


import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     List[str]					=					{}
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					tokenizer(example['content'],      truncation=A__				)['input_ids']
			SCREAMING_SNAKE_CASE_		:     Any					=					len(example['content']				) / len(output['input_ids']				)
			return output


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

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

lowerCAmelCase__			:      Optional[int]								=time.time()
lowerCAmelCase__			:      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""")

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""")









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__			:      str								={
    'configuration_clipseg': [
        'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPSegConfig',
        'CLIPSegTextConfig',
        'CLIPSegVisionConfig',
    ],
    'processing_clipseg': ['CLIPSegProcessor'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__			:      Optional[Any]								=[
				    'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'CLIPSegModel',
				    'CLIPSegPreTrainedModel',
				    'CLIPSegTextModel',
				    'CLIPSegVisionModel',
				    'CLIPSegForImageSegmentation',
				]

if TYPE_CHECKING:
				from .configuration_clipseg import (
				    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    CLIPSegConfig,
				    CLIPSegTextConfig,
				    CLIPSegVisionConfig,
				)
				from .processing_clipseg import CLIPSegProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_clipseg import (
								    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
								    CLIPSegForImageSegmentation,
								    CLIPSegModel,
								    CLIPSegPreTrainedModel,
								    CLIPSegTextModel,
								    CLIPSegVisionModel,
								)

else:
				import sys

				lowerCAmelCase__			:      str								=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





