(						lowercase		):







    """simple docstring"""
    a					={}
    a					=tokenizer(example['''content''']	,   truncation=lowercase		)['''input_ids''']
    a					=len(example['''content''']		) / len(output['''input_ids''']		)
    return output


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

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

lowerCamelCase_       :       List[str]      						=  time.time()
lowerCamelCase_       :       str      						=  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_       :       Union[str, 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')