

"""simple docstring"""




import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser
def 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 UpperCAmelCase__       :	List[Any]        =       {}
 UpperCAmelCase__       :	str        =       tokenizer(example["""content"""]				,     truncation=__UpperCamelCase   )["""input_ids"""]
 UpperCAmelCase__       :	str        =       len(example["""content"""]   ) / len(output["""input_ids"""]   )
 return output


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

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

__UpperCAmelCase			=					time.time()
__UpperCAmelCase			=					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			=					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 ...configuration_utils import PretrainedConfig
from ...utils import logging


A       :   Optional[int]					= logging.get_logger(__name__)

A       :   Dict					= {
    '''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}


class     A     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			__lowerCamelCase      :			Tuple							     =       '''glpn'''






			def __init__(     self  :       Optional[int]				,      __lowerCAmelCase  :       Union[str, Any]=3				,      __lowerCAmelCase  :       Any=4				,      __lowerCAmelCase  :       Tuple=[2, 2, 2, 2]				,      __lowerCAmelCase  :       int=[8, 4, 2, 1]				,      __lowerCAmelCase  :       int=[32, 64, 1_60, 2_56]				,      __lowerCAmelCase  :       str=[7, 3, 3, 3]				,      __lowerCAmelCase  :       Dict=[4, 2, 2, 2]				,      __lowerCAmelCase  :       List[str]=[1, 2, 5, 8]				,      __lowerCAmelCase  :       List[Any]=[4, 4, 4, 4]				,      __lowerCAmelCase  :       str="gelu"				,      __lowerCAmelCase  :       Union[str, Any]=0.0				,      __lowerCAmelCase  :       Tuple=0.0				,      __lowerCAmelCase  :       List[Any]=0.0_2				,      __lowerCAmelCase  :       Dict=0.1				,      __lowerCAmelCase  :       Dict=1e-6				,      __lowerCAmelCase  :       Tuple=64				,      __lowerCAmelCase  :       Tuple=10				,      __lowerCAmelCase  :       Union[str, Any]=-1				,      **__lowerCAmelCase  :       Any				,      )      ->      Tuple:







					"""simple docstring"""






					super().__init__(**__lowerCAmelCase  )

					A__     =  num_channels
					A__     =  num_encoder_blocks
					A__     =  depths
					A__     =  sr_ratios
					A__     =  hidden_sizes
					A__     =  patch_sizes
					A__     =  strides
					A__     =  mlp_ratios
					A__     =  num_attention_heads
					A__     =  hidden_act
					A__     =  hidden_dropout_prob
					A__     =  attention_probs_dropout_prob
					A__     =  initializer_range
					A__     =  drop_path_rate
					A__     =  layer_norm_eps
					A__     =  decoder_hidden_size
					A__     =  max_depth
					A__     =  head_in_index



