
"""simple docstring"""




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__      :	Optional[Any]          =    logging.get_logger(__name__)







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





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



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



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					str:
          UpperCAmelCase__							: str 							=						BeautifulSoup(__UpperCamelCase ,			"html.parser"  )

          UpperCAmelCase__							: List[Any] 							=						[]
          UpperCAmelCase__							: Union[str, Any] 							=						[]
          UpperCAmelCase__							: Optional[Any] 							=						[]

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

                    UpperCAmelCase__							: Union[str, Any] 							=						html.unescape(__UpperCamelCase  ).strip()
                    if not text_in_this_tag:
                         continue

                    all_doc_strings.append(__UpperCamelCase  )

                    UpperCAmelCase__							,	UpperCAmelCase__							: Optional[int] 							=						self.xpath_soup(__UpperCamelCase  )
                    stringaxtag_seq.append(__UpperCamelCase  )
                    stringaxsubs_seq.append(__UpperCamelCase  )

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

          return all_doc_strings, stringaxtag_seq, stringaxsubs_seq



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase  )->					Union[str, Any]:
          UpperCAmelCase__							: Tuple 							=						""
          for tagname, subs in zip(__UpperCamelCase ,			__UpperCamelCase  ):
               xpath += F"/{tagname}"
               if subs != 0:
                    xpath += F"[{subs}]"
          return xpath



     def __call__(      self ,			__UpperCamelCase  )->					BatchFeature:
          UpperCAmelCase__							: Union[str, Any] 							=						False

          # Check that strings has a valid type
          if isinstance(__UpperCamelCase ,			__UpperCamelCase  ):
               UpperCAmelCase__							: List[Any] 							=						True
          elif isinstance(__UpperCamelCase ,			(list, tuple)  ):
               if len(__UpperCamelCase  ) == 0 or isinstance(html_strings[0] ,			__UpperCamelCase  ):
                    UpperCAmelCase__							: Dict 							=						True

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

          UpperCAmelCase__							: Dict 							=						bool(isinstance(__UpperCamelCase ,			(list, tuple)  ) and (isinstance(html_strings[0] ,			__UpperCamelCase  ))  )

          if not is_batched:
               UpperCAmelCase__							: Optional[Any] 							=						[html_strings]

          # Get nodes + xpaths
          UpperCAmelCase__							: Optional[Any] 							=						[]
          UpperCAmelCase__							: List[Any] 							=						[]
          for html_string in html_strings:
               UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: int 							=						self.get_three_from_single(__UpperCamelCase  )
               nodes.append(__UpperCamelCase  )
               UpperCAmelCase__							: int 							=						[]
               for node, tag_list, sub_list in zip(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  ):
                    UpperCAmelCase__							: Dict 							=						self.construct_xpath(__UpperCamelCase ,			__UpperCamelCase  )
                    xpath_strings.append(__UpperCamelCase  )
               xpaths.append(__UpperCamelCase  )

          # return as Dict
          UpperCAmelCase__							: Union[str, Any] 							=						{"nodes": nodes, "xpaths": xpaths}
          UpperCAmelCase__							: Any 							=						BatchFeature(data=__UpperCamelCase ,			tensor_type=__UpperCamelCase  )

          return encoded_inputs





"""simple docstring"""




import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD



def 							a__ (				lowerCAmelCase	:       List[str]		):






     '''simple docstring'''
     def wrapper(*lowerCAmelCase	:       Any       ,    **lowerCAmelCase	:       Tuple		):
          UpperCAmelCase__							: Optional[int] 							=						timeit.default_timer()
          UpperCAmelCase__							: int 							=						func(*lowerCAmelCase       ,    **lowerCAmelCase		)
          UpperCAmelCase__							: List[Any] 							=						timeit.default_timer() - starttime
          return delta

     UpperCAmelCase__							: int 							=						func.__name__

     return wrapper



def 							a__ (				lowerCAmelCase	:       dict       ,    lowerCAmelCase	:       Optional[int]=100       ,    lowerCAmelCase	:       List[str]=None		):






     '''simple docstring'''
     UpperCAmelCase__							: str 							=						[]
     UpperCAmelCase__							: Optional[Any] 							=						seq_shapes or {}
     for i in range(lowerCAmelCase		):
          UpperCAmelCase__							: int 							=						{}
          for col_id, (k, v) in enumerate(features.items()		):
               if isinstance(lowerCAmelCase       ,    _ArrayXD		):
                    UpperCAmelCase__							: List[str] 							=						np.random.rand(*v.shape		).astype(v.dtype		)
               elif isinstance(lowerCAmelCase       ,    datasets.Value		):
                    if v.dtype == "string":
                         UpperCAmelCase__							: Dict 							=						"The small grey turtle was surprisingly fast when challenged."
                    else:
                         UpperCAmelCase__							: str 							=						np.random.randint(10       ,    size=1		).astype(v.dtype		).item()
               elif isinstance(lowerCAmelCase       ,    datasets.Sequence		):
                    while isinstance(lowerCAmelCase       ,    datasets.Sequence		):
                         UpperCAmelCase__							: List[str] 							=						v.feature
                    UpperCAmelCase__							: Optional[int] 							=						seq_shapes[k]
                    UpperCAmelCase__							: Optional[int] 							=						np.random.rand(*lowerCAmelCase		).astype(v.dtype		)
               UpperCAmelCase__							: Union[str, Any] 							=						data

          dummy_data.append((i, example)		)

     return dummy_data



def 							a__ (				lowerCAmelCase	:       List[str]       ,    lowerCAmelCase	:       Tuple       ,    lowerCAmelCase	:       List[str]=100       ,    lowerCAmelCase	:       Optional[int]=None		):






     '''simple docstring'''
     UpperCAmelCase__							: int 							=						generate_examples(lowerCAmelCase       ,    num_examples=lowerCAmelCase       ,    seq_shapes=lowerCAmelCase		)

     with ArrowWriter(features=lowerCAmelCase       ,    path=lowerCAmelCase		) as writer:
          for key, record in dummy_data:
               UpperCAmelCase__							: List[Any] 							=						features.encode_example(lowerCAmelCase		)
               writer.write(lowerCAmelCase		)

          UpperCAmelCase__							,	UpperCAmelCase__							: Optional[Any] 							=						writer.finalize()

     if not num_final_examples == num_examples:
          raise ValueError(
              F"Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."		)

     UpperCAmelCase__							: Optional[int] 							=						datasets.Dataset.from_file(filename=lowerCAmelCase       ,    info=datasets.DatasetInfo(features=lowerCAmelCase		)		)

     return dataset



