





'''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


UpperCAmelCase					:		Optional[int]		=							logging.get_logger(__name__)





class lowerCamelCase						(a__					):







  def __init__(		self     ,     **lowercase__		)		->						Union[str, Any]:






        """simple docstring"""





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







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						Dict:






        """simple docstring"""





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







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						Optional[int]:






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     BeautifulSoup(lowercase__     ,     '''html.parser'''		)

        _snake_case		:	List[str]					     =     []
        _snake_case		:	Tuple					     =     []
        _snake_case		:	Any					     =     []

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

                    _snake_case		:	str					     =     html.unescape(lowercase__		).strip()
                    if not text_in_this_tag:
                          continue

                    all_doc_strings.append(lowercase__		)

                    _snake_case     ,							_snake_case		:	List[str]					     =     self.xpath_soup(lowercase__		)
                    stringaxtag_seq.append(lowercase__		)
                    stringaxsubs_seq.append(lowercase__		)

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

        return all_doc_strings, stringaxtag_seq, stringaxsubs_seq







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__		)		->						Union[str, Any]:






        """simple docstring"""





        _snake_case		:	Tuple					     =     ''''''
        for tagname, subs in zip(lowercase__     ,     lowercase__		):
              xpath += F'''/{tagname}'''
              if subs != 0:
                    xpath += F'''[{subs}]'''
        return xpath







  def __call__(		self     ,     lowercase__		)		->						BatchFeature:






        """simple docstring"""





        _snake_case		:	Dict					     =     False

        # Check that strings has a valid type
        if isinstance(lowercase__     ,     lowercase__		):
              _snake_case		:	str					     =     True
        elif isinstance(lowercase__     ,     (list, tuple)		):
              if len(lowercase__		) == 0 or isinstance(html_strings[0]     ,     lowercase__		):
                    _snake_case		:	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(lowercase__		)}.'''		)

        _snake_case		:	Any					     =     bool(isinstance(lowercase__     ,     (list, tuple)		) and (isinstance(html_strings[0]     ,     lowercase__		))		)

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

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

        # return as Dict
        _snake_case		:	Optional[int]					     =     {'''nodes''': nodes, '''xpaths''': xpaths}
        _snake_case		:	Tuple					     =     BatchFeature(data=lowercase__     ,     tensor_type=lowercase__		)

        return encoded_inputs









'''simple docstring'''

import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
   import optax
   from flax.training.common_utils import onehot

   from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
   from transformers.models.ta.modeling_flax_ta import shift_tokens_right





@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class lowerCamelCase						(unittest.TestCase					):







  @slow
  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        _snake_case		:	Tuple					     =     FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''		)
        _snake_case		:	Any					     =     AutoTokenizer.from_pretrained('''google/mt5-small'''		)

        _snake_case		:	List[str]					     =     tokenizer('''Hello there'''     ,     return_tensors='''np'''		).input_ids
        _snake_case		:	Dict					     =     tokenizer('''Hi I am'''     ,     return_tensors='''np'''		).input_ids

        _snake_case		:	Any					     =     shift_tokens_right(lowercase__     ,     model.config.pad_token_id     ,     model.config.decoder_start_token_id		)

        _snake_case		:	Any					     =     model(lowercase__     ,     decoder_input_ids=lowercase__		).logits
        _snake_case		:	Tuple					     =     optax.softmax_cross_entropy(lowercase__     ,     onehot(lowercase__     ,     logits.shape[-1]		)		).mean()

        _snake_case		:	Tuple					     =     -(labels.shape[-1] * loss.item())

        _snake_case		:	Union[str, Any]					     =     -84.9_127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1E-4		)


