






"""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         =							logging.get_logger(__name__)



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	def __init__(self     ,     **lowerCAmelCase_							):
		requires_backends(self     ,     ["""bs4"""]							)
		super().__init__(**lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						Tuple	    =   []
		A_		:						Union[str, Any]	    =   []
		A_		:						Any	    =   element if element.name else element.parent
		for parent in child.parents:  # type: bs4.element.Tag
			A_		:						Optional[Any]	    =   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							)							)
			A_		:						List[str]	    =   parent
		xpath_tags.reverse()
		xpath_subscripts.reverse()
		return xpath_tags, xpath_subscripts





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						List[Any]	    =   BeautifulSoup(lowerCAmelCase_     ,     """html.parser"""							)

		A_		:						Union[str, Any]	    =   []
		A_		:						str	    =   []
		A_		:						Dict	    =   []

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

				A_		:						Tuple	    =   html.unescape(lowerCAmelCase_							).strip()
				if not text_in_this_tag:
					continue

				all_doc_strings.append(lowerCAmelCase_							)

				A_    ,     A_		:						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    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						Optional[int]	    =   """"""
		for tagname, subs in zip(lowerCAmelCase_     ,     lowerCAmelCase_							):
			xpath += f"""/{tagname}"""
			if subs != 0:
				xpath += f"""[{subs}]"""
		return xpath





	def __call__(self     ,     lowerCAmelCase_							):
		A_		:						Any	    =   False

		# Check that strings has a valid type
		if isinstance(lowerCAmelCase_     ,     lowerCAmelCase_							):
			A_		:						Union[str, Any]	    =   True
		elif isinstance(lowerCAmelCase_     ,     (list, tuple)							):
			if len(lowerCAmelCase_							) == 0 or isinstance(html_strings[0]     ,     lowerCAmelCase_							):
				A_		:						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_							)}."""							)

		A_		:						List[Any]	    =   bool(isinstance(lowerCAmelCase_     ,     (list, tuple)							) and (isinstance(html_strings[0]     ,     lowerCAmelCase_							))							)

		if not is_batched:
			A_		:						List[Any]	    =   [html_strings]

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

		# return as Dict
		A_		:						Tuple	    =   {"""nodes""": nodes, """xpaths""": xpaths}
		A_		:						List[Any]	    =   BatchFeature(data=lowerCAmelCase_     ,     tensor_type=lowerCAmelCase_							)

		return encoded_inputs









"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Optional[int]    =				"""facebook/bart-large-mnli"""
	_A       :				str    =				(
	    """This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which """
	    """should be the text to classify, and `labels`, which should be the list of labels to use for classification. """
	    """It returns the most likely label in the list of provided `labels` for the input text."""
	)
	_A       :				List[str]    =				"""text_classifier"""
	_A       :				Optional[int]    =				AutoTokenizer
	_A       :				Optional[Any]    =				AutoModelForSequenceClassification

	_A       :				List[str]    =				["""text""", ["""text"""]]
	_A       :				Dict    =				["""text"""]





	def    lowerCamelCase(self							):
		super().setup()
		A_		:						int	    =   self.model.config
		A_		:						List[str]	    =   -1
		for idx, label in config.idalabel.items():
			if label.lower().startswith("""entail"""							):
				A_		:						List[Any]	    =   int(lowerCAmelCase_							)
		if self.entailment_id == -1:
			raise ValueError("""Could not determine the entailment ID from the model config, please pass it at init."""							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						List[Any]	    =   labels
		return self.pre_processor(
		    [text] * len(lowerCAmelCase_							)     ,     [f"""This example is {label}""" for label in labels]     ,     return_tensors="""pt"""     ,     padding="""max_length"""     ,     )





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						str	    =   outputs.logits
		A_		:						Optional[int]	    =   torch.argmax(logits[:, 2]							).item()
		return self._labels[label_id]

