



"""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


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



class    _lowerCAmelCase    (				a__		):





		"""simple docstring"""






		def __init__(					self							,    **_lowercase   ) ->						int:







								'''simple docstring'''

								requires_backends(self							,    ["""bs4"""]   )
								super().__init__(**lowerCAmelCase__   )





		def    UpperCAmelCase__							(					self							,    _lowercase   ) ->						Union[str, Any]:







								'''simple docstring'''

								snake_case_   : Optional[int]						=			[]
								snake_case_   : Optional[Any]						=			[]
								snake_case_   : Dict						=			element if element.name else element.parent
								for parent in child.parents:  # type: bs4.element.Tag
														snake_case_   : 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   )   )
														snake_case_   : Optional[int]						=			parent
								xpath_tags.reverse()
								xpath_subscripts.reverse()
								return xpath_tags, xpath_subscripts





		def    UpperCAmelCase__							(					self							,    _lowercase   ) ->						int:







								'''simple docstring'''

								snake_case_   : str						=			BeautifulSoup(lowerCAmelCase__							,    """html.parser"""   )

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

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

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

																				all_doc_strings.append(lowerCAmelCase__   )

																				snake_case_   : 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    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase   ) ->						Union[str, Any]:







								'''simple docstring'''

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





		def __call__(					self							,    _lowercase   ) ->						BatchFeature:







								'''simple docstring'''

								snake_case_   : List[Any]						=			False

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

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

								if not is_batched:
														snake_case_   : Dict						=			[html_strings]

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

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

								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		):





		"""simple docstring"""






		@slow
		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : int						=			FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small"""   )
								snake_case_   : List[Any]						=			AutoTokenizer.from_pretrained("""google/mt5-small"""   )

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

								snake_case_   : Optional[Any]						=			shift_tokens_right(_lowercase							,    model.config.pad_token_id							,    model.config.decoder_start_token_id   )

								snake_case_   : Tuple						=			model(_lowercase							,    decoder_input_ids=_lowercase   ).logits
								snake_case_   : Tuple						=			optax.softmax_cross_entropy(_lowercase							,    onehot(_lowercase							,    logits.shape[-1]   )   ).mean()

								snake_case_   : List[str]						=			-(labels.shape[-1] * loss.item())

								snake_case_   : Optional[int]						=			-84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1E-4   )

