






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


snake_case__      :	Tuple       	=      logging.get_logger(__name__)


class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  def __init__(     self		,		**_UpperCAmelCase )			-> Dict:
     requires_backends(self		,		['bs4'] )
     super().__init__(**_UpperCAmelCase )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> List[str]:
     UpperCamelCase_									=     []
     UpperCamelCase_									=     []
     UpperCamelCase_									=     element if element.name else element.parent
     for parent in child.parents:  # type: bs4.element.Tag
        UpperCamelCase_									=     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_									=     parent
     xpath_tags.reverse()
     xpath_subscripts.reverse()
     return xpath_tags, xpath_subscripts




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> Any:
     UpperCamelCase_									=     BeautifulSoup(_UpperCAmelCase		,		'html.parser' )

     UpperCamelCase_									=     []
     UpperCamelCase_									=     []
     UpperCamelCase_									=     []

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

           UpperCamelCase_									=     html.unescape(_UpperCAmelCase ).strip()
           if not text_in_this_tag:
              continue

           all_doc_strings.append(_UpperCAmelCase )

           UpperCamelCase_		,      UpperCamelCase_									=     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    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> Tuple:
     UpperCamelCase_									=     ''
     for tagname, subs in zip(_UpperCAmelCase		,		_UpperCAmelCase ):
        xpath += f"""/{tagname}"""
        if subs != 0:
           xpath += f"""[{subs}]"""
     return xpath




  def __call__(     self		,		_UpperCAmelCase )			-> BatchFeature:
     UpperCamelCase_									=     False

     # Check that strings has a valid type
     if isinstance(_UpperCAmelCase		,		_UpperCAmelCase ):
        UpperCamelCase_									=     True
     elif isinstance(_UpperCAmelCase		,		(list, tuple) ):
        if len(_UpperCAmelCase ) == 0 or isinstance(html_strings[0]		,		_UpperCAmelCase ):
           UpperCamelCase_									=     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_									=     bool(isinstance(_UpperCAmelCase		,		(list, tuple) ) and (isinstance(html_strings[0]		,		_UpperCAmelCase )) )

     if not is_batched:
        UpperCamelCase_									=     [html_strings]

     # Get nodes + xpaths
     UpperCamelCase_									=     []
     UpperCamelCase_									=     []
     for html_string in html_strings:
        UpperCamelCase_		,      UpperCamelCase_		,      UpperCamelCase_									=     self.get_three_from_single(_UpperCAmelCase )
        nodes.append(_UpperCAmelCase )
        UpperCamelCase_									=     []
        for node, tag_list, sub_list in zip(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase ):
           UpperCamelCase_									=     self.construct_xpath(_UpperCAmelCase		,		_UpperCAmelCase )
           xpath_strings.append(_UpperCAmelCase )
        xpaths.append(_UpperCAmelCase )

     # return as Dict
     UpperCamelCase_									=     {'nodes': nodes, 'xpaths': xpaths}
     UpperCamelCase_									=     BatchFeature(data=_UpperCAmelCase		,		tensor_type=_UpperCAmelCase )

     return encoded_inputs













import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


snake_case__      :	Any       	=      logging.get_logger(__name__)


@dataclass
class 						_a       :






  """simple docstring"""





  A_						=   field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()    )}    )
  A_						=   field(
      metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}    )
  A_						=   field(
      default=128						,       metadata={
          """help""": (
              """The maximum total input sequence length after tokenization. Sequences longer """
              """than this will be truncated, sequences shorter will be padded."""
          )
      }						,       )
  A_						=   field(
      default=UpperCAmelCase__						,       metadata={"""help""": """Overwrite the cached training and evaluation sets"""}    )




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     self.task_name.lower()




class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   """train"""
  A_						=   """dev"""
  A_						=   """test"""




class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   42
  A_						=   42
  A_						=   42




  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = Split.train		,		_UpperCAmelCase = None		,		)			-> Tuple:
     warnings.warn(
         'This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '
         'library. You can have a look at this example script for pointers: '
         'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'		,		_UpperCAmelCase		,		)
     UpperCamelCase_									=     args
     UpperCamelCase_									=     glue_processors[args.task_name]()
     UpperCamelCase_									=     glue_output_modes[args.task_name]
     if isinstance(_UpperCAmelCase		,		_UpperCAmelCase ):
        try:
           UpperCamelCase_									=     Split[mode]
        except KeyError:
           raise KeyError('mode is not a valid split name' )
        # Load data features from cache or dataset file
     UpperCamelCase_									=     os.path.join(
         cache_dir if cache_dir is not None else args.data_dir		,		f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"""		,		)
     UpperCamelCase_									=     self.processor.get_labels()
     if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
         "RobertaTokenizer",
         "RobertaTokenizerFast",
         "XLMRobertaTokenizer",
         "BartTokenizer",
         "BartTokenizerFast",
     ):
        # HACK(label indices are swapped in RoBERTa pretrained model)
        UpperCamelCase_		,      UpperCamelCase_									=     label_list[2], label_list[1]
     UpperCamelCase_									=     label_list

     # Make sure only the first process in distributed training processes the dataset,
     # and the others will use the cache.
     UpperCamelCase_									=     cached_features_file + '.lock'
     with FileLock(_UpperCAmelCase ):
        if os.path.exists(_UpperCAmelCase ) and not args.overwrite_cache:
           UpperCamelCase_									=     time.time()
           UpperCamelCase_									=     torch.load(_UpperCAmelCase )
           logger.info(
               f"""Loading features from cached file {cached_features_file} [took %.3f s]"""		,		time.time() - start )
        else:
           logger.info(f"""Creating features from dataset file at {args.data_dir}""" )

           if mode == Split.dev:
              UpperCamelCase_									=     self.processor.get_dev_examples(args.data_dir )
           elif mode == Split.test:
              UpperCamelCase_									=     self.processor.get_test_examples(args.data_dir )
           else:
              UpperCamelCase_									=     self.processor.get_train_examples(args.data_dir )
           if limit_length is not None:
              UpperCamelCase_									=     examples[:limit_length]
           UpperCamelCase_									=     glue_convert_examples_to_features(
               _UpperCAmelCase		,		_UpperCAmelCase		,		max_length=args.max_seq_length		,		label_list=_UpperCAmelCase		,		output_mode=self.output_mode		,		)
           UpperCamelCase_									=     time.time()
           torch.save(self.features		,		_UpperCAmelCase )
           # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
           logger.info(
               f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]""" )




  def __len__(     self )			-> List[str]:
     return len(self.features )




  def __getitem__(     self		,		_UpperCAmelCase )			-> InputFeatures:
     return self.features[i]




  def    _UpperCAmelCase     (     self )			-> Tuple:
     return self.label_list





