

"""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		  =     logging.get_logger(__name__)





class        snake_case      (							UpperCAmelCase__    ):

				def __init__(      self				:							Union[str, Any]	,    **UpperCamelCase__				:							Optional[int])->  Optional[int]:



						'''simple docstring'''


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

				def 				lowercase_	(      self				:							Union[str, Any]	,    UpperCamelCase__				:							Dict)->  Optional[Any]:



						'''simple docstring'''


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

				def 				lowercase_	(      self				:							Dict	,    UpperCamelCase__				:							Dict)->  Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:			Optional[Any]        =     BeautifulSoup(UpperCAmelCase__	,    "html.parser")

						__lowerCAmelCase:			Optional[Any]        =     []
						__lowerCAmelCase:			Tuple        =     []
						__lowerCAmelCase:			Union[str, Any]        =     []

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

										__lowerCAmelCase:			Optional[Any]        =     html.unescape(UpperCAmelCase__).strip()
										if not text_in_this_tag:
												continue

										all_doc_strings.append(UpperCAmelCase__)

										__lowerCAmelCase ,					__lowerCAmelCase:			Dict        =     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 				lowercase_	(      self				:							List[str]	,    UpperCamelCase__				:							Any	,    UpperCamelCase__				:							Optional[int])->  Optional[Any]:



						'''simple docstring'''


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




				def __call__(      self				:							Optional[Any]	,    UpperCamelCase__				:							Tuple)->  BatchFeature:



						'''simple docstring'''


						__lowerCAmelCase:			List[str]        =     False

						# Check that strings has a valid type
						if isinstance(UpperCAmelCase__	,    UpperCAmelCase__):
								__lowerCAmelCase:			Optional[Any]        =     True
						elif isinstance(UpperCAmelCase__	,    (list, tuple)):
								if len(UpperCAmelCase__) == 0 or isinstance(html_strings[0]	,    UpperCAmelCase__):
										__lowerCAmelCase:			Tuple        =     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__)}.")

						__lowerCAmelCase:			Optional[int]        =     bool(isinstance(UpperCAmelCase__	,    (list, tuple)) and (isinstance(html_strings[0]	,    UpperCAmelCase__)))

						if not is_batched:
								__lowerCAmelCase:			Optional[int]        =     [html_strings]

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

						# return as Dict
						__lowerCAmelCase:			Optional[int]        =     {"nodes": nodes, "xpaths": xpaths}
						__lowerCAmelCase:			str        =     BatchFeature(data=UpperCAmelCase__	,    tensor_type=UpperCAmelCase__)

						return encoded_inputs








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase      :						Any							=  {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase      :						int							=  ["""XLNetTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase      :						int							=  ["""XLNetTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase      :						Union[str, Any]							=  [
      """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """XLNetForMultipleChoice""",
      """XLNetForQuestionAnswering""",
      """XLNetForQuestionAnsweringSimple""",
      """XLNetForSequenceClassification""",
      """XLNetForTokenClassification""",
      """XLNetLMHeadModel""",
      """XLNetModel""",
      """XLNetPreTrainedModel""",
      """load_tf_weights_in_xlnet""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase      :						Dict							=  [
      """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFXLNetForMultipleChoice""",
      """TFXLNetForQuestionAnsweringSimple""",
      """TFXLNetForSequenceClassification""",
      """TFXLNetForTokenClassification""",
      """TFXLNetLMHeadModel""",
      """TFXLNetMainLayer""",
      """TFXLNetModel""",
      """TFXLNetPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet import XLNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet_fast import XLNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlnet import (
        XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLNetForMultipleChoice,
        XLNetForQuestionAnswering,
        XLNetForQuestionAnsweringSimple,
        XLNetForSequenceClassification,
        XLNetForTokenClassification,
        XLNetLMHeadModel,
        XLNetModel,
        XLNetPreTrainedModel,
        load_tf_weights_in_xlnet,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlnet import (
        TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLNetForMultipleChoice,
        TFXLNetForQuestionAnsweringSimple,
        TFXLNetForSequenceClassification,
        TFXLNetForTokenClassification,
        TFXLNetLMHeadModel,
        TFXLNetMainLayer,
        TFXLNetModel,
        TFXLNetPreTrainedModel,
    )

else:
  import sys

  _lowerCamelCase      :						int							=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


