


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


__UpperCAmelCase : Any 			=				logging.get_logger(__name__)



class    UpperCAmelCase_	(							_a):
       '''simple docstring'''






       def __init__(     self ,					**__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              requires_backends(self ,					['''bs4''']						)
              super().__init__(**__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	int   	=		[]
              UpperCamelCase    :	Tuple   	=		[]
              UpperCamelCase    :	Any   	=		element if element.name else element.parent
              for parent in child.parents:  # type: bs4.element.Tag
                     UpperCamelCase    :	Dict   	=		parent.find_all(child.name ,					recursive=__SCREAMING_SNAKE_CASE						)
                     xpath_tags.append(child.name						)
                     xpath_subscripts.append(
                         0 if 1 == len(__SCREAMING_SNAKE_CASE						) else next(i for i, s in enumerate(__SCREAMING_SNAKE_CASE ,					1						) if s is child						)						)
                     UpperCamelCase    :	Optional[int]   	=		parent
              xpath_tags.reverse()
              xpath_subscripts.reverse()
              return xpath_tags, xpath_subscripts






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		BeautifulSoup(__SCREAMING_SNAKE_CASE ,					'''html.parser'''						)

              UpperCamelCase    :	Tuple   	=		[]
              UpperCamelCase    :	Optional[int]   	=		[]
              UpperCamelCase    :	Tuple   	=		[]

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

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

                            all_doc_strings.append(__SCREAMING_SNAKE_CASE						)

                            UpperCamelCase	, UpperCamelCase    :	Dict   	=		self.xpath_soup(__SCREAMING_SNAKE_CASE						)
                            stringaxtag_seq.append(__SCREAMING_SNAKE_CASE						)
                            stringaxsubs_seq.append(__SCREAMING_SNAKE_CASE						)

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

              return all_doc_strings, stringaxtag_seq, stringaxsubs_seq






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		''''''
              for tagname, subs in zip(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):
                     xpath += f"""/{tagname}"""
                     if subs != 0:
                            xpath += f"""[{subs}]"""
              return xpath






       def __call__(     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		False

              # Check that strings has a valid type
              if isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):
                     UpperCamelCase    :	Union[str, Any]   	=		True
              elif isinstance(__SCREAMING_SNAKE_CASE ,					(list, tuple)						):
                     if len(__SCREAMING_SNAKE_CASE						) == 0 or isinstance(html_strings[0] ,					__SCREAMING_SNAKE_CASE						):
                            UpperCamelCase    :	Optional[Any]   	=		True

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

              UpperCamelCase    :	Tuple   	=		bool(isinstance(__SCREAMING_SNAKE_CASE ,					(list, tuple)						) and (isinstance(html_strings[0] ,					__SCREAMING_SNAKE_CASE						))						)

              if not is_batched:
                     UpperCamelCase    :	int   	=		[html_strings]

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

              # return as Dict
              UpperCamelCase    :	Optional[Any]   	=		{'''nodes''': nodes, '''xpaths''': xpaths}
              UpperCamelCase    :	str   	=		BatchFeature(data=__SCREAMING_SNAKE_CASE ,					tensor_type=__SCREAMING_SNAKE_CASE						)

              return encoded_inputs





import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


__UpperCAmelCase : Optional[int] 			=				500000

__UpperCAmelCase			,    __UpperCAmelCase : Any 			=				os.path.split(__file__)
__UpperCAmelCase : int 			=				os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))


@get_duration
def      a	(							SCREAMING_SNAKE_CASE_      :		datasets.Dataset							,			**SCREAMING_SNAKE_CASE_      :		Union[str, Any]       ):






       """simple docstring"""





       UpperCamelCase    :	Tuple   	=		dataset.map(**SCREAMING_SNAKE_CASE_       )


@get_duration
def      a	(							SCREAMING_SNAKE_CASE_      :		datasets.Dataset							,			**SCREAMING_SNAKE_CASE_      :		Any       ):






       """simple docstring"""





       UpperCamelCase    :	int   	=		dataset.filter(**SCREAMING_SNAKE_CASE_       )


def      a	(							):






       """simple docstring"""





       UpperCamelCase    :	Optional[int]   	=		{'''num examples''': SPEED_TEST_N_EXAMPLES}
       with tempfile.TemporaryDirectory() as tmp_dir:
              UpperCamelCase    :	Dict   	=		datasets.Features({'''text''': datasets.Value('''string'''       ), '''numbers''': datasets.Value('''float32'''       )}       )
              UpperCamelCase    :	List[str]   	=		generate_example_dataset(
                  os.path.join(SCREAMING_SNAKE_CASE_							,			'''dataset.arrow'''       )							,			SCREAMING_SNAKE_CASE_							,			num_examples=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	Union[str, Any]   	=		transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''							,			use_fast=SCREAMING_SNAKE_CASE_       )

              def tokenize(SCREAMING_SNAKE_CASE_      :		Dict       ):
                     return tokenizer(examples['''text''']       )

              UpperCamelCase    :	List[Any]   	=		map(SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[str]   	=		map(SCREAMING_SNAKE_CASE_							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[Any]   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''numpy'''       ):
                     UpperCamelCase    :	Tuple   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''pandas'''       ):
                     UpperCamelCase    :	int   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''torch'''							,			columns='''numbers'''       ):
                     UpperCamelCase    :	Dict   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''tensorflow'''							,			columns='''numbers'''       ):
                     UpperCamelCase    :	Tuple   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[str]   	=		map(SCREAMING_SNAKE_CASE_							,			function=SCREAMING_SNAKE_CASE_							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	Dict   	=		filter(SCREAMING_SNAKE_CASE_       )

              # Activate later when tokenizer support batched inputs
              # with dataset.formatted_as(type='numpy'):
              #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

       with open(SCREAMING_SNAKE_CASE_							,			'''wb'''       ) as f:
              f.write(json.dumps(SCREAMING_SNAKE_CASE_       ).encode('''utf-8'''       )       )


if __name__ == "__main__":  # useful to run the profiler
  benchmark_map_filter()

