






'''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	:			Dict         =logging.get_logger(__name__)

class _lowercase	(				_lowercase		):




							def __init__(							self:				Dict			,							**UpperCamelCase__:				str     ):
														requires_backends(self			,							["""bs4"""]     )
														super().__init__(**UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Optional[int]			,							UpperCamelCase__:				int     ):
														lowerCamelCase__					:	Any         =       []
														lowerCamelCase__					:	Tuple         =       []
														lowerCamelCase__					:	Union[str, Any]         =       element if element.name else element.parent
														for parent in child.parents:  # type: bs4.element.Tag
																					lowerCamelCase__					:	Optional[int]         =       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__					:	Dict         =       parent
														xpath_tags.reverse()
														xpath_subscripts.reverse()
														return xpath_tags, xpath_subscripts




							def 		lowerCamelCase_				(							self:				Dict			,							UpperCamelCase__:				Optional[int]     ):
														lowerCamelCase__					:	Union[str, Any]         =       BeautifulSoup(UpperCamelCase__			,							"""html.parser"""     )

														lowerCamelCase__					:	Tuple         =       []
														lowerCamelCase__					:	Optional[Any]         =       []
														lowerCamelCase__					:	str         =       []

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

																												lowerCamelCase__					:	int         =       html.unescape(UpperCamelCase__     ).strip()
																												if not text_in_this_tag:
																																			continue

																												all_doc_strings.append(UpperCamelCase__     )

																												lowerCamelCase__  ,		lowerCamelCase__					:	Tuple         =       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 		lowerCamelCase_				(							self:				int			,							UpperCamelCase__:				List[Any]			,							UpperCamelCase__:				List[Any]     ):
														lowerCamelCase__					:	List[Any]         =       """"""
														for tagname, subs in zip(UpperCamelCase__			,							UpperCamelCase__     ):
																					xpath += F'''/{tagname}'''
																					if subs != 0:
																												xpath += F'''[{subs}]'''
														return xpath




							def __call__(							self:				List[str]			,							UpperCamelCase__:				Tuple     ):
														lowerCamelCase__					:	List[Any]         =       False

														# Check that strings has a valid type
														if isinstance(UpperCamelCase__			,							UpperCamelCase__     ):
																					lowerCamelCase__					:	List[Any]         =       True
														elif isinstance(UpperCamelCase__			,							(list, tuple)     ):
																					if len(UpperCamelCase__     ) == 0 or isinstance(html_strings[0]			,							UpperCamelCase__     ):
																												lowerCamelCase__					:	str         =       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__					:	List[Any]         =       bool(isinstance(UpperCamelCase__			,							(list, tuple)     ) and (isinstance(html_strings[0]			,							UpperCamelCase__     ))     )

														if not is_batched:
																					lowerCamelCase__					:	int         =       [html_strings]

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

														# return as Dict
														lowerCamelCase__					:	int         =       {"""nodes""": nodes, """xpaths""": xpaths}
														lowerCamelCase__					:	Optional[Any]         =       BatchFeature(data=UpperCamelCase__			,							tensor_type=UpperCamelCase__     )

														return encoded_inputs









'''simple docstring'''







import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor

@require_tokenizers
@require_vision
class _lowercase	(				unittest.TestCase		):




							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	int         =       tempfile.mkdtemp()

														# fmt: off
														lowerCamelCase__					:	int         =       ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
														# fmt: on
														lowerCamelCase__					:	Union[str, Any]         =       os.path.join(self.tmpdirname			,							VOCAB_FILES_NAMES["""vocab_file"""]     )
														with open(self.vocab_file			,							"""w"""			,							encoding="""utf-8"""     ) as vocab_writer:
																					vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]     )     )

														lowerCamelCase__					:	Tuple         =       {
														    """do_resize""": True,
														    """size""": {"""height""": 18, """width""": 18},
														    """do_normalize""": True,
														    """image_mean""": [0.5, 0.5, 0.5],
														    """image_std""": [0.5, 0.5, 0.5],
														}
														lowerCamelCase__					:	Tuple         =       os.path.join(self.tmpdirname			,							UpperCamelCase__     )
														with open(self.image_processor_file			,							"""w"""			,							encoding="""utf-8"""     ) as fp:
																					json.dump(UpperCamelCase__			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				str			,							**UpperCamelCase__:				List[str]     ):
														return BertTokenizer.from_pretrained(self.tmpdirname			,							**UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				int			,							**UpperCamelCase__:				Tuple     ):
														return ViTImageProcessor.from_pretrained(self.tmpdirname			,							**UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														shutil.rmtree(self.tmpdirname     )




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__					:	List[Any]         =       [np.random.randint(255			,							size=(3, 30, 400)			,							dtype=np.uinta     )]

														lowerCamelCase__					:	Tuple         =       [Image.fromarray(np.moveaxis(UpperCamelCase__			,							0			,							-1     )     ) for x in image_inputs]

														return image_inputs




							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	Optional[Any]         =       self.get_tokenizer()
														lowerCamelCase__					:	Dict         =       self.get_image_processor()

														lowerCamelCase__					:	Optional[Any]         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														processor.save_pretrained(self.tmpdirname     )
														lowerCamelCase__					:	int         =       VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname     )

														self.assertEqual(processor.tokenizer.get_vocab()			,							tokenizer.get_vocab()     )
														self.assertIsInstance(processor.tokenizer			,							(BertTokenizer, BertTokenizerFast)     )

														self.assertEqual(processor.image_processor.to_json_string()			,							image_processor.to_json_string()     )
														self.assertIsInstance(processor.image_processor			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Tuple     ):
														lowerCamelCase__					:	Dict         =       VisionTextDualEncoderProcessor(
														    tokenizer=self.get_tokenizer()			,							image_processor=self.get_image_processor()     )
														processor.save_pretrained(self.tmpdirname     )

														lowerCamelCase__					:	int         =       self.get_tokenizer(bos_token="""(BOS)"""			,							eos_token="""(EOS)"""     )
														lowerCamelCase__					:	List[Any]         =       self.get_image_processor(do_normalize=UpperCamelCase__			,							padding_value=1.0     )

														lowerCamelCase__					:	Tuple         =       VisionTextDualEncoderProcessor.from_pretrained(
														    self.tmpdirname			,							bos_token="""(BOS)"""			,							eos_token="""(EOS)"""			,							do_normalize=UpperCamelCase__			,							padding_value=1.0     )

														self.assertEqual(processor.tokenizer.get_vocab()			,							tokenizer_add_kwargs.get_vocab()     )
														self.assertIsInstance(processor.tokenizer			,							(BertTokenizer, BertTokenizerFast)     )

														self.assertEqual(processor.image_processor.to_json_string()			,							image_processor_add_kwargs.to_json_string()     )
														self.assertIsInstance(processor.image_processor			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														lowerCamelCase__					:	Optional[Any]         =       self.get_image_processor()
														lowerCamelCase__					:	Union[str, Any]         =       self.get_tokenizer()

														lowerCamelCase__					:	Any         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	List[Any]         =       self.prepare_image_inputs()

														lowerCamelCase__					:	List[str]         =       image_processor(UpperCamelCase__			,							return_tensors="""np"""     )
														lowerCamelCase__					:	Optional[Any]         =       processor(images=UpperCamelCase__			,							return_tensors="""np"""     )

														for key in input_feat_extract.keys():
																					self.assertAlmostEqual(input_feat_extract[key].sum()			,							input_processor[key].sum()			,							delta=1e-2     )




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	Any         =       self.get_image_processor()
														lowerCamelCase__					:	List[str]         =       self.get_tokenizer()

														lowerCamelCase__					:	List[Any]         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	List[Any]         =       """lower newer"""

														lowerCamelCase__					:	Union[str, Any]         =       processor(text=UpperCamelCase__     )

														lowerCamelCase__					:	Any         =       tokenizer(UpperCamelCase__     )

														for key in encoded_tok.keys():
																					self.assertListEqual(encoded_tok[key]			,							encoded_processor[key]     )




							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	Optional[Any]         =       self.get_image_processor()
														lowerCamelCase__					:	List[Any]         =       self.get_tokenizer()

														lowerCamelCase__					:	List[Any]         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	Any         =       """lower newer"""
														lowerCamelCase__					:	Dict         =       self.prepare_image_inputs()

														lowerCamelCase__					:	Tuple         =       processor(text=UpperCamelCase__			,							images=UpperCamelCase__     )

														self.assertListEqual(list(inputs.keys()     )			,							["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]     )

														# test if it raises when no input is passed
														with self.assertRaises(UpperCamelCase__     ):
																					processor()




							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	List[str]         =       self.get_image_processor()
														lowerCamelCase__					:	List[str]         =       self.get_tokenizer()

														lowerCamelCase__					:	int         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	Dict         =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

														lowerCamelCase__					:	Union[str, Any]         =       processor.batch_decode(UpperCamelCase__     )
														lowerCamelCase__					:	Dict         =       tokenizer.batch_decode(UpperCamelCase__     )

														self.assertListEqual(UpperCamelCase__			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	Any         =       self.get_image_processor()
														lowerCamelCase__					:	Union[str, Any]         =       self.get_tokenizer()

														lowerCamelCase__					:	int         =       VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	Optional[Any]         =       """lower newer"""
														lowerCamelCase__					:	str         =       self.prepare_image_inputs()

														lowerCamelCase__					:	int         =       processor(text=UpperCamelCase__			,							images=UpperCamelCase__     )

														self.assertListEqual(list(inputs.keys()     )			,							processor.model_input_names     )

