

'''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


__snake_case :	List[str]      						=			logging.get_logger(__name__)



class lowerCamelCase (  __a						):



			'''simple docstring'''






			def __init__(      self       :						List[Any]		,			**lowerCAmelCase_       :						List[Any] )			->		str:





									'''simple docstring'''
									requires_backends(self		,			["""bs4"""] )
									super().__init__(**a_ )


			def lowercase__						(      self       :						List[Any]		,			lowerCAmelCase_       :						Optional[Any] )			->		List[Any]:





									'''simple docstring'''
									A__     :   Any      =[]
									A__     :   List[Any]      =[]
									A__     :   Dict      =element if element.name else element.parent
									for parent in child.parents:  # type: bs4.element.Tag
															A__     :   Any      =parent.find_all(child.name		,			recursive=a_ )
															xpath_tags.append(child.name )
															xpath_subscripts.append(
															    0 if 1 == len(a_ ) else next(i for i, s in enumerate(a_		,			1 ) if s is child ) )
															A__     :   Union[str, Any]      =parent
									xpath_tags.reverse()
									xpath_subscripts.reverse()
									return xpath_tags, xpath_subscripts


			def lowercase__						(      self       :						List[str]		,			lowerCAmelCase_       :						Dict )			->		Optional[Any]:





									'''simple docstring'''
									A__     :   Tuple      =BeautifulSoup(a_		,			"""html.parser""" )

									A__     :   Any      =[]
									A__     :   Dict      =[]
									A__     :   str      =[]

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

																					A__     :   Dict      =html.unescape(a_ ).strip()
																					if not text_in_this_tag:
																											continue

																					all_doc_strings.append(a_ )

																					A__     :   Dict      =self.xpath_soup(a_ )
																					stringaxtag_seq.append(a_ )
																					stringaxsubs_seq.append(a_ )

									if len(a_ ) != len(a_ ):
															raise ValueError("""Number of doc strings and xtags does not correspond""" )
									if len(a_ ) != len(a_ ):
															raise ValueError("""Number of doc strings and xsubs does not correspond""" )

									return all_doc_strings, stringaxtag_seq, stringaxsubs_seq


			def lowercase__						(      self       :						List[str]		,			lowerCAmelCase_       :						List[Any]		,			lowerCAmelCase_       :						List[str] )			->		int:





									'''simple docstring'''
									A__     :   str      =""""""
									for tagname, subs in zip(a_		,			a_ ):
															xpath += f"/{tagname}"
															if subs != 0:
																					xpath += f"[{subs}]"
									return xpath



			def __call__(      self       :						Tuple		,			lowerCAmelCase_       :						List[str] )			->		Optional[int]:





									'''simple docstring'''
									A__     :   List[Any]      =False

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

									A__     :   int      =bool(isinstance(a_		,			(list, tuple) ) and (isinstance(html_strings[0]		,			a_ )) )

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

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

									# return as Dict
									A__     :   int      ={"""nodes""": nodes, """xpaths""": xpaths}
									A__     :   Tuple      =BatchFeature(data=a_		,			tensor_type=a_ )

									return encoded_inputs







"""simple docstring"""


import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
     import onnxruntime as ort






@nightly
@require_onnxruntime
@require_torch_gpu
class 					lowercase(     unittest.TestCase ):







 '''simple docstring'''





 @property
 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



   return (
       "CUDAExecutionProvider",
       {
           "gpu_mem_limit": "15000000000",  # 15GB
           "arena_extend_strategy": "kSameAsRequested",
       },
   )


 @property
 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



   _snake_case							:		Any								=  ort.SessionOptions()
   _snake_case							:		Union[str, Any]								=  False
   return options


 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		Any								=  load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/overture-creations-5sI6fQgYIuo.png"""		)
   _snake_case							:		Union[str, Any]								=  load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""		)
   _snake_case							:		Union[str, Any]								=  load_numpy(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"""		)

   # using the PNDM scheduler by default
   _snake_case							:		Optional[Any]								=  OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
       """CompVis/stable-diffusion-v1-4""",      revision="""onnx""",      safety_checker=a_,      feature_extractor=a_,      provider=self.gpu_provider,      sess_options=self.gpu_options,      )
   pipe.set_progress_bar_config(disable=a_		)

   _snake_case							:		Optional[Any]								=  """A red cat sitting on a park bench"""

   _snake_case							:		Optional[int]								=  np.random.RandomState(0		)
   _snake_case							:		Any								=  pipe(
       prompt=a_,      image=a_,      mask_image=a_,      strength=0.75,      guidance_scale=7.5,      num_inference_steps=15,      generator=a_,      output_type="""np""",      )

   _snake_case							:		Dict								=  output.images[0]

   assert image.shape == (512, 512, 3)
   assert np.abs(expected_image - image		).max() < 1E-2


