





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


class 			__UpperCamelCase							(		a__		):






							def __init__(  self ,   **lowerCAmelCase__ )      ->					Union[str, Any]:
								requires_backends(self ,   ["bs4"] )
								super().__init__(**lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ )      ->					str:
								a :						int      =							[]
								a :						Optional[Any]      =							[]
								a :						List[Any]      =							element if element.name else element.parent
								for parent in child.parents:  # type: bs4.element.Tag
									a :						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 ) )
									a :						int      =							parent
								xpath_tags.reverse()
								xpath_subscripts.reverse()
								return xpath_tags, xpath_subscripts






							def        __a  (  self ,   lowerCAmelCase__ )      ->					Tuple:
								a :						int      =							BeautifulSoup(lowerCAmelCase__ ,   "html.parser" )

								a :						Dict      =							[]
								a :						Optional[int]      =							[]
								a :						List[str]      =							[]

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

										a :						Dict      =							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        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					str:
								a :						Any      =							""
								for tagname, subs in zip(lowerCAmelCase__ ,   lowerCAmelCase__ ):
									xpath += f"""/{tagname}"""
									if subs != 0:
										xpath += f"""[{subs}]"""
								return xpath






							def __call__(  self ,   lowerCAmelCase__ )      ->					BatchFeature:
								a :						Optional[int]      =							False

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

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

								if not is_batched:
									a :						Any      =							[html_strings]

								# Get nodes + xpaths
								a :						Tuple      =							[]
								a :						str      =							[]
								for html_string in html_strings:
									a,			a,			a :						Tuple      =							self.get_three_from_single(lowerCAmelCase__ )
									nodes.append(lowerCAmelCase__ )
									a :						List[str]      =							[]
									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 :						Optional[Any]      =							{"nodes": nodes, "xpaths": xpaths}
								a :						Dict      =							BatchFeature(data=lowerCAmelCase__ ,   tensor_type=lowerCAmelCase__ )

								return encoded_inputs









"""simple docstring"""




from typing import TYPE_CHECKING

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


a		:					Optional[Any]	      =     {
    '''configuration_owlvit''': [
        '''OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''OwlViTConfig''',
        '''OwlViTOnnxConfig''',
        '''OwlViTTextConfig''',
        '''OwlViTVisionConfig''',
    ],
    '''processing_owlvit''': ['''OwlViTProcessor'''],
}


try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:					str	      =     ['''OwlViTFeatureExtractor''']
		a		:					List[Any]	      =     ['''OwlViTImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:					List[Any]	      =     [
		    '''OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''OwlViTModel''',
		    '''OwlViTPreTrainedModel''',
		    '''OwlViTTextModel''',
		    '''OwlViTVisionModel''',
		    '''OwlViTForObjectDetection''',
		]

if TYPE_CHECKING:
		from .configuration_owlvit import (
		    OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    OwlViTConfig,
		    OwlViTOnnxConfig,
		    OwlViTTextConfig,
		    OwlViTVisionConfig,
		)
		from .processing_owlvit import OwlViTProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_owlvit import OwlViTFeatureExtractor
				from .image_processing_owlvit import OwlViTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_owlvit import (
				    OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    OwlViTForObjectDetection,
				    OwlViTModel,
				    OwlViTPreTrainedModel,
				    OwlViTTextModel,
				    OwlViTVisionModel,
				)

else:
		import sys

		a		:					List[str]	      =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


