





import html

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends


if is_bsa_available():
		import bsa
		from bsa import BeautifulSoup


a		:				Union[str, Any]  			=    logging.get_logger(__name__)


class  __UpperCamelCase       (		a__		):






							def __init__(     self   ,      **lowerCAmelCase__   )			->				int:
								requires_backends(self   ,      ["bs4"]   )
								super().__init__(**lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Union[str, Any]:
								a   :	Optional[int]			=       []
								a   :	Optional[Any]			=       []
								a   :	Dict			=       element if element.name else element.parent
								for parent in child.parents:  # type: bs4.element.Tag
									a   :	List[str]			=       parent.find_all(child.name   ,      recursive=lowerCAmelCase__   )
									xpath_tags.append(child.name   )
									xpath_subscripts.append(
									    0 if 1 == len(lowerCAmelCase__   ) else next(i for i, s in enumerate(lowerCAmelCase__   ,      1   ) if s is child   )   )
									a   :	Optional[int]			=       parent
								xpath_tags.reverse()
								xpath_subscripts.reverse()
								return xpath_tags, xpath_subscripts






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				int:
								a   :	str			=       BeautifulSoup(lowerCAmelCase__   ,      "html.parser"   )

								a   :	Optional[int]			=       []
								a   :	List[Any]			=       []
								a   :	List[str]			=       []

								for element in html_code.descendants:
									if type(lowerCAmelCase__   ) == bsa.element.NavigableString:
										if type(element.parent   ) != bsa.element.Tag:
											continue

										a   :	List[Any]			=       html.unescape(lowerCAmelCase__   ).strip()
										if not text_in_this_tag:
											continue

										all_doc_strings.append(lowerCAmelCase__   )

										a   :	List[Any]			=       self.xpath_soup(lowerCAmelCase__   )
										stringaxtag_seq.append(lowerCAmelCase__   )
										stringaxsubs_seq.append(lowerCAmelCase__   )

								if len(lowerCAmelCase__   ) != len(lowerCAmelCase__   ):
									raise ValueError("Number of doc strings and xtags does not correspond"   )
								if len(lowerCAmelCase__   ) != len(lowerCAmelCase__   ):
									raise ValueError("Number of doc strings and xsubs does not correspond"   )

								return all_doc_strings, stringaxtag_seq, stringaxsubs_seq






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Union[str, Any]:
								a   :	int			=       ""
								for tagname, subs in zip(lowerCAmelCase__   ,      lowerCAmelCase__   ):
									xpath += f"""/{tagname}"""
									if subs != 0:
										xpath += f"""[{subs}]"""
								return xpath






							def __call__(     self   ,      lowerCAmelCase__   )			->				BatchFeature:
								a   :	List[Any]			=       False

								# Check that strings has a valid type
								if isinstance(lowerCAmelCase__   ,      lowerCAmelCase__   ):
									a   :	Tuple			=       True
								elif isinstance(lowerCAmelCase__   ,      (list, tuple)   ):
									if len(lowerCAmelCase__   ) == 0 or isinstance(html_strings[0]   ,      lowerCAmelCase__   ):
										a   :	int			=       True

								if not valid_strings:
									raise ValueError(
									    "HTML strings must of type `str`, `List[str]` (batch of examples), "
									    f"""but is of type {type(lowerCAmelCase__   )}."""   )

								a   :	Optional[Any]			=       bool(isinstance(lowerCAmelCase__   ,      (list, tuple)   ) and (isinstance(html_strings[0]   ,      lowerCAmelCase__   ))   )

								if not is_batched:
									a   :	Dict			=       [html_strings]

								# Get nodes + xpaths
								a   :	Optional[Any]			=       []
								a   :	Dict			=       []
								for html_string in html_strings:
									a   :	str			=       self.get_three_from_single(lowerCAmelCase__   )
									nodes.append(lowerCAmelCase__   )
									a   :	Dict			=       []
									for node, tag_list, sub_list in zip(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ):
										a   :	Union[str, Any]			=       self.construct_xpath(lowerCAmelCase__   ,      lowerCAmelCase__   )
										xpath_strings.append(lowerCAmelCase__   )
									xpaths.append(lowerCAmelCase__   )

								# return as Dict
								a   :	Optional[Any]			=       {"nodes": nodes, "xpaths": xpaths}
								a   :	List[Any]			=       BatchFeature(data=lowerCAmelCase__   ,      tensor_type=lowerCAmelCase__   )

								return encoded_inputs









"""simple docstring"""




import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[str]      )				->int:






	'''simple docstring'''






	a   :	int			=       {}
	a   :	Union[str, Any]			=       tokenizer(example["content"]					, truncation=_lowercase      )["input_ids"]
	a   :	Any			=       len(example["content"]      ) / len(output["input_ids"]      )
	return output


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

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

a		:				Dict  			=    time.time()
a		:				Tuple  			=    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''')

a		:				Tuple  			=    time.time()
ds.push_to_hub(args.tokenized_data_repo)







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


